System and method for assessing social jet LAG and providing behavioral insights thereof

Abstract

A system (200) for assessing social jet lag comprises a wearable device (100) to be worn by a user (202), the wearable device (100) including an accelerometer to detect sleep data such as sleep onset time, wake-up time, and restlessness patterns, a physiological temperature sensor (110) to capture body temperature during sleep, an ambient temperature sensor (112) to detect room temperature, and a wireless module (206) to transmit sleep and temperature data A user device (204) receives the sleep data and temperature, determines sleep midpoints for workdays and weekends, correlates physiological and ambient temperature, calculates a sleep midpoint deviation score, and compares restlessness and temperature data against a personalized thirty-day baseline. The system (200) segments the user (202) into a balanced, socially skewed, or work-life skewed sleep category and generates a notification with personalized, context-aware recommendations to help recognize and manage social jet lag.

Description

SYSTEM AND METHOD FOR ASSESSING SOCIAL JET LAG AND PROVIDING BEHAVIORAL INSIGHTS THEREOFFIELD OF INVENTION

[0001] The present invention relates to social jet lag detection. More specifically, the present invention relates to a system that involves a wearable device for assessing social jet lag of a user, comparing it to relevant demographic data, aligning with other data streams, and providing cross-functional insights to the user.BACKGROUND

[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.

[0003] Regular health monitoring plays a critical role in helping individuals achieve and maintain long-term wellness goals. In recent years, the widespread adoption of wearable technology, such as smart watches, smart bands, and electronic rings, has enabled users to continuously and non-invasively monitor a wide range of physiological and lifestyle-related parameters. Such wearable devices typically incorporate multiple sensors, including but not limited to heart rate sensors, SpCh sensors, photoplethsmography (PPG) sensors, electrocardiogram (ECG) sensors, temperature sensors, and accelerometers, which collectively allow users to track vital health indicators in real time. Information captured by these sensors is often displayed directly on the wearable device or transmitted wirelessly to a user device, such as a smartphone or tablet, where dedicated software applications process the information and present it in a comprehensible format. The aim is to provide users context- specific information which when compared with a relevant population group (examples: same city, same age, same gender etc.) can provide actionable insights to the user on their lifestyle with the aim to support them to improve their lifestyle, health and overall wellbeing.

[0004] The increasing popularity of wearable health devices has significantly contributed to raising awareness about daily recovery and activity, encouraging users to make healthierchoices and monitor metrics such as steps taken, resting heart rate, heart rate variability, VO2 max, sleep quality or blood oxygen saturation. While many existing devices excel at capturing these fundamental health signals, certain aspects of individual health remain insufficiently addressed by conventional solutions, particularly when it comes to interpreting how day-to-day lifestyle behaviors influence long-term health and individual circadian alignment.

[0005] One such aspect is the phenomenon of social jet lag. Although many wearable devices today include basic sleep tracking features, such as measuring sleep duration or differentiating between light and deep sleep phases, they often fail to capture how behavioral patterns specifically originating for a user’s professional and family commitments impact these metrics and can cause chronic misalignment of an individual’s biological clock. For instance- an individual working in a high-pressure job copes with considerable stress during the workweek. However, instead of optimizing on recovery, this person strives to fulfil family commitments over the weekend which accumulate stress and fatigue. Hence the social jet lag pertain here is not due to travel across time zones but from the lifestyle-driven discrepancy between a person’s socially imposed schedule and their natural circadian rhythm. Over time, this misalignment may lead to chronic fatigue, cognitive impairments, mood swings, and an increased risk of various long-term health concerns including metabolic disorders with known mortality risks.

[0006] Existing solutions tend to focus on broad measures of sleep quality and circadian alignment but often lack dedicated mechanisms to detect, quantify, and contextualize social jet lag for individuals who maintain a consistent geographic location yet frequently shift their sleep schedules due to social or work commitments. Moreover, conventional systems generally do not account for the subtle ways in which physiological signals, such as body temperature fluctuations, interact with external environmental factors to influence sleep phase detection and circadian interpretation.

[0007] Additionally, current wearable devices do not provide personalized, actionable guidance that distinguishes whether an irregular sleep pattern stems from social habits, work demands, environmental disruptions or a balanced routine. Without such context- specific awareness and meaningful feedback, users may struggle to identify the root cause of their misaligned sleep schedules and may lack the insights needed to adjust daily behaviors effectively.

[0008] Accordingly, there remains a need for a system that can go beyond simple sleep duration or sleep stage tracking and provide precise, behaviorally grounded assessments of social jet lag.SUMMARY OF THE INVENTION

[0009] This summary is provided to introduce aspects related to a method and a system for assessing social jet lag, and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining or limiting the scope of the claimed subject matter.

[0010] In an embodiment of the present disclosure, a method for assessing social jet lag is disclosed. The method includes detecting, by a sleep tracking sensor of a wearable device, sleep data including sleep onset time, wake-up time, disparity between sleep wake and first consistent movement in the day (related to day time fatigue) and restlessness patterns of the user for a plurality of workdays and weekends. Further, the method includes detecting, by a physiological temperature sensor of the wearable device, physiological temperature data of the user during sleep for the plurality of workdays and weekends. Furthermore, the method includes detecting, by an ambient temperature sensor of the wearable device, temperature of a room in which the user sleeps. The method further includes transmitting, by a wireless module of the wearable device, the sleep data, the physiological temperature of the user, and the temperature of the room to a user device.

