U.S. patent application number 15/199113 was filed with the patent office on 2017-12-07 for systems and techniques for tracking sleep consistency and sleep goals.
The applicant listed for this patent is Fitbit, Inc.. Invention is credited to Jacob Antony Arnold, Yasaman Baiani, Yeqing Cheng, Allison Maya Russell.
Application Number | 20170347949 15/199113 |
Document ID | / |
Family ID | 60482006 |
Filed Date | 2017-12-07 |
United States Patent
Application |
20170347949 |
Kind Code |
A1 |
Arnold; Jacob Antony ; et
al. |
December 7, 2017 |
SYSTEMS AND TECHNIQUES FOR TRACKING SLEEP CONSISTENCY AND SLEEP
GOALS
Abstract
Methods, techniques, apparatuses, and systems for setting up and
tracking sleep consistency goals of users are provided. In one
example, a computing system for setting a sleep schedule of a user
of a biometric monitoring device may obtain sleep data derived from
sensor data generated by the biometric monitoring device, store the
sleep data in a sleep log data store as one or more sleep logs
associated with an account assigned to the user, and calculate a
target bedtime based on a scheduled waketime of the user and a
sleep efficiency derived, at least in part, from the sleep data for
one or more users stored in the sleep log data store. The computing
system may also be configured to provide a number of personalized
user interfaces to an individual for the purposes of setting a
sleep schedule. Such interfaces may include parameters that are
tailored to the individual sleep needs and/or characteristics of
the individual's sleep.
Inventors: |
Arnold; Jacob Antony;
(Fremont, CA) ; Cheng; Yeqing; (San Carlos,
CA) ; Baiani; Yasaman; (San Francisco, CA) ;
Russell; Allison Maya; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Fitbit, Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
60482006 |
Appl. No.: |
15/199113 |
Filed: |
June 30, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15171049 |
Jun 2, 2016 |
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15199113 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G09B 19/00 20130101;
G06F 3/0484 20130101; A61B 5/7405 20130101; A61B 5/4806 20130101;
A61B 5/7475 20130101; A61B 5/4809 20130101; G06F 3/04842 20130101;
A61B 5/4812 20130101; A61B 5/7455 20130101; A61B 5/486 20130101;
G06F 3/0481 20130101; G09B 5/00 20130101; A61B 5/4815 20130101;
A61B 5/743 20130101; A61B 5/1118 20130101; A61B 5/742 20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; G06F 3/0481 20130101 G06F003/0481; G06F 3/0484 20130101
G06F003/0484 |
Claims
1. A method of generating one or more graphical user interfaces for
setting a sleep schedule of a user of a biometric monitoring
device, the biometric monitoring device comprising one or more
sensors, the method comprising: determining that an account
assigned to the user is associated with less than a first number of
sleep logs in a sleep log data store, wherein each sleep log
includes sleep data derived from sensor data generated by the
biometric monitoring device for a sleep session, the sensor data
including data generated by at least one of the one or more sensors
in the biometric monitoring device responsive to movement of the
user of the biometric monitoring device, the sleep data for each
sleep log specifying various sleep states of the user for that
respective sleep session; causing, responsive, at least in part, to
the determination that the account assigned to the user is
associated with less than the first number of sleep logs in the
sleep log data store, a first set of one or more graphical user
interfaces to be displayed to the user, wherein at least one
graphical user interface of the first set of one or more graphical
user interfaces to be displayed to the user includes a sleep
duration user interface element configured to allow the user to
specify a selected sleep duration of the user for one or more first
future sleep sessions of the user, and the first set of one or more
graphical user interfaces lacks a scheduled waketime user interface
element configured to allow the user to specify a scheduled
waketime in association with the selected sleep duration of the
user; obtaining the selected sleep duration specified by the user;
obtaining sleep data for one or more sleep sessions of the user,
wherein the sleep data for each sleep session includes sleep state
duration data representative of the total amount of time the user
spent in a subset of non-awake sleep states during that sleep
session; storing the sleep data in the sleep log data store as one
or more sleep logs associated with the account assigned to the
user; and comparing the sleep state duration data for one or more
of the sleep logs stored in the sleep log data store with the
selected sleep duration.
2. The method of claim 1, wherein causing the first set of one or
more graphical user interfaces to be displayed further includes:
causing a suggested sleep duration to be displayed in a graphical
user interface element of at least one graphical user interface of
the first set of one or more graphical user interfaces, wherein the
suggested sleep duration is based on one or more of: a default
value and a target sleep duration determined based on one or more
of sleep logs stored in the sleep log data store and including
sleep data derived from sensor data generated by other biometric
monitoring devices of other users the other sensor data including
other data generated by at least one of the one or more other
sensors in the other biometric monitoring devices responsive to
movement by the other users of the other biometric monitoring
devices.
3. The method of claim 2, further comprising: obtaining, after the
display of the suggested sleep duration, a second selected sleep
duration specified by the user.
4. The method of claim 1, further comprising: determining that the
selected sleep duration is not within a threshold amount of a
default sleep duration value; and causing, based on the
determination that the selected sleep duration is not within the
threshold amount of the default sleep duration value, a suggested
sleep duration to be displayed in a graphical user interface
element of at least one graphical user interface of a second set of
the one or more graphical user interfaces, wherein the suggested
sleep duration is based on one or more of: a default value and a
target sleep duration determined based on one or more sleep logs
stored in the sleep log data store and including sleep data derived
from sensor data generated by other biometric monitoring devices of
other users, the other sensor data including other data generated
by at least one of the one or more other sensors in the other
biometric monitoring devices responsive to movement by the other
users of the other biometric monitoring devices.
5. The method of claim 4, further comprising: obtaining a second
selected sleep duration after the display of the suggested sleep
duration.
6. The method of claim 1, wherein the determining that the account
assigned to the user is associated with less than the first number
of sleep logs in the sleep log data store is evaluated based on
sleep logs associated with the account assigned to the user and
associated with sleep sessions that occurred within a first period
of time.
7. The method of claim 1, wherein causing the first set of one or
more graphical user interfaces to be displayed further includes:
causing information associated with average recommended sleep
durations of a group of people to be displayed in a graphical user
interface element of at least one graphical user interface of the
first set of one or more graphical user interfaces.
8. The method of claim 1, wherein causing the first set of one or
more graphical user interfaces to be displayed further includes
causing information associated with the selected sleep duration,
other than the selected sleep duration, to be displayed in a
graphical user interface element of at least one graphical user
interface of the first set of one or more graphical user
interfaces.
9. The method of claim 1, wherein causing the first set of one or
more graphical user interfaces to be displayed further includes:
causing the display of sleep state duration data of the user for at
least one of the one or more sleep sessions.
10. The method of claim 1, further comprising: determining, after
the obtaining sleep data for one or more sleep sessions of the user
and the storing the sleep data in the sleep log data store, that
the account assigned to the user is associated with at least one or
more of: a second number of sleep logs in the sleep log data store
and a second time period elapsed since the selected sleep duration
was obtained; and causing, in response to the determination that
the account assigned to the user is associated with at least one or
more of: the second number of sleep logs in the sleep log data
store and the second time period elapsed since the selected sleep
duration was obtained, a second set of one or more graphical user
interfaces to be displayed to the user.
11. The method of claim 10, wherein causing the second set of one
or more graphical user interfaces to be displayed further includes:
calculating, in response to a determination that the sleep state
duration data of the user for one or more of the one or more sleep
sessions is outside a threshold amount from the selected sleep
duration, a recommended sleep duration, wherein the recommended
sleep duration is based on sleep data in sleep logs for one or more
users that are stored in the sleep log data store, and causing the
recommended sleep duration to be displayed in a graphical user
interface element of at least one graphical user interface of the
second set of one or more graphical user interfaces.
12. The method of claim 11, wherein the causing the recommended
sleep duration to be displayed in a graphical user interface
element of at least one graphical user interface of the second set
of one or more graphical user interfaces includes: causing an
incremented recommended sleep duration to be displayed in a
graphical user interface element in each of a series graphical user
interfaces of the second set of one or more graphical user
interfaces, wherein each incremented recommended sleep duration is
greater than the previously displayed incremented recommended sleep
duration, and wherein each incremented recommended sleep duration
is associated with a different future sleep session.
13. The method of claim 11, further comprising: obtaining a second
selected sleep duration after causing the recommended sleep
duration to be displayed.
14. The method of claim 11, wherein the recommended sleep duration
is further based on one or more of: a look-up table, demographics
of the user, one or more specific days of the week, a specific time
of year, holidays, workdays of the user, non-workdays of the user,
a seasonal time change, a geographic location, travel by the user
between at least two time zones, exercise of the user, and a
duration of daylight in a day.
15. The method of claim 10, further comprising: obtaining a
scheduled waketime of the user for one or more second future sleep
sessions of the user after determining that the account assigned to
the user is associated with at least the second number of sleep
logs in the sleep log data store.
16. The method of claim 15, wherein causing the second set of one
or more graphical user interfaces to be displayed further includes:
calculating, after determining that the account assigned to the
user is associated with at least a third number of sleep logs in
the sleep log data store, a recommended waketime based on waketimes
derived from the sleep data associated with the user stored in the
sleep log data store, and causing the recommended waketime to be
displayed in a graphical user interface element of at least one
graphical user interface of the second set of one or more graphical
user interfaces.
17. The method of claim 15, wherein causing the second set of one
or more graphical user interfaces to be displayed further includes:
obtaining a recommended bedtime based on a calculation accounting
for, at least in part, the scheduled waketime and a sleep
efficiency based on sleep data associated with one or more users
stored in the sleep log data store, and causing the recommended
bedtime to be displayed in a graphical user interface element of at
least one graphical user interface of the second set of one or more
graphical user interfaces.
18. The method of claim 17, wherein: the calculation on which the
recommended bedtime is based further accounts for, at least in
part, the selected sleep duration of the user.
19. The method of claim 17, wherein: obtaining the recommended
bedtime is further based on a recommended waketime that is used as
the scheduled waketime.
20. The method of claim 17, wherein: the sleep efficiency is
representative, at least in part, of a correlation between sleep
state duration data for one or more sleep sessions associated with
one or more sleep logs stored in the sleep log data store and sleep
session duration data for the corresponding plurality of sleep
sessions, the sleep state duration data for each sleep session is
representative of the total amount of time the user that is
associated with the sleep session spent in a subset of non-awake
sleep states during that sleep session, and the sleep session
duration data for each sleep session is representative of the total
duration of that sleep session.
21. The method of claim 17, further comprising: obtaining timing
information indicating one or more selected reminder times for a
bedtime reminder, and generating the bedtime reminder based on the
timing information.
22. The method of claim 21, wherein the bedtime reminder includes
information regarding the recommended bedtime.
23. The method of claim 21, wherein causing the second set of one
or more graphical user interfaces to be displayed further includes:
calculating a recommended reminder time for the bedtime reminder
based on the recommended bedtime, and causing the recommended
reminder time to be displayed in a graphical user interface element
of at least one graphical user interface of the second set of one
or more graphical user interfaces.
24. The method of claim 21, further comprising: obtaining day
information indicating one or more selected reminder days for the
bedtime reminder, wherein the generating of the bedtime reminder is
further based on the day information.
25. The method of claim 21, wherein: the generating the bedtime
reminder includes providing a notification, and the notification is
one or more of: a message, an auditory output, an electronic
communication, an electromagnetic communication, a visual output,
and a tactile output.
26. The method of claim 11, further comprising: causing, based on a
determination that the sleep state duration data of the user is not
within a threshold amount of the selected sleep duration, one or
more of: a second recommended bedtime, a recommended sleep
duration, and a recommended waketime to be displayed in a graphical
user interface element of at least one graphical user interface of
a third set of one or more graphical user interfaces.
27. The method of claim 1, further comprising: determining whether
the sleep state duration data of the user for at least one of the
one or more sleep sessions of the user is within a threshold amount
of the selected sleep duration, and causing information associated
with the determination of whether the sleep state duration data of
the user for one of the sleep sessions of the user is within the
threshold amount of the selected sleep duration to be displayed in
a graphical user interface element of at least one graphical user
interface of a second set of one or more graphical user
interfaces.
28. The method of claim 27, wherein the causing the information
associated with the determination of whether the sleep state
duration data of the user for one of the sleep sessions of the user
is within the threshold amount of the selected sleep duration to be
displayed includes: causing a timeline to be displayed showing a
graphical indication of the threshold amount relative to the
timeline and a graphical indication of the sleep state duration
data relative to the timeline.
29. The method of claim 1, wherein causing the first set of one or
more graphical user interfaces to be displayed further includes:
causing instructional information about using the biometric
monitoring device to be displayed.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of, and claims priority
to, U.S. patent application Ser. No. 15/171,049, filed Jun. 2,
2016, titled "SYSTEMS AND TECHNIQUES FOR TRACKING SLEEP CONSISTENCY
AND SLEEP GOALS," which is hereby incorporated herein by reference
in its entirety.
BACKGROUND
[0002] Personal fitness and health monitoring devices, which may be
referred to as biometric monitoring devices herein, may include a
variety of different sensors that are used to provide feedback
regarding various physiological characteristics of a person. Such
sensors may be used to track sleep of a user.
SUMMARY
[0003] Details of one or more implementations of the subject matter
described in this specification are set forth in the accompanying
drawings and the description below. Other features, aspects, and
advantages will become apparent from the description, the drawings,
and the claims.
[0004] In one embodiment, a method of generating one or more
personalized graphical user interfaces for setting a sleep schedule
of a user of a biometric monitoring device may be provided. The
method may include obtaining sleep data derived from sensor data
generated by the biometric monitoring device, the sleep data
including data regarding a plurality of sleep sessions and
specifying various sleep states of the user for the respective
sleep sessions; storing the sleep data in a sleep log data store as
one or more sleep logs associated with an account assigned to the
user, the sleep log data store also including sleep logs including
sleep data derived from sensor data generated by other biometric
monitoring devices of other users; and causing the one or more
personalized graphical user interfaces to be displayed to the user.
Causing the one or more personalized graphical user interfaces to
be displayed to the user may include obtaining a scheduled waketime
of the user, obtaining a recommended bedtime based on a calculation
accounting for, at least in part, the scheduled waketime and a
sleep efficiency based on the sleep data for one or more users
stored in the sleep log data store, and causing the recommended
bedtime to be displayed in a graphical user interface element of at
least one of the one or more personalized graphical user
interfaces.
[0005] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
obtaining a selected sleep duration of the user, and calculating
the recommended bedtime may be further based on the selected sleep
duration of the user.
[0006] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
obtaining timing information indicating one or more selected
reminder times for a bedtime reminder, and the method may further
include generating the bedtime reminder based on the timing
information.
[0007] In some further embodiments, the bedtime reminder may
include information regarding the recommended bedtime.
[0008] In some other further embodiments, causing the one or more
personalized graphical user interfaces to be generated may further
include calculating a recommended reminder time for the bedtime
reminder based on the recommended bedtime and causing the
recommended reminder time to be displayed in a graphical user
interface element of the one or more personalized graphical user
interfaces.
[0009] In some other further embodiments, causing the one or more
personalized graphical user interfaces to be generated may further
include obtaining day information indicating one or more selected
reminder days for the bedtime reminder, and the generating of the
bedtime reminder may be further based on the day information.
[0010] In some other further embodiments, the method may further
include providing the bedtime reminder via a notification such as a
message, an auditory output, an electronic communication, an
electromagnetic communication, a visual output, or a tactile
output.
[0011] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
determining whether sleep state duration data of the user that is
representative of the time the user spent in one or more non-awake
sleep states during one of the sleep sessions of the user is within
a threshold of the selected sleep duration, and causing information
associated with the determination to be displayed in a graphical
user interface element of at least one of the one or more
personalized graphical user interfaces.
[0012] In some further embodiments, causing the information
associated with the determination to be displayed may include
include causing a timeline to be displayed showing a graphical
indication of the threshold relative to the timeline and a
graphical indication of the sleep state duration data relative to
the timeline.
[0013] In some other further embodiments, causing the one or more
personalized graphical user interfaces to be generated may further
include causing, based on a determination that the sleep state
duration data of the user is not within the threshold of the
selected sleep duration, one or more of: a second recommended
bedtime, a recommended sleep duration, and a recommended waketime
to be displayed in a graphical user interface element of at least
one of the one or more personalized graphical user interfaces.
