Heart rate detection by Fitbit ChargeHR™ : A validation study versus portable polysomnography

Deriving Accurate Nocturnal Heart Rate, rMSSD and Frequency HRV from the Oura Ring

Cardiovascular diseases are a major cause of mortality worldwide. Long-term monitoring of nighttime heart rate (HR) and heart rate variability (HRV) may be useful in identifying latent cardiovascular risk. The Oura Ring has shown excellent correlation only with ECG-derived HR, but not HRV. We thus assessed if stringent data quality filters can improve the accuracy of time-domain and frequency-domain HRV measures. 92 younger (<45 years) and 22 older (≥45 years) participants from two in-lab sleep studies with concurrent overnight Oura and ECG data acquisition were analyzed. For each 5 min segment during time-in-bed, the validity proportion (percentage of interbeat intervals rated as valid) was calculated. We evaluated the accuracy of Oura-derived HR and HRV measures against ECG at different validity proportion thresholds: 80%, 50%, and 30%; and aggregated over different durations: 5 min, 30 min, and Night-level. Strong correlation and agreements were obtained for both age groups across all HR and HRV metrics and window sizes. More stringent validity proportion thresholds and averaging over longer time windows (i.e., 30 min and night) improved accuracy. Higher discrepancies were found for HRV measures, with more than half of older participants exceeding 10% Median Absolute Percentage Error. Accurate HRV measures can be obtained from Oura's PPG-derived signals with a stringent validity proportion threshold of around 80% for each 5 min segment and aggregating over time windows of at least 30 min.

Real-time biopsychosocial antecedents and correlates of functional neurological symptoms in daily life: A pilot remote monitoring technology study

Functional neurological symptom disorder (FNSD) is a neuropsychiatric diagnosis referring to symptoms resembling those of neurological disorders, occurring without causal neuropathology. FNSD has a complex biopsychosocial aetiology but its mechanisms are poorly understood. Remote monitoring technologies (RMT) could provide critical insights into functional neurological symptoms (FNS) in real-world contexts. We examined the feasibility and acceptability of a novel RMT protocol, to identify psychobiological correlates and antecedents of FNS in everyday life. Seventeen individuals with FNS (seizures/motor) and 17 healthy controls (HC) completed ecological momentary assessments (EMA) eight times daily for 1-week, reporting FNS severity, associated physical and psychological symptoms, and subjectively significant events. Sleep quality was reported daily. Physiological variables were measured using wearable Fitbit 5 devices. Multilevel modelling examined variables associated with FNS variability. Average EMA completion rates were good in both groups (≥80%). At week-level, the FNS group reported significantly greater subjective arousal, pain, fatigue, dissociation, negative affect, daily events, stressful events, and sleep duration, compared to HC. Objective sleep disturbance and duration, and resting heartrate, were also significantly greater in the FNS sample. FNS severity correlated significantly with daily events, affect, subjective arousal, pain, fatigue and sleep disturbance, at day- or within-day levels. Daily events and negative affect were the most prominent time-lagged predictors of within-day moment-to-moment FNS severity. RMTs are feasible and acceptable tools for investigation of FNS in real-world settings, revealing daily events and negative affect as possible triggers of FNS. Interventions targeting affective reactivity and regulation might be beneficial in this group. Larger-scale, longer-term RMT studies are needed in this population.

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study

BACKGROUND

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations.

OBJECTIVE

We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting.

METHODS

Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA.

RESULTS

All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001).

CONCLUSIONS

Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.3390/clockssleep6010010.

Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease

STUDY OBJECTIVES

Wearable devices that monitor sleep stages and heart rate offer the potential for longitudinal sleep monitoring in patients with neurodegenerative diseases. Sleep quality reduces with disease progression in Huntington's disease (HD). However, the involuntary movements characteristic of HD may affect the accuracy of wrist-worn devices. This study compares sleep stage and heart rate data from the Fitbit Charge 4 (FB) against polysomnography (PSG) in participants with HD.