[0011] The method further includes determining, by an application executed by the user device, a sleep midpoint for the workdays and a sleep midpoint for the weekends based on the sleep data. Further, the method includes adjusting, by the application, the sleep midpoint using the physiological temperature data in correlation with the temperature of the room to distinguish internal physiological temperature changes from external ambient influences. Furthermore, the method includes calculating, by the application and use of the wearable device over time, a sleep midpoint deviation score based on a difference between the workday sleep midpoint and the weekend sleep midpoint. The method further includes comparing, by the application, the restlessness patterns and physiological temperature changes against a personalized baseline to detect deviations from the user’s typical sleep behavior, the personalized baseline being generated from at least thirty days of sleep data.

[0012] Further, the method includes segmenting the user into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and physiological temperature. Further, the method includes aggregating user uploaded information for the week, indicating activities that may have impacted sleep quality. Furthermore, the method includes generating, by the application, a notification including personalized recommendations to increase awareness and assist the user in becoming aware of the consequences of continued social jet lag, recognizing and managing the social jet lag by modifying existent weekday vs weekend sleep schedule variations.

[0013] In an aspect of the present disclosure, the method further includes adjusting, by the application, the personalized baseline and the category using gender-specific and age-specific physiological parameters or including weightage of night shift work.

[0014] In an aspect of the present disclosure, the personalized baseline is dynamically updated based on most recent thirty days of sleep data and maintained in historical demographic database which can be cloud or decentralized based.

[0015] In an aspect of the present disclosure, the method includes user journaling that provides cause and effect information for existence of a social jet lag by tagging daily activities.

[0016] In an aspect of the present disclosure, the method further includes providing the notification and visualization of the sleep midpoint deviation score and behavioral category on a user interface of the user device overlaying with journaling information to provide personalized insights to the user.

[0017] In an aspect of the present disclosure, the personalized recommendations include suggested bedtimes and wake-up times for workdays and weekends, and context-aware social and environmental suggestions based on the ambient temperature.

[0018] In an aspect of the present disclosure, the method further includes receiving, from the wearable device, heart rate variability data. Further, the method includes adjusting, by the application, the personalized baseline based on the detected heart rate variability deviations from the personalized baseline.

[0019] In an aspect of the present disclosure, a light weight executable version of the application is configured to be executed by the wearable device.

[0020] In an aspect of the present disclosure, the method further includes detecting the temperature of the room in which the user sleeps, by an external device.

[0021] In an embodiment of the present disclosure, a system for assessing social jet lag is disclosed. The system includes a wearable device to be worn by a user. The wearable device includes an accelerometer configured to detect sleep data including sleep onset time, wake-up time, and restlessness patterns of the user for a plurality of workdays and weekends, a physiological temperature sensor configured to detect physiological temperature data of the user during sleep, an ambient temperature sensor configured to detect a temperature of a room in which the user sleeps, and a wireless module configured to transmit the sleep data and the temperature of the room.

[0022] The system further includes a user device communicatively coupled to the wearable device, the user device includes a processor and a memory. The memory is communicatively coupled with the processor, and the memory stores program instructions executable by the processor to run an application configured to receive the sleep data, the physiological temperature data of the user, and the temperature of the room from the wearable device.

[0023] Further, the application is configured to determine a sleep midpoint for the workdays and a sleep midpoint for the weekends based on the sleep data. Further, the application is configured to adjust the sleep midpoint using the physiological temperature data in correlation with the temperature of the room to distinguish internal physiological temperature changes from external ambient influences. Further, the application is configured to calculate a sleep midpoint deviation score based on a difference between the workday sleep midpoint and the weekend sleep midpoint. Further, the application is configured to compare the restlessness patterns and internal physiological temperature changes against a personalized baseline to detect deviations from the user’s typical sleep behavior, the personalized baseline being generated from at least thirty days of historical sleep data.

[0024] Further, the application is configured to segment the user into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and physiological temperature. Further, the application is configured to aggregate user uploaded information forthe week, indicating activities that may have impacted sleep quality. Furthermore, the application is configured to generate a notification including personalized recommendations to increase awareness and assist the user in becoming aware of the consequences of continued social jet lag, recognizing and managing the social jet lag by modifying existent weekday vs weekend sleep schedule variations.

[0025] In an aspect of the present disclosure, the application is further configured to adjust the personalized baseline and the category using gender- specific and age-specific physiological parameters or including weightage of night shift work.

[0026] In an aspect of the present disclosure, the personalized baseline is dynamically updated based on most recent thirty days of sleep data and maintained in a historical demographic database which can be cloud or decentralized based.

[0027] In an aspect of the present disclosure, the application is further configured to provide the notification and visualization of the sleep midpoint deviation score and behavioral category on a user interface of the user device overlaying with journaling information to provide personalized insights to the user.

[0028] In an aspect of the present disclosure, the personalized recommendations include suggested bedtimes and wake-up times for workdays and weekends, and context-aware social and environmental suggestions based on the ambient temperature.

[0029] In an aspect of the present disclosure, the application is further configured to receive, from the wearable device, heart rate variability data. Further, the application is configured to adjust, by the application, the personalized baseline based on heart rate variability deviations from the personalized baseline.

[0030] In an aspect of the present disclosure, a light weight executable version of the application is configured to run on the wearable device.

[0031] In an aspect of the present disclosure, the system includes an external device configured to detect the temperature of the room in which the user sleeps.