[0014] In some other further such embodiments, the one or more of
the second recommended bedtime, the recommended sleep duration, and
the recommended waketime that are displayed in the graphical user
interface element may be, respectively, different from the
recommended bedtime by a first time increment, different from the
selected sleep duration by a second time increment, and different
from the scheduled waketime by a third time increment.
[0015] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
calculating a recommended waketime based on waketimes derived from
the sleep data for the user stored in the sleep log data store and
causing the recommended waketime to be displayed in a graphical
user interface element of at least one of the one or more
personalized graphical user interfaces.
[0016] In some further embodiments, obtaining the recommended
bedtime may be further based on the recommended waketime.
[0017] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
calculating a recommended sleep duration based on the sleep data
for one or more users stored in the sleep log data store, and
causing the recommended sleep duration to be displayed in a
graphical user interface element of at least one of the one or more
personalized graphical user interfaces.
[0018] In some further embodiments, obtaining the recommended
bedtime may be further based on the recommended sleep duration.
[0019] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
determining, based on the sleep data of the user, whether a
waketime of the user for one of the sleep sessions of the user is
within a threshold of the scheduled waketime, and causing
information associated with the determination to be displayed in a
graphical user interface element of at least one of the one or more
personalized graphical user interfaces.
[0020] In some further embodiments, causing the information
associated with the determination to be displayed may include
causing a timeline to be displayed showing a graphical indication
of the threshold relative to the timeline and a graphical
indication of the waketime relative to the timeline.
[0021] In some other further embodiments, causing the one or more
personalized graphical user interfaces to be generated may further
include causing, based on a determination that the waketime of the
user is not within the threshold of the scheduled waketime, one or
more of a second recommended bedtime, a recommended sleep duration,
and a recommended waketime to be displayed in a graphical user
interface element of at least one of the one or more personalized
graphical user interfaces.
[0022] In some other further such embodiments, the one or more of
the second recommended bedtime, the recommended sleep duration, and
the recommended waketime that are displayed in the graphical user
interface element may be, respectively, different from the
recommended bedtime by a first time increment, different from the
selected sleep duration by a second time increment, and different
from the scheduled waketime by a third time increment.
[0023] In some embodiments, causing the one or more personalized
graphical user interfaces to be generated may further include
determining, based on the sleep data of the user, whether a bedtime
of the user for one of the sleep sessions of the user is within a
threshold of the scheduled bedtime, and causing information
associated with the determination to be displayed in a graphical
user interface element of at least one of the one or more
personalized graphical user interfaces.
[0024] In some further embodiments, causing the information
associated with the determination to be displayed may include
causing a timeline to be displayed showing a graphical indication
of the threshold relative to the timeline and a graphical
indication of the bedtime relative to the timeline.
[0025] In some other further embodiments, causing the one or more
personalized graphical user interfaces to be generated may further
include causing, based on a determination that the bedtime of the
user is not within the threshold of the scheduled bedtime, one or
more of a second recommended bedtime, a recommended sleep duration,
and a recommended waketime to be displayed in a graphical user
interface element of at least one of the one or more personalized
graphical user interfaces.
[0026] In some other further embodiments, the one or more of the
second recommended bedtime, the recommended sleep duration, and the
recommended waketime that are displayed in the graphical user
interface element may be, respectively, different from the
recommended bedtime by a first time increment, different from the
selected sleep duration by a second time increment, and different
from the scheduled waketime by a third time increment.
[0027] In some embodiments, the sleep efficiency may be
representative, at least in part, of a correlation between sleep
state duration data for one or more sleep sessions stored in the
sleep log data store and sleep session duration data for the
corresponding plurality of sleep sessions, the sleep state duration
data may be representative of the total amount of time the user
that is associated with the sleep session spent in a subset of
non-awake sleep states during the sleep session, and the sleep
session duration data may be representative of the total duration
of the sleep session.
[0028] In some embodiments, the method may further include, prior
to obtaining the recommended bedtime and causing the recommended
bedtime to be displayed in the graphical user interface element of
the at least one of the one or more personalized graphical user
interfaces determining that the user is a new user without sleep
data stored in the sleep log data store, and causing a suggested
sleep duration to be displayed in a graphical user interface
element of at least one of the one or more personalized graphical
user interfaces. The suggested sleep duration may based on one or
more of a default value and a target sleep duration determined
based on one or more of the sleep logs including sleep data derived
from the sensor data generated by the other biometric monitoring
devices of the other users.
[0029] In some embodiments, the method may further include
determining that a first number of sleep logs associated with the
account assigned to the user exist in the sleep log data store, and
the causing the one or more personalized graphical user interfaces
to be displayed may occur after the determination.
[0030] In one embodiment, a system for setting a sleep schedule of
a user of a biometric monitoring device may be provided. The system
may include one or more processors and a memory. The one or more
processors may be communicatively connected with the memory, and
the memory may store instructions that, when executed, cause the
one or more processors to obtain sleep data derived from sensor
data generated by the biometric monitoring device, the sleep data
including data regarding a plurality of sleep sessions and
specifying various sleep states of the user for the respective
sleep sessions; store the sleep data in a sleep log data store as
one or more sleep logs associated with an account assigned to the
user, the sleep log data store also storing other sleep logs
including other sleep data derived from other sensor data generated
by other biometric monitoring devices of other users; and calculate
a target bedtime based on a scheduled waketime of the user and a
sleep efficiency derived, at least in part, from the sleep data for
one or more users stored in the sleep log data store.
[0031] In some embodiments, the target bedtime may be based on the
sleep efficiency of the other users of the other biometric
monitoring devices. In some other embodiments, the target bedtime
may be based on the sleep efficiency of the user of the biometric
monitoring device.
[0032] In some embodiments, the memory may further store
instructions that, when executed, cause the one or more processors
to obtain sleep state duration data for a sleep session stored in
the sleep log data store, the sleep state duration data being
representative of the total amount of time the user that is
associated with the sleep session spent in one or more sleep states
during the sleep session, determine sleep session duration data for
the sleep session stored in the sleep log data store, the sleep
session duration data being representative of the total time of
that sleep session. The sleep efficiency may be representative, at
least in part, of a correlation between sleep state duration data
for one or more sleep sessions and sleep session duration data for
the corresponding one or more sleep sessions.
[0033] In some further embodiments, the memory may further store
instructions for controlling the one or more processors to obtain
the scheduled waketime of the user from the user via a graphical
user interface.
[0034] In some other further embodiments, the calculation of the
target bedtime may be further based on the sleep efficiency of the
user.
[0035] In some other further embodiments, the calculation of the
target bedtime may be further based on a selected sleep duration of
the user for a sleep session.
[0036] In some other further embodiments, the memory may further
store instructions for controlling the one or more processors to
obtain the selected sleep duration of the user for the sleep
session.
[0037] In some such embodiments, the memory may further store
instructions for controlling the one or more processors to
determine a regression model that relates the sleep session
duration data for a plurality of sleep sessions stored in the sleep
log data store to corresponding sleep state duration data for the
plurality of sleep sessions, and the calculation of the target
bedtime may be further based on the regression model.
[0038] In some further such embodiments, the sleep efficiency for
each sleep session may be calculated, at least in part, by dividing
the sleep state duration data for that sleep session by the sleep
session duration data for that sleep session.
[0039] In some other further such embodiments, the plurality of
sleep sessions may be for the other users of the other biometric
monitoring devices.
[0040] In some other further such embodiments, the regression model
may be a regression model such as a linear regression model, a
nonlinear regression model, a parametric regression model, a
nonparametric regression model, a semiparametric regression model,
and a multivariate linear regression model.
[0041] In some other further such embodiments, the sleep session
duration data and corresponding sleep state duration data for the
plurality of sleep sessions that may be included in the regression
model are associated with sleep sessions with waketimes that are
within a first threshold amount of the scheduled waketime.
[0042] In some other further such embodiments, the sleep session
duration data and corresponding sleep state duration data for the
plurality of sleep sessions that may be included in the regression
model are associated with sleep state duration data that are within
a second threshold amount of the selected sleep duration.
[0043] In some other further such embodiments, the sleep session
duration data and corresponding sleep state duration data for the
plurality of sleep sessions that may be included in the regression
model are associated with sleep sessions with waketimes that have
occurred on the same day of the week as the day of the week on
which the scheduled waketime occurs.
[0044] In some other further such embodiments, the regression model
may account for one or more of one or more specific days of the
week, a specific time of year, holidays, workdays of the user,
non-workdays of the user, a seasonal time change, a geographic
location, travel by the user between at least two time zones,
exercise of the user, and a duration of daylight in a day.
[0045] In some embodiments, the sleep efficiency for each sleep
session may be calculated, at least in part, by dividing the sleep
state duration data for each sleep session by the sleep session
duration data for each corresponding sleep session, respectively,
and the sleep efficiency may be accounted for, at least in part, by
multiplying the selected sleep duration by a factor that is based
on the sleep efficiencies for the plurality of sleep sessions to
determine a predicted sleep session duration.
[0046] In some other embodiments, the memory may store instructions
for further controlling the one or more processors to cause a
notification mechanism to produce a notification relating to a
comparison of sleep state duration data for one or more sleep
sessions with the selected sleep duration data.
[0047] In some embodiments, the memory may store instructions for
further controlling the one or more processors to determine a
recommended bedtime based on the target bedtime. The recommended
bedtime may be further based on one or more of one or more specific
days of the week, a specific time of year, holidays, workdays of
the user, non-workdays of the user, a seasonal time change, a
geographic location, travel by the user between at least two time
zones, exercise of the user, and a duration of daylight in a
day.
[0048] In some embodiments, the sleep efficiency may be further
based on of historical sleep efficiency data associated with one or
more of a proper subset of one or more specific days of the week, a
time of year, holidays, workdays of the user, non-workdays of the
user, a seasonal time change, a geographic location, travel by the
user between at least two time zones, exercise of the user, and a
duration of daylight in a day.
[0049] In some embodiments, the system may further include the
biometric monitoring device that includes one or more sensors
configured to generate the sensor data, and a communications
interface to communicate the sensor data to the one or more
processors.
BRIEF DESCRIPTION OF THE DRAWINGS
[0050] The various implementations disclosed herein are illustrated
by way of example, and not by way of limitation, in the figures of
the accompanying drawings, in which like reference numerals refer
to similar elements.
[0051] FIG. 1 depicts a high-level flow diagram of sleep scheduling
and sleep tracking techniques discussed herein.
[0052] FIG. 2 depicts a high-level flow diagram that outlines
techniques for obtaining a selected sleep duration of an
individual.
[0053] FIG. 3 depicts a high-level flow diagram of techniques for
obtaining a scheduled waketime.
[0054] FIG. 4 depicts a high-level flow diagram of techniques for
obtaining a selected bedtime.
[0055] FIG. 5 is a block diagram of an example computational
environment that may be used to implement the techniques and
methods discussed herein.
[0056] FIG. 6 depicts a high-level schematic of a sleep tracking
system.
[0057] FIG. 7 is an example graphical user interface for a user new
to sleep tracking.
[0058] FIG. 8 is another example graphical user interface for a
user new to sleep tracking.
[0059] FIG. 9 is an example of a graphical user interface that may
be generated for a user new to sleep tracking.
[0060] FIG. 10 is an example of a graphical user interface that may
be generated in order to allow a user to provide the amount of
sleep that they believe they typically attain each night.
[0061] FIG. 11 is an example of a graphical user interface that may
be used to confirm whether a selected sleep duration for a user is
sufficient.
[0062] FIG. 12 is an example of a graphical user interface that may
be presented to a user if the user selects a selected sleep
duration that is less than a recommended amount of sleep
duration.
[0063] FIG. 13 is an example of a graphical user interface that may
be presented to a user if the user previously indicated that they
would like to increase their selected sleep duration.
[0064] FIG. 14 is an example of a graphical user interface that may
be presented to a user in order to allow the user to change their
selected sleep duration.
[0065] FIG. 15 is an example of a graphical user interface that may
be used to provide instructions to a user of a wearable biometric
tracking device with manually activated sleep tracking.
[0066] FIG. 16 is an example of a graphical user interface that may
be used to provide instructions to a user of a wearable biometric
tracking device with automatic sleep tracking.
[0067] FIG. 17 is an example of a graphical user interface that may
be used to provide instructions to a user once initial sleep
tracking parameters have been established.
[0068] FIG. 18 is an example of a graphical user interface that may
be used to convey sleep performance data to a user.
[0069] FIG. 19 is an example of a graphical user interface that may
be used to introduce a user to sleep scheduling features.
[0070] FIG. 20 is an example of a graphical user interface that may
be used to present typical sleep duration of the user and inquire
whether that amount of sleep duration is sufficient.
[0071] FIG. 21 is another example of a graphical user interface
that may be used to present typical sleep duration of the user and
inquire whether that amount of sleep duration is sufficient.
[0072] FIG. 22 is an example of a graphical user interface that may
be used to present a recommended sleep duration and allow a user to
confirm or modify that recommended sleep duration.
[0073] FIG. 24 is an example of a graphical user interface that may
be used to present a recommended sleep duration to the user and to
inquire whether that amount of sleep duration is sufficient.
[0074] FIG. 23 is an example of a graphical user interface that may
be used to allow the user to update a selected sleep duration.
[0075] FIG. 25 is an example of a graphical user interface that may
be used to introduce a user to the concept of a sleep schedule.
[0076] FIG. 26 is an example of a graphical user interface that may
be used to present the user's typical waketime to the user and to
inquire whether that waketime is acceptable as a scheduled
waketime.
[0077] FIG. 27 is an example of a graphical user interface that may
be used to allow the user to adjust their scheduled waketime.
[0078] FIG. 28 is an example of a graphical user interface that may
be used to allow the user to indicate whether or not an alarm
should be set to wake the user up at the scheduled waketime.
[0079] FIG. 29 is a plot of an example linear regression model fit
to a plurality of sleep data points.
[0080] FIG. 30 is a plot of an example non-linear regression model
fit to the plurality of sleep data points of FIG. 29.
[0081] FIG. 31 is an example of a graphical user interface that may
be used to recommend a personalized recommended bedtime to a user
and to allow the user to confirm or reject the recommended bedtime
for use as a selected bedtime.
[0082] FIG. 32 is an example of a graphical user interface that may
be used to allow the user to adjust the selected bedtime.
[0083] FIG. 33 is an example of a graphical user interface that may
be used to establish a reminder in advance of a selected
bedtime.
[0084] FIG. 34 is an example of a graphical user interface that may
be used to allow the user to change timing information for a
bedtime reminder.
[0085] FIG. 35 is an example of a graphical user interface that may
be used to allow a user to specify on which days to provide a
bedtime reminder.
[0086] FIG. 36 is an example of a graphical user interface that may
be used to provide summary information regarding sleep data and to
compare sleep data against desired sleep goals.
[0087] FIG. 37 is another example of a graphical user interface
that may be used to provide summary information regarding sleep
data and to compare sleep data against desired sleep goals.
[0088] FIG. 38 is an example of a graphical user interface that may
be used to allow a user to edit sleep scheduling parameters.
DETAILED DESCRIPTION
[0089] An aspect of the present disclosure relates to assisting and
guiding users to set goals that promote consistent sleep behavior.
Consistent sleep behavior is a foundation of healthy sleep due in
part to its relation to the circadian rhythm which regulates many
bodily functions, including sleep. For example, this rhythm affects
when a person is awake, aware, tired, and ready to sleep. The more
regular a person's sleep cycles are, the easier it may be for the
person's body to function in accordance with the circadian rhythm.
Consistent sleep behavior, which may include a consistent waketime,
bedtime, and/or sleep duration, helps establish this rhythm and
keep a person's sleep well patterned and healthy. The present
disclosure is directed to methods, techniques, apparatuses,
systems, and the like which help guide a user to set goals for
sleep duration, wakeup time, and/or bedtime that will in turn help
establish consistent sleep behaviors. Some of the techniques
discussed in this disclosure utilize "sleep tracking," which
includes the monitoring, i.e., tracking, of a user's sleep by, at
least in part, analyzing sleep data that is derived from sensor
data generated by a biometric monitoring device, in order to better
guide a person to a more consistent sleep cycle. The present
inventors have determined that the methods, techniques,
apparatuses, systems, and the like disclosed herein may, in some
cases, be used to promote better sleep consistency and improve the
sleep of users.
