Recommendations for Assessment of the Reliability, Sensitivity, and Validity of Data Provided by Wearable Sensors Designed for Monitoring Physical Activity

Digital health interventions for postoperative recovery in children: a systematic review

BACKGROUND

Digital health interventions offer a promising approach for monitoring during postoperative recovery. However, the effectiveness of these interventions remains poorly understood, particularly in children. The objective of this study was to assess the efficacy of digital health interventions for postoperative recovery in children.

METHODS

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the use of automation tools for searching and screening. We searched five electronic databases for randomised controlled trials or non-randomised studies of interventions that utilised digital health interventions to monitor postoperative recovery in children. The study quality was assessed using Cochrane Collaboration's Risk of Bias tools. The systematic review protocol was prospectively registered with PROSPERO (CRD42022351492).

RESULTS

The review included 16 studies involving 2728 participants from six countries. Tonsillectomy was the most common surgery and smartphone apps (WeChat) were the most commonly used digital health interventions. Digital health interventions resulted in significant improvements in parental knowledge about the child's condition and satisfaction regarding perioperative instructions (standard mean difference=2.16, 95% confidence interval 1.45-2.87; z=5.98, P<0.001; I2=88%). However, there was no significant effect on children's pain intensity (standard mean difference=0.09, 95% confidence interval -0.95 to 1.12; z=0.16, P=0.87; I2=98%).

CONCLUSIONS

Digital health interventions hold promise for improving parental postoperative knowledge and satisfaction. However, more research is needed for child-centric interventions with validated outcome measures. Future work should focus development and testing of user-friendly digital apps and wearables to ease the healthcare burden and improve outcomes for children.

SYSTEMATIC REVIEW PROTOCOL

PROSPERO (CRD42022351492).

Proximity tracking using ultra-wideband technology for equine social behaviour research

Sociopositive interactions with conspecifics are essential for equine welfare and quality of life. This study aimed to validate the use of wearable ultra-wideband (UWB) technology to quantify the spatial relationships and dynamics of social behaviour in horses by continuous (1/s) measurement of interindividual distances. After testing the UWB devices' spatiotemporal accuracy in a static environment, the UWB measurement validity, feasibility and utility under dynamic field conditions was assessed in a group of 8 horses. Comparison of the proximity measurements with video surveillance data established the measurement accuracy and validity (r = 0.83, p < 0.0001) of the UWB technology. The utility for social behaviour research was demonstrated by the excellent accordance of affiliative relationships (preferred partners) identified using UWB with video observations. The horses remained a median of 5.82 m (95% CI 5.13-6.41 m) apart from each other and spent 20% (median, 95% CI 14-26%) of their time in a distance ≤ 3 m to their preferred partner. The proximity measurements and corresponding speed calculation allowed the identification of affiliative versus agonistic approaches based on differences in the approach speed and the distance and duration of the resulting proximity. Affiliative approaches were statistically significantly slower (median: 1.57 km/h, 95% CI 1.26-1.92 km/h, p = 0.0394) and resulted in greater proximity (median: 36.75 cm, 95% CI 19.5-62 cm, p = 0.0003) to the approached horse than agonistic approaches (median: 3.04 km/h, 95% CI 2.16-3.74 km/h, median proximity: 243 cm, 95% CI 130-319 cm), which caused an immediate retreat of the approached horse at a significantly greater speed (median: 3.77 km/h, 95% CI 3.52-5.85 km/h, p < 0.0001) than the approach.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Are individuals with disabilities using wearable devices? A secondary data analysis of 2017 BRFSS

PURPOSE

The study aims to investigate the prevalence of individuals with disabilities who reported using wearable devices, to examine the association between wearable device usage and disability status, and to determine the characteristic of individuals with disabilities associated with wearable device usage using the 2017 Behavioural Risk Factor Surveillance System (BRFSS) through secondary data analysis.

MATERIALS AND METHODS

Data from the 2017 BRFSS of eight states were used in the analysis. Descriptive analysis, chi-square analysis, and multivariable logistic regressions were performed. Subsample analyses were also conducted for individuals with disabilities and different types of disability, including visual impairments, hearing impairments, cognitive disability, independent living disability, self-care disability, and mobility disability on wearable device usage.

RESULTS

14.6% (95% CI [11.7, 17.5]) of participants with disabilities were wearable device users. Individuals with disabilities were .63 (95% CI [.48, .83], p < 0.001) and .67 (95% CI [.50, .90], p = 0.007) times the odds of individuals without disabilities in using wearable devices, respectively, according to unadjusted and adjusted logistic regression. Individuals with mobility disability were less likely to utilise wearable devices than their counterparts. Among individuals with disabilities, those who were age 65 years or older had a lower odds of using wearable devices (OR = .55, 95% CI [0.35, 0.85), p = 0.007).

CONCLUSION

Individuals with disabilities are using wearable devices in collecting various health-related information. Further research is needed to determine reasons why individuals with disabilities are not using wearable devices and how individuals with disabilities are using wearable devices.IMPLICATIONS FOR REHABILITATIONWearable devices can track various health-related information such as physical activity levels, sleep patterns, calories intakes, and chronic health conditions.Using nationally represent data, individuals with disabilities have access and utilise wearable devices in free living setting.Compare to individuals without disabilities, individuals with disabilities are less likely to utilise wearable devices in free living setting.Further research is needed to determine the accessibility of wearable devices for individuals with disabilities and its usage in rehabilitation setting.

Toward Personalized Orthopedic Care: Validation of a Smart Knee Brace

Introduction

Wearable technology offers a promising solution to advance current rehabilitation strategies for post-operative orthopedic care. The aim of this study was to determine the level of agreement and concurrent validity of a smart knee brace compared to the gold standard measurement system GAITRite® for assessing lower limb gait parameters.

