Comparison of Wearable Activity Tracker with Actigraphy for Sleep Evaluation and Circadian Rest-Activity Rhythm Measurement in Healthy Young Adults

Impact of Volume and Type of Overnight Pages on Resident Sleep During Home Call

BACKGROUND

Little research exists regarding factors that contribute to resident fatigue during home call.

OBJECTIVE

We objectively tracked the number and type of pages received, as well as residents' sleep time, during home call. We then examined the relationship between paging volume, resident sleep, and resident fatigue.

METHODS

A total of 4 of 4 urology residents (100%) at a single institution wore a FitBit Charge HR device from July 2015 to July 2016 to track sleep. Between January and July 2016, pages received by the on-call resident were counted as either floor (urology inpatient unit), clinic (after-hours answering service), or other. Postcall residents were defined as fatigued and excused at noon if they reported they were too tired to safely perform clinical duties.

RESULTS

Residents slept an average of 408 minutes per night while not on call, versus 368 minutes while on call but not fatigued, and 181 minutes while on call and fatigued (P < .05). The most senior resident received fewer pages per night on average than the most junior resident. Each page was associated with 4.71 fewer minutes asleep on average for all residents. Pages in the other category were associated with 7.74 fewer minutes asleep per page for all residents, but only the most junior resident had significantly less sleep, 9.02 minutes, per floor page.

CONCLUSIONS

Objective sleep data correlate with subjective assessment of resident fatigue and with volume and type of pages received. Senior residents spent less time awake per page and received fewer pages.

Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data

Background

Although designed as a consumer product to help motivate individuals to be physically active, Fitbit activity trackers are becoming increasingly popular as measurement tools in physical activity and health promotion research and are also commonly used to inform health care decisions.

Objective

The objective of this review was to systematically evaluate and report measurement accuracy for Fitbit activity trackers in controlled and free-living settings.

Methods

We conducted electronic searches using PubMed, EMBASE, CINAHL, and SPORTDiscus databases with a supplementary Google Scholar search. We considered original research published in English comparing Fitbit versus a reference- or research-standard criterion in healthy adults and those living with any health condition or disability. We assessed risk of bias using a modification of the Consensus-Based Standards for the Selection of Health Status Measurement Instruments. We explored measurement accuracy for steps, energy expenditure, sleep, time in activity, and distance using group percentage differences as the common rubric for error comparisons. We conducted descriptive analyses for frequency of accuracy comparisons within a ±3% error in controlled and ±10% error in free-living settings and assessed for potential bias of over- or underestimation. We secondarily explored how variations in body placement, ambulation speed, or type of activity influenced accuracy.

Results

We included 67 studies. Consistent evidence indicated that Fitbit devices were likely to meet acceptable accuracy for step count approximately half the time, with a tendency to underestimate steps in controlled testing and overestimate steps in free-living settings. Findings also suggested a greater tendency to provide accurate measures for steps during normal or self-paced walking with torso placement, during jogging with wrist placement, and during slow or very slow walking with ankle placement in adults with no mobility limitations. Consistent evidence indicated that Fitbit devices were unlikely to provide accurate measures for energy expenditure in any testing condition. Evidence from a few studies also suggested that, compared with research-grade accelerometers, Fitbit devices may provide similar measures for time in bed and time sleeping, while likely markedly overestimating time spent in higher-intensity activities and underestimating distance during faster-paced ambulation. However, further accuracy studies are warranted. Our point estimations for mean or median percentage error gave equal weighting to all accuracy comparisons, possibly misrepresenting the true point estimate for measurement bias for some of the testing conditions we examined.

Conclusions

Other than for measures of steps in adults with no limitations in mobility, discretion should be used when considering the use of Fitbit devices as an outcome measurement tool in research or to inform health care decisions, as there are seemingly a limited number of situations where the device is likely to provide accurate measurement.

Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep

The objectives of this review were to evaluate the use of consumer-targeted wearable and mobile sleep monitoring technology, identify gaps in the literature and determine the potential for use in behavioral interventions. We undertook a scoping review of studies conducted in adult populations using consumer-targeted wearable technology or mobile devices designed to measure and/or improve sleep. After screening for inclusion/exclusion criteria, data were extracted from the articles by two co-authors. Articles included in the search were using wearable or mobile technology to estimate or evaluate sleep, published in English and conducted in adult populations. Our search returned 3897 articles and 43 met our inclusion criteria. Results indicated that the majority of studies focused on validating technology to measure sleep (n = 23) or were observational studies (n = 10). Few studies were used to identify sleep disorders (n = 2), evaluate response to interventions (n = 3) or deliver interventions (n = 5). In conclusion, the use of consumer-targeted wearable and mobile sleep monitoring technology has largely focused on validation of devices and applications compared with polysomnography (PSG) but opportunities exist for observational research and for delivery of behavioral interventions. Multidisciplinary research is needed to determine the uses of these technologies in interventions as well as the use in more diverse populations including sleep disorders and other patient populations.

Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables

Background

New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects.

Objective

The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research.

Methods

We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands.

Results

We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands.

Conclusions

The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.

Pulmonary exposure to metal fume particulate matter cause sleep disturbances in shipyard welders

Sleep disorders may pose a risk to workers in the workplace. We aimed to investigate the associations between metal fume fine particulate matter (PM_2.5) and sleep quality in workers. We assessed the effects of personal exposure to metal fume PM_2.5 on lung functions, urinary biomarkers, and sleep quality in shipyard welding workers. In total, 96 welding workers and 54 office workers were recruited in the present study; office workers were exposed to 82.1 ± 94.1 μg/m³ PM_2.5 and welding workers were exposed to 2166.5 ± 3149.1 μg/m³. Welding workers had significantly lower levels of FEV25-75 than office workers (p < 0.05). An increase in 1 μg/m³ PM_2.5 was associated with a decrease of 0.003 ng/mL in urinary serotonin (95% CI = -0.007-0.000, p < 0.05) in all workers and with a decrease of 0.001 ng/mL in serotonin (95% CI = -0.004-0.002, p < 0.05) in welding workers, but these were not observed in office workers. There was no significant association of PM_2.5 with urinary cortisol observed in any workers. Urinary serotonin was associated with urinary Cu, Mn, Co, Ni, Cd, and Pb. Urinary cortisol was associated with Cu, Mn, Co, Ni, Cd, and Pb. Sixteen subjects were randomly selected from each of the office and welding workers for personal monitoring of sleep quality using a wearable device. We observed that welding workers had greater awake times than did office workers (p < 0.05). Our study observed that exposure to heavy metals in metal fume PM_2.5 may disrupt sleep quality in welding workers.

An International Study on the Determinants of Poor Sleep Amongst 15,000 Users of Connected Devices

BACKGROUND

Sleep is a modifiable lifestyle factor that can be a target for efficient intervention studies to improve the quality of life and decrease the risk or burden of some chronic conditions. Knowing the profiles of individuals with poor sleep patterns is therefore a prerequisite. Wearable devices have recently opened new areas in medical research as potential efficient tools to measure lifestyle factors such as sleep quantity and quality.

OBJECTIVES

The goal of our research is to identify the determinants of poor sleep based on data from a large population of users of connected devices.

METHODS

We analyzed data from 15,839 individuals (13,658 males and 2181 females) considered highly connected customers having purchased and used at least 3 connected devices from the consumer electronics company Withings (now Nokia). Total and deep sleep durations as well as the ratio of deep/total sleep as a proxy of sleep quality were analyzed in association with available data on age, sex, weight, heart rate, steps, and diastolic and systolic blood pressures.

RESULTS

With respect to the deep/total sleep duration ratio used as a proxy of sleep quality, we have observed that those at risk of having a poor ratio (≤0.40) were more frequently males (odds ratio [OR]_{female vs male}=0.45, 95% CI 0.38-0.54), younger individuals (OR_{>60 years vs 18-30 years}=0.47, 95% CI 0.35-0.63), and those with elevated heart rate (OR_{>78 bpm vs ≤61 bpm}=1.18, 95% CI 1.04-1.34) and high systolic blood pressure (OR_{>133 mm Hg vs ≤116 mm Hg}=1.22, 95% CI 1.04-1.43). A direct association with weight was observed for total sleep duration exclusively.

CONCLUSIONS

Wearables can provide useful information to target individuals at risk of poor sleep. Future alert or mobile phone notification systems based on poor sleep determinants measured with wearables could be tested in intervention studies to evaluate the benefits.