[0090] As noted above, one factor for consistent sleep behavior is
sleep duration of the user for a given sleep session; however, each
person does not require the same sleep duration. For example, while
a typical sleep duration recommendation for adults is 7-9 hours of
sleep, different people may need different sleep durations in order
to be healthy, productive, and/or to feel rested. Therefore, the
present disclosure is intended, at least in part, to determine,
recommend, and/or set an individualized or personalized recommended
sleep duration for each user. Further, some of the embodiments
described herein may provide recommended sleep duration with higher
fidelity as compared to the two hour window provided by a generic
recommendation of 7-9 hours of sleep. For example, the embodiments
described herein may generate a sleep duration recommendation that
is, in some cases, a range of durations (e.g., 8+/-0.5 hours) or,
in other cases, a specific duration (e.g., 8 hours).
[0091] A "sleep session," as used herein, may generally be
considered data and/or logic that represents a period of time
during which an individual is attempting to sleep or is actually
sleeping. The precise data or logic of a sleep session may vary
depending on a variety of factors. For example, the start time and
end time of a sleep session may be established according to a
variety of different techniques. For example, the start time of a
sleep session may be derived from sensors when an individual first
enters a bed in an attempt to sleep (such as may be measured by a
biometric monitoring device that includes a pressure sensor located
in a bed--if the sensor detects pressure commensurate with the
presence of the individual, then the biometric monitoring device
may determine that the individual in in bed and is attempting to
sleep), when the individual manually indicates to a biometric
monitoring device the beginning of a sleep session (such as by
pressing a button on a biometric monitoring device to indicate the
start of a sleep session), and/or when a processor has determined,
from data generated by, for instance, the biometric monitoring
device, that a person has engaged in behavior indicative of the
start of a sleep session (e.g., by remaining generally motionless
for an extended period of time, such as may be detected by
accelerometers or other motion sensors in the biometric monitoring
device).
[0092] In some cases, the start of a sleep session may be
contemporaneous or near-contemporaneous with when a person actually
falls asleep. For example, if data from a wearable biometric
monitoring device worn by an individual indicates that the person
has transitioned from an awake state to an asleep state (without
any data indicating that the individual is trying to go to sleep
beforehand), then such a transition may be deemed to be the start
or beginning of the sleep session. Similarly, for example, the
sleep session may end when a person exits a bed, when the
individual manually inputs to the biometric monitoring device the
end of the sleep session, and/or when the processor has determined,
from data generated by the biometric monitoring device, that the
person has stopped sleeping (for example, accelerometers in the
biometric monitoring device indicate that the person is walking or
otherwise engaged in prolonged movement). In some embodiments, each
sleep session may be bounded by a respective bedtime of the user
and respective waketime of the user. It is to be understood that a
sleep session may be punctuated by periods of wakefulness or
restlessness--for example, an individual may get up to go to the
bathroom, let a pet out, or nurse a baby. Such short periods of
wakefulness, when followed by a return to sleep, would not be
viewed as the "end" of a sleep session. As will be clear, the exact
start and end times of a sleep session may be subject to some
variability depending on what criteria are used to determine such
parameters; it is to be understood that the concepts and techniques
discussed herein may be practiced using any of a variety of such
different definitions of what a sleep session is. In the context of
computing or processing systems that be used to implement the
techniques and methods discussed herein and to collect
physiological data, e.g., heart rate, movement, breathing rate,
etc., of an individual during sleep and log such data, the term
"sleep session" is to be understood to refer to the window of time
that such a computing or processing system establishes as being
representative of the individual's actual sleep session. The
duration of a sleep session, which may include periods of both
sleep and wakefulness, as described above, is referred to herein as
the "sleep session duration."
[0093] The biometric monitoring devices that may be used to collect
biometric sensor data, e.g., physiological data, that may be
evaluated to determine one or more types of sleep data usable with
the techniques discussed herein may be wearable biometric
monitoring devices, e.g., such as a wrist-wearable fitness tracker
such as the Fitbit.TM. Charge.TM., Surge.TM., or other similar
device; non-wearable biometric monitoring devices, such as
sleep-monitoring sensor systems that are integrated into a mattress
pad, mattress, or similar bed-integrated system or that are
configured to operate from a location remote from a bed, e.g., such
as a nightstand adjacent to a bed; or combinations thereof.
[0094] As an individual sleeps or prepares to sleep, the
embodiments discussed herein may detect that the individual passes
through a variety of different states, such as an awake state, an
asleep state, a waking state (in which the individual is
transitioning from an asleep state to an awake state), etc. At a
minimum, the embodiments may determine that an individual is in one
of at least two states, "awake" and "asleep," at one or more given
times during a sleep session. In some cases, additional
determinations may be made to identify when an individual is in one
or more other sleep states, e.g., a restless state, a waking or
starting-to-wake state, etc. Moreover, in some implementations,
sleep states may be further granularized into multiple sleep stages
or sleep phases. For example, an asleep state may include one or
more periods when the individual is in stage 1, e.g., NREM1, N1,
somnolence, light, or drowsy sleep) sleep, one or more further
periods where the individual is in stage 2, e.g., NREM2 or N2)
sleep, one or more further periods where the individual is in stage
3, e.g., NREM3, N3, deep, delta, or slow-wave sleep) sleep, and one
or more periods of random eye movement (REM) sleep; it is not
necessarily the case that an individual will pass through all of
these stages of sleep in any given sleep session--each of these
sleep stages may also be viewed as a type of sleep state or as part
of a sleep state (if a sleep state includes time spent in multiple
sleep stages). An individual's sleep duration is a characterization
of the amount of time that that individual spends in one or more
sleep states other than the awake state during a sleep session. The
sleep states of interest for determining sleep duration (which may
also be referred to herein as "sleep state duration") may include
sleep states that represent one or more of the different stages of
sleep. In some cases, sleep duration may simply be a measure of how
much time a given individual is asleep during a sleep
session--regardless of whether the individual is experiencing
restless, light, deep, or REM sleep. In other cases, such a system
may be configured to only credit time spent in deep or REM sleep
towards an individual's sleep duration. Thus, sleep state duration
may, in some implementations, be inclusive of all of the time that
a user spent in any of the non-awake sleep states. The sleep state
duration may, in other implementations, be inclusive of all of the
time that a user spent in a non-empty proper subset of the
non-awake sleep states.
[0095] As noted above, the user's sleep duration, including the
time spent in various sleep states, as well as other sleep-related
information about the user, may be determined and/or obtained from
sleep data derived from sensor data generated by a biometric
monitoring device. For example, the biometric monitoring device may
include a plurality of sensors which are configured to generate
data indicative of the movements and physiological state of the
user; when the user wears the biometric monitoring device to bed,
at least some of the biometric monitoring device's sensors may
generate data that is collected and analyzed by one or more
processors to derive sleep data from this sensor data. The sensors
collecting such data may be, for instance, a motion sensor like a
multi-axis accelerometer or an optical heart rate monitor module
including a photoplethysmographic sensor. The generated sensor data
may include, for instance, movement data of the user or heart rates
of the user. The generated sensor data may be analyzed and/or
categorized into sleep data, which may include, for example, the
sleep duration, sleep states, sleep stages, sleep state duration,
sleep session duration, waketime, and/or bedtime of the user for
each sleep session--for example, various Fitbit.TM. biometric
monitoring devices, such as the Charge.TM., Charge HR.TM.,
Pulse.TM., utilize accelerometer data to track a person's movement
during sleep and identify periods or epochs during which the person
is awake, restless, or awake. Sleep data may also include various
biometric measurements that, while not directly related to sleep,
may nonetheless be useful in determining various sleep-related
data, e.g., heart rate (heart rate may indicate, in part, a
particular sleep state or sleep stage of a person), respiratory
rate (respiratory rate may also indicate, in part, a particular
sleep state or sleep stage of a person), movement levels, etc.
Based on this data, a total sleep session duration, total amount of
time actually spent asleep, and total amount of time spent awake or
restless may be determined. For example, for a given sleep session,
the sleep data for that session may indicate that the individual
went to bed at 11:06 PM and woke up at 6:33 AM (for a sleep session
duration of 7 hours and 27 minutes), was restless 15 times during
the night, woke up 3 times, spent 37 minutes total in an awake or
restless state, and was actually asleep for 6 hours and 50 minutes.
It is to be understood that "based on," in the context of this
disclosure, also means "based, at least in part, on," and that
these two phrases may be used interchangeably. Moreover, it is to
be understood that in instances where "based on" or "based, at
least in part, on" are used, one of the implementations that is
embraced by such language, unless the context of such usage
indicates otherwise, is the singular case, e.g., where a
determination of a parameter is "based exclusively on" a particular
type of data. For example, if a parameter A is calculated based on
(or based, at least in part, on) a parameter B, this is to be
understood as encompassing situations in which parameter A may be
calculated solely based on parameter B as well as situations in
which parameter A may be calculated based on parameter B in
combination with other parameters, such as parameter(s) C (and
D).
[0096] For example, the biometric monitoring device's sensors may
generate movement data of the user which may be analyzed by one or
more processors to determine a user's sleep state, e.g., based on
sleep stage or on other determinations without reference to a
particular sleep stage, such as overall restlessness. One such
analysis may include the generation of a movement measure or sleep
coefficient based on, at least in part, the movement data that is
collected for a period of time, e.g., 30 seconds. Then, based on
the movement measure of a larger period of time, e.g., a window of
time larger than the period of time, an activity level of the user
is calculated; the activity level may be active or inactive. A
user's sleep state may then be classified based, at least in part,
on the activity levels of the user calculated for a contiguous time
period over a number of windows of time.
[0097] A further example of obtaining, measuring, and/or
determining sleep data, such as a user's sleep duration and sleep
states, is more fully described in U.S. patent application Ser. No.
14/859,192, filed Sep. 18, 2015, titled "Movement Measure
Generation In A Wearable Electronic Device", which is hereby
incorporated herein by reference for all purposes.
[0098] Some techniques of the present disclosure include generating
one or more personalized graphical user interfaces for setting a
sleep schedule of a user of a biometric monitoring device. At least
some of the graphical user interfaces may be generated in a display
of the biometric monitoring device while at least some others may
be generated in a display of an electronic device such as a
smartphone, tablet, and/or personal computer.
[0099] As noted earlier, the techniques and systems discussed
herein may be used to guide users of biometric monitoring devices
to engage in more consistent sleep behavior.
[0100] FIG. 1 depicts a high-level flow diagram of sleep scheduling
and sleep tracking techniques as discussed in more detail below. As
can be seen in FIG. 1, the overall technique may involve obtaining,
through various mechanisms, one or more parameters affecting an
individual's sleep schedule--for example, obtaining one or more of
a selected sleep duration 101, a scheduled waketime 102, and a
selected bedtime 103 may be obtained through one or more different
techniques, as discussed in more detail below. After one or more of
such parameters have been obtained, the individual's sleep may be
tracked 104 to determine various characteristics of the person's
sleep behaviors, which may be stored as sleep data. The sleep data
may be used for a variety of purposes, including for presentation
105 to the individual and for providing personalized recommended
bedtimes, as discussed in more detail later.
[0101] The various sleep parameters that are obtained, e.g.,
selected sleep duration, scheduled waketime, and selected bedtime,
may be obtained relatively simultaneously, e.g., by a user entering
all three parameters into a common user interface (see, for
example, the GUI of FIG. 38, which is discussed later), or in a
staged or staggered manner. For example, for an individual who is
new to sleep tracking or who has not previously set up sleep
tracking on a biometric monitoring device, a selected sleep
duration may first be obtained and then the individual's sleep may
then be tracked in order to determine how consistently the
individual is meeting such a sleep duration goal--at some later
point, further sleep parameters may be obtained, such as the
individual's scheduled waketime and selected bedtime. Such a staged
progression may allow for the gradual introduction of the
individual to the concept of sleep tracking and scheduling, and may
serve to be a more effective mechanism for encouraging more
consistent sleep behavior. Alternatively, if a user waits for some
period of time before exploring the sleep tracking functionality of
their biometric monitoring device, the biometric monitoring device
may nonetheless collect sleep data for the user if the user wears
it bed. Thus, when the user initially explores the sleep tracking
functionality, the biometric monitoring device may already have
collected some initial sleep data that may be used to suggest a
selected sleep duration that is based on the user's actual sleep
behavior. In a further variation, a user's past sleep data that was
collected with another biometric monitoring device may be used in
place of sleep data collected with the biometric monitoring device
being set up, and a selected sleep duration (and other parameters)
may be provided based on such earlier-collected sleep data.
[0102] FIG. 2 depicts a high-level flow diagram that outlines
techniques for obtaining a selected sleep duration of an
individual. To start with, an initial selected sleep duration may
be obtained, e.g., by querying the individual to enter a typical
sleep duration (as estimated and reported by the individual) in
block 201 or by analyzing historical sleep data of the individual
in block 202 to determine, for example, an average sleep duration
for the individual for some period of time. Once an initial
selected sleep duration has been obtained, it may optionally be
used directly as the selected sleep duration or may, for example,
be subject to modification. For example, the initial selected sleep
duration may be presented to the individual and the individual may
be prompted in block 203 to indicate if the initial selected sleep
duration is acceptable or if the individual wishes to change it. If
the individual indicates that the initial selected sleep duration
is acceptable, then the initial selected sleep duration may be used
as the selected sleep duration in block 205. If the individual
indicates the initial selected sleep duration is not acceptable,
then the individual may be queried in block 204 for an alternate
selected sleep duration. Once the individual has either confirmed
the initial selected sleep duration as the selected sleep duration
or selected an alternate selected sleep duration, the selected
sleep duration may be considered to be obtained in block 205.
[0103] As mentioned earlier, an individual's actual sleep duration
may be tracked, e.g., via sleep data obtained from a biometric
monitoring device, in order to determine to what degree the
individual's actual sleep duration correlates with the individual's
selected sleep duration. In some implementations, the individual
may be prompted to re-evaluate their selected sleep duration to
determine if a more achievable selected sleep duration should be
selected--for example, if the individual is consistently sleeping
only 6 hours a night but has a selected sleep duration of 7 hours,
then the individual may be prompted to select a lower selected
sleep duration initially. If the individual's sleep data then
indicates a subsequent increase in the individual's actual sleep
duration, then the individual may be intermittently encouraged to
raise the selected sleep duration, e.g., by 15 minutes or 30
minutes at a time, in order to gradually reach the original
selected sleep duration.
[0104] Once a selected sleep duration for an individual has been
obtained, such information may eventually be used with an obtained
scheduled waketime and collected sleep data in order to provide a
recommended bedtime.
[0105] FIG. 3 depicts a high-level flow diagram of techniques for
obtaining a scheduled waketime. To begin with, the individual may
be queried in block 301 to provide a typical waketime, e.g., the
time at which the individual normally wakes up, or, alternatively
(or additionally), the individual may be presented in block 302
with a typical waketime, e.g., an average waketime, as determined
from historical sleep data of the individual. After the typical
waketime is obtained, the typical waketime may optionally be
presented to the individual and the individual prompted to confirm
whether or not the typical waketime is acceptable to the individual
in block 303. If the individual indicates in block 303 that the
typical waketime is not acceptable in block 303, the individual may
be prompted to select an alternate scheduled waketime in block 304,
which may then be used as the scheduled waketime in block 306. If
the individual indicates in block 303 that the typical waketime is
acceptable, then the typical waketime may be used as the scheduled
waketime in block 305. Once the scheduled waketime has been
obtained, then, in some implementations, the technique may
optionally also include configuring a waketime alarm. For example,
in block 306, the individual may be presented with a query as to
whether or not the individual wishes to set a waketime alarm. If
the individual indicates that they wish to set a waketime alarm in
block 306, a waketime alarm, e.g., an audible alarm on a smartphone
or a vibratory alarm on a wearable biometric monitoring device, may
be set to activate at the scheduled waketime in block 307 before
the waketime alarm settings may be considered obtained in block
308. If the individual indicates that no waketime alarm is to be
set in block 306, then the waketime alarm settings may be
considered to be obtained (with no alarm set) in block 308.