METHODS

Ten participants with manifest HD wore an FB during overnight hospital-based PSG, and 9 of these participants continued to wear the FB for 7 nights at home. Sleep stages (30-second epochs) and minute-by-minute heart rate were extracted and compared against PSG data.

RESULTS

FB-estimated total sleep and wake times and sleep stage times were in good agreement with PSG, with intraclass correlations of 0.79-0.96. However, poor agreement was observed for wake after sleep onset and the number of awakenings. FB detected waking with 68.6 ± 15.5% sensitivity and 93.7 ± 2.5% specificity, rapid eye movement sleep with high sensitivity and specificity (78.7 ± 31.9%, 95.6 ± 2.3%), and deep sleep with lower sensitivity but high specificity (56.4 ± 28.8%, 95.0 ± 4.8%). FB heart rate was strongly correlated with PSG, and the mean absolute error between FB and PSG heart rate data was 1.16 ± 0.42 beats/min. At home, longer sleep and shorter wake times were observed compared with hospital data, whereas percentage sleep stage times were consistent with hospital data.

CONCLUSIONS

Results suggest the potential for long-term monitoring of sleep patterns using wrist-worn wearable devices as part of symptom management in HD.

CITATION

Doheny EP, Renerts K, Braun A, et al. Assessment of Fitbit Charge 4 for sleep stage and heart rate monitoring against polysomnography and during home monitoring in Huntington's disease. J Clin Sleep Med. 2024;20(7):1163-1171.

Comparison of Polysomnography, Single-Channel Electroencephalogram, Fitbit, and Sleep Logs in Patients With Psychiatric Disorders: Cross-Sectional Study

BACKGROUND

Sleep disturbances are core symptoms of psychiatric disorders. Although various sleep measures have been developed to assess sleep patterns and quality of sleep, the concordance of these measures in patients with psychiatric disorders remains relatively elusive.

OBJECTIVE

This study aims to examine the degree of agreement among 3 sleep recording methods and the consistency between subjective and objective sleep measures, with a specific focus on recently developed devices in a population of individuals with psychiatric disorders.

METHODS

We analyzed 62 participants for this cross-sectional study, all having data for polysomnography (PSG), Zmachine, Fitbit, and sleep logs. Participants completed questionnaires on their symptoms and estimated sleep duration the morning after the overnight sleep assessment. The interclass correlation coefficients (ICCs) were calculated to evaluate the consistency between sleep parameters obtained from each instrument. Additionally, Bland-Altman plots were used to visually show differences and limits of agreement for sleep parameters measured by PSG, Zmachine, Fitbit, and sleep logs.

RESULTS

The findings indicated a moderate agreement between PSG and Zmachine data for total sleep time (ICC=0.46; P<.001), wake after sleep onset (ICC=0.39; P=.002), and sleep efficiency (ICC=0.40; P=.006). In contrast, Fitbit demonstrated notable disagreement with PSG (total sleep time: ICC=0.08; wake after sleep onset: ICC=0.18; sleep efficiency: ICC=0.10) and exhibited particularly large discrepancies from the sleep logs (total sleep time: ICC=-0.01; wake after sleep onset: ICC=0.05; sleep efficiency: ICC=-0.02). Furthermore, subjective and objective concordance among PSG, Zmachine, and sleep logs appeared to be influenced by the severity of the depressive symptoms and obstructive sleep apnea, while these associations were not observed between the Fitbit and other sleep instruments.

CONCLUSIONS

Our study results suggest that Fitbit accuracy is reduced in the presence of comorbid clinical symptoms. Although user-friendly, Fitbit has limitations that should be considered when assessing sleep in patients with psychiatric disorders.

Smartwatch-derived sleep and heart rate measures complement step counts in explaining established metrics of MS severity

BACKGROUND

Passive remote monitoring of patients with MS (PwMS) with sensor-based wearable technologies promises near-continuous evaluation with high ecological validity. Step counts correlate strongly with traditional measures of MS severity. We hypothesized that remote monitoring of sleep and heart rate will yield complementary information.

