Logan Douglas Schneider
Dr. Logan Schneider is a sleep & cognitive neurologist and the Clinical Lead for Sleep Health at Alphabet. He maintains his clinical expertise through service as a consultant neurologist at the Stanford/VA Alzheimer’s Research Center and Adjunct Clinical Associate Professor of Sleep Medicine at Stanford. Following postdoctoral training in sleep, genetics, and big data with Dr. Emmanuel Mignot, Dr. Schneider advised multiple startups: a mental health technology company, Medibio; an infant, sleep-aid device by Hatch (subsequently funded by Amazon); and a computer-vision, sleep-monitoring system that was acquired by Alphabet. Since that time he has worked within xFN teams, spanning multiple PAs, to launch several sleep-health products and features: from the Sleep Sensing and Twilight Coaching program in the Nest Hub 2nd gen to the Fitbit Sleep Profiles and Monthly Sleep Analysis. Dr. Schneider has also dedicated his time to democratizing sleep health and awareness through leadership roles within the American Academy of Neurology and American Academy of Sleep Medicine.
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Performance analysis of updated Sleep Tracking algorithms across Google and Fitbit wearable devices
Arno Charton
Linda Lei
Siddhant Swaroop
Marius Guerard
Michael Dixon
Logan Niehaus
Shao-Po Ma
Ross Wilkinson
Ryan Gillard
Conor Heneghan
Pramod Rudrapatna
Mark Malhotra
Shwetak Patel
Google, Google, 1600 Amphitheatre Parkway
Mountain View, CA 94043 (2026) (to appear)
Preview abstract
Background: The general public has increasingly adopted consumer wearables for sleep tracking over the past 15 years, but reports on performance versus gold standards such as polysomnogram (PSG), high quality sleep diaries and at-home portable EEG systems still show potential for improved performance. Two aspects in particular are worthy of consideration: (a) improved recognition of sleep sessions (times when a person is in bed and has attempted to sleep), and (b) improved accuracy on recognizing sleep stages relative to an accepted standard such as PSG.
Aims: This study aimed to: 1) provide an update on the methodology and performance of a system for correctly recognizing valid sleep sessions, and 2) detail an updated description of how sleep stages are calculated using accelerometer and inter-beat intervals
Methods: Novel machine learning algorithms were developed to recognize sleep sessions and sleep stages using accelerometer sensors and inter-beat intervals derived from the watch or tracker photoplethysmogram. Algorithms were developed on over 3000 nights of human-scored free-living sleep sessions from a representative population of 122 subjects, and then tested on an independent validation set of 47 users. Within sleep sessions, an algorithm was developed to recognize periods when the user was attempting to sleep (Time-Attempting-To-Sleep = TATS). For sleep stage estimation, an algorithm was trained on human expert-scored polysomnograms, and then tested on 50 withheld subject nights for its ability to recognize Wake, Light (N1/N2), Deep (N3) and REM sleep relative to expert scored labels.
Results: For sleep session estimation, the algorithm had at least 95% overlap on TATS with human consensus scoring for 94% of nights from healthy sleepers. For sleep stage estimation, comparing with the current Fitbit algorithm, Cohen’s kappa for four-class determination of sleep stage increased from an average of 0.56 (std 0.13) to 0.63 (std 0.12), and average accuracy increased from 71% (std 0.10) to 77% (std 0.078)
Conclusion: A set of new algorithms has been developed and tested on Fitbit and Pixel Watches and is capable of providing robust and accurate measurement of sleep in free-living environments.
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Capturing Real-World Habitual Sleep Patterns with a Novel User-centric Algorithm to Pre-Process Fitbit Data in the All of Us Research Program: Retrospective observational longitudinal study
Hiral Master
Jeffrey Annis
Karla Gleichauf
Lide Han
Peyton Coleman
Kelsie Full
Neil Zheng
Doug Ruderfer
Evan Brittain
Journal of Medical Internet Research (2025)
Preview abstract
Background:
Commercial wearables such as Fitbit quantify sleep metrics using fixed calendar times as default measurement periods, which may not adequately account for individual variations in sleep patterns. To address this limitation, experts in sleep medicine and wearable technology developed a user-centric algorithm designed to more accurately reflect actual sleep behaviors and improve the validity of wearable-derived sleep metrics.