[0106] Once a scheduled waketime and a selected sleep duration have
been obtained, a selected bedtime may be obtained. FIG. 4 depicts a
high-level flow diagram of techniques for obtaining a selected
bedtime. To obtain a selected bedtime, a target bedtime may first
be determined, taking into account the sleep efficiency of the
individual (or of other individuals), in block 401; such a
determination may also be based on the selected sleep duration and
the scheduled waketime. Once the target bedtime has been
determined, a recommended bedtime may be optionally determined in
block 402 based on the target bedtime, as discussed in more detail
below--the recommended bedtime may, for example, be modified from
the target bedtime to be the closest 15-minute time on the hour to
the target bedtime (in other implementations, the target bedtime
may simply be used as the recommended bedtime). Once determined,
the recommended bedtime may be presented to the individual in block
403 and the individual may be requested to confirm if the
recommended bedtime is acceptable to the individual as a selected
bedtime in block 404. If the individual indicates in block 404 that
the recommended bedtime is acceptable, then the recommended bedtime
may be used as the selected bedtime in block 406. If the individual
indicates in block 404 that the recommended bedtime is not
acceptable as the selected bedtime, then the individual may be
prompted in block 405 to provide an alternate selected bedtime in
block 405, which may then be used as the selected bedtime in block
406.
[0107] After a selected bedtime has been determined, the individual
may be optionally presented with an opportunity to set a bedtime
reminder in block 407. If the individual indicates in block 408
that no bedtime reminder is desired, then the bedtime reminder
settings may be considered to have been obtained (with no reminder
set) in block 412. If the individual indicates in block 407 that a
bedtime reminder is desired, then initial bedtime reminder timing
information may be presented to the individual in block 408, after
which the individual may be presented in block 409 with an
opportunity to indicate whether the initial bedtime reminder timing
information is acceptable. If so, then the technique may proceed to
block 411 using the initial bedtime reminder timing information as
the bedtime reminder setting. If not, then the technique may first
query the individual to enter alternate bedtime reminder timing
information in block 410 before proceeding to block 412.
[0108] The techniques outlined above may be implemented in a
variety of ways; the following discussion examines some of these
implementations in the context of some example graphical user
interfaces (GUIs). It is to be understood that GUIs may include a
multitude of different graphical user interface elements, e.g.,
fields, controls, images, charts, graphs, buttons, sliders, etc.
that may be used to convey or receive data to or from a user. While
discrete GUIs are discussed below, such discrete GUIs may also be
provided by simply replacing or updating a GUI element in one GUI
to provide the information conveyed in another GUI (for example,
the GUIs of FIGS. 12 and 13, which are discussed later, may be the
same GUI with the same GUI elements, but with different data placed
in two fields--"Adults are recommended to get 7-9 hours but
everyone is different. For now, let's set your sleep goal to" is
replaced by "Adults are recommended to get 7-9 hours. For now,
let's set your sleep goal to," and "6 hr 30 min" is replaced by "7
hr 00 min"). Thus, in the discussions below, it is to be understood
that reference to providing or generating a GUI is inclusive of
causing a GUI element of a GUI to act in a particular way or
display particular data or information that is to be conveyed via
that GUI.
Computational Environment
[0109] The techniques and methods discussed herein are
implementable in a variety of different computational environments
involving a variety of computing platforms and may make use of a
number of different computing systems. It is to be understood that
the techniques and methods discussed herein are not to be limited
to a particular computational environment, but may be implemented
in a number of different specific ways consistent with this
disclosure, and that all of these different implementations are
within the scope of this disclosure.
[0110] FIG. 5 depicts a block diagram of an example computational
environment that may be used to practice the techniques and methods
discussed herein. As can be seen, the computational environment may
include a remote server or servers, e.g., a cloud-based computing
system, that may include one or more processors, memory (such as
random-access memory that may be used to store computer-executable
instructions during program execution and for temporary data
storage), storage (such as hard disk arrays or other non-volatile
storage media), and a communications interface (such as a TCP/IP
network connection or similar communications interface). Such
remote servers may be communicatively connected with a plurality of
mobile communications devices, e.g., smartphones, tablets, etc.,
that may be owned and/or used by individuals owning and/or using
biometric monitoring devices. The mobile communications devices may
each similarly have one or more processors, memory, storage, and
communications interfaces that allow them to communicate with the
remote servers via a network. The communications interfaces of the
mobile communications devices may also include a communications
interface for communicating with the biometric monitoring devices,
e.g., a Bluetooth communications interface or similar short-range,
low-power link. The biometric monitoring devices may also have
their own processors, memory, storage, and communications
interfaces, as well as one or more biometric sensors that may be
used to monitor activities of their users.
[0111] These various components or elements of the example
computational environment may be configured to individually or
cooperatively perform the various techniques and methods discussed
herein. For example, the mobile communications device(s) may serve
as an information relay between the remote server(s) and the
biometric monitoring device(s), and may also serve to provide the
GUIs discussed herein using, for example, a GUI engine that
includes logic or computer-executable instructions for controlling
the one or more processors of the mobile communications device to
provide the various GUIs discussed herein. The biometric monitoring
device(s) may, for example, collect data from the biometric
sensors, store such collected data locally in the biometric
monitoring device(s), and then upload the collected data to the
remote server via the mobile communications device(s). The
biometric monitoring device or the mobile communications device may
also provide alarm functionality, e.g., the waketime and/or bedtime
reminder alarms discussed later herein, via an alarm engine that
includes logic or computer-executable instructions for controlling
the one or more processors of such elements to provide such alarms.
The remote servers, for example, may provide long-term storage of
collected sleep data, as well as performing sleep data analysis,
such as identifying periods spent in each sleep state, the timing
of any waking events, etc., such as may be performed by a sleep
data analysis engine. Such sleep data analysis may alternatively or
additionally be performed by the mobile communications device
and/or the biometric monitoring device, depending on the particular
architecture that is implemented. A bedtime recommendation, as
discussed later herein, may also be provided by a bedtime
recommendation engine, which may include computer-executable
instructions or logic for controlling one or more of the processors
of one or more of the elements discussed above to determine a
bedtime recommendation according to the techniques discussed
herein.
[0112] The particular example depicted in FIG. 5 is only one
representative example of a system and computational environment
that may be used to implement the techniques and methods discussed
herein, and it is to be understood that other systems and
computational environments that may provide one or more aspects of
the methods and techniques discussed herein are also considered
within the scope of this disclosure.
Sleep Data Collection and Storage
[0113] As noted earlier, sleep data that is derived from sensor
data that is generated by a biometric monitoring device may be used
to evaluate how well the user is adhering to their desired sleep
duration, as well as to provide further tools that may assist the
user in attaining their desired sleep duration. The sleep data may
include data for a plurality of sleep sessions and may specify
various sleep states of the user for the respective sleep sessions,
as well as bedtimes, and/or waketimes of the user for each
respective sleep session. As stated above, the various sleep states
of the user may also include or be representative of one or more
various sleep stages of the user.
[0114] Sleep monitoring and/or tracking may also include storing
the sleep data in a sleep log data store as one or more sleep logs
associated with an account assigned to or associated with the user
as well as sleep logs that include sleep data of other users of
other biometric monitoring devices. The sleep log data store may be
located in one or more memories of one or more databases that may
be communicatively connected with each other as well as with the
biometric monitoring device of the user and with biometric
monitoring devices of the other users, whether directly or
indirectly. For example, a biometric monitoring device may include
a Bluetooth communications interface that may allow the biometric
monitoring device to connect with a user's smartphone, which may,
in turn, have a network communications interface that allows the
smartphone to wirelessly connect with a remote server or servers,
such as a cloud-based data storage system that may store the sleep
log data store. The smartphone may thus act to relay information
from the biometric monitoring device to the sleep log data store.
FIG. 6 depicts an example system for implementing such sleep
monitoring and/or tracking. As shown, the example system 600
includes a memory 602 and a processor 604 that are communicatively
connected with each other, as indicated by line 606.
[0115] In some embodiments, the system may include a biometric
monitoring device 608 that includes one or more sensors 610 and a
communications interface 612 to communicate with the one or more
processors 604 as indicated by line 614. The communications
interface may use any known hard-wired, wireless, or other
communication technique. The one or more sensors 610 may be
configured to generate biometric sensor data that may be used to
generate sleep data. In some embodiments, the sensor 610 of the
biometric monitoring device 608 may generate sensor data and the
memory 602 may contain instructions for controlling the processor
604 to obtain sleep data that is derived from the sensor data. In
some such embodiments, the processor 604 may be part of the
biometric monitoring device 608, on the user's computing device
(e.g., a computer or a smart phone), and/or on one or more
computing devices that are separate from the biometric monitoring
device 608.
[0116] It is to be understood that, in actual practice, the
processing and storage functionality discussed herein may be
distributed among a number of different devices, rather than a
single processor 604 and a single memory 602. For example, a
biometric monitoring device may have one or more processors and a
memory that are communicatively connected with one another and with
one or more sensors of the biometric monitoring device. The memory
of the biometric monitoring device may store computer-executable
instructions for controlling the one or more processors of the
biometric monitoring device to obtain sensor data from the one or
more sensors. In some implementations, the memory of the biometric
monitoring device may also store computer-executable instructions
for controlling the one or more processors of the biometric
monitoring device to analyze such collected sensor data to
determine one or more biometric measurements, e.g., heart rate,
steps taken, sleep state or stage, etc., as well as to communicate
collected sensor data and/or determined biometric measurement data
to a remote device, such as to a smartphone that may be
communicatively connected with the biometric monitoring device
(e.g., via Bluetooth connection) and/or a server, such as a
cloud-based server. The remote device, which may have its own one
or more processors and one or more memories, may store data
provided by the biometric monitoring device in the one or more
memories of the remote device, and may, in some implementations,
perform further analysis on such data in order to determine
additional biometric measurements or to refine
previously-determined biometric measurements. For example, a
biometric monitoring device may determine some biometric
measurements using simplified computational methods that, while
less accurate, may consume far less battery power than more
involved computational methods. If the sensor data used to
determine such biometric measurements is later transmitted to a
remote device, e.g., to a server, the sensor data may be
re-analyzed by the one or more processors of the remote device
using more computationally-intensive techniques that provide a more
accurate determination of that same biometric measurement. Thus, it
is to be understood that the determination and storage of sleep
data may be performed in any of a number of different devices
and/or locations, and the techniques discussed herein are not to be
viewed as being limited to implementation on only one type of
device or system.
[0117] As discussed above, sleep data may be stored in a sleep log
data store, which may be a data structure that stores sleep data
and associates such sleep data with particular sleep sessions.
There may be multiple instances of a sleep log data store. For
example, a biometric monitoring device may store several days'
worth of sleep sessions and sleep data, and a smartphone that is
communicatively connected with the biometric monitoring device may
store several weeks' or months' worth of sleep log data, and a
remote device, e.g., a cloud-based storage system, may store months
or years of sleep log data (or even store such data
indefinitely).
[0118] It is also to be understood that the techniques discussed
herein may involve calculations and/or determinations that may be
made by processors located in a variety of different locations. For
example, in some implementations, a target bedtime determination
(discussed later herein) may be made by a processor or processors
of a wearable biometric monitoring device using data stored on the
biometric monitoring device. In other implementations, such a
target bedtime determination may be made by a processor or
processors of a mobile communications device, e.g., a smartphone,
that is paired with the wearable biometric monitoring device. In
yet other implementations, such a target bedtime determination may
be made by a processor or processors of a remote device, such as a
cloud server system, and the target bedtime determination (or a
recommended bedtime determination based thereupon, also discussed
later herein) may be sent to a mobile communications device, e.g.,
a smartphone, afterwards to allow such information to be provided
to the user via a GUI of the mobile communications device.
New User/User New to Sleep Tracking--Initial Selected Sleep
Duration Setup
[0119] As discussed earlier, in some implementations, a selected
sleep duration may be obtained for a new user. Various further
details of how such selected sleep durations may be obtained are
discussed in more detail below, along with graphical user
interfaces that may be used to assist in obtaining such selected
sleep durations.
[0120] FIGS. 7 and 8 depict examples of two different graphical
user interfaces that may be generated for a new user (each
represents a different option for introducing the new user to sleep
tracking). As can be seen, such graphical user interfaces (referred
hereinafter as a "GUI" or "GUIs") may include placeholders 701 for
information such as historical sleep data of the user, as well as a
welcome message--such historical sleep data may be collected by the
biometric monitoring device even if the user has not previously
used or otherwise enabled the sleep tracking functionality of the
biometric monitoring device before, although in this case, there is
no historical sleep data, so "no data" is displayed (in the GUI of
FIG. 8) and there are no indications of actual hours slept. These
GUIs may also include a "Get Started" prompt which may obtain input
from the user to setup sleep tracking for the user. As used herein,
a "prompt" may receive information and/or input from a user.
Further, also as used herein, any time a user is described as
inputting any data or information into a GUI, including selecting a
prompt in a GUI, it may be considered that a processor providing
such a GUI or otherwise linked or associated with the GUI, e.g., a
processor at a remote server that is provided such data or
information by, for example, accessing such data from a memory
where it has been stored, obtains such data, information, and/or
selection. Such setup for sleep tracking may include the generation
of additional GUIs to obtain sleep-related information from the
user and to display sleep-related information, as will be described
in more detail below. FIG. 9 depicts another GUI that may be
generated for a new user or a user being introduced to sleep
tracking. If the user responds to the prompt to "Get Started" with
sleep tracking, the new user may, in some implementations, be
introduced to the concept and importance of sleep tracking, e.g.,
by one or more follow-on GUIs such as the GUI of FIG. 9. The GUI of
FIG. 9 may include sleep-related information, such as information
regarding the importance of sleep, as well as a prompt, e.g., "OK",
which may be selected in order to transition to another GUI, such
as the GUI of FIG. 10. The GUI of FIG. 9 may be generated before or
after FIGS. 7 and 8 or may, in some cases, be omitted entirely.
[0121] FIG. 10 depicts a GUI that may be generated as part of a
technique for obtaining a selected sleep duration of the user. As
used herein, "selected sleep duration" is used synonymously with
"sleep goal," and represents the amount of actual sleep (as may be
estimated by a biometric monitoring device) that the user would
like to achieve during a sleep session or sleep sessions. As can be
seen, the GUI of FIG. 10 includes a prompt 1002 that can be
user-adjusted to specify an amount of sleep 1001 that the user
believes he or she gets during a typical sleep session, such as
typical night's sleep. In some implementations, the amount of sleep
entered into the prompt may be used as a starting point for
obtaining a selected sleep duration via additional GUIs, as is
discussed further below. In other implementations, the amount of
sleep input by the user to the GUI, i.e., obtained by the GUI of
FIG. 10, may be considered the selected sleep duration (or sleep
goal) of the user which may be used in other aspects of sleep
tracking as discussed below. In this later implementation, the call
of the GUI shown in FIG. 10 may specify "Please set an amount of
sleep you would like to get" or some variant thereof.
[0122] In some implementations, another GUI may be generated after
the amount of sleep is input to the GUI of FIG. 10. For instance,
FIG. 11 depicts a graphical user interface that may be generated
after FIG. 10. As can be seen, the GUI of FIG. 11 may include,
e.g., display, the amount of sleep 1101 obtained by the GUI of FIG.
10 and may provide a prompt 1102 to allow the user to confirm that
the typical amount of sleep input by the user is enough sleep or to
indicate that the user would like to get more sleep. The GUI may
obtain inputs from the user when the user selects the "Yes" or "No,
I Want More" prompts (or similar prompts) which may be included in
the GUI.
[0123] In some such implementations, if the user inputs in a GUI,
such as the GUI of FIG. 11, that the amount of sleep is sufficient,
then another GUI may be generated, such as FIG. 12, which depicts a
GUI that may be generated after FIG. 11. Here, as with the GUI of
FIG. 11, the GUI of FIG. 12 may include the amount of sleep 1201
obtained in FIG. 10 as well as information related to the amount of
sleep obtained in FIG. 10. For example, if the amount of sleep
obtained in FIG. 10 is outside a range of predetermined sleep
durations, then information may be included in the GUI that is
intended to educate the user about recommended sleep and/or
encourage the user to change the selected sleep duration to a value
within the range of predetermined sleep durations. For instance, in
FIG. 12, the information included in the GUI informs the user that
the selected sleep duration of 6 hours and 30 minutes is outside a
recommended sleep duration range of 7-9 hours for adults. However,
the GUI of FIG. 12 also acknowledges that every person is
different, thereby gently educating the user as to the recommended
sleep duration.