METHODS

We recruited 31 PwMS and 31 age- and sex-matched healthy volunteers (HV) as part of the dreaMS feasibility study (NCT04413032). Fitbit Versa 2 smartwatches were worn for 6 weeks and provided a total of 25 features for activity, heart rate, and sleep. Features were selected based on their pairwise intercorrelation (Pearson |r| < 0.6), test-retest reliability (intraclass correlation coefficient ≥ 0.6 or median coefficient of variation < 0.2) and group comparisons between HV and PwMS with moderate disability (expanded disability status scale (EDSS) ≥ 3.5) (rank-biserial |r| ≥ 0.5). These selected features were correlated with clinical reference tests (EDSS, timed 25-foot walk (T25FW), MS-walking scale (MSWS-12)) in PwMS, and multivariate models adjusted for age, sex, and disease duration were compared.

RESULTS

We analyzed 28 PwMS (68% female, mean age 44 years, median EDSS 3.0) and 26 HV in our primary analysis. The objectively selected features discriminated well between HV and PwMS with moderate disability with rank-biserial r = 0.83 for Total number of steps, 0.51 for Deep sleep proportion, -0.51 for Median heart rate, 0.85 for Proportion very active, and 0.65 for Total number of floors. In PwMS they correlated strongly with the three clinical reference tests EDSS (strongest Spearman ρ = -0.75 for Proportion very active), T25FW (-0.75 for Total number of floors), and MSWS-12 (-0.72 for Total number of floors). Deep sleep proportion and Median heart rate complemented Total number of steps in explaining the variance of reference tests.

CONCLUSIONS

Activity, deep sleep and heart rate measures can be derived reliably from smartwatches and contain independent clinically meaningful information about MS severity, highlighting their potential for continuous passive monitoring in both clinical trials and clinical care of PwMS.

Remote monitoring technologies for measuring cardiovascular functions in community-dwelling adults: a systematic review

Remote monitoring technologies (RMTs) allow continuous, unobtrusive, and real-time monitoring of the cardiovascular system. An overview of existing RMTs measuring cardiovascular physiological variables is lacking. This systematic review aimed to describe RMTs measuring cardiovascular functions in community-dwelling adults. An electronic search was conducted via PubMed, EMBASE, and Cochrane Library from January 1, 2020, to April 7, 2022. Articles reporting on non-invasive RMTs used unsupervised in community-dwelling adults were included. Reviews and studies in institutionalized populations were excluded. Two reviewers independently assessed the studies and extracted the technologies used, cardiovascular variables measured, and wearing locations of RMTs. Validation of the RMTs was examined based on the COSMIN tool, and accuracy and precision were presented. This systematic review was registered with PROSPERO (CRD42022320082). A total of 272 articles were included representing 322,886 individuals with a mean or median age from 19.0 to 88.9 years (48.7% female). Of all 335 reported RMTs containing 216 distinct devices, photoplethysmography was used in 50.3% of RMTs. Heart rate was measured in 47.0% of measurements, and the RMT was worn on the wrist in 41.8% of devices. Nine devices were reported in more than three articles, of which all were sufficiently accurate, six were sufficiently precise, and four were commercially available in December 2022. The top four most reported technologies were AliveCor KardiaMobile®, Fitbit Charge 2, and Polar H7 and H10 Heart Rate Sensors. With over 200 distinct RMTs reported, this review provides healthcare professionals and researchers an overview of available RMTs for monitoring the cardiovascular system.