Objective:
This study aims to describe the development of a new user-centric algorithm, compare its performance with the default calendar-relative algorithm, and provide a practical guide for analyzing All of Us Fitbit sleep data on a cloud-based platform.
Methods:
The default and user-centric algorithms were implemented to preprocess and compute sleep metrics related to schedule, duration, and disturbances using high-resolution Fitbit sleep data from 8563 participants (median age 58.1 years, 6002/8341, 71.96%, female) in the All of Us Research Program (version 7 Controlled Tier). Variations in typical sleep patterns were calculated by examining the differences in the mean number of primary sleep logs classified by each algorithm. Linear mixed-effects models were used to compare differences in sleep metrics across quartiles of variation in typical sleep patterns.
Results:
Out of 8,452,630 total sleep logs collected over a median of 4.2 years of Fitbit monitoring, 401,777 (4.75%) nonprimary sleep logs identified by the default algorithm were reclassified as primary sleep by the user-centric algorithm. Variation in typical sleep patterns ranged from –0.08 to 1. Among participants with the greatest variation in typical sleep patterns, the user-centric algorithm identified significantly more total sleep time (by 17.6 minutes; P<.001), more wake after sleep onset (by 13.9 minutes; P<.001), and lower sleep efficiency (by 2.0%; P<.001), on average. Differences in sleep stage metrics between the 2 algorithms were modest.
Conclusions:
The user-centric algorithm captures the natural variability in sleep schedules, providing an alternative approach to preprocess and evaluate sleep metrics related to schedule, duration, and disturbances. A publicly available R package facilitates the implementation of this algorithm for clinical and translational research.
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A personal health large language model for sleep and fitness coaching
Anastasiya Belyaeva
Zhun Yang
Nick Furlotte
Chace Lee
Erik Schenck
Yojan Patel
Jian Cui
Robby Bryant
Ryan Gomes
Allen Jiang
Roy Lee
Javier Perez
Jamie Rogers
Cathy Speed
Shyam Tailor
Megan Walker
Jeffrey Yu
Tim Althoff
Conor Heneghan
Mark Malhotra
Leor Stern
Shwetak Patel
Shravya Shetty
Jiening Zhan
Daniel McDuff
Nature Medicine (2025)
Preview abstract
Although large language models (LLMs) show promise for clinical healthcare applications, their utility for personalized health monitoring using wearable device data remains underexplored. Here we introduce the Personal Health Large Language Model (PH-LLM), designed for applications in sleep and fitness. PH-LLM is a version of the Gemini LLM that was finetuned for text understanding and reasoning when applied to aggregated daily-resolution numerical sensor data. We created three benchmark datasets to assess multiple complementary aspects of sleep and fitness: expert domain knowledge, generation of personalized insights and recommendations and prediction of self-reported sleep quality from longitudinal data. PH-LLM achieved scores that exceeded a sample of human experts on multiple-choice examinations in sleep medicine (79% versus 76%) and fitness (88% versus 71%). In a comprehensive evaluation involving 857 real-world case studies, PH-LLM performed similarly to human experts for fitness-related tasks and improved over the base Gemini model in providing personalized sleep insights. Finally, PH-LLM effectively predicted self-reported sleep quality using a multimodal encoding of wearable sensor data, further demonstrating its ability to effectively contextualize wearable modalities. This work highlights the potential of LLMs to revolutionize personal health monitoring via tailored insights and predictions from wearable data and provides datasets, rubrics and benchmark performance to further accelerate personal health-related LLM research.
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Circadian rhythm of heart rate and activity: a cross-sectional study
Maryam Khalid
Aravind Natarajan
Conor Heneghan
Karla Gleichauf
Chronobiology International (2025)
Preview abstract
ABSTRACT
Background: Circadian rhythms are commonly observed in a number of physiological processes. Consumer wearable devices have made it possible to obtain continuous time series data from a large number of individuals. We study circadian rhythms from measurements of heart rate, movement, and sleep, from a cohort of nearly 20,000 participants over the course of 30 days.