[0032] In an embodiment of the present disclosure, a non-transitory computer-readable storage medium including computer program code for execution by one or more processors of an apparatus, the computer program code configured to, when executed by the one or moreprocessors, cause the apparatus to detect sleep data of a user including sleep onset time, wakeup time, and restlessness patterns of the user for a plurality of workdays and weekends. Further, the computer program code causes the apparatus to detect physiological temperature data of the user during sleep for the plurality of workdays and weekends.

[0033] Further, the computer program code causes the apparatus to detect temperature of a room in which the user sleeps. Further, the computer program code causes the apparatus to transmit the sleep data, physiological temperature of the user, and the temperature of the room to a user device. Further, the computer program code causes the apparatus to determine a sleep midpoint for the workdays and a sleep midpoint for the weekends based on the sleep data. Further, the computer program code causes the apparatus to utilize physiological temperature data in conjunction with ambient room temperature to differentiate internal physiological temperature changes from external ambient influences. Further, the computer program code causes the apparatus to calculate a sleep midpoint deviation score based on a difference between the workday sleep midpoint and the weekend sleep midpoint.

[0034] Further, the computer program code causes the apparatus to compare the restlessness patterns and physiological temperature changes against a personalized baseline to detect deviations from the user’s typical sleep behavior, the personalized baseline being generated from at least thirty days of sleep data. Further, the computer program code causes the apparatus to segment the user into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and the physiological temperature. Further, the computer program code causes the apparatus to generate a notification including personalized recommendations to increase awareness and assist the user in becoming aware of the consequences of continued social jet lag, recognizing and managing the social jet lag by modifying existent weekday vs weekend sleep schedule variations.

[0035] Other aspects and advantages of the invention will become apparent from the following description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the invention.BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings constitute a part of the description and are used to provide further understanding of the present invention. Such accompanying drawings illustratethe embodiments of the present invention which are used to describe the principles of the present invention. The embodiments are illustrated by way of example and not by way of limitation in the figures of the accompanying drawings in which like references indicate similar elements. It should be noted that references to “an” or “one” embodiment in this invention are not necessarily to the same embodiment, and they mean at least one. In the drawings:

[0037] Fig. 1 illustrates a wearable device capable of assessing social jet lag, in accordance with an embodiment of the present invention;

[0038] Fig. 2 illustrates a block diagram of a system for assessing social jet lag, in accordance with an embodiment of the present invention; and

[0039] Figs. 3A and 3B cumulatively illustrate a flow chart of a method of assessing social jet lag, in accordance with an embodiment of the present invention.

[0040] A more complete understanding of the present invention and its embodiments thereof may be acquired by referring to the following description and the accompanying drawings.DETAILED DESCRIPTION OF THE INVENTION

[0041] Exemplary embodiments will now be described with reference to the accompanying drawings. The disclosure may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey its scope to those skilled in the art. The terminology used in the detailed description of the particular exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting. In the drawings, like numbers refer to like elements.

[0042] It is to be noted, however, that the reference numerals used herein illustrate only typical embodiments of the present subject matter, and are therefore not to be considered for limiting its scope, for the subject matter may admit to other equally effective embodiments.

[0043] The detailed description includes specific details for the purpose of providing a thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without these specific details.

[0044] The proposed invention relates to a wearable device capable of assessing social jet lag of a user. The wearable device may be a smart watch, smart band, or an electronic ring. Although the details have been provided successively with reference to a smart ring merely for the sake of explanation, it must be understood that the invention could be fairly implemented in a similar manner using any other wearable device.

[0045] Fig. 1 illustrates a wearable device (100) capable of assessing social jet lag of a user, in accordance with an embodiment of the present invention. The wearable device (100) may be a smart ring. In one embodiment, the wearable device (100) may be but is not limited to, a smart watch, a smart band or any device capable of detecting sleep data and temperature data.

[0046] The wearable device (100) may be made using a hypoallergenic material to allow comfortable and continuous wear by the user. The wearable device (100) may include an outer surface (102), a middle layer (104), and an inner surface (106). The outer surface (102) may be made of any scratch-proof hard material. The middle layer (104), positioned between the outer layer (102) and the inner surface (106), may be a Printed Circuit Board (PCB). In one embodiment, the PCB may be flexible. In another embodiment, the PCB may be rigid.

[0047] The wearable device (100) may include a plurality of sensors configured to capture various health parameters of the user in a continuous and non-invasive manner. The plurality of sensors may include a sleep tracking sensor (108), such as an accelerometer, configured to detect motion of the user and determine sleep data, including but not limited to sleep onset time, wake-up time, and restlessness patterns during sleep sessions.

[0048] Additionally, the plurality of sensors may include a physiological temperature sensor (110) configured to monitor the user’s body temperature (physiological temperature data) during defined sleep intervals. The plurality of sensors may further include an ambient temperature sensor (112) configured to detect temperature of the room in which the user sleeps, thereby enabling contextual adjustment of the physiological temperature data.

[0049] Further, a microcontroller may be mounted on the flexible PCB and may be operatively connected to the plurality of sensors. The sleep tracking sensor (108) may monitor the user’s sleep data including but not limited to, sleep onset, wake-up time, and restlessness patterns throughout a plurality of workdays and weekends. The physiological temperature sensor (110) may record body temperature (physiological temperature data) trends during thesame period. The ambient temperature sensor (112) may capture the room temperature to allow differentiation between internal physiological changes and external environmental influences. The plurality of sensors may transmit the sleep and temperature data to the microcontroller in real-time. The microcontroller may store the sleep and temperature data in an onboard memory (208) or a separate memory element mounted on the flexible PCB. Further, a battery may be provided to power the sleep tracking sensor (108), the physiological temperature sensor (110), the ambient temperature sensor (112), the microcontroller, and the wireless module, ensuring continuous monitoring and data transmission while the wearable device (100) is worn by the user.