[0124] If the user inputs that more sleep is desired in the prompt
1202 of the GUI of FIG. 10, then the GUI of FIG. 13 may be
generated. The GUI of FIG. 13 includes sleep-related information
that may be displayed to the user, similar to that displayed in
FIG. 12, which here is information that informs the user about
recommended sleep durations for adults; since the user has already
indicated dissatisfaction with their typical sleep duration (by way
of selecting the "No, I Want More" prompt), the information
regarding the typical sleep durations of adults may be presented in
a more emphatic manner than in the GUI of FIG. 12, e.g., the phrase
"but everyone is different" may be omitted. The GUI of FIG. 13 also
includes a recommended sleep duration 1301 which may be determined
in any number of various ways, such as are described in detail
below. In some embodiments, the recommended sleep duration may be
calculated based, at least in part, on past or historical
user-selected sleep duration; sleep data, including the selected
sleep duration, of other users of sleep tracking and/or biometric
monitoring devices; a look-up table; demographics of the user
(e.g., age, weight, gender); a geographic location of the user;
specific days of the week; a specific time of year; holidays; a
seasonal time change; exercise of the user; duration of daylight;
and/or general guidelines of sleep durations for users. For
example, in FIG. 13, the system may set the recommended sleep
duration to 7 hours and 00 minutes, which is the lowest sleep
duration in the depicted range of recommended sleep durations for
adults. Such recommended sleep duration may also be calculated by
adding an incremental amount to the selected sleep duration
specified in the prompt 1002 in FIG. 10, such as increment the
selected sleep duration by 15 minutes or 30 minutes. The user may
then indicate that they find the selected sleep duration acceptable
or unacceptable via a prompt 1302.
[0125] The GUIs of both FIGS. 12 and 13 also include prompts which
may obtain the user's selection of whether the recommended or
selected sleep duration that is depicted is acceptable ("Sounds
Good V") or whether the user would like to choose her own sleep
duration ("Choose My Own"). If the user selects the "Sounds Good!"
prompt, then the recommended sleep duration as shown on either of
the GUIs shown in FIG. 12 or 13 may be stored as the selected sleep
duration.
[0126] If a GUI obtains the user's input to choose her own sleep
duration, e.g., the user selects the "choose my own" prompt in
either of the GUIs of FIG. 12 or 13, then another GUI may be
generated. FIG. 14 depicts a graphical user interface that may be
generated after 13 and, as can be seen, this GUI includes a
selected sleep duration 1401 and a prompt 1402 to allow the user to
change the selected sleep duration. This selected sleep duration
may, like in FIG. 13, override any previously obtained selected
sleep durations and become the selected sleep duration.
[0127] Once the selected sleep duration is obtained by the GUI, a
memory communicatively connected with one or more processors may
include instructions for controlling the one or more processors to
store the selected sleep duration in the memory or in a sleep log
data store such that the selected sleep duration is associated with
an account assigned to or associated with the user, as discussed
further below. The sleep log data store may, for example and as
discussed earlier, be a database or other structured data construct
that stores sleep logs associated with one or more user accounts
(which are each associated with a particular user). A sleep log,
for example, may be a record that is representative of sleep data
from a user's sleep session, e.g., it may have data indicating the
start and end of the sleep session, the amounts of time spent in
various sleep states, the number of times the person woke up during
the sleep session, etc. There may be multiple instances of sleep
log data stores that may be utilized in the various implementations
discussed herein--for example, a local sleep log data store may be
maintained in the memory of a user's biometric monitoring device or
smartphone and may be used to store sleep logs for that user.
Alternatively, or additionally, an external server or servers,
e.g., a cloud-based system, may provide a global sleep log data
store that may be used to store sleep logs for that user as well as
multiple other users; processors having access to either type of
sleep log data store may be able to access sleep data from the
sleep logs stored therein and to perform calculations utilizing
such data.
Instructing Users on how to Enable Sleep Tracking in a Biometric
Monitoring Device
[0128] After the user's selected sleep duration is obtained, in
some implementations a GUI may be generated that includes
information related to the user's biometric monitoring device, such
as further instructions for how to use their biometric monitoring
device to track sleep. In some embodiments, a determination may be
made as to whether the biometric monitoring device of the user is a
biometric monitoring device with automatic sleep tracking
functionality or a biometric monitoring device with
manually-triggered sleep tracking functionality. This may be
determined, for example, by comparing the model of the biometric
monitoring device against a list or lists of biometric monitoring
devices having automatic sleep tracking or manual sleep tracking.
For some biometric monitoring devices that may not be configured to
automatically detect, track, and/or monitor the user's sleep, the
GUI of FIG. 15 may be generated. FIG. 15 depicts a graphical user
interface generated for biometric monitoring devices that do not
include automatic sleep detection, tracking, and/or monitoring. As
can be seen, the GUI of FIG. 15 includes information confirming
that the selected sleep duration, i.e., sleep goal, has been set.
As noted above, once the selected sleep duration is input, it may
be stored in a memory and/or sleep log data store such that it is
associated with the user. The GUI of FIG. 15 also includes
instructional information about using the biometric monitoring
device in order to monitor and track the user's sleep--in this
case, the user is instructed to push and hold a button on the
biometric monitoring device to mark the start and stop sleep
tracking, e.g., the beginning and end of a sleep session. For some
biometric monitoring devices that may be configured to
automatically detect, track, and/or monitor the user's sleep, the
GUI of FIG. 16 may be generated. Here, the GUI of FIG. 16 includes
information confirming that the selected sleep duration, i.e.,
sleep goal, has been set, as well as instructional information
about using the biometric monitoring device in order to monitor and
track the user's sleep--in this case, the user need only wear the
biometric monitoring device and the biometric monitoring device may
automatically identify when the user is in a sleep session, so the
user is only prompted to remember to wear the biometric monitoring
device to bed. It will be appreciated that the particular GUI
displayed, e.g., such as the GUIs of FIGS. 15 and 16, may be
selected based on the determination as to whether or not the user's
biometric monitoring device has automatic sleep tracking.
[0129] FIG. 17 depicts a graphical user interface that may be
generated after FIG. 15 or 16. Here, the GUI may include
information about making changes to the selected sleep duration,
i.e., the sleep goal, as well as information related to sleep data
of the user, such as sleep duration data, waketimes, and/or
bedtimes. For example, the "You can edit your sleep goal here"
callout may be used to indicate a user interface element that may
allow a user to access, for example, a dashboard GUI such as is
shown in FIG. 38, which is discussed later, which may allow the
user to easily change various sleep scheduling parameters. For a
new user, the GUI of FIG. 17 may include the capability to display
sleep data, but there may not yet be enough (or any) sleep data
generated and/or collected by the user's biometric monitoring
device to display in the GUI of FIG. 17.
Establishing a Sleep Schedule--Potential Recalibration of Selected
Sleep Duration
[0130] FIGS. 18 and 19 depict graphical user interfaces that may be
generated after some sleep data derived from sensor data generated
by the biometric monitoring device have been obtained and stored in
the sleep log data store. Such sleep data may indicate that the
user is consistently unable to meet the selected sleep duration,
and may the display of a GUI that allows for recalibration of the
selected sleep duration to a selected sleep duration that is
perhaps easier for the user to achieve. Such interfaces, or similar
interfaces, may also be used during initial sleep duration setting
if sleep data has already been obtained at the time that the
initial selected sleep duration is set.
[0131] The GUIs in FIGS. 18 and 19 include the graphical
representation of at least some sleep data of the user relating to
actual time slept. For instance, each of the two GUIs include a
graphical representation 1801 or 1901 of the "Hours Slept" of the
user for particular days of a week as well as an average amount of
the "Hours Slept" for the week. The "Hours Slept" information may
be derived from the obtained and stored sleep data, and may be
representative of various sleep states of the user, depending on
what sleep states are considered to be "sleeping." As also seen in
these Figures, the GUIs may include a prompt for the user to set a
sleep schedule.
[0132] In some implementations of generating one or more
personalized GUIs for setting the sleep schedule of the user of the
biometric monitoring device, a GUI may be generated that is similar
to the GUIs of FIGS. 11 through 13, except that instead of a
user-supplied typical sleep duration, the GUI may provide a
calculated typical sleep duration based on the actual sleep
durations determined for one or more sleep sessions for which
biometric sensor data has been obtained. FIGS. 20 and 21, for
example, depict graphical user interfaces that may be used, at
least in part, to obtain a selected sleep duration of the user
after sleep data for some period of time has been obtained. As can
be seen in FIGS. 20 and 21, the GUIs include information 2001 or
2101 related to and/or based, at least in part, on the sleep data
of the user that has been collected. Such information may include
sleep duration, e.g., average time spent in the various sleep
states during some number of past sleep sessions and/or during a
specific period of time, e.g., the past week or past two weeks.
Here, the GUI of FIG. 20 includes an average amount of sleep for
the user for the last five sleep sessions (i.e., "sleeps"), whereas
the GUI of FIG. 21 includes an average amount of sleep for the user
during the previous 14 day period. These GUIs may also include
prompts 2002 or 2102 which may receive input from the user to
either accept the presented sleep duration ("Yes") as the selected
sleep duration, i.e., sleep goal, or to select a different selected
sleep duration ("No, I Want More").
[0133] FIG. 22 depicts a graphical user interface that may be
generated to obtain an updated selected sleep duration. The GUI of
FIG. 22 may be generated after FIG. 20 or 21 if the user input that
the information displayed in the GUI of FIG. 20 or 21, e.g.,
average sleep duration of the user, is acceptable as the selected
sleep duration. The GUI of FIG. 22 may also include a recommended
sleep duration 2201 that is related to the information presented in
the GUI of FIG. 20 or 21, such as a sleep duration within a
particular amount of time to the information presented in the GUI
of FIG. 20 or 21. For example, FIGS. 20 and 21 included actual
measured average sleep durations and the GUI of FIG. 22 includes a
recommended sleep duration that is not the same as these average
sleep durations, but is within three minutes of these average sleep
durations. In some embodiments, the recommended sleep duration may,
for example, be rounded to the closest 15-minute increment to the
average sleep duration presented in the GUI of FIGS. 20 and 21. In
some other embodiments, the recommended sleep duration of the GUI
in FIG. 22 may be the same as the sleep duration presented in FIG.
20 or 21.
[0134] Prompts 2202 are included in FIG. 22 for the user to either
accept the recommended sleep duration as the selected sleep
duration, i.e., sleep goal, or to choose a different selected sleep
duration. If the user accepts the recommended sleep duration, then
the recommended sleep duration is stored as the selected sleep
duration of the user. If the user elects to choose a different
selected sleep duration, then FIG. 23 may be generated. FIG. 23
depicts a graphical user interface that may be generated to, at
least in part, obtain a user-specified selected sleep duration.
FIG. 23 includes prompts to obtain an input sleep duration by the
user which may be stored as the selected sleep duration.
[0135] FIG. 24 depicts a different graphical user interface that
may be generated to obtain a selected sleep duration, for example,
in response to the user selecting "no, I want more" in the GUI of
FIG. 21. Here, the GUI may include a recommended sleep duration
2401 that is more than the sleep duration information included in
the GUI of FIG. 20 or 21. As stated above, the GUIs of FIGS. 20 and
21 included average sleep durations of the user and here in FIG.
24, the recommended sleep duration is greater than those average
sleep durations. The amount by which the recommended sleep duration
is greater than the presented sleep duration may vary. In some
implementations the amount may be 30 minutes from the sleep
duration information previously presented to the user; for example,
if the sleep duration information presented to the user, as in FIG.
21, is an average sleep duration of 6 hours and 48 minutes, then
the recommended sleep duration may be 7 hours and 18 minutes. In
some implementations, the amount may be 30 minutes from the nearest
15-minute increment of the presented sleep duration information;
for instance, if the sleep duration information presented to the
user, as in FIG. 21, is an average sleep duration of 6 hours and 48
minutes, then the closest 15 minute increment to that sleep
duration is 6 hours and 45 minutes and therefore the recommended
sleep duration may be 7 hours and 15 minutes, as depicted in FIG.
24.
[0136] Similar to FIG. 22, FIG. 24 includes prompts 2402 for the
user to either accept the recommended sleep duration as the
selected sleep duration or to choose a different selected sleep
duration. If the user accepts the recommended sleep duration, then
the recommended sleep duration may be stored as the selected sleep
duration of the user. If the user elects to choose a different
selected sleep duration, then FIG. 23 may be generated which
includes prompts 2302 to obtain an input sleep duration 2301 by the
user which may be stored as the selected sleep duration.
[0137] FIG. 25 depicts a graphical user interface that may be
generated after a selected sleep duration has been selected by the
user. This GUI may be generated after, for instance, the selection
of the selected sleep duration in FIGS. 22 through 23. This GUI may
serve as an introduction to setting up a sleep schedule.
Establishing a Sleep Schedule--Establishing a Scheduled
Waketime
[0138] In some implementations of generating one or more
personalized GUIs for setting the sleep schedule of the user of the
biometric monitoring device, the generation may include generating
one or more GUIs to obtain the scheduled waketime of the user. FIG.
26 depicts a graphical user interface that may be generated, at
least in part, to obtain the scheduled waketime of the user. The
GUI of FIG. 26 may include information 2601 related to and/or
based, at least in part, on the sleep data of the user, which may
include past waketimes of the user. Such past waketimes may be an
average waketime for a specific period of time, such as two weeks;
an average waketime for a specific number of sleep sessions; a
single past waketime of the user; or another metric of past
waketimes of the user. This information may, in some embodiments,
be considered a recommended waketime. Such a past waketime may, for
example, be rounded to the nearest 15-minute interval on-the-hour
to make it easier for the user to interpret.
[0139] FIG. 26 may also include prompts 2602 for the user to accept
the displayed information (e.g., an average waketime) as the target
or scheduled waketime. In some such embodiments, as discussed
above, this information may be a recommended waketime which the
user may accept by selecting one prompt of FIG. 26 (e.g., "Set As
Target") or may change by selecting another prompt of FIG. 26
(e.g., "Choose A Different Time"). If the user selected the
recommended waketime as the scheduled waketime, then the
recommended waketime is stored as the scheduled waketime. The
scheduled waketime may be used to set an alarm to wake the user up
at the scheduled waketime, but, more importantly, the scheduled
waketime may be used in determining a recommended bedtime for the
user based on their selected sleep duration.
[0140] FIG. 27 depicts a different graphical user interface that
may be generated, at least in part, to obtain the scheduled
waketime of the user. The GUI of FIG. 27 may be generated if the
user selects to choose a different waketime in the GUI of FIG. 26.
Here, the user may be provided with a prompt 2702 to allow the user
to input any waketime 2701 which will be received by the GUI and
stored as the scheduled waketime of the user.
Establishing a Sleep Schedule--Setting Waketime Alarms
[0141] FIG. 28 depicts a graphical user interface generated to
obtain user preferences regarding a waketime alarm. The GUI of FIG.
28 may, for example, provide an indication 2801 of when the
waketime alarm would be set for, as well as prompts 2802 to allow
the user to enable or disable the waketime alarm. The GUI of FIG.
28 may alternatively or additionally obtain timing and/or day
information regarding the waketime alarm such as a time, a date,
and/or a specific day for which the waketime is alarm is to be
activated. In some embodiments, the GUI of FIG. 28, or another GUI,
may obtain a selected notification for the scheduled waketime
alarm; the user may select the mechanisms in which the waketime
alarm will occur, such as an auditory output, an electronic
communication, an electromagnetic communication, a visual output
(such as very bright light that may simulate daylight), and/or a
tactile output that is described in greater detail below. In some
such implementations, the biometric monitoring device may include a
notification mechanism that is configured to provide such
notifications; alternatively, such a notification may be generated
by a smartphone that is paired with the biometric monitoring
device.