Methods

Thirty-four healthy participants were fitted with the smart knee brace (Digital Knee®) on their dominant limb. Gait parameters (stride length, stride time, and gait velocity) were measured simultaneously using the Digital Knee® and the GAITRite® electronic walkway. Two walks were performed at a comfortable speed and two at a fast-walking speed.

Results

At a comfortable walking speed, stride time was moderately valid (ICC2,1 = 0.66 s), and stride length and gait velocity demonstrated poor validity (ICC2,1 = 0.29; ICC2,1 = 0.41). All gait parameters demonstrated poor validity at a fast-walking speed (ICC2,1 = -0.16 to -0.01). Bias ranged from -0.08 to 0.28, with more clinically acceptable percentage errors at a comfortable walking speed (14.1-30%) versus at a fast-walking speed (26.4-42.6%). Gait velocity and stride length had substantially higher biases in the fast-walking speed compared to the comfortable walking speed (0.28 ± 0.39 m s-1 vs. 0.02 ± 0.21 m s-1; 0.15 ± 0.23 m vs. -0.04 ± 0.17 m). Limits of agreement were considered narrower for stride time compared to stride length and gait velocity.

Conclusion

The Digital Knee® is a promising approach to improving post-operative rehabilitation outcomes in patients with osteoarthritis. The Digital Knee® demonstrated good agreement and moderate concurrent validity for measuring gait metrics at a comfortable walking speed. These findings highlight the opportunity of the wearable sensor as an intervention for post-operative orthopedic care. This was a laboratory-based study; thus, further research is required to validate the wearable sensor in real-world contexts and in patients with knee pathologies. Further, refinement of the algorithm for measuring gait metrics at slow- and fast-walking speed with the Digital Knee® is warranted.

Comparative Analysis of ActiGraph Step Counting Methods in Adults: A Systematic Literature Review and Meta-Analysis

PURPOSE

The primary aim of this study was to compare steps per day across ActiGraph models, wear locations, and filtering methods. A secondary aim was to compare ActiGraph steps per day to those estimated by the ankle-worn StepWatch.

METHODS

We conducted a systematic literature review to identify studies of adults published before May 12, 2022, that compared free-living steps per day of ActiGraph step counting methods and studies that compared ActiGraph to StepWatch. Random-effects meta-analysis compared ActiGraph models, wear locations, filter mechanisms, and ActiGraph to StepWatch steps per day. A sensitivity analysis of wear location by younger and older age was included.

RESULTS

Twelve studies, with 46 comparisons, were identified. When worn on the hip, the AM-7164 recorded 123% of the GT series steps (no low-frequency extension (no LFE) or default filter). However, the AM-7164 recorded 72% of the GT series steps when the LFE was enabled. Independent of the filter used (i.e., LFE, no LFE), ActiGraph GT series monitors captured more steps on the wrist than on the hip, especially among older adults. Enabling the LFE on the GT series monitors consistently recorded more steps, regardless of wear location. When using the default filter (no LFE), ActiGraph recorded fewer steps than StepWatch (ActiGraph on hip 73% and ActiGraph on wrist 97% of StepWatch steps). When LFE was enabled, ActiGraph recorded more steps than StepWatch (ActiGraph on the hip, 132%; ActiGraph on the wrist, 178% of StepWatch steps).

CONCLUSIONS

The choice of ActiGraph model, wear location, and filter all impacted steps per day in adults. These can markedly alter the steps recorded compared with a criterion method (StepWatch). This review provides critical insights for comparing studies using different ActiGraph step counting methods.

The Use of Wearable Devices to Measure Sedentary Behavior during COVID-19: Systematic Review and Future Recommendations

The SARS-CoV-2 pandemic resulted in approximately 7 million deaths and impacted 767 million individuals globally, primarily through infections. Acknowledging the impactful influence of sedentary behaviors, particularly exacerbated by COVID-19 restrictions, a substantial body of research has emerged, utilizing wearable sensor technologies to assess these behaviors. This comprehensive review aims to establish a framework encompassing recent studies concerning wearable sensor applications to measure sedentary behavior parameters during the COVID-19 pandemic, spanning December 2019 to December 2022. After examining 582 articles, 7 were selected for inclusion. While most studies displayed effective reporting standards and adept use of wearable device data for their specific research aims, our inquiry revealed deficiencies in apparatus accuracy documentation and study methodology harmonization. Despite methodological variations, diverse metrics, and the absence of thorough device accuracy assessments, integrating wearables within the pandemic context offers a promising avenue for objective measurements and strategies against sedentary behaviors.

The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program

Wearable digital health technologies (DHTs) have become increasingly popular in recent years, enabling more capabilities to assess behaviors and physiology in free-living conditions. The All of Us Research Program (AoURP), a National Institutes of Health initiative that collects health-related information from participants in the United States, has expanded its data collection to include DHT data from Fitbit devices. This offers researchers an unprecedented opportunity to examine a large cohort of DHT data alongside biospecimens and electronic health records. However, there are existing challenges and sources of error that need to be considered before using Fitbit device data from the AoURP. In this viewpoint, we examine the reliability of and potential error sources associated with the Fitbit device data available through the AoURP Researcher Workbench and outline actionable strategies to mitigate data missingness and noise. We begin by discussing sources of noise, including (1) inherent measurement inaccuracies, (2) skin tone–related challenges, and (3) movement and motion artifacts, and proceed to discuss potential sources of data missingness in Fitbit device data. We then outline methods to mitigate such missingness and noise in the data. We end by considering how future enhancements to the AoURP’s Fitbit device data collection methods and the inclusion of new Fitbit data types would impact the usability of the data. Although the reliability considerations and suggested literature are tailored toward Fitbit device data in the AoURP, the considerations and recommendations are broadly applicable to data from wearable DHTs in free-living conditions.