Methods: Participation was restricted to Fitbit users of age 21 years or older residing in the United States or Canada. Participants were enrolled through a recruitment banner shown on the Fitbit App. The advertisement was shown to 531,359 Fitbit users, and 23,239 enrolled in the program. Of these, we obtained heart rate data from 19,350 participants. We obtain the underlying circadian rhythm from time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics. The first Fourier harmonic accounts for the 24-hour rhythmicity, while the second harmonic accounts for non-sinusoidal perturbations.
Findings: We observe a circadian rhythm in both heart rate and acceleration. From the diurnal modulation, we obtain the following circadian parameters: (i) amplitude of modulation, (ii) bathyphase, (iii) acrophase, (iv) non-sinusoidal fraction, and (v) fraction of day when the heart rate is greater than the mean. The amplitude, bathyphase, and acrophase depend on sex, and decrease with age. The waketime on average, follows the bathyphase by 2.4 hours. In most individuals, the circadian rhythm of heart rate lags the circadian rhythm of activity.
Interpretation: Circadian metrics for heart rate and activity can be reliably obtained from commercially available wearable devices. Distributions of circadian metrics can be valuable tools for individual-level interpretation.
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Unprecedented Insights into Maternal Sleep: A Large-scale Longitudinal Analysis of Real-world Wearable Device Data Before, During, and After Pregnancy
Nichole Young-Lin
Conor Heneghan
Logan Niehaus
Ariel Haney
Karla Gleichauf
Jacqueline Shreibati
Belen Lafon
Lancet eBioMedicine (2025)
Preview abstract
Introduction: Current understanding of pregnancy and postpartum sleep is driven by limited lab or self-reported data. Consumer wearable devices may help reveal longitudinal, real-world sleep patterns.
Methods: We analyzed de-identified wearable device data from 2,540 users in the United States and Canada who met strict wear-time requirements (≥80% daily usage for ≥80% of the time periods of interest [12 weeks prepregnancy, throughout pregnancy, and 20 weeks immediately postpartum]). We tracked sleep time and staging using Fitbit devices.
Results: Compared to prepregnancy, total sleep time (TST) increased from an average of 425.3±43.5 min to a peak of 447.6±47.6 min at gestational week 10 with ongoing declines throughout pregnancy. Time in bed (TIB) followed a similar pattern. Increased light sleep drove the initial TST rise. Deep and REM sleep decreased significantly throughout pregnancy, with maximum reductions of 19.2±13.8 min (p<0.01) and 9.0±19.2 min (p<0.01) respectively by pregnancy end. Sleep efficiency also declined slightly during pregnancy (median drop from 88.3% to 86.8%). After delivery, TIB remained below the prepregnancy baseline by 14.7±45.7 min at one year postpartum and 15.2±47.7 min at 1.5 years postpartum.
Conclusion: This unprecedented look at large-scale, real-world sleep and pregnancy patterns revealed a previously unquantified initial increase in sleep followed by decreases in both quantity and quality as pregnancy progresses. Sleep deficits persist for at least 1.5 years postpartum. These quantified trends can assist clinicians and patients in understanding what to expect.
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Sleep patterns and risk of chronic disease as measured by long-term monitoring with commercial wearable devices in the All of Us Research Program
Neil S. Zheng
Jeffrey Annis
Hiral Master
Lide Han
Karla Gleichauf
Melody Nasser
Peyton Coleman
Stacy Desine
Douglas M. Ruderfer
Evan L. Brittain
Nature Medicine (2024)
Preview abstract
Poor sleep health is associated with increased all-cause mortality and incidence of many chronic conditions. Previous studies have relied on cross-sectional and self-reported survey data or polysomnograms, which have limitations with respect to data granularity, sample size and longitudinal information. Here, using objectively measured, longitudinal sleep data from commercial wearable devices linked to electronic health record data from the All of Us Research Program, we show that sleep patterns, including sleep stages, duration and regularity, are associated with chronic disease incidence. Of the 6,785 participants included in this study, 71% were female, 84% self-identified as white and 71% had a college degree; the median age was 50.2 years (interquartile range = 35.7, 61.5) and the median sleep monitoring period was 4.5 years (2.5, 6.5). We found that rapid eye movement sleep and deep sleep were inversely associated with the odds of incident atrial fibrillation and that increased sleep irregularity was associated with increased odds of incident obesity, hyperlipidemia, hypertension, major depressive disorder and generalized anxiety disorder. Moreover, J-shaped associations were observed between average daily sleep duration and hypertension, major depressive disorder and generalized anxiety disorder. These findings show that sleep stages, duration and regularity are all important factors associated with chronic disease development and may inform evidence-based recommendations on healthy sleeping habits.