Establishing a Sleep Schedule--Establishing a Selected Bedtime
[0142] In some embodiments, once the user's selected sleep
duration, i.e., sleep goal, has been obtained and stored, the
user's sleep may be tracked for a specified or predetermined period
of time and/or number of sleep sessions before the generation of
additional personalized GUIs, including a GUI based on the
calculation of a specifically tailored target bedtime for the user,
as is described in more detail in a later section. These additional
GUIs may be used to assist the user with modifying their behavior
so as to better achieve their selected sleep duration. As most
people have little flexibility in the time they wake up, e.g., they
must get up at a certain time in order to arrive at work or school
at a scheduled time, such GUIs may generally be designed to promote
the selection of a recommended bedtime that will allow the user to
reliably achieve their desired sleep duration--a user typically has
much more control over their bedtime, so helping the user establish
and adhere to an appropriate bedtime may be the most effective
technique for promoting regular sleep patterns.
[0143] In some implementations, a recommended bedtime may only be
provided after a minimum amount of sleep data for the user is
acquired. For instance, the minimum amount of sleep data may be
sleep data gathered over a certain period of weeks, such as three
weeks; it may be sleep data gathered for a certain number of sleep
sessions, such as five; or it may be a combination in which the
minimum is sleep data gathered for a certain number of sleep
sessions over a certain period of time, such as five sleep sessions
over two weeks. In some embodiments, once the minimum amount of
sleep data is obtained, additional personalized GUIs may be
generated based on at least that minimum amount of sleep data. In
other implementations, the collection of sleep data of the user
before generating the additional personalized GUIs for recommending
a bedtime may be skipped, and the recommended bedtime may be
determined according to other techniques, as discussed in more
detail later.
[0144] In some implementations of generating one or more
personalized GUIs for setting the sleep schedule of the user of the
biometric monitoring device, the generation may include calculating
a target bedtime based, at least in part, on the scheduled waketime
and a sleep efficiency based on the sleep data for one or more
users stored in the sleep log data store. The target bedtime may be
calculated in order to provide the user with a recommended bedtime
that increases the likelihood of consistent and healthy sleep. The
target bedtime may also be calculated in order to provide the user
with the recommended bedtime that will most likely result in the
selected sleep duration, e.g., sleep goal, based on the scheduled
waketime.
[0145] Sleep efficiency, as used herein, refers to the total time
in a sleep session that an individual is in one or more sleep
states that is or are not an awake state, divided by the sleep
session duration, or a similarly equivalent measure of sleep
efficiency. For example, the individual may get into bed at 11:00
p.m., take 30 minutes to fall asleep, wake up periodically during
the night for a total of 30 minutes, and then wake up at 7:00 a.m.
Here, the individual was attempting to sleep for a total of 8 hours
(11:00 p.m.-7:00 a.m.), but was actually in the non-awake sleep
state for 7 hours (8 hours in bed less 30 minutes to fall asleep
and less 30 minutes in an awake sleep state during the night). The
individual's sleep efficiency for this example night is 7 hours
divided by 8 hours, or 0.875. Each user will have a different
average sleep efficiency based on their physiology, sleep
environment, etc. Sleep efficiency may also change night-to-night
due to various factors such as whether or not the user consumed
alcohol and/or caffeine, was traveling, is sick, etc. Sleep
efficiency may also change depending on when a person goes to
bed--for example, if a person goes to bed too early, they will
likely be less tired and it will be harder for them to fall asleep
quickly. If the person goes to bed later, they will be more tired
and spend less time falling asleep, thereby likely increasing the
amount of time that is spent sleeping in that sleep session.
[0146] It is to be understood that sleep efficiency may be
evaluated based on how much total time a person spends in one or
more designated sleep states during a sleep session divided by the
sleep session duration. In some implementations, the designated
sleep states may include all non-awake sleep states, in which case
the sleep efficiency may be the total time in a sleep session spent
asleep (regardless of how restless the person is while sleeping)
divided by the sleep session duration. This is perhaps the simplest
type of sleep efficiency to determine. In other implementations,
however, sleep efficiency may be more nuanced, e.g., the designated
sleep states may only include a non-empty subset of the non-awake
sleep states, such as the sleep states that are known to be most
restful and recuperative for the human body. In such an
implementation, the sleep efficiency may be much lower than in the
total-time-asleep example discussed previously. The selection of
which sleep states to include in the determination of sleep
efficiency may be pre-set or may be selectable by the user.
[0147] Because sleep efficiency is typically less than 1 (while it
is never more than 1, it can, theoretically, be equal to 1 if a
person immediately falls asleep when going to bed and otherwise
spends the entire sleep session in the desired sleep states),
recommending a bedtime by simply subtracting the selected sleep
duration from the scheduled waketime does not result in an
effective recommended bedtime. For example, if the user wants to
wake up at 7:00 a.m. and get 8 hours of sleep, then subtracting 8
hours from 7:00 a.m. to reach a recommended bedtime of 11:00 p.m.
(which is exactly 8 hours before 7:00 a.m.), will generally result
in the user not receiving the full 8 hours in the various desired
non-awake sleep states. As discussed herein, the sleep efficiency
and various other factors may be used to calculate a more accurate,
personalized, recommended bedtime for the user that will tend to
cause the user to achieve the selected sleep duration.
[0148] As stated above, the system depicted in FIG. 6 may be used
to implement such calculations of the target bedtime. Similar to
above, the memory, which is communicatively connected with the one
or more processors, may store instructions for controlling the one
or more processors to obtain sleep data derived from sensor data
generated by the biometric monitoring device, the sleep data
including a plurality of sleep sessions that each specify various
sleep states of the user for the respective sleep sessions, and to
store the sleep data in the sleep log data store as one or more
sleep logs associated with an account assigned to the user. In some
implementations, the sleep log data store may also include sleep
logs including sleep data derived from sensor data generated by
other biometric monitoring devices of other users. The memory may
also store instructions to calculate the target bedtime based, at
least in part, on the scheduled waketime of the user and the sleep
efficiency that is based on the sleep data for one or more users
stored in the sleep log data store.
[0149] In some implementations, the recommended bedtime may be
based, at least in part, on the sleep efficiency of the user of the
biometric monitoring device. In some other implementations, the
recommended bedtime may be based, at least in part, on the sleep
efficiency of other users of other biometric monitoring devices;
these other users may be filtered and selected in any number of
various ways, which is discussed below.
[0150] The sleep efficiency may thus be representative, at least in
part, of a correlation between sleep state duration data for one or
more sleep sessions and sleep session duration data for those
respective one or more sleep sessions. The memory may store
instructions for controlling the one or more processors to obtain
sleep state duration data for a sleep session stored in the sleep
log data store and to determine sleep session duration data for a
sleep session stored in the sleep log data store; the sleep state
duration data and sleep session duration data may both be based, at
least in part, on the sleep data derived from the sensor data
generated by the biometric monitoring device. The sleep state
duration data, as mentioned above, may be representative of the
total amount of time the user that is associated with the sleep
session spent in a designated subset of sleep states during the
sleep session. The subset of sleep states may include non-awake
sleep states, but does not include awake sleep states. While most
sleep data may be obtained directly from sensor measurements, some
sleep data, e.g., the start and end of a sleep session, may
potentially be obtained from user input, e.g., when a user pushes a
button on a biometric monitoring device to mark the start and/or
end of a sleep session.
[0151] The sleep efficiency that is calculated for a person may be
used to better predict how long of a sleep session that person will
require in order to achieve the selected sleep duration for that
person, which may, in turn, drive the determination of a target
bedtime for that person. For example, the sleep efficiency may, in
some embodiments, be modeled as a constant, e.g., an average, value
based on one or more sleep sessions and sleep session duration data
for those respective one or more sleep sessions. For instance, a
user may have a sleep efficiency of 0.94 which may be an average
sleep efficiency over a specific period, such as the last 7 days,
one month, or longer. If the user typically desires 8 hours of
total sleep duration per sleep session, i.e., the user's selected
sleep duration is 8 hours, then the sleep session duration that the
user will likely need in order to attain 8 hours of total sleep may
be estimated by dividing the selected sleep duration by the sleep
efficiency, e.g., 8 hours divided by 0.94, which results in a
target sleep session duration of approximately 8 hours and 30
minutes. This may also be thought of as multiplying the selected
sleep duration by a factor that is based on the sleep efficiency,
e.g., a factor such as 1/sleep efficiency. This target sleep
session duration, in turn, may be subtracted from the scheduled
waketime in order to determine a target bedtime, e.g., if the
user's scheduled waketime is 7:00 a.m., then to reach 8 hours of
total sleep duration in the designated sleep states, the target
sleep duration of 8.5 hours may be subtracted from the scheduled
waketime to yield a target bedtime of 10:30 PM the night
before.
[0152] Sleep efficiency may be accounted for in determining a
target bedtime in a variety of different ways. In some target
bedtime determination techniques, sleep efficiency may be
determined directly, as in the above example, and then applied to a
selected sleep duration to arrive at a target sleep session
duration which may then be subtracted from a scheduled waketime to
determine the target bedtime. In other techniques, the sleep
efficiency may be inherently accounted for in the target bedtime
without ever directly calculating it--in either case, however,
sleep efficiency is accounted for in the target bedtime. An example
of such inherent accounting for of sleep efficiency is discussed
below.
[0153] FIG. 29 depicts a graph of a linear relationship between
sleep state duration data for one or more sleep sessions and sleep
session bedtime data for those respective one or more sleep
sessions. Such data for a plurality of sleep sessions with
generally consistent waketimes (e.g., approximately 7:30 AM) are
plotted in the graph with sleep state duration in hours on the
x-axis and bedtime on the y-axis; the line through the data points
represents a linear relationship, e.g., as determined by applying a
linear regression model to the data, between these two data points
that inherently accounts for sleep efficiency. Thus, for example,
if the scheduled waketime is 7:30 AM, then the regression model
that is applied to the data of FIG. 29 may be used to determine the
target bedtime as a function of the selected sleep duration. In
this example, if the selected sleep duration is 8 hours, then the
target bedtime that may be calculated based on the regression model
may be 10:22 PM, which represents a target sleep session duration
of 9 hours and 8 minutes; the sleep efficiency for such a sleep
session would be 8 hours divided by 9.14 hours, or 87.5%.
Similarly, if the selected sleep duration is 7 hours, then the
target bedtime that may be calculated based on the linear
regression model in this example is 11:30 PM, which represents a
target sleep session duration of 8 hours; the sleep efficiency for
such a sleep session would also be 87.5% since the sleep efficiency
that is inherently reflected by the linear regression model is a
constant sleep efficiency. This constant sleep efficiency is
similarly observable if one calculates a target bedtime using the
linear regression model for a selected sleep duration of 6
hours--the target bedtime for such a sleep duration would be 12:39
AM and the sleep session duration would be 6 hours 51 minutes,
which, again, reflects an 87.5% sleep efficiency. Since the sleep
data store may include the start time (the bedtime) and the end
time (the waketime) for each sleep session in a plurality of
historical sleep sessions, applying a linear regression model (or
other data-fitting model) to such data may allow for direct
determination of a target bedtime that accounts for sleep
efficiency without requiring the separate determination of the
actual sleep efficiency.
[0154] Sleep efficiency may also be accounted for in more
sophisticated ways to allow for more refined determinations of
target bedtime that are even more customized or personalized to the
sleep habits of the user. In the example above, the linear
relationship between bedtime and sleep duration assumes that sleep
efficiency is individually uncorrelated to bedtime, waketime,
and/or sleep state duration, e.g., constant, and for many users
this assumption may not hold true in practice. For example, a user
that wakes up at the same time every day (e.g., 7:00 a.m.) and goes
to bed at different times may consequently spend different amounts
of time in non-awake sleep states for each sleep session, e.g.,
each night. For sleep sessions where the user goes to bed late, the
user may be more tired than usual, may fall asleep faster than
normal, and therefore may have more efficient sleep. Conversely, if
the user goes to sleep earlier than normal, they may not be as
tired and it may take them more time to fall asleep, thereby
lowering their sleep efficiency. This may create a non-linearity in
the relationship between sleep state duration data for one or more
sleep sessions and sleep session duration data for the
corresponding one or more sleep sessions because the user's sleep
efficiency is correlated with bedtime such that, for instance,
later bedtimes may result in increasingly efficient sleep and thus
more sleep than a linear relationship may predict.
[0155] Therefore, as discussed herein below, in some embodiments
the calculation of the target bedtime may account for a variable
sleep efficiency that may be affected by one or more factors and
thus be more accurate than a sleep efficiency that is assumed to be
constant.
[0156] In regression model-based approaches to determining target
bedtime, the regression model may be any type of regression model
including, but not limited to, a linear regression model, a
nonlinear regression model, a parametric regression model, a
nonparametric regression model, a semiparametric regression model,
and a multivariate linear regression model. For instance, a
parametric model (e.g., a polynomial) may be fit to sleep data
including sleep state duration data, waketimes, sleep session
duration data, and/or bedtimes to account for non-linear
relationships between the variables. For instance, the recommended
bedtime may be provided using a function modeled on:
target bedtime=.SIGMA..sub.n=0.sup.N.differential..sub.n (sleep
state duration data).sup.n
in which .varies..sub.n are the weights for each polynomial term
(up to order N) that could be determined via regression fitting.
For example, in the context of a linear regression, an example of
such a function may have only two terms, the coefficients of which
(.varies..sub.0 and .varies..sub.1) are determined via linear
regression analysis of sleep data for one or more users, e.g., of
the user for which the target bedtime is being determined, and
which may inherently account for the effects of sleep efficiency on
the target bedtime for a given selected sleep duration since the
linear regression model (or whatever regression model is used) will
have been "trained" or calibrated using sleep data that is
reflective of such sleep efficiency. In such an example,
.varies..sub.0 may represent the individual's average waketime less
the average amount of time spent in a non-efficient sleep state
during an average sleep session, and .varies..sub.1 may be another
coefficient that encodes sleep efficiency with respect to sleep
duration--if sleep efficiency changes linearly with sleep state
duration, then .varies..sub.1 would be above or below -1, but not
exactly -1, such that:
target bedtime = .varies. 0 + .varies. 1 ( selected sleep duration
) = average waketime taking into account sleep efficiency -
selected sleep duration taking into account sleep efficiency
##EQU00001##
The training of the regression model may be further enhanced, for
example, by using a subset of training data (sleep data) that is
within a certain threshold range of the scheduled waketime, if
desired. FIG. 30 depicts a parametric regression model fit to the
data of FIG. 29. As can be seen, the curve in FIG. 30 conforms more
closely to the data than in FIG. 29, and may therefore more
accurately account for sleep efficiency. It is to be understood
that the above example is merely one example of how sleep data may
be used to train a regression model. It will be appreciated that
other variations may train a regression model using other
combinations of sleep data, e.g., target bedtime may be determined
according to a regression model that is a function of scheduled
waketime rather than selected sleep duration (using a subset of
sleep data that is inclusive of the selected sleep duration, for
example), and that the coefficients used may represent a variety of
different.
[0157] Other regression model techniques may include lasso, ridge,
Bayesian, and maximum likelihood methods. In some such embodiments,
the regression model may explicitly include waketimes and/or the
measured sleep efficiency in the regression model. Collinearity in
the curve fit may also be mitigated by any known technique
including, for instance, Principal Components Analysis.
Additionally, known uncertainties in the calculations and/or
estimates of bedtime and/or sleep state duration may be included in
the regression model, e.g., via maximum likelihood fitting. In some
embodiments, outliers may be accounted for in the regression model
to improve the fit by, for example, using Random Sample Consensus
or other known iterative fitting methods.
[0158] In some implementations, the influence of various factors,
such as those discussed herein, may be accounted for as co-variates
that are used in the regression model. For example, if desired
waketime is to be accounted for, then the above framework may be
modified to:
target bedtime = n = 0 N .alpha. n ( selected sleep duration ) n +
.beta. ( scheduled waketime - average waketime ) ##EQU00002##
in which .beta. may be a factor determined using regression
analysis and the covariate term .beta. (scheduled waketime-average
waketime) may shift the target bedtime by an amount dependent on
the difference between the average waketime and the desired
waketime. For example, if a linear regression model were to be
applied, then the alpha and beta parameters or coefficients would
be determined according using regression fitting.