[0050] Now referring to Fig. 2, a block diagram of a system (200) for assessing social jet lag of the user (202) is illustrated in accordance with an embodiment of the present invention. The system (200) includes a wearable device (100), and the user device (204) interacting with the user (202). The user (202) may want to assess social jet lag. Thus, the user (202) may wear the wearable device (100) on one of their fingers continuously for at least a week.

[0051] Further, as explained in detail in Fig. 1, the wearable device (100) includes a sleep tracking sensor (108) configured to monitor the sleep pattern of the user (202). Further, the sleep tracking sensor (108) detects the sleep duration and sleep time of the user throughout the workdays, such as Monday to Thursday, and throughout the weekends, such as Friday to Sunday. The wearable device (100) may store the sleep duration and sleep time in a memory (208) of the wearable device (100).

[0052] A wireless module (206) of the wearable device (100) may wirelessly communicate the sleep and temperature data to a wireless module (210) of the user device (204), such as a smartphone or a laptop, for further analysis by a dedicated application. The wireless module (206) may operate over one or more of Bluetooth, Wi-Fi, radio frequency, or Near Field Communication (NFC).

[0053] In an embodiment, the system (200) may include an external device configured to detect the temperature (ambient temperature) of the room in which the user (202) sleeps. Further, the external device, the user device (204), and the wearable device (100) may be independently connected to a cloud for storing the data that the external device, the user device (204), and the wearable device (100) may collect.

[0054] The user device (204) includes a processor (216) and a memory (218). The memory (218) is communicatively coupled with the processor (216), and the memory (218) stores program instructions executable by the processor (216) to run an application (212). In one embodiment, a lightweight executable version of the application (212) may be configured to run on the wearable device (100).

[0055] The processor (216) may include one or more general purpose processors (e.g., INTEL® or Advanced Micro Devices® (AMD) microprocessors) and / or one or more special purpose processors (e.g., digital signal processors or Xilinx® System On Chip (SOC) Field Programmable Gate Array (FPGA) processor), MIPS / ARM-class processor, a microprocessor, a digital signal processor, an application specific integrated circuit, a microcontroller, a state machine, or any type of programmable logic array.

[0056] The memory (218) may include, but is not limited to, non-transitory machine- readable storage devices such as hard drives, magnetic tape, floppy diskettes, optical disks, Compact Disc Read-Only Memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, Random Access Memories (RAMs), Programmable Read-Only Memories (PROMs), Erasable PROMs (EPROMs), Electrically Erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media / machine- readable medium suitable for storing electronic instructions.

[0057] The memory (218) may include a plurality of storage locations that are addressable by the processor (216) and one or more interfaces for storing software programs and other necessary information (program instructions, personalized baseline, personalized recommendations, and application (212)) associated with the embodiments described herein. The processor (216) assesses social jet lag by executing program instructions stored in the memory (218). The processor (218) may include hardware elements or hardware logic adapted to execute the software programs and manipulate data structures.

[0058] It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while the processes have been shown separately,those skilled in the art will appreciate that processes may be routines or modules within other processes.

[0059] The application (212) may determine a sleep midpoint deviation score by first calculating a sleep midpoint for the workdays and a separate sleep midpoint for the weekends. The sleep midpoint for a given sleep session may be determined by identifying a time halfway between the sleep onset time and the wake-up time, as captured by the sleep tracking sensor (108) of the wearable device (100). The sleep midpoint may be different from temporal midpoint of sleep and can be mean a variety of things in different systems for instance midpoint of all actual deep sleep session in the night during the middle of the week, all REM+NREM sleep medians for the workweek, median of sleep onset times or temporal values of middle of the week REM+NREM, HRV, Temp, movement etc.

[0060] To improve the accuracy of midpoint determination, the application (212) may analyze the physiological temperature data recorded by the wearable device (100) during the sleep session. Specifically, body temperature fluctuations, such as a drop-in core temperature during the onset of sleep and a rise toward wake-up, may be used to confirm or adjust motion- derived estimates of sleep onset and wake-up times.

[0061] In an embodiment, the application may evaluate non-physiological temperature, sleep, and movement measures, which are also checked against a user’s normal profile. For instance, temperature cannot exceed 105 degrees C, sleep cannot exceed 14 hours, environmental temperature cannot exceed 55 degrees C. This removes outliers and makes the system for accurate. Secondly the user has a pattern for all these and over time a profile builds. Using that as well to refine outputs and signals.

[0062] Additionally, the application (212) may correlate the physiological temperature data with the ambient temperature data (the temperature of the room the user sleeps in) captured by the ambient temperature sensor (112) to distinguish genuine internal body temperature changes from external environmental influences that could otherwise skew the analysis.

[0063] Once the sleep midpoints for the workdays and weekends are determined, the application (212) may calculate sleep midpoint deviation score by computing time difference between an average sleep midpoint for the workdays and an average sleep midpoint for the weekends. The sleep midpoint deviation score may then be used as an indicator of extent of misalignment between biological clock and social or occupational schedules of the user (202).

[0064] A larger deviation score may indicate a greater degree of social jet lag, while a smaller score may indicate a more consistent sleep routine. The deviation score may further be used, in combination with restlessness patterns and temperature trends compared against a personalized baseline, to classify the user’s sleep behavior into one of several categories and generate targeted recommendations to raise awareness and support healthier sleep alignment.