The Validity of a Three-Dimensional Motion Capture System and the Garmin Running Dynamics Pod in Connection with an Assessment of Ground Contact Time While Running in Place

A three-dimensional motion capture system (MoCap) and the Garmin Running Dynamics Pod can be utilised to monitor a variety of dynamic parameters during running. The present investigation was designed to examine the validity of these two systems for determining ground contact times while running in place by comparing the values obtained with those provided by the bilateral force plate (gold standard). Eleven subjects completed three 20-s runs in place at self-selected rates, starting slowly, continuing at an intermediate pace, and finishing rapidly. The ground contact times obtained with both systems differed significantly from the gold standard at all three rates, as well as for all the rates combined (p < 0.001 in all cases), with the smallest mean bias at the fastest step rate for both (11.5 ± 14.4 ms for MoCap and -81.5 ± 18.4 ms for Garmin). This algorithm was developed for the determination of ground contact times during normal running and was adapted here for the assessment of running in place by the MoCap, which could be one explanation for its lack of validity. In conclusion, the wearables developed for monitoring normal running cannot be assumed to be suitable for determining ground contact times while running in place.

Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study

BACKGROUND

Mobility is a meaningful aspect of an individual's health whose quantification can provide clinical insights. Wearable sensor technology can quantify walking behaviors (a key aspect of mobility) through continuous passive monitoring.

OBJECTIVE

Our objective was to characterize the analytical performance (accuracy and reliability) of a suite of digital measures of walking behaviors as critical aspects in the practical implementation of digital measures into clinical studies.

METHODS

We collected data from a wrist-worn device (the Verily Study Watch) worn for multiple days by a cohort of volunteer participants without a history of gait or walking impairment in a real-world setting. On the basis of step measurements computed in 10-second epochs from sensor data, we generated individual daily aggregates (participant-days) to derive a suite of measures of walking: step count, walking bout duration, number of total walking bouts, number of long walking bouts, number of short walking bouts, peak 30-minute walking cadence, and peak 30-minute walking pace. To characterize the accuracy of the measures, we examined agreement with truth labels generated by a concurrent, ankle-worn, reference device (Modus StepWatch 4) with known low error, calculating the following metrics: intraclass correlation coefficient (ICC), Pearson r coefficient, mean error, and mean absolute error. To characterize the reliability, we developed a novel approach to identify the time to reach a reliable readout (time to reliability) for each measure. This was accomplished by computing mean values over aggregation scopes ranging from 1 to 30 days and analyzing test-retest reliability based on ICCs between adjacent (nonoverlapping) time windows for each measure.

RESULTS

In the accuracy characterization, we collected data for a total of 162 participant-days from a testing cohort (n=35 participants; median observation time 5 days). Agreement with the reference device-based readouts in the testing subcohort (n=35) for the 8 measurements under evaluation, as reflected by ICCs, ranged between 0.7 and 0.9; Pearson r values were all greater than 0.75, and all reached statistical significance (P<.001). For the time-to-reliability characterization, we collected data for a total of 15,120 participant-days (overall cohort N=234; median observation time 119 days). All digital measures achieved an ICC between adjacent readouts of >0.75 by 16 days of wear time.

CONCLUSIONS

We characterized the accuracy and reliability of a suite of digital measures that provides comprehensive information about walking behaviors in real-world settings. These results, which report the level of agreement with high-accuracy reference labels and the time duration required to establish reliable measure readouts, can guide the practical implementation of these measures into clinical studies. Well-characterized tools to quantify walking behaviors in research contexts can provide valuable clinical information about general population cohorts and patients with specific conditions.

Velocity-Based Strength Training: The Validity and Personal Monitoring of Barbell Velocity with the Apple Watch

Velocity-based training (VBT) is a method to monitor resistance training based on measured kinematics. Often, measurement devices are too expensive for non-professional use. The purpose of this study was to determine the accuracy and precision of the Apple Watch 7 and the Enode Pro device for measuring mean, peak, and propulsive velocity during the free-weighted back squat (in comparison to Vicon as the criterion). Velocity parameters from Vicon optical motion capture and the Apple Watch were derived by processing the motion data in an automated Python workflow. For the mean velocity, the barbell-mounted Apple Watch (r = 0.971-0.979, SEE = 0.049), wrist-worn Apple Watch (r = 0.952-0.965, SEE = 0.064) and barbell-mounted Enode Pro (r = 0.959-0.971, SEE = 0.059) showed an equal level of validity. The barbell-mounted Apple Watch (Vpeak: r = 0.952-0.965, SEE = 0.092; Vprop: r = 0.973-0.981, SEE = 0.05) was found to be the most valid for assessing propulsive and peak lifting velocity. The present results on the validity of the Apple Watch are very promising, and may pave the way for the inclusion of VBT applications in mainstream consumer wearables.

E-Textile by Printing an All-through Penetrating Copper Complex Ink

Wearable electronics is an emerging field in academics and industry, in which electronic devices, such as smartwatches and sensors, are printed or embedded within textiles. The electrical circuits in electronics textile (e-textile) should withstand many cycles of bending and stretching. Direct printing of conductive inks enables the patterning of electrical circuits; however, while using conventional nanoparticle-based inks, printing onto the fabric results in a thin layer of a conductor, which is not sufficiently robust and impairs the reliability required for practical applications. Here, we present a new process for fabricating robust stretchable e-textile using a thermodynamically stable, solution-based copper complex ink, which is capable of full penetrating the fabric. After printing on knitted stretchable fabrics, they were heated, and the complex underwent an intermolecular self-reduction reaction. The continuously formed metallic copper was used as a seed layer for electroless plating (EP) to form highly conductive circuits. It was found that the stretching direction has a significant role in resistivity. This new approach enables fabricating e-textiles with high stretchability and durability, as demonstrated for wearable gloves, toward printing functional e-textile.