Acute social jetlag augments morning blood pressure surge: a randomized crossover trial

Although social jetlag (SJL) is generally considered a chronic condition, even acute SJL may have unfavorable effects on the cardiovascular system. We focused on the acute effects of SJL on morning blood pressure (BP) surge. This randomized crossover trial recruited 20 healthy men. In the SJL trial, participants delayed their bedtime by three hours on Friday and Saturday nights. Participants in the control (CON) trial implemented the same sleep-wake timing as on weekdays. Pre- and post-intervention measurements were performed to evaluate resting cardiovascular variables on Friday and Monday mornings, respectively. The ambulatory BP was automatically measured during the sleep and awake periods for 2 h after the participant woke up at night before pre- and post-intervention measurements. SJL (average mid-sleep time on weekends - average mid-sleep time on weekdays) occurred only in the SJL trial (SJL: 181 ± 24 min vs. CON: 8 ± 47 min). Carotid-femoral pulse wave velocity (cfPWV) and morning BP surge on Monday in the SJL trial were significantly higher than those on Friday in the SJL trial (cfPWV: P = 0.001, morning BP surge: P < 0.001), and those on Monday in the CON trial (cfPWV: P = 0.007; morning BP surge: P < 0.001). Furthermore, a significant positive correlation was found between ΔcfPWV and Δmorning BP surge (R = 0.587, P = 0.004). These results suggest that even acute SJL augments morning BP surge. This phenomenon may correspond to increased central arterial stiffness.State the details of Clinical Trials: Name: Effect of acute social jetlag on risk factors of lifestyle-related diseases. URL: https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000053204 . Unique identifier: UMIN000046639. Registration date: 17/01/2022.

Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease

Background

Fatigue is a subjective, complex and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. It is a well-recognized manifestation in chronic inflammatory rheumatic diseases, such as Sjögren's Syndrome and Systemic Lupus Erythematosus and an important predictor of patient's health-related quality of life (HRQoL). Patient reported outcome questions are the key instruments to assess fatigue. To date, there is no consensus about reliable quantitative assessments of fatigue.

Method

Observational data for a period of one month were collected from 296 participants in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity and sleep features, and app-based daily and weekly questions covering various HRQoL factors including pain, mood, general physical activity and fatigue. Descriptive statistics and hierarchical clustering of digital data were used to describe behavioural phenotypes. Gradient boosting classifiers were trained to classify participant-reported weekly fatigue and daily tiredness from multi-sensor and other participant-reported data, and extract a set of key predictive features.

Results

Cluster analysis of Fitbit parameters highlighted multiple digital phenotypes, including sleep-affected, fatigued and healthy phenotypes. Features from participant-reported data and Fitbit data both contributed as key predictive features of weekly physical and mental fatigue and daily tiredness. Participant answers to pain and depressed mood-related daily questions contributed the most as top features for predicting physical and mental fatigue, respectively. To classify daily tiredness, participant answers to questions on pain, mood and ability to perform daily activities contributed the most. Features related to daily resting heart rate and step counts and bouts were overall the most important Fitbit features for the classification models.

Conclusion

These results demonstrate that multimodal digital data can be used to quantitatively and more frequently augment pathological and non-pathological participant-reported fatigue.

Linking Electronic Health Records With Wearable Technology From the All of Us Research Program

OBJECTIVE

To evaluate the feasibility of using electronic health records (EHRs) and wearable data to describe patterns of longitudinal change in day-level heart rate before, during, and after pregnancy and how these patterns differ by age and body mass index.

DESIGN

Descriptive secondary analysis feasibility study using data from the National Institutes of Health All of Us Research Program.

SETTING

United States.

PARTICIPANTS

Women (N = 89) who had a birth or length of gestation code in the EHR and at least 60 days of Fitbit heart rate data during pregnancy.

METHODS

We estimated pregnancy-related episodes using EHR codes. Time consisted of five 3-month periods: before pregnancy, first trimester, second trimester, third trimester, and after birth. We analyzed data using descriptive statistics and locally estimated scatterplot smoothing.

RESULTS

An average of 330 days (SD = 112) of Fitbit heart rate data (29,392 days) were available from participants. During pregnancy, distinct peaks in heart rate occurred during the first trimester (6% increase) and third trimester (15% increase).