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Analysis of objective and subjective sleep metrics and smartphone usage patterns
Conor Heneghan
Daniel McDuff
Ari Winbush
Nicholas Allen
Allen Jiang
Andrew Barakat
Benjamin Nelson
Ben Yetton
2024
Preview abstract
Analysis of objective and subjective sleep metrics and smartphone usage patterns
Conor Heneghan, , Daniel McDuff, Ari Winbush, Nicholas Allen, John Hernandez, Allen Jiang,, Andrew Barakat, Logan Schneider, Benjamin Nelson, Ben Yetton
Consumer Health Research Team, Google Inc.
Department of Psychology, University of Oregon
Verily Life Sciences
Department of Psychiatry, Harvard Medical School and Beth Israel Deaconess Medical Center
Introduction: The Digital Wellbeing Study is an IRB approved joint study between the University of Oregon and Google to investigate how smartphone usage interacts with objective and
subjective parameters of well-being such as sleep, exercise and stress. The study recruited a demographically diverse population who each wore a smartwatch and installed a smartphone app linked to the study. Participants completed demographic and health questionnaires including the PROMIS Sleep Disturbance (SD) Short Form. Aims of the study included (a) whether objective sleep duration was correlated with smartphone use, and (b) whether smartphone usage could predict the subjective self reported sleep instrument.
Methods: There was sufficient data from 7,499 users to conduct a population modeling analysis. An Ordinary Least Squares linear model was used as a predictor of each subject’s average total sleep time (TST) and their SD t-score. The inputs to the model included demographics, and population z-scored activity measures (steps, sedentary time, time driving, time at work, home and other locations, phone screen time, frequency of phone unlocks)
over seven days prior to the survey.
Results: The activity measures and baseline demographics could only explain a small amount of the overall variance in TST and SD (R^2=0.04 for TST and R^2=0.05 for SD). Phone screen
time was a statistically significant predictor of both TST (-8.19 mins, p< 0.001) and self-reported sleep disruption (0.611 t-score units, p< 0.001). The number of phone unlocks was a predictor of variability in TST (-3.33 mins, p< 0.001) suggesting that longer session times are correlated with greater TST variability. The effects are minimal (e.g., a subject who has one standard
deviation greater phone screen time than average would be predicted to only see a 2% reduction in TST, and a 0.6% increase in perceived sleep disturbance). Time driving and step count were
also minor predictors of SD and TST.
Conclusion: At a population level, average activity measures from wearables and smartphones such as steps, smartphone usage time, sedentary activity etc. are limited predictors of
objective sleep metrics such as Total Sleep Time, and subjective sleep metrics such as the PROMIS Sleep Disturbance t-score.
Support (if any): This research was funded by Google Inc.
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Predicting subjective sleep impairment and disturbance from wearable sleep data
Conor Heneghan
Ben Yetton
Daniel McDuff
Nicholas Allen
Andrew Barakat
Allen Jiang
Benjamin Nelson
Ari Winbush
2024
Preview abstract
Introduction:
Wearables offer a scalable, passive and objective measure of sleep health. However, prior reported correlations (spearman) between subjective and wearable derived sleep measures have been modest (rS=0.3-0.46). We set out to determine if wearables adequately capture subjective feelings of sleep disturbance and impairment in a large, diverse ecologically valid sleep study.
Methods:
Subject data (n=2922, mean age= 45.4 (12.6), 74% female) came from the Digital Wellbeing Study: a joint study between the University of Oregon and Google to investigate how smartphone usage impacts well-being. Wearable (Fitbit) derived sleep metrics were summarized across the week prior to the administration of the PROMIS Sleep Disturbance (SD) and Sleep Related Impairment (SR) Short Form surveys. A series of stepwise OLS regressions were used to test the predictive power of each sleep metric over a baseline model of age and sex.
