User behavior recommendations for improving sleep

Patent metadata

Field	Value
Publication number	US-2021287776-A1
Application number	US-202017131830-A
Country	US
Kind code	A1
Filing date	Dec 23, 2020
Priority date	Mar 16, 2020
Publication date	Sep 16, 2021
Grant date	—

Abstract

A method is provided of generating behavior recommendations for a user, and communicating these to a user by means of a linguistic message, and wherein the recommended behavior or properties of the linguistic message are configured based on a measure of circadian inconsistency for the user. The measure of circadian inconsistency is derived by comparing an expected circadian curve of the user (e.g. an average curve derived from historical data for the user) with an empirical circadian curve for a given day. A deviation between the two provides an indication of the circadian inconsistency for the given day, and this is used to inform content, timing, wording, or other properties of the behavior recommendations.

First claim

1 . A computer-implemented method comprising:
determining an expected circadian pattern for a user based on a reference dataset comprising historical data for the user; determining an actual circadian pattern for a given day d based on user sensor data gathered for day d; determining a measure of circadian inconsistency for day d based on a deviation between the expected circadian pattern and actual circadian pattern for day d; determining a linguistic message for delivery to the user using a sensory output means, the linguistic message comprising a recommended user behavior for improving sleep, and wherein the recommended user behavior and/or one or more properties of the linguistic message are configured based at least in part on the circadian inconsistency; and generating a control signal for controlling a sensory output means to generate a sensory output representative of the linguistic message.

2 . A method as claimed in claim 1 , wherein the measure of circadian inconsistency comprises a measure of said deviation between the expected and actual circadian patterns as a function of time over day d, or at a particular time point in day d.

3 . A method as claimed in claim 1 , wherein the method comprises determining a message delivery time corresponding to a time at which the message is to be communicated to the user by means of the sensory output means, and wherein the message delivery time is determined based at least in part on the measure of circadian inconsistency.

4 . A method as claimed in claim 3 , wherein the delivery time of the message is selected to be at a time of day in which the circadian inconsistency for day d is below a defined threshold.

5 . A method as claimed in claim 1 , wherein the recommended behavior includes a recommended commencement time by the user of the behavior, and wherein the recommended commencement time is determined based at least in part on the determined measure of circadian inconsistency.

6 . A method as claimed in claim 1 , wherein the reference dataset comprises historical circadian pattern data, or historical sleep and/or activity data for the user.

7 . A method as claimed in claim 6 , wherein the expected circadian pattern is determined based on fitting parameters of a pre-determined circadian curve equation, the fitting being based on the historical sleep and/or activity data for the patient over one or more days.

8 . A method as claimed in claim 1 , wherein the sensor data comprises physiological parameter data, and/or movement data.

9 . A method as claimed in claim 1 , wherein
the measure of circadian inconsistency comprises an index of circadian inconsistency for day d, determined based on deriving a correlation between the expected and actual circadian patterns, and optionally wherein the method comprises monitoring the index of circadian inconsistency over a plurality of days and determining a measure of adherence based on changes in the index of circadian inconsistency.

10 . A method as claimed in claim 1 , wherein the recommended behavior is determined based on the circadian inconsistency, and based on use of a lookup table.

11 . A method as claimed in claim 1 , wherein the method comprises configuring one or more properties of the linguistic message based on the circadian inconsistency, and wherein the properties include a wording of the linguistic message.

12 . A method as claimed in claim 11 , wherein the configuring of the wording of the linguistic message, for communicating a particular behavior change, is performed by selecting from a lookup table one of a plurality of pre-determined linguistic messages conveying said behavior change and each with a different wording.

13 . A method as claimed in claim 11 , wherein the configuring of the wording of the message is performed using a machine learning algorithm.

14 . A computer program product comprising computer program code, the computer program code being configured, when executed on a processor, to cause the processor to perform a method in accordance with claim 1 .

15 . A processing arrangement comprising:
an input/output; and one or more processors adapted to:
determine an expected circadian pattern for a user based on a reference dataset for the user accessed using the input/output;
determine an actual circadian pattern for a given day d based on patient sensor data gathered for day d, the sensor data received at the input/output;
determine a measure of circadian inconsistency for day d based on a deviation between the expected and actual circadian patterns for day d;
determine a linguistic message for delivery to the user using a sensory output means, the message comprising a recommended user behavior, wherein the recommended behavior and/or one or more properties of the linguistic message are configured based on the circadian inconsistency; and
generate a control signal for controlling a sensory output means to generate a sensory output representative of the linguistic message.

Assignees

• Koninklijke Philips Nv

Inventors

• Garcia Molina Gary Nelson
• Chen Xia
• Mokashi Yash Parag
• Aloia Mark Steven

Classifications

• G16H50/30 Primary

  for calculating health indices; for individual health risk assessment · CPC title

• G09B19/00

  Teaching not covered by other main groups of this subclass (teaching or practice apparatus for gun-aiming or gun-laying F41G3/26) · CPC title

• A61B5/374

  Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves · CPC title

• A61B5/02116

  of pulse wave amplitude (A61B5/02225 takes precedence) · CPC title

• G16H20/30 Primary

  relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising · CPC title