CONCLUSION

Future researchers can examine whether longitudinal timing and patterns of heart rate from wearable devices could be leveraged to detect health problems early in pregnancy.

Role of Wearable Sensing Technology to Manage Long COVID

Long COVID consequences have changed the perception towards disease management, and it is moving towards personal healthcare monitoring. In this regard, wearable devices have revolutionized the personal healthcare sector to track and monitor physiological parameters of the human body continuously. This would be largely beneficial for early detection (asymptomatic and pre-symptomatic cases of COVID-19), live patient conditions, and long COVID monitoring (COVID recovered patients and healthy individuals) for better COVID-19 management. There are multitude of wearable devices that can observe various human body parameters for remotely monitoring patients and self-monitoring mode for individuals. Smart watches, smart tattoos, rings, smart facemasks, nano-patches, etc., have emerged as the monitoring devices for key physiological parameters, such as body temperature, respiration rate, heart rate, oxygen level, etc. This review includes long COVID challenges for frequent monitoring of biometrics and its possible solution with wearable device technologies for diagnosis and post-therapy of diseases.

The feasibility of multimodality remote monitoring of maternal physiology during pregnancy

To ascertain whether remote multimodality cardiovascular monitoring of health in pregnancy is feasible, 24 participants were asked to daily monitor body weight, heart rate, blood pressure, activity levels, and sleep patterns. Study participants took on average 4.3 (standard deviation = 2.20) home recordings of each modality per week across the 3 trimesters and 2.0 postpartum (standard deviation = 2.41), out of a recommended maximum of 7. Thus, remote monitoring indicative of cardiovascular health throughout and after pregnancy might be feasible for routine clinical care or within the context of a research study.

Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach

Purpose

A large portion of the adult population is thought to suffer from obstructive sleep apnoea syndrome (OSAS), a sleep-related breathing disorder associated with increased morbidity and mortality. International guidelines include the polysomnography and the cardiorespiratory monitoring (CRM) as diagnostic tools for OSAS, but they are unfit for a large-scale screening, given their invasiveness, high cost and lengthy process of scoring. Current screening methods are based on self-reported questionnaires that suffer from lack of objectivity. On the contrary, commercial smartbands are wearable devices capable of collecting accelerometric and photoplethysmographic data in a user-friendly and objective way. We questioned whether machine-learning (ML) classifiers trained on data collected through these wearable devices would help predict OSAS severity.

Patients and Methods

Each of the patients (n = 78, mean age ± SD: 57.2 ± 12.9 years; 30 females) underwent CRM and concurrently wore a commercial wrist smartband. CRM's traces were scored, and OSAS severity was reported as apnoea hypopnoea index (AHI). We trained three pairs of classifiers to make the following prediction: AHI <5 vs AHI ≥5, AHI <15 vs AHI ≥15, and AHI <30 vs AHI ≥30.

Results

According to the Matthews correlation coefficient (MCC), the proposed algorithms reached an overall good correlation with the ground truth (CRM) for AHI <5 vs AHI ≥5 (MCC: 0.4) and AHI <30 vs AHI ≥30 (MCC: 0.3) classifications. AHI <5 vs AHI ≥5 and AHI <30 vs AHI ≥30 classifiers' sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and diagnostic odds ratio (DOR) are comparable with the STOP-Bang questionnaire, an established OSAS screening tool.

Conclusion

Machine learning algorithms showed an overall good performance. Unlike questionnaires, these are based on objectively collected data. Furthermore, these commercial devices are widely distributed in the general population. The aforementioned advantages of machine-learning algorithms applied to smartbands' data over questionnaires lead to the conclusion that they could serve a population-scale screening for OSAS.