Characterization of Female US Marine Recruits: Workload, Caloric Expenditure, Fitness, Injury Rates, and Menstrual Cycle Disruption during Bootcamp

Basic training is centered on developing the physical and tactical skills essential to train a recruit into a Marine. The abrupt increase in activity and energy expenditure in young recruits may contribute to high rates of musculoskeletal injuries, to which females are more susceptible. To date, the total workload of United State Marine Corps (USMC) bootcamp is unknown and should include movement around the military base (e.g., to and from dining facilities, training locations, and classrooms). Thus, the purpose of this effort was to quantify workload and caloric expenditure, as well as qualitatively assess the impact of female reproductive health and injury rates in female recruits. Female recruits (n = 79; age: 19.1 ± 0.2 years, weight: 59.6 ± 0.8 kg, height: 161.6 ± 0.7 cm) wore physiological monitors daily throughout 10 weeks of USMC bootcamp. Physical fitness test scores, physiological metrics from wearables, injury data, and menstrual cycle information were obtained. Female recruits on average expended 3096 ± 9 kcal per day, walked 11.0 ± 0.1 miles per day, and slept 5:43 ± 1:06 h:min per night throughout the 10 weeks of bootcamp. About one-third (35%) of female recruits sustained an injury. In a subset of females that were not taking birth control and had previously been menstruating, 85% experienced cycle dysfunction during boot camp. High levels of physical activity and caloric expenditure, coupled with the stress of a new environment and insufficient sleep, may lead to alterations in female reproductive cycles and musculoskeletal injuries in young USMC recruits.

Major steps in 2021

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Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?

The market for wrist-worn devices is growing at previously unheard-of speeds. A consequence of their fast commercialization is a lack of adequate studies testing their accuracy on varied populations and pursuits. To provide an understanding of wearable sensors for sports medicine, the present study examined heart rate (HR) measurements of four popular wrist-worn devices, the (Fitbit Charge (FB), Apple Watch (AW), Tomtom runner Cardio (TT), and Samsung G2 (G2)), and compared them with gold standard measurements derived by continuous electrocardiogram examination (ECG). Eight athletes participated in a comparative study undergoing maximal stress testing on a cycle ergometer or a treadmill. We analyzed 1,286 simultaneous HR data pairs between the tested devices and the ECG. The four devices were reasonably accurate at the lowest activity level. However, at higher levels of exercise intensity the FB and G2 tended to underestimate HR values during intense physical effort, while the TT and AW devices were fairly reliable. Our results suggest that HR estimations should be considered cautiously at specific intensities. Indeed, an effective intervention is required to register accurate HR readings at high-intensity levels (above 150 bpm). It is important to consider that even though none of these devices are certified or sold as medical or safety devices, researchers must nonetheless evaluate wrist-worn wearable technology in order to fully understand how HR affects psychological and physical health, especially under conditions of more intense exercise.

Wrist-worn devices for the measurement of heart rate and energy expenditure: A validation study for the Apple Watch 6, Polar Vantage V and Fitbit Sense

The purpose of this study was to investigate the accuracy of 3 recently released wrist-worn devices (Apple Watch 6, Polar Vantage V and Fitbit Sense) for heart rate and energy expenditure during various activities. The study population consisted of 60 young healthy individuals (30 men and 30 women; age: 24.9 ± 3.0 years, BMI: 23.1 ± 2.7 kg/m2). Heart rate and energy expenditure were measured using the Polar H10 and Metamax 3B, respectively (reference measures) as well as with the 3 wrist-worn devices during 5 different activities (sitting, walking, running, resistance exercises and cycling). The Apple Watch 6 displayed the highest level of accuracy for heart rate measurement with a coefficient of variation (CV) (%) of less than 5% for all 5 activities, whereas the Polar Vantage V and the Fitbit Sense presented various degrees of accuracy (from high to poor accuracy) dependent on the activity (CVs between 2.44-8.80% and 4.14-10.76%, respectively). As for energy expenditure, all 3 devices displayed poor accuracy for all 5 physical activities (CVs between 14.68-24.85% for Apple Watch 6, 16.54-25.78% for Polar Vantage V and 13.44-29.66% for Fitbit Sense). Results of the present study indicate that the Apple Watch 6 was the most accurate for measuring heart rate across all 5 activities, whereas variable levels of accuracy for heart rate measurement for the Polar Vantage V and the Fitbit Sense were observed depending on the activity. As for energy expenditure, all 3 devices showed poor accuracy during all activities.Highlights The Apple Watch 6 was the most accurate for measuring heart rate, whereas the Polar Vantage V and Fitbit Sense showed variable results dependent on the activityThe Apple Watch 6, Polar Vantage V and Fitbit Sense showed poor accuracy for energy expenditure during 5 different physical activitiesHealthcare care professionals, athletes/coaches and the general population may want to proceed with caution on the clinical utility of energy expenditure of these devices during the implementation of an exercise training or nutritional programme.

Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis

The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.

Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients

Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.

Addressing privacy concerns for mobile and wearable devices sensors: Small-group interviews with healthy adults and cancer survivors

Objective

Mobile and wearable sensor technology is increasingly common and accessible. The aim of this study was to explore individuals' perceptions and acceptability of mobile and wearable sensors, as well as concerns.

Methods

Purposive sampling was used to recruit non-patient adults (n = 22) and cancer survivors (n = 17) for face-to-face and virtual small-group interviews. Reflexive thematic analysis of the data focused on privacy concerns.

Results

Participants reported that privacy was generally not a concern for sensor adoptions for physical activity health interventions except for health insurer access.

Conclusion

The patient perspectives as reported in the findings illustrate the need for transparency between potential adopters and users of mobile and wearable devices and health care practitioners, as well as secure privacy policies for health insurers.

Innovation

Older adults often are perceived as unwilling to adopt mHealth technologies for many reasons, including privacy concerns. This study examined an important patient population, cancer survivors, who are often overlooked yet may benefit from targeted health interventions using mHealth technologies, and compared their responses with a non-patient population for prevention purposes. Our findings suggest that one's lived health experiences (cancer survivorship) are more influential than one's age in adopting mHealth technologies.