[0065] Further, the user device (204) may categorize the user (202) into one of a balanced sleep trends category, an imbalanced sleep due to social life category, or an imbalanced sleep due to work life category, based on the sleep midpoint deviation score in combination with the compared restlessness patterns and physiological temperature changes compared against a personalized baseline.

[0066] The personalized baseline may be generated using historical sleep data collected over at least thirty consecutive days, including typical sleep onset and wake-up times, average restlessness levels, and normal physiological temperature trends for the user under similar ambient conditions. The personalized baseline is dynamically updated based on most recent thirty days of sleep data. Further, the personalized recommendations include suggested bedtimes and wake-up times for workdays and weekends, and context-aware environmental suggestions based on the ambient temperature.

[0067] In an embodiment, the application may verify social jet lag by comparing changes in sleep environment that can impact sleep quality independently.

[0068] By referencing the personalized baseline, the system (200) may identify significant deviations from the user’s usual sleep behavior and distinguish whether irregularities are temporary fluctuations or indicate a persistent pattern of social jet lag. The balanced sleep trends category may correspond to a healthy, stable sleep pattern with minimal or negligible difference between the sleep midpoints on workdays and weekends, indicating that the user’s daily sleep routine remains consistent and well aligned with their natural circadian rhythm.

[0069] In an embodiment, the application may verify, using external inputs, whether the change in user physiology is due to social jet lag or other factors.

[0070] Additionally, the personalized baseline may be dynamically updated using at least thirty days of historical sleep data, allowing the system to adapt to gradual changes in the user’s lifestyle, work schedules, or physiological conditions. Such continuous calibration supports thegeneration of precise, context-aware recommendations that remain relevant as the user’s behavioral trends evolve, ensuring that the user receives meaningful insights to recognize, understand, and manage social jet lag in a way that aligns with their unique biological and social circumstances.

[0071] The imbalanced sleep due to the social life category may correspond to a sleep midpoint deviation score skewed toward later midpoints on weekends compared to workdays. Such a pattern may indicate that the user (202) tends to stay awake or sleep in later during weekends due to social or recreational activities, leading to a misalignment between the user’s biological clock and their weekday obligations.

[0072] Conversely, the imbalanced sleep due to the work-life category may correspond to a sleep midpoint deviation score skewed toward earlier midpoints on workdays compared to weekends. This may indicate that the user (202) adjusts their sleep-wake schedule earlier on workdays due to occupational demands or commuting requirements, which can also contribute to circadian misalignment over time.

[0073] In an embodiment, the categorization may further be refined by correlating the user’s detected restlessness patterns and physiological temperature changes with the ambient temperature data, allowing the system (200) to more accurately distinguish whether sleep inconsistencies are driven primarily by behavioral factors or external conditions. By assigning the user (202) to a specific behavioral category, the system (200) may generate tailored, non- interventional recommendations designed to raise awareness and help the user recognize the root causes of their social jet lag, empowering them to adopt more consistent sleep habits aligned with their biological needs.

[0074] In an embodiment, the system (200) may further refine the behavioral categorization by incorporating user-specific physiological factors, such as gender-related variations that can affect sleep and recovery signals. For example, the application (212) may account for cyclical hormonal influences, such as progesterone-driven changes in body temperature and heart rate variability, which may impact baseline sleep patterns, particularly in female users. By integrating such gender-sensitive adjustments into the personalized baseline, the system (202) can enhance the accuracy of sleep midpoint calculations, restlessness pattern interpretation, and temperature fluctuation analysis.

[0075] In an embodiment, the application (212) may generate personalized recommendations for optimizing the user’s sleep schedule and improving overall sleep health based on the sleep midpoint deviation score, the user’s categorized sleep behavior, restlessness patterns, and physiological temperature data compared against the personalized baseline. The recommendations may be designed to help the user recognize patterns in their sleep timing and adopt voluntary adjustments to reduce circadian misalignment caused by social jet lag, without directly intervening or controlling the user’s behavior. The personalized recommendations may include suggested bedtimes and wake-up times for workdays and weekends, or context- aw are notifications such an anxiety reducing routine before bed for a person with routine professional late night calls and context-aware environmental suggestions based on the ambient temperature such as telling user that the impaired recovery is due to hotter climate than usual rather than social jet lag.

[0076] For example, if the user (202) is categorized in the imbalanced sleep due to social life category, the application (212) may recommend gradually shifting bedtime on weekends closer to workday sleep times, limiting late-night social activities that extend sleep midpoints, or using relaxing pre-sleep routines to encourage earlier sleep onset. If the user is categorized in the imbalanced sleep due to work life category, the application (212) may suggest practical strategies such as consistent wake-up times on weekends, short naps to manage accumulated sleep debt, or adjusting evening activities to allow for sufficient wind-down before earlier bedtimes required by work obligations.

[0077] In some embodiments, the application (212) may further personalize the recommendations by considering gender- specific factors that influence sleep physiology, such as advising female users on how hormone-driven temperature fluctuations across different phases of the menstrual cycle may naturally affect sleep patterns and recovery needs. Additionally, the recommendations may provide context for environmental influences detected by the ambient temperature sensor (112), for instance, suggesting adjustments to room temperature or bedding if ambient conditions are found to contribute to unusual restlessness or body temperature variations that deviate from the personalized baseline.