Results:
Sleep variables of total sleep time, resting heart rate, and the variability in total sleep time and restlessness (accelerometer based metric) improved both SI and SD above a baseline model (SIBaseline adjR2=0.087, SDBaseline adjR2=0.024). Deep (e.g. N3) minutes uniquely improved SI model fit, while longest wake length and total wake minutes improved SD fit. REM percent and normalized nightly heart rate did not improve model fit. The final model explained 12.9% of the variance of SI, and 8.4% of the variance of SD. The most predictive single sleep metric was the variability in total sleep time (adjR2=0.104) for SI, and total sleep time for SD (age & sex included). Fitbit’s composite “Sleep Score” was the single best predictor of SD when included in analysis (age and sex excluded).
Conclusion: As demonstrated in previous studies, wearable derived sleep metrics are modest predictors of perceived sleep disturbance or sleep related impairment. Composite metrics that include measures of sleep variability are recommended.
Support: This research was funded by Google Inc.
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Preview abstract
Introduction:
Wearables offer a scalable, passive and objective measure of sleep health. However, prior reported correlations (spearman) between subjective and wearable derived sleep measures have been modest (rS=0.3-0.46). We set out to determine if wearables adequately capture subjective feelings of sleep disturbance and impairment in a large, diverse ecologically valid sleep study.
Methods:
Subject data (n=2922, mean age= 45.4 (12.6), 74% female) came from the Digital Wellbeing Study: a joint study between the University of Oregon and Google to investigate how smartphone usage impacts well-being. Wearable (Fitbit) derived sleep metrics were summarized across the week prior to the administration of the PROMIS Sleep Disturbance (SD) and Sleep Related Impairment (SR) Short Form surveys. A series of stepwise OLS regressions were used to test the predictive power of each sleep metric over a baseline model of age and sex.
Results:
Sleep variables of total sleep time, resting heart rate, and the variability in total sleep time and restlessness (accelerometer based metric) improved both SI and SD above a baseline model (SIBaseline adjR2=0.087, SDBaseline adjR2=0.024). Deep (e.g. N3) minutes uniquely improved SI model fit, while longest wake length and total wake minutes improved SD fit. REM percent and normalized nightly heart rate did not improve model fit. The final model explained 12.9% of the variance of SI, and 8.4% of the variance of SD. The most predictive single sleep metric was the variability in total sleep time (adjR2=0.104) for SI, and total sleep time for SD (age & sex included). Fitbit’s composite “Sleep Score” was the single best predictor of SD when included in analysis (age and sex excluded).
Conclusion: As demonstrated in previous studies, wearable derived sleep metrics are modest predictors of perceived sleep disturbance or sleep related impairment. Composite metrics that include measures of sleep variability are recommended.
Support: This research was funded by Google Inc.
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Preview abstract
Introduction
Typical data derived from a wrist worn device include accelerometer and photoplethysmogram (PPG) sensor signals . These reflect underlying movement, heart rate, and vascular dynamics that contain sleep stage information. We investigated the ability of a deep learning network to map raw data from such sensors to estimated sleep stages defined by full polysomnography scoring.
Methods
A convolutional neural network (CNN) was proposed for application to raw PPG (green light at 25 Hz) and 3D accelerometer data (also sampled at 25 Hz). The CNN had 70 hidden layers and output labels were mapped to four classes (wake, light sleep, deep sleep, and REM sleep) where light sleep is defined as Stages N1 and N2. The CNN was pretrained using 1654 records of finger PPG data from the Multi-Ethnic Study of Atherosclerosis (MESA) sleep records. The system was then further trained and evaluated on an internal set of 184 records obtained from adults (mean age = 68) with corresponding scored PSG sleep stage labels. Data augmentation techniques were used to create additional training data. The system was then tested using a withheld data set of 16 records. The overall performance of the system was evaluated by calculating two stage (wake versus sleep) and four stage accuracy and Cohen’s kappa values (?).
Results: The overall performance for two-stage wake/sleep classification was an accuracy of 0.94 and ?=0.79. For four stage classification, the accuracy was 0.79 and ?=0.66. A comparable figure for expert human scoring four-stage class is accuracy of 0.8-0.85 and ?=0.7-0.75.
Conclusion: Raw accelerometer and PPG signals contain a significant amount of information related to underlying sleep stages, and can be trained to produce hypnograms which approach the accuracy of human scorers. This may provide utility for both multi-night clinical use and underlying research in sleep science.
Support: This research was funded by Google Inc.
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