Using Fitbit data to monitor the heart rate evolution patterns of college students

Using Wear Time for the Analysis of Consumer-Grade Wearables' Data: Case Study Using Fitbit Data

Background

Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users' engagement remains a challenge and can lead to a decrease in compliance (eg, wear time in the context of wearable sensors) over time (eg, "wearables' abandonment").

Objective

In this work, we analyzed datasets from diverse populations (eg, caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants' wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrate the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time.

Methods

We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining "valid" days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored (1) the estimation of the average daily step count and (2) the estimation of the average heart rate while walking.

Results

For Aim 1, we found that the changes in the population sample average daily step count could reach 2000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (less than 15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1000 steps for 4 of the 6 population samples analyzed when using different data processing methods. Those individual differences were higher than 3000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. For Aim 2, we found that, for 5 population samples out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count.

Conclusions

We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants' wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity.

METHODS

Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions.

RESULTS

Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms.

CONCLUSIONS

Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

The Impact of Wearable Technologies in Health Research: Scoping Review

BACKGROUND

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

OBJECTIVE

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

METHODS

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

RESULTS

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations-wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

CONCLUSIONS

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

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.

Using Fitbit data to examine factors that affect daily activity levels of college students

To date, the effect of both fixed and time-varying individual, social, psychological, environmental, and behavioral characteristics on temporal growth trends in physical activity (PA) among younger individuals remains an under-studied topic. In this paper, we address this gap in previous work by examining how temporal growth trends in PA respond to changing social, environmental, and behavioral characteristics using a large sample of college students (N = 692) who participated in the NetHealth project at the University of Notre Dame and from which fine-grained longitudinal data on physical activity and social interaction were collected unobtrusively via the use of wearables for 637 days (August 16, 2015 to May 13, 2017). These data are augmented by periodic survey data on fixed sociodemographic and psychological variables. We estimate latent growth-curve models for daily activity status, steps, active minutes, and activity calories. We find evidence of both a generalized friendship paradox and a peer effect for PA, with the average PA level of study participants' contacts being on average larger than their own, and with this average level exerting a statistically significant effect on individual PA levels. Notably, there was limited evidence of temporal growth in PA across the 637 days of observation with null temporal effects for three out of the four PA indicators, except for daily steps taken. Finally, we find that social, psychological, and behavioral factors (e.g., large network size, high extroversion levels, and more courses taken) are systematically associated with higher PA levels in this sample. Overall, our findings highlight the importance of social, environmental, and behavioral factors (such as peer networks and daily sociability) in modulating the dynamics of PA levels among college students.