[0078] Further, the user device (204) may include a user interface (UI) (214) configured to render the detected sleep pattern, sleep duration, sleep onset and wake-up times, calculated sleep midpoint deviation score, behavioral sleep category, and the generated recommendations in an easy-to-read, interactive format. The UI (214) may also display historical trends andcomparisons to the user’s established baseline, using visual cues or alerts to help the user identify improvements or recurring misalignments. The UI (214) enables the user (202) to make informed, proactive adjustments to their daily routine to better manage and reduce social jet lag.

[0079] In an embodiment, If the user's wearable device is not worn or is temporarily unavailable (e.g., due to charging, replacement, or malfunction), the system is configured to generate predictive estimations of physiological or behavioral patterns for 2-3 days using previously collected historical data, thereby maintaining continuity in monitoring and insights despite data interruptions.

[0080] Figs. 3A and 3B cumulatively illustrate a flow chart of a method (300) of assessing social jet lag, in accordance with an embodiment of the present invention. In this regard, each block may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the drawings.

[0081] For example, two blocks shown in succession in Figs. 3A and 3B may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. Any process descriptions or blocks in flow charts should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the example embodiments in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved. In addition, the process descriptions or blocks in flow charts should be understood as representing decisions made by a hardware structure such as a state machine.

[0082] The order in which method (300) is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein.

[0083] Furthermore, the method (300) can be implemented in any suitable hardware, software, firmware, or combination thereof. Furthermore, the above-mentioned methods maybe implemented in suitable hardware, computer-readable instructions, or a combination thereof. The steps of such methods may be performed by either a system under the instruction of machine-executable instructions stored on a non-transitory computer-readable medium or by dedicated hardware circuits, microcontrollers, or logic circuits. The method may include the following steps:

[0084] At step (302), sleep data of a user, including sleep onset time, wake-up time, and restlessness patterns of the user, may be detected for a plurality of workdays and weekends.

[0085] At step (304), physiological temperature data of the user during sleep may be detected for the plurality of workdays and weekends.

[0086] At step (306), temperature of a room in which the user sleeps may be detected. Detection of the temperature of the room may allow differentiation between internal physiological changes and external environmental influences.

[0087] At step (308), the sleep data, the physiological temperature of the user, and the temperature of the room may be transmitted to a user device. The user device may be configured to execute an application to assess social jet lag for the user.

[0088] At step (310), a sleep midpoint for the workdays and a sleep midpoint for the weekends may be determined based on the sleep data. The sleep midpoint for a given sleep session may be determined by identifying a time halfway between the sleep onset time and the wake-up time.

[0089] At step (312), the physiological temperature data may be utilized in conjunction with the ambient temperature to differentiate internal physiological temperature changes from external ambient influences. Specifically, body temperature fluctuations, such as a drop-in core temperature during the onset of sleep and a rise toward wake-up, may be used to confirm or adjust motion-derived estimates of sleep onset and wake-up times.

[0090] At step (314), a sleep midpoint deviation score may be calculated based on a difference between the workday sleep midpoint and the weekend sleep midpoint. The sleep midpoint deviation score may be calculated by computing time difference between an average sleep midpoint for the workdays and an average sleep midpoint for the weekends. The sleepmidpoint deviation score may then be used as an indicator of extent of misalignment between biological clock and social or occupational schedules of the user.

[0091] In an embodiment, the method may include verifying using external inputs whether the change in user physiology is due to social jet lag or other factors.

[0092] At step (316), the restlessness patterns and physiological temperature changes may be compared against a personalized baseline generated from at least thirty days of sleep data. The comparison may identify significant deviations from the user’s usual sleep behavior and distinguish whether irregularities are temporary fluctuations or indicate a persistent pattern of social jet lag.

[0093] At step (318), the user may be segmented into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and physiological temperature.

[0094] The balanced sleep trends category may correspond to a healthy, stable sleep pattern with minimal or negligible difference between the sleep midpoints on workdays and weekends, indicating that the user’s daily sleep routine remains consistent and well aligned with their natural circadian rhythm.

[0095] The imbalanced sleep due to the social life category may correspond to a sleep midpoint deviation score skewed toward later midpoints on weekends compared to workdays. Such a pattern may indicate that the user tends to stay awake or sleep in later during weekends due to social or recreational activities, leading to a misalignment between the user’s biological clock and their weekday obligations.

[0096] Conversely, the imbalanced sleep due to the work-life category may correspond to a sleep midpoint deviation score skewed toward earlier midpoints on workdays compared to weekends. This may indicate that the user adjusts their sleep-wake schedule earlier on workdays due to occupational demands or commuting requirements, which can also contribute to circadian misalignment over time.

[0097] In an embodiment, the behavioral categorization may be refined by incorporating user-specific physiological factors, such as gender-related variations that can affect sleep and recovery signals. For example, the application may account for cyclical hormonal influences,such as progesterone-driven changes in body temperature and heart rate variability, which may impact baseline sleep patterns, particularly in female users. By integrating such gendersensitive adjustments into the personalized baseline, the accuracy of sleep midpoint calculations, restlessness pattern interpretation, and temperature fluctuation analysis may be enhanced.

[0098] At step (320), user uploaded information for the week may be aggregated. The user uploaded information may indicate activities that may have impacted sleep quality.

[0099] At step (322), a notification may be generated including personalized recommendations to increase awareness and assist the user in recognizing and managing social jet lag due to sleep schedule variations.