Agreement between two photoplethysmography-based wearable devices for monitoring heart rate during different physical activity situations: a new analysis methodology

Wearables are being increasingly used to monitor heart rate (HR). However, their usefulness for analyzing continuous HR in research or at clinical level is questionable. The aim of this study is to analyze the level of agreement between different wearables in the measurement of HR based on photoplethysmography, according to different body positions and physical activity levels, and compared to a gold-standard ECG. The proposed method measures agreement among several time scales since different wearables obtain HR at different sampling rates. Eighteen university students (10 men, 8 women; 22 ± 2.45 years old) participated in a laboratory study. Participants simultaneously wore an Apple Watch and a Polar Vantage watch. ECG was measured using a BIOPAC system. HR was recorded continuously and simultaneously by the three devices, for consecutive 5-min periods in 4 different situations: lying supine, sitting, standing and walking at 4 km/h on a treadmill. HR estimations were obtained with the maximum precision offered by the software of each device and compared by averaging in several time scales, since the wearables obtained HR at different sampling rates, although results are more detailed for 5 s and 30 s epochs. Bland–Altman (B-A) plots show that there is no noticeable difference between data from the ECG and any of the smartwatches while participants were lying down. In this position, the bias is low when averaging in both 5 s and 30 s. Differently, B-A plots show that there are differences when the situation involves some level of physical activity, especially for shorter epochs. That is, the discrepancy between devices and the ECG was greater when walking on the treadmill and during short time scales. The device showing the biggest discrepancy was the Polar Watch, and the one with the best results was the Apple Watch. We conclude that photoplethysmography-based wearable devices are suitable for monitoring HR averages at regular intervals, especially at rest, but their feasibility is debatable for a continuous analysis of HR for research or clinical purposes, especially when involving some level of physical activity. An important contribution of this work is a new methodology to synchronize and measure the agreement against a gold standard of two or more devices measuring HR at different and not necessarily even paces.

Accuracy and Precision of Consumer-Grade Wearable Activity Monitors for Assessing Time Spent in Sedentary Behavior in Children and Adolescents: Systematic Review

Background

A large number of wearable activity monitor models are released and used each year by consumers and researchers. As more studies are being carried out on children and adolescents in terms of sedentary behavior (SB) assessment, knowledge about accurate and precise monitoring devices becomes increasingly important.

Objective

The main aim of this systematic review was to investigate and communicate findings on the accuracy and precision of consumer-grade physical activity monitors in assessing the time spent in SB in children and adolescents.

Methods

Searches of PubMed (MEDLINE), Scopus, SPORTDiscus (full text), ProQuest, Open Access Theses and Dissertations, DART Europe E-theses Portal, and Networked Digital Library of Theses and Dissertations electronic databases were performed. All relevant studies that compared different types of consumer-grade monitors using a comparison method in the assessment of SB, published in European languages from 2015 onward were considered for inclusion. The risk of bias was estimated using Consensus-Based Standards for the Selection of Health Status Measurement Instruments. For enabling comparisons of accuracy measures within the studied outcome domain, measurement accuracy interpretation was based on group mean or percentage error values and 90% CI. Acceptable limits were predefined as –10% to +10% error in controlled and free-living settings. For determining the number of studies with group error percentages that fall within or outside one of the sides from previously defined acceptable limits, two 1-sided tests of equivalence were carried out, and the direction of measurement error was examined.

Results

A total of 8 studies complied with the predefined inclusion criteria, and 3 studies provided acceptable data for quantitative analyses. In terms of the presented accuracy comparisons, 14 were subsequently identified, with 6 of these comparisons being acceptable in terms of quantitative analysis. The results of the Cochran Q test indicated that the included studies did not share a common effect size (Q₅=82.86; P<.001). I², which represents the percentage of total variation across studies due to heterogeneity, amounted to 94%. The summary effect size based on the random effects model was not statistically significant (effect size=14.36, SE 12.04, 90% CI −5.45 to 34.17; P=.23). According to the equivalence test results, consumer-grade physical activity monitors did not generate equivalent estimates of SB in relation to the comparison methods. Majority of the studies (3/7, 43%) that reported the mean absolute percentage errors have reported values of <30%.

Conclusions

This is the first study that has attempted to synthesize available evidence on the accuracy and precision of consumer-grade physical activity monitors in measuring SB in children and adolescents. We found very few studies on the accuracy and almost no evidence on the precision of wearable activity monitors. The presented results highlight the large heterogeneity in this area of research.

Trial Registration

PROSPERO CRD42021251922; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=251922

A Comparison of a Novel Stretchable Smart Patch for Measuring Runner’s Step Rates with Existing Measuring Technologies

A novel wearable smart patch can monitor various aspects of physical activity, including the dynamics of running, but like any new device developed for such applications, it must first be tested for validity. Here, we compare the step rate while running in place as measured by this smart patch to the corresponding values obtained utilizing ‘‘gold standard’’ MEMS accelerometers in combination with bilateral force plates equipped with HBM load cells, as well as the values provided by a three-dimensional motion capture system and the Garmin Dynamics Running Pod. The 15 healthy, physically active volunteers (age = 23 ± 3 years; body mass = 74 ± 17 kg, height = 176 ± 10 cm) completed three consecutive 20-s bouts of running in place, starting at low, followed by medium, and finally at high intensity, all self-chosen. Our major findings are that the rates of running in place provided by all four systems were valid, with the notable exception of the fast step rate as measured by the Garmin Running Pod. The lowest mean bias and LoA for these measurements at all rates were associated consistently with the smart patch.

Wearable activity trackers-advanced technology or advanced marketing?