[0159] Some example non-parametric methods for the regression model
include, for instance, K Nearest Neighbors (KNN), Random Forest
regression, and Gaussian process regression. For example, in the
KNN case, a user with historical sleep data, a scheduled waketime,
and/or a selected sleep duration, the sleep session duration data
and corresponding sleep state duration data for the plurality of
sleep sessions included in the regression model may be associated
with, e.g., may be the nearest, K sleep sessions with similar
waketimes, e.g., waketimes that are within a first threshold amount
of the scheduled waketime. For example, the first threshold may be
.+-.15 minutes of the scheduled waketime of 7:30 a.m. and the sleep
sessions include in the regression model are those sleep sessions
with waketimes ranging from 7:15 a.m. to 7:45 a.m.
[0160] Similarly, the sleep session duration data and corresponding
sleep state duration data for the plurality of sleep sessions
included in the regression model may be associated with, e.g., may
be the nearest, K sleep sessions with similar sleep state duration
data, e.g., with selected sleep duration data that is within a
second threshold amount, e.g., within .+-.15 minutes, of the
selected sleep duration.
[0161] In some implementations, the sleep session duration data and
corresponding sleep state duration data for the plurality of sleep
sessions included in the regression model may also be associated
with, e.g., may be the nearest, K sleep sessions with waketimes
that have occurred on the same day of the week as the day of the
week on which the scheduled waketime occurs. For example, if the
user has a scheduled waketime that occurs on a Monday, then the
data included in the regression model may be associated with sleep
sessions with waketimes that have also occurred on a Monday. The
resulting bedtime recommendation may be the average bedtime from
those K nights, or some pre-chosen quantile from the distribution
of bedtimes. This may allow for variations in sleep efficiency that
are day-dependent to be taken into account--for example, a person
may generally be more tired on Sunday nights due to participating
in soccer games on Sunday afternoons, and may therefore have a
higher sleep efficiency during sleep sessions starting on Sunday
evenings and ending on Monday mornings.
[0162] In some embodiments, the regression model may account for
one or more of: one or more specific days of the week, a specific
time of year, holidays, workdays of the user, non-workdays of the
user, a seasonal time change, a geographic location, travel by the
user between at least two time zones, exercise of the user, and a
duration of daylight in a day. In some instances, a particular
user's sleep data may not have data points that allow for such
factors to be taken into account, in which case data from other
users may be analyzed and used as a stand-in for data associated
with that user.
[0163] For example, day of the week may be included in the
regression model since many users have distinct bedtimes,
waketimes, and sleep state durations on weekdays and weekends. For
instance, some users tend to go to bed later, sleep longer, and/or
sleep in later on weekends as compared to weekdays. The
relationships between these variables can be distinct on weekdays
and weekends. Therefore, for instance, the regression model may be
fit to sleep data associated with sleep sessions that have
waketimes on weekdays (when calculating target bedtimes that are
for waketimes that are on weekdays), while in another instance, the
regression model may be fit to sleep data associated with sleep
sessions that have waketimes on weekends (when calculating target
bedtimes that are for waketimes on weekends).
[0164] Similarly, time of year may be an included covariate to the
regression model. Sleep behavior generally changes with season, and
is typically correlated with the amount of sunlight per day (i.e.,
day length), which in turn generally has a strong effect on the
circadian rhythm. For example, a user may sleep 15 minutes more on
average during the winter than the summer. Specific nights of data,
or sleep sessions that may start and/or end near a holiday (e.g., a
user may start a sleep session past midnight of a holiday) may be
excluded or de-weighted to improve the regression model. For
example, sleep behavior on officially recognized holidays, even if
on a weekday, may look more like a weekend (e.g., with later
bedtimes, longer sleep state durations, and later waketimes). Time
changes, such as seasonal and daylight savings, may also be
accounted for by the regression model; such time changes generally
effect sleep behavior due to the circadian rhythm not adjusting to
time changes immediately. The regression model may exclude sleep
data or de-weight sleep data around time change dates because that
data may, in some instances, be less representative of the
underlying relationship between bedtime, waketime, and sleep state
duration for a user.
[0165] Activity level in the hours before bedtime may also be
accounted for in the regression model. Such activity levels may be
used to predict time to fall asleep; for example, if a user is
highly active within a certain time period before the scheduled
bedtime, the user may have more trouble falling asleep and may need
to try to fall asleep sooner to achieve the sleep goal. Likewise,
heart rate in the hours before bedtime may also be accounted for in
the regression model and may be used to predict time to fall
asleep. For example, if the user has elevated heart rate with a
certain time period before the scheduled bedtime, the user may have
more trouble falling asleep and may need to try to fall asleep
sooner to achieve the sleep goal. The target bedtime may, in such
cases, be determined based on bedtimes for sleep sessions in the
sleep data store that are associated with similar elevated heart
rates that occur within that time period prior to the sleep session
bedtime. As noted above, the target bedtime may be based, at least
in part, on the sleep efficiency of other users of other biometric
monitoring devices or the sleep efficiency of the user of the
biometric monitoring device. Accordingly, the target bedtime may be
based, at least in part, on sleep data associated with the user of
the biometric monitoring device and/or sleep data associated with
other users of other biometric monitoring devices. As stated above,
the sleep data associated with the other users may be stored as
corresponding sleep logs in the sleep log data store; such sleep
data may also be derived from sensor data generated by the other
biometric monitoring devices of the other users.
[0166] Generally speaking, the target bedtime (as well as
recommended sleep durations, as discussed earlier with respect to
the various GUIs dealing with establishing a selected sleep
duration) for a user may be determined from data that is associated
with that user, as such data will best reflect that particular
user's sleep behavior, efficiency, and sleep needs. However, in
situations where there is insufficient data or no data associated
with that user that may be used to determine a target bedtime (or,
for example, a typical sleep duration), sleep data from other users
may be used as a stand-in for data associated with that user.
[0167] For instance, the aforementioned regression models may be
applied to sleep data associated with the user and/or sleep data
associated with other users. In some such embodiments, there may
not be sufficient to model the user's sleep data, but it may be
beneficial to determine a target bedtime for the user even when
little or no sleep data has been obtained from the user. For
example, the target bedtime for the user may be determined based,
at least in part, on the application of a regression model to sleep
data from other users of similar demographics and/or living in the
same geographic region.
[0168] In some implementations, the target bedtime may also be
based, at least in part, on a proper subset of one or more of one
or more specific days of the week, a specific time of year,
holidays, workdays of the user and/or other users, non-workdays of
the user and/or other users, a seasonal time change, a geographic
location, travel by the user and/or other users between at least
two time zones, exercise of the user, and a duration of daylight in
a day. For example, if the user's scheduled waketime is on a
weekday, then the target bedtime may be based, at least in part, on
the user's historical sleep data associated with that same weekday.
For another example, as mentioned above, if the user's scheduled
waketime is on a weekday but that weekday is also a holiday, then
the target bedtime may be based, at least in part, on the user's
historical sleep data associated with that same holiday, a weekend
day, a different holiday, and/or other users' sleep data associated
with that same holiday. In such implementations, the target bedtime
may account for a sleep efficiency that may vary based on such
factors, e.g., the sleep efficiency may be based, at least in part,
on one or more of historical sleep efficiency data associated with
one or more of: a proper subset of one or more specific days of the
week, a specific time of year, holidays, workdays of the user,
non-workdays of the user, a seasonal time change, a geographic
location, travel by the user between at least two time zones,
exercise of the user, and a duration of daylight in a day. For
example, if the user's scheduled waketime is on a weekday, then the
sleep efficiency may be based, at least in part, on the user's
historical sleep efficiency data associated with that same
weekday.
[0169] The target bedtime discussed above may be considered an
"optimal bedtime" and such optimal bedtime may be further
calculated, analyzed, and/or modified before being presented as a
recommended bedtime that is included in the GUI. In some
embodiments, the recommended bedtime discussed above is the optimal
bedtime that is included in the GUI. Even if the target bedtime is
not used as the recommended bedtime, the recommended bedtime will
still be based on the target bedtime in some fashion, as will be
seen in the examples below.
[0170] In some embodiments, the recommended bedtime may be selected
so as to be at some regular hourly division, e.g., the closest
quarter hour on the hour to the target bedtime. Thus, for example,
a target bedtime of 11:24 PM may cause a recommended bedtime of
11:30 PM to be selected, whereas a target bedtime of 11:21 PM may
cause a recommended bedtime of 11:15 PM to be selected. This may
make it easier for a user to remember the recommended bedtime.
[0171] In some other such embodiments, the recommended bedtime may
be before or after the optimal bedtime by a particular time, such
as between about 1 and 30 minutes. For example, 15 minutes may be
subtracted from the optimal bedtime such that the recommended
bedtime is 15 minutes earlier than the optimal bedtime, thereby
causing the user to think about going to bed earlier. This may be
useful, for example, in situations where the sleep data for the
user indicates that the user is consistently late by 10 to 20
minutes in actually going to bed by their recommended bedtime.
[0172] In some embodiments, the recommended bedtime may be based,
at least in part, on the user's optimal weekday or optimal weekend
bedtime, or on days of the week specified by the user.
[0173] In some other embodiments, the recommended bedtime may be
based, at least in part, on the differences between average sleep
state duration data for weekdays and weekends. For example, if the
user sleeps longer on weekends than weekdays, it may indicate that
the user is accumulating sleep debt during the week that making up
for it on the weekend. Averaging sleep state duration data for the
entire 7-day week may provide a more accurate representation of the
user's sleep need than for just the work week (e.g., Monday through
Friday). If the user's selected sleep duration is lower than his
inferred sleep need, the recommended bedtime may be adjusted, e.g.,
with time added or subtracted to the target bedtime, to reduce the
accumulation of sleep debt.
[0174] In some embodiments, the recommended bedtime may be based,
at least in part, on how inconsistent the user's bedtime is for a
certain period of time. For example, a user with an inconsistent
bedtime may be provided with a recommended bedtime that is later
than the optimal bedtime in order to achieve the user's selected
sleep duration because such recommended bedtime may be a more
achievable bedtime for the user.
[0175] In some embodiments, the recommended bedtime may be based,
at least in part, on a forecast of the user's future bedtime which
may be more appropriate for near-future behavior. For example, for
a user with a long baseline of data (e.g., numerous months or
years), the user's own sleep data may be modeled for the forecast
while for users with insufficient data, sleep data of other similar
users (e.g., similar demographics, activity level, and/or
geographic location) may be used for the forecast. In some such
embodiments, both the data of the user and other users may be used
for the forecast. For instance, as noted above, the user's optimal
bedtime may be based, at least in part, on the season or
time-of-year. Because sleep behavior may have a seasonal component
that is tied to the amount of daylight, sleep state duration may be
lower in the summer compared to the winter on average, bedtimes may
be later, and the accumulation of weekday sleep debt may be
different. Such trends may be incorporated into the recommended
bedtime by forecasting how the user's behavior may change in the
near or long future. For example, when calculating a recommended
bedtime for a user in North America in April, the average bedtime
will trend later as the summer solstice approaches and the amount
of daily sunlight increases. Therefore, the recommended bedtime may
be adjusted earlier in anticipation of this trend so that the user
is more likely to achieve their selected sleep duration in the
coming months as their bedtime naturally trends a bit later,
thereby making it easier for the user to achieve the recommended
bedtime and increasing user satisfaction (this may result in a
shorter sleep duration than the selected sleep duration,
however--this may nonetheless be desirable if it positively
reinforces the user experience with the sleep tracking and
scheduling functionality discusses herein). Alternatively, the
recommended bedtime may be adjusted to be earlier than it otherwise
would in anticipation of the user wanting to stay up later due to
the longer days. The user may decide to stay up an extra hour past
the recommended bedtime, but if the recommended bedtime has been
shifted earlier by half an hour, then the user may only be going to
bed by half an hour later with respect to the target bedtime than
they otherwise would. Similarly, the recommended bedtime may be
shifted in the opposite directions in preparation for winter
months, e.g., a later bedtime may be provided that is more in-line
with what the user is likely to do in the coming months.
Recommending a bedtime that is responsive to the user's natural
behavior is more likely to keep the user engaged with healthy sleep
behavior rather than suggesting a bedtime that will be difficult to
achieve.
[0176] As mentioned above, season may be a factor included in
modeling sleep behavior and making bedtime recommendations. The
seasonal changes are location dependent, largely on latitude, but
may also depend on local climatic patterns, time zones, and/or
cultural customs. When retrieving sleep data of other similar users
for modeling and/or forecasting, location may be used as part of
the similarity measure to account for these dependencies.
[0177] In some embodiments, long term behavior, rather than short
term behavior, may be forecast and the recommended bedtime may
therefore average out seasonal modulations. In some other
embodiments, the recommended bedtime may be based, at least in
part, on whether a time change (e.g., daylight savings time) has
recently changed or is just about to. As mentioned above, time
changes (e.g., daylight savings time) can disrupt a user's sleep
behavior and bedtime. The recommended bedtime may be shifted
earlier or later to help mitigate the effects of these changes. In
some embodiments, the recommended bedtime may be based, at least in
part, on age and/or knowledge of whether the user is working or
retired. Sleep duration, sleep efficiency, bedtime, and/or amount
of deep sleep may change with age. For example, weekday sleep
duration decreases on average with age, except after retirement age
where sleep duration starts to starts to increase again. Such
trends may also be accounted for in the recommended bedtime.
[0178] In some embodiments, the optimal bedtime recommendation may
be shifted from the target bedtime based, at least in part, on a
value that is calculated experimentally (e.g., via A/B testing,
multi-armed bandit testing, or parameter optimization experiments).
For example, a cohort of users may be defined, (e.g., male runners
in New York City, between 20 and 25 years of age, etc.), and some
of these users may be separated into a control group while the
remaining users may be placed into a testing group. Example tests
may include determining if recommending a particular bedtime leads
to more or less adoption from users in the control group as
compared to a variant (e.g., that bedtime minus 10 minutes) that is
recommended to the testing group. Accordingly, the recommended
bedtime may be adjusted because a user is in a particular test or
control group. Additionally, if such experiments indicate that a
particular bedtime recommendation methodology is more likely to
lead to a desired outcome (e.g., a more consistent bedtime), then
the recommended bedtime for all users that are demographically
similar to the cohort users may be adjusted accordingly.
[0179] As stated above, generating the one or more personalized
GUIs may include generating a GUI that includes the recommended
bedtime (or the target bedtime, if the target bedtime is used as
the recommended bedtime). As noted and described at length above,
the recommended bedtime may be based, at least in part, on the
sleep data for one or more users stored in the sleep log data
store. FIG. 31 depicts a graphical user interface that includes a
recommended bedtime 3102. As can be seen, the recommended bedtime
of "11:45 pm" is included in the GUI, along with the selected sleep
duration 3101 ("6 hr 45 min"). Two prompts 3103 may also be
included in the GUI of FIG. 31 which may allow the user to confirm
and store the recommended bedtime as the user's selected bedtime or
to input a different time for the selected bedtime of the user.
FIG. 32 depicts a graphical user interface to obtain a different
recommended bedtime 3201 using prompt 3202. Similar to other GUIs
described above, the user may input a bedtime which may be received
and stored as the selected bedtime.
Establishing a Sleep Schedule--Setting Bedtime Reminders
[0180] In some embodiments, a bedtime reminder may also be
displayed and/or presented to the user in advance of the selected
bedtime order to remind the user to go to bed. In some embodiments,
the memory may store instructions for further controlling the one
or more processors to present to the user, via a notification
mechanism, the bedtime reminder.
[0181] The bedtime reminder may be provided, at least in part, as a
"notification" which may be one or more of a message, an auditory
output, an electronic communication, an electromagnetic
communication, a visual output, and a tactile output. For instance,
the mechanism through which the notification may be conveyed may,
generally speaking, be referred to as a notification mechanism.
Notifications may be provided through a variety of media, and may,
in some cases, require further action by an intermediate device
before being perceptible by the person. For example, a biometric
monitoring device may have a notification mechanism that includes a
display or lights that are configured to display graphics or light
up in order to catch the attention of a person (the notification,
in this case, may refer to a signal that is sent to the lights or
display that cause these components to light up or display graphics
to a person; it may also refer to the light or graphics that is
emitted or displayed by components receiving the signal in response
to the signal). In some examples, the biometric monitoring device
may have a notification mechanism that includes a speaker or other
device capable of generating auditory output that may be used to
provide the notification (the notification in this case may be a
signal that is sent to a speaker or other audio device; it may also
refer to the actual audio output that is generated by the audio
device in response to the signal). In some other or additional
examples, the notification mechanism may include a wireless
interface and the notification may take the form of an electronic
or electromagnetic communication, e.g., a wireless signal, that is
sent to another device, e.g., a wearable fitness monitoring device
such as a Fitbit activity tracker or a smartphone, associated with
the person (the notification in this case may be an electromagnetic
signal; it may also refer to any audio, visual, tactile, or other
output generated by the receiving device in response to receipt of
the signal). In such scenarios, the notification may still be
generated or initiated by the notification mechanism even if the
intended recipient device of the communication fails to be
activated or otherwise fails to convey the notification to the
person. The notification mechanism may be configured to generate
and/or provide one or more notifications to the user, and may
include one or more components that may be used to generate audio,
visual, tactile, electromagnetic, or other types of
notifications.