[0100] The recommendations may be designed to help the user recognize patterns in their sleep timing and adopt voluntary adjustments to reduce circadian misalignment caused by social jet lag, without directly intervening or controlling the user’s behavior. The personalized recommendations may include suggested bedtimes and wake-up times for workdays and weekends, and context-aware environmental suggestions based on the ambient temperature.

[0101] For example, if the user is categorized in the imbalanced sleep due to social life category, the recommendations may include gradually shifting bedtime on weekends closer to workday sleep times, limiting late-night social activities that extend sleep midpoints, or using relaxing pre-sleep routines to encourage earlier sleep onset. If the user is categorized in the imbalanced sleep due to work life category, the recommendations may include practical strategies such as consistent wake-up times on weekends, short naps to manage accumulated sleep debt, or adjusting evening activities to allow for sufficient wind-down before earlier bedtimes required by work obligations.

[0102] In some embodiments, the personalized recommendations may consider genderspecific factors that influence sleep physiology, such as advising female users on how hormone-driven temperature fluctuations across different phases of the menstrual cycle may naturally affect sleep patterns and recovery needs. Additionally, the recommendations may provide context for environmental influences, for instance, suggesting adjustments to room temperature or bedding if ambient conditions are found to contribute to unusual restlessness or body temperature variations that deviate from the personalized baseline.Technical Advancement and Economic Significance

[0103] The system and the method disclosed in the present invention, for assessing social jet lag, may have the following advantages over conventional art:Behavioral sleep midpoint deviation scoring enables precise detection of social jet lag caused by lifestyle-driven schedule shifts.Integrated motion, restlessness, and physiological temperature tracking ensures accurate sleep onset and wake-up time and recovery detection for each sleep session.Correlation of internal body temperature with ambient room temperature isolates true circadian signals from external environmental factors, since both can impact sleep quality.Personalized baseline generation using at least thirty days of historical sleep data enhances sensitivity to gradual behavioral and physiological changes and also includes weather pattern changes that can change environmental temperature.User journaling provided unique user scenarios that refines insights and analysis of social jet lag and improves personalized notifications.Gender-sensitive modeling accounts for hormone-driven temperature variations, improving relevance and inclusivity of sleep insights.Distinct categorization into balanced, socially skewed, or work-life skewed sleep trends supports clear behavioral profiling.Non-interventional, context-aware recommendations increase user awareness and support voluntary adjustments to align sleep routines with natural circadian rhythms.

[0104] The specification may refer to “an”, “another”, “one”, or “some” embodiment(s) in several locations.

[0105] This does not necessarily imply that each such reference is to the same embodiment(s), or that the feature only applies to a single embodiment. Single features of different embodiments may also be combined to provide other embodiments.

[0106] The terms “or” and “and / or” as used herein are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and / or C” mean “anyof the following: A; B; C; A and B; A and C; B and C; A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0107] As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms “includes”, “includes”, “including” and / or “including” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Furthermore, “connected” or “coupled” as used herein may include operatively connected or coupled. As used herein, the term “and / or” includes any and all combinations and arrangements of one or more of the associated listed items.

[0108] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skills in the art to which this disclosure pertains. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0109] Although implementations of methods and systems for assessing social jet lag have been described in language specific to structural features and / or methods, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations of a method and a system for assessing social jet lag.

[0110] The invention has been described above with reference to numerous embodiments and specific examples. Many variations will suggest themselves to those skilled in this art in light of the above-detailed description. All such obvious variations are within the full intended scope of the appended claims.

Claims

WE CLAIM:

1. A method comprising: detecting, by a sleep tracking sensor (108) of a wearable device (100) worn by a user (202), sleep data comprising sleep onset time, wake-up time, disparity between sleep wake and first consistent movement in the day (related to day time fatigue) and restlessness patterns of the user for a plurality of workdays and weekends; detecting, by a physiological temperature sensor (110) of the wearable device (100), physiological temperature data of the user (202) during sleep for the plurality of workdays and weekends; detecting, by an ambient temperature sensor (112) of the wearable device (100), temperature of a room in which the user (202) sleeps; transmitting, by a wireless module (206) of the wearable device (100), the sleep data, the physiological temperature of the user (202), and the temperature of the room to a user device (204); determining, by an application (212) executed by the user device (204), a sleep midpoint for the workdays and a sleep midpoint for the weekends based on the sleep data; utilizing the physiological temperature data in conjunction with the ambient temperature to differentiate internal physiological temperature changes from external ambient influences; calculating, by the application (212), a sleep midpoint deviation score based on a difference between the workday sleep midpoint and the weekend sleep midpoint; comparing, by the application (212), the restlessness patterns and physiological temperature changes against a personalized baseline, to detect deviations from the user’s typical sleep behavior, the personalized baseline being generated from at least thirty days of sleep data; segmenting the user (202) into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and physiological temperature;aggregating user uploaded information for the week, indicating activities that may have impacted sleep quality; and generating, by the application (212), a notification comprising personalized recommendations to increase awareness and assist the user (202) in becoming aware of the consequences of continued social jet lag, recognizing and managing the social jet lag by modifying existent weekday vs weekend sleep schedule variations.

2. The method as claimed in claim 1 , further comprises adjusting, by the application (212), the personalized baseline and the category using gender- specific and age-specific physiological parameters or including weightage of night shift work.

3. The method as claimed in claim 2, wherein the personalized baseline is dynamically updated based on most recent thirty days of sleep data and maintained in historical demographic database which can be cloud or decentralized based.

4. The method as claimed in claim 1 , further comprises user journaling that provides cause and effect information for existence of a social jet lag by tagging daily activities.