Wearable devices represent one of the most popular trends in health and fitness. Rapid advances in wearable technology present a dizzying display of possible functions: from thermometers and barometers, magnetometers and accelerometers, to oximeters and calorimeters. Consumers and practitioners utilize wearable devices to track outcomes, such as energy expenditure, training load, step count, and heart rate. While some rely on these devices in tandem with more established tools, others lean on wearable technology for health-related outcomes, such as heart rhythm analysis, peripheral oxygen saturation, sleep quality, and caloric expenditure. Given the increasing popularity of wearable devices for both recreation and health initiatives, understanding the strengths and limitations of these technologies is increasingly relevant. Need exists for continued evaluation of the efficacy of wearable devices to accurately and reliably measure purported outcomes. The purposes of this review are (1) to assess the current state of wearable devices using recent research on validity and reliability, (2) to describe existing gaps between physiology and technology, and (3) to offer expert interpretation for the lay and professional audience on how best to approach wearable technology and employ it in the pursuit of health and fitness. Current literature demonstrates inconsistent validity and reliability for various metrics, with algorithms not publicly available or lacking high-quality validation studies. Advancements in wearable technology should consider standardizing validation metrics, providing transparency in used algorithms, and improving how technology can be tailored to individuals. Until then, it is prudent to exercise caution when interpreting metrics reported from consumer-wearable devices.

Heart Rate Measurement Accuracy of Fitbit Charge 4 and Samsung Galaxy Watch Active2: Device Evaluation Study

Background

Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research.

Objective

This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2.

Methods

Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed.

Results

A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm).

Conclusions

Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners

Peak oxygen uptake (˙VO _2peak ) is an important factor contributing to running performance. Wearable technology may allow the assessment of ˙VO _2peak more frequently and on a larger scale. We aim to i) validate the ˙VO _2peak assessed by a smartwatch (Garmin Forerunner 245), and ii) discuss how this parameter may assist to evaluate and guide training procedures. A total of 23 runners (12 female, 11 male; ˙VO _2peak : 48.6±6.8 ml∙min ⁻¹ ∙kg ⁻¹ ) visited the laboratory twice to determine their ˙VO _2peak during a treadmill ramp test. Between laboratory visits, participants wore a smartwatch and performed three outdoor runs to obtain ˙VO _2peak values provided by the smartwatch. The ˙VO _2peak obtained by the criterion measure ranged from 38 to 61 ml∙min ⁻¹ ∙kg ⁻¹ . The mean absolute percentage error (MAPE) between the smartwatch and the criterion ˙VO _2peak was 5.7%. The criterion measure revealed a coefficient of variation of 4.0% over the VO2peak range from 38–61 ml∙min ⁻¹ ∙kg ⁻¹ . MAPE between the smartwatch and criterion measure was 7.1, 4.1 and −6.2% when analyzing ˙VO _2peak ranging from 39–45 ml∙min ⁻¹ ∙kg ⁻¹ , 45–55 ml∙min ⁻¹ ∙kg ⁻¹ or 55–61 ml∙min ⁻¹ ∙kg ⁻¹ , respectively.

Proof-of-concept and concurrent validity of a prototype headset to assess peak oxygen uptake without a face mask

Objective

Portable gas exchange instruments allow the assessment of peak oxygen uptake (V̇O_2peak) but are often bulky, expensive and require wearing a face mask thereby limiting their routine application. A newly developed miniaturized headset (VitaScale, Nuremberg, Germany) may overcome these barriers and allow measuring V̇O_2peak without applying a face mask. Here we aimed (i) to disclose the technical setup of a headset incorporating a gas and volume sensor to measure volume flow and expired oxygen concentration and (ii) to assess the concurrent criterion-validity of the headset to measure V̇O_2peak in 44 individuals exercising on a stationary cycle ergometer in consideration of the test–retest reliability of the criterion measure.

Results

The coefficient of variation (CV%) while measuring V̇O_2peak during incremental cycling with the headset was 6.8%. The CV% for reliability of the criterion measure was 4.0% for V̇O_2peak. Based on the present data, the headset might offer a new technology for V̇O_2peak measurement due to its low-cost and mask-free design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-021-05850-y.

Lessons from a multicenter clinical trial with an approved wearable electrocardiogram: issues and practical considerations

Although wearable electrocardiograms (ECGs) are being increasingly applied in clinical settings, validation methods have not been standardized. As an exploratory evaluation, we performed a multicenter clinical trial implementing an approved wearable patch ECG. Healthy male adults were enrolled in 2 study centers. The approved ECGs were deployed for 6 hours, and pulse rates were measured independently with conventional pulse oximetry at selected time points for correlation analyses. The transmission status of the data was evaluated by heart rates and classified into valid, invalid, and missing. A total of 55 subjects (40 in center 1 and 15 in center 2) completed the study. Overall, 77.40% of heart rates were within the valid range. Invalid and missing data accounted for 1.42% and 21.23%, respectively. There were significant differences in valid and missing data between centers. The proportion of missing data in center 1 (24.77%) was more than twice center 2 (11.77%). Heart rates measured by the wearable ECG and conventional pulse oximetry showed a poor correlation (intraclass correlation coefficient = 0.0454). In conclusion, we evaluated the multicenter feasibility of implementing wearable ECGs. The results suggest that systems to mitigate multicenter discrepancies and remove artifacts should be implemented prior to performing a clinical trial.

Heart Rate and Distance Measurement of Two Multisport Activity Trackers and a Cellphone App in Different Sports: A Cross-Sectional Validation and Comparison Field Study

Options for monitoring sports have been continuously developed by using activity trackers to determine almost all vital and movement parameters. The aim of this study was to validate heart rate and distance measurements of two activity trackers (Polar Ignite; Garmin Forerunner 945) and a cellphone app (Polar Beat app using iPhone 7 as a hardware platform) in a cross-sectional field study. Thirty-six moderate endurance-trained adults (20 males/16 females) completed a test battery consisting of walking and running 3 km, a 1.6 km interval run (standard 400 m outdoor stadium), 3 km forest run (outdoor), 500/1000 m swim and 4.3/31.5 km cycling tests. Heart rate was recorded via a Polar H10 chest strap and distance was controlled via a map, 400 m stadium or 50 m pool. For all tests except swimming, strong correlation values of r > 0.90 were calculated with moderate exercise intensity and a mean absolute percentage error of 2.85%. During the interval run, several significant deviations (p < 0.049) were observed. The swim disciplines showed significant differences (p < 0.001), with the 500 m test having a mean absolute percentage error of 8.61%, and the 1000 m test of 55.32%. In most tests, significant deviations (p < 0.001) were calculated for distance measurement. However, a maximum mean absolute percentage error of 4.74% and small mean absolute error based on the total route lengths were calculated. This study showed that the accuracy of heart rate measurements could be rated as good, except for rapid changing heart rate during interval training and swimming. Distance measurement differences were rated as non-relevant in practice for use in sports.