Effects of Sleep Restriction on Self-Reported Putting Performance in Golf

In the present study, we aimed to explore the effects of sleep restriction (SR) on self-reported golf putting skills. Eleven collegiate golfers participated in a self-reported, counterbalanced experimental study under two conditions: (a) a SR condition in which sleep on the night prior to putting was restricted to 4-5 hours, and (b) a habitual normal sleep (NS) condition on the night before the putting test. Following each sleep condition, participants engaged in ten consecutive putting tests at 7 am, 11 am, and 3 pm. Participants reported their subjective sleepiness before each time frame, and their chronotype, defined as their individual circadian preference, was scored based on a morningness-eveningness questionnaire (MEQ). Participants restricted sleep to an average period of 267.6 minutes/night (SD = 51.2) in the SR condition and 426.2 (SD =38.0) minutes/night in the NS condition. A two-way analysis of variance revealed a significant main effect of the sleep condition on the lateral displacement of putts from the target (lateral misalignment) (p = 0.002). In addition, there was a significant main effect of time on distance from the target (distance misalignment) (p = 0.017), indicating less accuracy of putting in the SR condition. In the SR condition, the MEQ score was positively correlated with distance misalignment at 3 pm (ρ = 0.650, p = 0.030), suggesting that morningness types are susceptible to the effects of SR on putting performance. Our findings suggest that golfers should obtain sufficient sleep to optimize putting performance.

Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review

The emergence of physiological monitoring technologies has produced exceptional opportunities for real-time collection and analysis of workers' physiological information. To benefit from these safety and health prognostic opportunities, research efforts have explored the applicability of these devices to control workers' wellbeing levels during occupational activities. A systematic review is proposed to summarise up-to-date progress in applying physiological monitoring systems for occupational groups. Adhering with the PRISMA Statement, five databases were searched from 2014 to 2021, and 12 keywords were combined, concluding with the selection of 38 articles. Sources of risk of bias were assessed regarding randomisation procedures, selective outcome reporting and generalisability of results. Assessment procedures involving non-invasive methods applied with health and safety-related goals were filtered. Working-age participants from homogeneous occupational groups were selected, with these groups primarily including firefighters and construction workers. Research objectives were mainly directed to assess heat stress and physiological workload demands. Heart rate related variables, thermal responses and motion tracking through accelerometry were the most common approaches. Overall, wearable sensors proved to be valid tools for assessing physiological status in working environments. Future research should focus on conducting sensor fusion assessments, engaging wearables in real-time evaluation methods and giving continuous feedback to workers and practitioners.

The impact of social networks on sleep among a cohort of college students

Background

Sleep duration and quality are associated with physical and mental wellbeing. This paper examines social network effects on individual level change in the sleep quantity and quality from late adolescence to emerging adulthood and its associated factors, including the influence of peers on sleep behavior and the impact of changes in network size.

Methods

We use sleep data from 619 undergraduates at the University of Notre Dame obtained via Fitbit devices as part of the NetHealth project. The data were collected between August 16, 2015 and May 13, 2017. We model trends in sleep behaviors using latent growth-curve models.

Results

Controlling for the many factors known to impact sleep quantity and quality, we find two social network effects: increasing network size is associated with less sleep and a student's sleep levels are influenced by his or her peers. While we do not find any consistent decline in sleep quantity over the 637 days, daily fluctuations in sleep quantity are associated with changes in network size and the composition of a student's network. As a student's network gets bigger, s/he sleeps less, and when a student's contacts sleep more (or less) than s/he does, the student becomes more like his or her contacts and sleeps more (or less).

Conclusions

Social networks can and do impact sleep, especially sleep quantity. In contexts where students want to have larger networks, the difficulties of increasing network size and maintaining larger networks negatively impact sleep. Because of peer influence, the effectiveness of interventions designed to improve sleep practices could be increased by leveraging student social networks to help diffuse better sleep habits.

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The sleep habits of 619 undergraduates were traced with Fitbit devices for 637 days.

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Their sleep quantity measures were relatively stable but affect by peers.

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Their sleep quality was slightly getting worse but not affected by peers.