5. The method as claimed in claim 1, further comprises providing the notification and visualization of the sleep midpoint deviation score and behavioral category on a user interface (214) of the user device (204) overlaying with journaling information to provide personalized insights to the user (202).

6. The method as claimed in claim 1, wherein the personalized recommendations comprise suggested bedtimes and wake-up times for workdays and weekends, and context- aware environmental suggestions based on the ambient temperature.

7. The method as claimed in claim 1, further comprises: receiving, from the wearable device (100), heart rate variability data; and adjusting, by the application (212), the personalized baseline based on heart rate variability deviations from the personalized baseline.

8. The method as claimed in claim 1, wherein a light weight executable version of the application (212) is configured to be executed by the wearable device (100).

9. The method as claimed in claim 1, further comprises detecting the temperature of the room in which the user (202) sleeps by an external device.

10. A system (200) comprises: a wearable device (100) to be worn by a user (202), the wearable device (100) comprises an accelerometer configured to detect sleep data comprising sleep onset time, wake-up time, disparity between sleep wake and first consistent movement in the day (related to day time fatigue) and restlessness patterns of the user (202) for a plurality of workdays and weekends, a physiological temperature sensor (110) configured to detect physiological temperature data of the user (202) during sleep, an ambient temperature sensor (112) configured to detect a temperature of a room in which the user (202) sleeps, and a wireless module (206) configured to transmit the sleep data and the temperature of the room; a user device (204) communicatively coupled to the wearable device (100), the user device (204) comprises a processor (216) and a memory (218), wherein the memory (218) is communicatively coupled with the processor (216), and the memory (218) stores program instructions executable by the processor (216) to run an application (212) configured to: receive the sleep data, the physiological temperature data of the user (202), and the temperature of the room from the wearable device (100); determine a sleep midpoint for the workdays and a sleep midpoint for the weekends based on the sleep data; utilizing the physiological temperature data in conjunction with the ambient temperature to differentiate internal physiological temperature changes from external ambient influences; calculate a sleep midpoint deviation score based on a difference between the workday sleep midpoint and the weekend sleep midpoint; compare the restlessness patterns and internal physiological temperature changes against a personalized baseline to detect deviations from the user’s typicalsleep behavior, the personalized baseline being generated from at least thirty days of historical sleep data; segment the user (202) into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and physiological temperature; aggregate user uploaded information for the week, indicating activities that may have impacted sleep quality; and generate a notification comprising personalized recommendations to increase awareness and assist the user (202) in becoming aware of the consequences of continued social jet lag, recognizing and managing the social jet lag by modifying existent weekday vs weekend sleep schedule variations.

11. The system (200) as claimed in claim 10, wherein the application (212) is further configured to adjust the personalized baseline and the category using gender- specific and agespecific physiological parameters or including weightage of night shift work.

12. The system (200) as claimed in claim 11, wherein the personalized baseline is dynamically updated based on most recent thirty days of sleep data and maintained in historical demographic database which can be cloud or decentralized based.

13. The system (200) as claimed in claim 10, the application is further configured to enable user journaling that provides cause and effect information for existence of a social jet lag by tagging daily activities.

14. The system (200) as claimed in claim 10, wherein the application (212) is further configured to provide the notification and visualization of the sleep midpoint deviation score and behavioral category on a user interface (214) of the user device (204) overlaying with journaling information to provide personalized insights to the user (202).

15. The system (200) as claimed in claim 10, wherein the personalized recommendations comprise suggested bedtimes and wake-up times for workdays and weekends, and context- aware environmental suggestions based on the ambient temperature.

16. The system (200) as claimed in claim 10, wherein the application (212) is further configured to: receive, from the wearable device (100), heart rate variability data; and adjust, by the application (212), the personalized baseline based on heart rate variability deviations from the personalized baseline.

17. The system (200) as claimed in claim 10, wherein a light weight executable version of the application (212) is configured to run on the wearable device (100).

18. The system (200) as claimed in claim 10, wherein the system (200) includes an external device configured to detect the temperature of the room in which the user (202) sleeps.

19. A non-transitory computer-readable storage medium comprising computer program code for execution by one or more processors of an apparatus, the computer program code configured to, when executed by the one or more processors, cause the apparatus to: detect sleep data of a user comprising sleep onset time, wake-up time, and restlessness patterns of the user for a plurality of workdays and weekends; detect physiological temperature data of the user during sleep for the plurality of workdays and weekends; detect temperature of a room in which the user sleeps; transmit the sleep data, the physiological temperature of the user, and the temperature of the room to a user device; determine a sleep midpoint for the workdays and a sleep midpoint for the weekends based on the sleep data; utilize the physiological temperature data in conjunction with the ambient temperature to differentiate internal physiological temperature changes from external ambient influences; calculate a sleep midpoint deviation score based on a difference between the workday sleep midpoint and the weekend sleep midpoint;compare the restlessness patterns and physiological temperature changes against a personalized baseline to detect deviations from the user’s typical sleep behavior, the personalized baseline being generated from at least thirty days of sleep data; segment the user into one of a balanced sleep category, a socially skewed sleep category, or a work-life skewed sleep category based on the sleep midpoint deviation score and the compared restlessness and physiological temperature; aggregate user uploaded information for the week, indicating activities that may have impacted sleep quality; and generate a notification comprising personalized recommendations to increase awareness and assist the user in becoming aware of the consequences of continued social jet lag, recognizing and managing the social jet lag by modifying existent weekday vs weekend sleep schedule variations.

Images