Establishing a Global Standard for Wearable Devices in Sport and Exercise Medicine: Perspectives from Academic and Industry Stakeholders

Millions of consumer sport and fitness wearables (CSFWs) are used worldwide, and millions of datapoints are generated by each device. Moreover, these numbers are rapidly growing, and they contain a heterogeneity of devices, data types, and contexts for data collection. Companies and consumers would benefit from guiding standards on device quality and data formats. To address this growing need, we convened a virtual panel of industry and academic stakeholders, and this manuscript summarizes the outcomes of the discussion. Our objectives were to identify (1) key facilitators of and barriers to participation by CSFW manufacturers in guiding standards and (2) stakeholder priorities. The venues were the Yale Center for Biomedical Data Science Digital Health Monthly Seminar Series (62 participants) and the New England Chapter of the American College of Sports Medicine Annual Meeting (59 participants). In the discussion, stakeholders outlined both facilitators of (e.g., commercial return on investment in device quality, lucrative research partnerships, and transparent and multilevel evaluation of device quality) and barriers (e.g., competitive advantage conflict, lack of flexibility in previously developed devices) to participation in guiding standards. There was general agreement to adopt Keadle et al.'s standard pathway for testing devices (i.e., benchtop, laboratory, field-based, implementation) without consensus on the prioritization of these steps. Overall, there was enthusiasm not to add prescriptive or regulatory steps, but instead create a networking hub that connects companies to consumers and researchers for flexible guidance navigating the heterogeneity, multi-tiered development, dynamicity, and nebulousness of the CSFW field.

Reliability and Validity of the CORE Sensor to Assess Core Body Temperature during Cycling Exercise

Monitoring core body temperature (T_c) during training and competitions, especially in a hot environment, can help enhance an athlete's performance, as well as lower the risk for heat stroke. Accordingly, a noninvasive sensor that allows reliable monitoring of T_c would be highly beneficial in this context. One such novel non-invasive sensor was recently introduced onto the market (CORE, greenTEG, Rümlang, Switzerland), but, to our knowledge, a validation study of this device has not yet been reported. Therefore, the purpose of this study was to evaluate the validity and reliability of the CORE sensor. In Study I, 12 males were subjected to a low-to-moderate heat load by performing, on two separate occasions several days apart, two identical 60-min bouts of steady-state cycling in the laboratory at 19 °C and 30% relative humidity. In Study II, 13 males were subjected to moderate-to-high heat load by performing 90 min of cycling in the laboratory at 31 °C and 39% relative humidity. In both cases the core body temperatures indicated by the CORE sensor were compared to the corresponding values obtained using a rectal sensor (T_rec). The first major finding was that the reliability of the CORE sensor is acceptable, since the mean bias between the two identical trials of exercise (0.02 °C) was not statistically significant. However, under both levels of heat load, the body temperature indicated by the CORE sensor did not agree well with T_rec, with approximately 50% of all paired measurements differing by more than the predefined threshold for validity of ≤0.3 °C. In conclusion, the results obtained do not support the manufacturer's claim that the CORE sensor provides a valid measure of core body temperature.

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

Gait, balance, and coordination are important in the development of chronic disease, but the ability to accurately assess these in the daily lives of patients may be limited by traditional biased assessment tools. Wearable sensors offer the possibility of minimizing the main limitations of traditional assessment tools by generating quantitative data on a regular basis, which can greatly improve the home monitoring of patients. However, these commercial sensors must be validated in this context with rigorous validation methods. This scoping review summarizes the state-of-the-art between 2010 and 2020 in terms of the use of commercial wearable devices for gait monitoring in patients. For this specific period, 10 databases were searched and 564 records were retrieved from the associated search. This scoping review included 70 studies investigating one or more wearable sensors used to automatically track patient gait in the field. The majority of studies (95%) utilized accelerometers either by itself (N = 17 of 70) or embedded into a device (N = 57 of 70) and/or gyroscopes (51%) to automatically monitor gait via wearable sensors. All of the studies (N = 70) used one or more validation methods in which “ground truth” data were reported. Regarding the validation of wearable sensors, studies using machine learning have become more numerous since 2010, at 17% of included studies. This scoping review highlights the current state of the ability of commercial sensors to enhance traditional methods of gait assessment by passively monitoring gait in daily life, over long periods of time, and with minimal user interaction. Considering our review of the last 10 years in this field, machine learning approaches are algorithms to be considered for the future. These are in fact data-based approaches which, as long as the data collected are numerous, annotated, and representative, allow for the training of an effective model. In this context, commercial wearable sensors allowing for increased data collection and good patient adherence through efforts of miniaturization, energy consumption, and comfort will contribute to its future success.