[0182] In various implementations, the notification may ultimately
be provided using any of a variety of output mechanisms, i.e.
notification mechanisms. In some implementations, the notification
may include nothing more than a light on the fitness monitoring
device that blinks intermittently when the user is to be reminded
to go to bed. In other additional or alternative implementations,
the notification may include other visual feedback, e.g., graphics,
text on a display, etc.; audio feedback, e.g., melodies, speech,
sound effects, etc.; tactile feedback, e.g., vibration, mild
electric shock, etc.; electromagnetic signals to other devices to
cause those other devices to provide feedback perceptible to the
person, e.g., signals sent to smartphones, laptops, desktops,
tablets, other fitness monitoring devices, etc.; and other forms of
communicating with the person.
[0183] In some embodiments, generating the one or more personalized
GUIs may further include generating a GUI to obtain timing
information indicating one or more selected reminder times for a
bedtime reminder and generating, for instance via the notification
mechanism, the bedtime reminder based, at least in part, on the
timing information. The reminder time may be the time at which the
bedtime reminder is presented to the user; the timing information
may be information that defines when the bedtime reminder is to be
provided relative to the selected bedtime. FIG. 33 depicts a
graphical user interface that may be used, at least in part, to
obtain timing information 3301 related to a bedtime reminder. As
can be seen, the GUI of FIG. 33 includes timing information 3301
that defines or establishes a selected reminder time for the
bedtime reminder to be presented to the user, which here is 30
minutes before the selected bedtime, i.e., the recommended
bedtime.
[0184] The time included in the GUI of FIG. 33 (e.g., "10:45 p.m.")
may also be considered a recommended reminder time for the bedtime
reminder. In some such embodiments, generating the one or more
personalized GUIs may further include calculating the recommended
reminder time for the bedtime reminder based, at least in part, on
the recommended bedtime, and displaying the recommended reminder
time as part of the GUI, like in FIG. 33. Here, the recommended
reminder time is 10:45 p.m. which was calculated by subtracting 30
minutes from the selected bedtime. In other implementations, the
user may simply define an offset time, e.g., 30 minutes before the
selected bedtime.
[0185] There are also two prompts 3302 in FIG. 33 which allow the
user to select the displayed selected reminder time as the selected
reminder time such that it may be obtained and stored by the GUI,
or to change the selected reminder time. FIG. 34 depicts a
graphical user interface that may be used, at least in part, to
obtain timing information related to a bedtime reminder. As can be
seen, the user may, via prompts 3402 and 3403, input timing
information 3401, such as a specific time as well as day, for the
bedtime reminder to be generated, e.g., presented via the
notification mechanism, to the user. Therefore, the GUI that is
generated may obtain both timing information indicating one or more
selected reminder times for a bedtime reminder as well as day
information indicating one or more selected reminder days for the
bedtime reminder. In such embodiments, generating the bedtime
reminder, e.g., via the notification mechanism, may be based, at
least in part, on the timing information and/or the day
information.
[0186] Similarly, in some embodiments, a different GUI may be
generated to obtain day information indicating one or more selected
reminder days for the bedtime reminder, and the generating of the
bedtime reminder may be further based, at least in part, on the day
information. For example, FIG. 35 depicts a graphical user
interface that may be used, at least in part, to obtain day
information related to a bedtime reminder. Like in FIG. 33, the GUI
of FIG. 35 may obtain information 3501 relating to a reminder day
or reminder days on which the bedtime reminder will be displayed.
Such information may be a specific day and/or a grouping of days,
such as weekdays or weekends.
[0187] As noted above, the bedtime reminder may be generated, such
as by the notification mechanism, based, at least in part, on the
day information and/or the timing information. In some embodiments,
the bedtime reminder may include the recommended bedtime.
[0188] It should be noted that in many embodiments, the bedtime
reminder may be sent before the user's recommended bedtime so that
the user has adequate time to prepare for bed. This notification,
e.g., warning window, may be set in several ways. For example, the
user may set how many minutes before the recommended bedtime, e.g.,
the scheduled bedtime, the user would like to receive the bedtime
reminder. The notification may also be personalized for each user
based on how many minutes of low activity the user typically logs
before the user falls asleep. Users who tend to wind down quickly
before sleep time would need a shorter window to prepare for bed
compared with users who typically have long periods of low activity
before bed. The warning window may be adjusted based on whether or
not the user has a history of reducing his activity level after
receiving the reminder. If the user is detected to be more active
than usual (e.g., by steps, metabolic equivalent units, workouts,
elevated heart rate) in the hours before bed, the user's bedtime
reminder may be adjusted earlier to recommend a longer winding down
period so that the user is able to fall asleep in time to meet his
selected sleep duration and scheduled waketime, e.g., a sleep goal
and a wake goal.
[0189] The bedtime reminder may also have a snooze option. If the
user does begin to lower their activity level, the alarm will go
off again. The bedtime reminder may also use location as an input
for determining whether or not the user is able to react to the
alarm. For example, if the user is not at home, the alarm may be
disabled to avoid discouraging the user who is in a situation out
of their control (e.g., still at work, or at a bar).
[0190] The bedtime reminder may also function as a feedback loop or
as an online learning model and learn over time when to remind a
user at a time that is most likely to get the user to go to bed on
time. For example, the bedtime reminder may be a system that may
remind the user to go to bed 15 minutes before her recommended
bedtime on one or more days. Then the bedtime reminder system may
switch behavior and remind the user 20 minutes before her
recommended bedtime for one or more days. It may similarly try
different variants, and if one of those variants was more
successful in getting the user to go to bed at the selected
bedtime, then that variant may be adopted. Similarly, learnings
about the most successful bedtime reminder variants from other
users may be used to set bedtime reminders for users new to the
feature. In the more general case, the bedtime reminder model may
optimize for some other behavior of interest besides just
maximizing the probability that the user will go to bed on time.
For example, the system may try to optimize a more general cost
function that maximizes engagement, and/or minimizes sleep debt
accumulation, etc.
[0191] Scheduling the bedtime reminder may also be recommended in
the morning, based on the sleep state duration data of the user for
the previous sleep session. The schedule of the bedtime reminder
may be adjusted to address accumulating sleep debt. The user may
also be congratulated after a sleep session in the morning if the
user achieved his or her bedtime target. Success may be defined as
the user is asleep by her bedtime, and/or defined as the user is
relaxed and lying down by her bedtime, attempting to meet the goal
even if the user is unable to fall asleep. The bedtime may also be
scheduled or recommended by a friend in a challenge form.
Consistent bedtimes may also be a household, team, or group
challenge where all members of the household need to achieve the
bedtime in order to succeed.
Providing Feedback Regarding Individual's Sleep Behavior
[0192] As part of the present disclosure, determinations may be
made from the sleep data as to how the user is performing with
respect to the user's selected bedtime, scheduled waketime, and/or
selected sleep duration, and additional GUIs may be generated that
include such determinations and/or include new recommendations for
a bedtime, waketime, and/or sleep duration based, at least in part,
on such determinations.
[0193] In some embodiments, generating the one or more personalized
GUIs may include determining, based on the sleep data of the user,
how the user's sleep performance compares with the user's selected
or desired sleep performance. For example, a determination may be
made, for each of a plurality of sleep sessions, as to whether or
not the user's actual bedtime, actual waketime, sleep state
duration, and/or sleep session duration were within some acceptable
range or threshold of the selected bedtime, the scheduled waketime,
selected sleep duration, and/or the predicted sleep session
duration (which may be based on the target sleep session duration
and which, generally speaking, is equivalent to the time span
bracketed between the selected bedtime and the scheduled waketime).
The user's performance for such sleep sessions, as compared against
the selected values, may be displayed on a GUI such as that shown
in FIG. 36, which shows various sleep data points 3601 on timelines
3604 for the most recent seven days of sleep logs. As can be seen
in FIG. 36, the selected bedtimes and the scheduled waketimes may
be indicated with cross-hatched areas 3602 and 3603 that represent
tolerance bands or thresholds, e.g., .+-.15 minutes, from the
selected bedtimes and the scheduled waketimes (which would be
located, for example, in the middle of such tolerance bands). The
user's sleep data may be considered to be "on target" when the
actual bedtimes and the actual waketimes that the user experiences
are within the tolerance bands or thresholds, and such on-target
behavior may be indicated using a graphical indicator, such as goal
achievement indicator 3606. More informative timelines 3605 may
also be included to show periods of restlessness or wakefulness
during a sleep session for such days. FIG. 37 depicts a different
graphical user interface that includes summary information 3702
regarding a single sleep session, including total sleep duration,
the actual bedtime, the actual sleep time, how long it took the
user to fall asleep (based on biometric monitoring device data),
and total amount of time awake and/or restless during the sleep
session. Also displayed is a timeline 3701 indicating which sleep
states the person was in at any given point in the sleep session.
In the case where a user is on-target for one or more of these
parameters for a given sleep session, the GUI may be configured to
display a congratulatory message, e.g., "On target!," regarding the
user's achievement with respect to that parameter.
[0194] If the user is not on-target with respect to one or more of
the selected bedtime, the scheduled waketime, selected sleep
duration, and/or the predicted sleep session duration, then a
personalized GUI may be generated that suggests one or more
alternate settings for the selected bedtime, the scheduled
waketime, the selected sleep duration, and/or the predicted sleep
session duration. For example, if the user consistently misses
attaining the selected sleep duration by 20 minutes, then the user
may be presented with a personalized GUI that suggests that the
user should consider a selected bedtime that is 30 minutes earlier
than the current setting. In other implementations, the sleep data
from other users that exhibited a similar inability to meet a
similar target sleep metric but who then adjusted their sleep
patterns to meet that metric may be analyzed to determine which of
these parameters typically resulted in successful behavior
modification of the users when adjusted. For example, in the
example above, the sleep logs in the sleep log data store may be
analyzed to locate other users that had similar (within .+-.15
minutes, for example) selected bedtimes, scheduled waketimes, and
selected sleep durations and that also consistently missed their
selected sleep durations by approximately 20 minutes (e.g., 20
minutes .+-.10 minutes); the sleep data from these identified users
may be further analyzed to determine if they made any adjustments
to their selected bedtime and whether they attained their selected
sleep duration subsequent to such an adjustment. If so, then that
adjustment (or a representation thereof, e.g., an average of the
adjustment) may be used as the basis for recommending a similar
adjustment to the user. For example, if such users are identified
and analysis indicates that 70% of those users re-scheduled their
selected bedtime for 30 minutes earlier, on average, than their
previous settings, then the user may be provided with a revised
recommended bedtime that is 30 minutes earlier than their current
bedtime.
[0195] Once a user has been guided through setting a selected sleep
duration and a selected bedtime, one or more additional GUIs may be
generated to allow the user to easily edit and revise these
settings without repeating the more involved guided/coached set up
discussed above. For example, FIG. 38 depicts a GUI that may be
generated to allow a user to easily change their scheduled waketime
3803, selected bedtime 3802, and/or selected sleep duration 3801,
as well as a prompt 3804 to turn on or off a bedtime reminder and
another prompt 3805 to allow the user to change the bedtime
reminder timing information. By tapping on any of the indicated
settings, the user may be taken to a new GUI (not shown) that
allows for modification of such parameters by the user.
[0196] It is to be understood that the GUIs discussed above are
merely one example of how sleep tracking functionality may be
implemented for a user of a biometric monitoring device, and that
there may be other techniques and GUIs that may be used instead
that are nonetheless within the scope of this disclosure. The GUIs
discussed above may also be implemented in a partial manner, e.g.,
the GUIs may not include GUIs geared towards introducing sleep
tracking to a new user, but may instead proceed directly to the
setting of a selected bedtime based on a user's accumulated sleep
data (or on other users' sleep data, e.g., if the user has not
accumulated sufficient sleep data of their own yet).
[0197] Some aspects of the present disclosure may occur in
particular sequences or orders. For a first example, a
determination may be made as to whether a user has a particular
number of sleep logs stored in the sleep log data store (e.g., more
than 5 sleep logs). If the user does not have the particular number
of sleep logs, then GUIs associated with a new user may be
generated, such as FIGS. 7 through 17. Once the GUIs associated
with the new user have been generated, the sleep data (as described
above) may be obtained and stored in the sleep log data store (as
also described above); another determination may also be made as to
whether a given number of sleep logs have been stored within a
period of time (e.g., whether more than 5 sleep logs have been
stored within 14 days).
[0198] If the determination is that the requisite number of sleep
logs have been gathered, then additional determinations may be made
as to whether the user has edited a selected sleep duration (i.e.,
sleep goal), whether the user has more a specific number of sleep
logs within a period of time (e.g., more than 5 sleep logs within
14 days), and/or whether the user's sleep state duration data
indicates that the user is within a specific threshold of the
selected sleep duration. If the user has not edited this sleep
goal, does not have more than the requisite number of sleep logs
within the period of time, and/or the user's sleep state duration
data does not indicate that the user is within the specific
threshold, then then GUIs associated with selecting a selected
sleep duration, waketime, waketime alarm, recommended bedtime,
and/or bedtime reminder may be generated. For instance, this may
include FIGS. 18-28 and 31-34. However, if one or more of the
determinations are opposite, then different GUIs may be generated,
such as GUIs associated with selecting a waketime, waketime alarm,
recommended bedtime, and/or bedtime reminder.
[0199] It should be noted that each of the GUIs described herein
and shown is not an exhaustive list of the GUIs that may be
generated as part of this disclosure. Furthermore, each and every
feature described and shown may be included in one or more of the
other GUIs; such features and information in the GUIs are not
mutually exclusive of each and every other feature and
information.
[0200] It is also to be understood that the techniques discussed
above may be implemented as methods, as well as systems or devices
configured, using computer-executable instructions stored in
memory, so as to cause one or more processors to perform such
methods. It is to be further understood that such
computer-executable instructions may also be implemented in
non-transitory form as computer-executable instructions that are
stored on a storage device, e.g., in a computer-readable
memory.
[0201] Importantly, the present invention is neither limited to any
single aspect nor implementation, nor to any combinations and/or
permutations of such aspects and/or implementations. Moreover, each
of the aspects of the present invention, and/or implementations
thereof, may be employed alone or in combination with one or more
of the other aspects and/or implementations thereof. For the sake
of brevity, many of those permutations and combinations will not be
discussed and/or illustrated separately herein.
[0202] Embodiments discussed herein may provide solutions to a
number of technical problems. For example, embodiments that
generate suggested sleep-tracking parameters solve the technical
problem of providing a meaningful user interface to a user. Such
may be the case because those embodiments may generate a user
interface with information that is relevant to the user, rather
than provide default values that characterize, say, a general
population. As described above, these parameters may include
suggested waketime, sleep duration, bedtime, and the like. Thus,
such embodiments may provide comparatively accurate information and
suggestions to a user base that includes users with a wide variety
of different sleep habits and needs.
[0203] Embodiments discussed herein may also provide solutions for
increasing a user's engagement with a fitness monitor that tracks
sleep. For example, embodiments may determine when to trigger the
presentation or introduction of a sleep-related tracking feature.
Such may be the case when embodiments selectively determine when to
introduce a sleep consistency goal. In some cases, an embodiment
may first introduce a sleep duration goal and then, after a
determinable period (as may be determined based on historical sleep
logs, for example), introduce a sleep consistency goal, e.g., to
promote or encourage consistent bedtimes and waketimes, on-top of
the sleep duration goal. Thus, such embodiments may provide
comparative improvements for systems to retain and engage a user
base compared, for example, with systems that do not provide such a
staged or gradual introduction to sleep consistency tracking.
* * * * *