Exploring the Use of Mobile and Wearable Technology among University Student Athletes in Lebanon: A Cross-Sectional Study

The markets of commercial wearables and health and fitness apps are constantly growing globally, especially among young adults and athletes, to track physical activity, energy expenditure and health. Despite their wide availability, evidence on use comes predominantly from the United States or Global North, with none targeting college student-athletes in low- and middle-income countries. This study was aimed to explore the use of these technologies among student-athletes at the American University of Beirut (AUB). We conducted a cross-sectional survey of 482 participants (average age 20 years) enrolled in 24 teams during Fall 2018; 230 students successfully completed the web-based survey, and 200 provided valid data. Fifty-three (26.5%) have owned a fitness tracker, mostly for self-monitoring. The most popular were Fitbit, Apple Watch, and Garmin. Similarly, 82 students (40%) used apps, primarily MyFitnessPal, Apple Health, and Samsung Health. Nevertheless, many participants discontinued use due to loss of interest or technical issues (breaking, usability, obsolescence, or lack of engagement). Wearable devices were considered superior to mobile phones alone as physical activity monitors. However, forming regular habits made self-monitoring via technology irrelevant. Further research is needed to better understand what motivates continuous use among student-athletes, who could use trackers to improve athletic performance and overall health.

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Smartphone-Based Passive Sensing for Behavioral and Physical Monitoring in Free-Life Conditions: Technical Usability Study

Background

Smartphone use is widely spreading in society. Their embedded functions and sensors may play an important role in therapy monitoring and planning. However, the use of smartphones for intrapersonal behavioral and physical monitoring is not yet fully supported by adequate studies addressing technical reliability and acceptance.

Objective

The objective of this paper is to identify and discuss technical issues that may impact on the wide use of smartphones as clinical monitoring tools. The focus is on the quality of the data and transparency of the acquisition process.

Methods

QuantifyMyPerson is a platform for continuous monitoring of smartphone use and embedded sensors data. The platform consists of an app for data acquisition, a backend cloud server for data storage and processing, and a web-based dashboard for data management and visualization. The data processing aims to extract meaningful features for the description of daily life such as phone status, calls, app use, GPS, and accelerometer data. A total of health subjects installed the app on their smartphones, running it for 7 months. The acquired data were analyzed to assess impact on smartphone performance (ie, battery consumption and anomalies in functioning) and data integrity. Relevance of the selected features in describing changes in daily life was assessed through the computation of a k-nearest neighbors global anomaly score to detect days that differ from others.

Results

The effectiveness of smartphone-based monitoring depends on the acceptability and interoperability of the system as user retention and data integrity are key aspects. Acceptability was confirmed by the full transparency of the app and the absence of any conflicts with daily smartphone use. The only perceived issue was the battery consumption even though the trend of battery drain with and without the app running was comparable. Regarding interoperability, the app was successfully installed and run on several Android brands. The study shows that some smartphone manufacturers implement power-saving policies not allowing continuous sensor data acquisition and impacting integrity. Data integrity was 96% on smartphones whose power-saving policies do not impact the embedded sensor management and 84% overall.

Conclusions

The main technological barriers to continuous behavioral and physical monitoring (ie, battery consumption and power-saving policies of manufacturers) may be overcome. Battery consumption increase is mainly due to GPS triangulation and may be limited, while data missing because of power-saving policies are related only to periods of nonuse of the phone since the embedded sensors are reactivated by any smartphone event. Overall, smartphone-based passive sensing is fully feasible and scalable despite the Android market fragmentation.

Visualising the knowledge structure and evolution of wearable device research

In recent years, the literature associated with wearable devices has grown rapidly, but few studies have used bibliometrics and a visualisation approach to conduct deep mining and reveal a panorama of the wearable devices field. To explore the foundational knowledge and research hotspots of the wearable devices field, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the distribution of countries, a keyword co-occurrence analysis, theme evolution analysis and research hotspots and trends for the future. By conducting a literature content analysis and structure analysis, we found the following: (a) The subject evolution path includes sensor research, sensitivity research and multi-functional device research. (b) Wearable device research focuses on information collection, sensor materials, manufacturing technology and application, artificial intelligence technology application, energy supply and medical applications. The future development trend will be further studied in combination with big data analysis, telemedicine and personalised precision medical application.

Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

Background

There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse.

Objective

This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research.

Methods

The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well.

Results

A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy.

Conclusions

Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

Using the VERT wearable device to monitor jumping loads in elite volleyball athletes

Sport is becoming increasingly competitive and athletes are being exposed to greater physical demands, leaving them prone to injuries. Monitoring athletes with the use of wearable technology could provide a way to potentially manage training and competition loads and reduce injuries. One such technology is the VERT inertial measurement unit, a commercially available discrete wearable device containing a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer. Some of the main measurement outputs include jump count, jump height and landing impacts. While several studies have examined the accuracy of the VERT's measures of jump height and jump count, landing impact force has not yet been investigated. The objective of this research study was to explore the validity of the VERT landing impact values. We hypothesized that the absolute peak VERT acceleration values during a jump-land cycle would fall within 10% of the peak acceleration values derived simultaneously from a research-grade accelerometer (Shimmer). Fourteen elite university-level volleyball players each performed 10 jumps while wearing both devices simultaneously. The results showed that VERT peak accelerations were variable (limits of agreement of -84.13% and 52.37%) and had a propensity to be lower (mean bias of -15.88%) when compared to the Shimmer. In conclusion, the validity of the VERT device's landing impact values are generally poor, when compared to the Shimmer.

Validity and Reliability of Physiological Data in Applied Settings Measured by Wearable Technology: A Rapid Systematic Review

The purpose of this review was to evaluate the current state of the literature and to identify the types of study designs, wearable devices, statistical tests, and exercise modes used in validation and reliability studies conducted in applied settings/outdoor environments. This was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We identified nine articles that fit our inclusion criteria, eight of which tested for validity and one tested for reliability. The studies tested 28 different devices with exercise modalities of running, walking, cycling, and hiking. While there were no universally common analytical techniques used to measure accuracy or validity, correlative measures were used in 88% of studies, mean absolute percentage error (MAPE) in 75%, and Bland–Altman plots in 63%. Intra-class correlation was used to determine reliability. There were not any universally common thresholds to determine validity, however, of the studies that used MAPE and correlation, there were only five devices that had a MAPE of < 10% and a correlation value of > 0.7. Overall, the current review establishes the need for greater testing in applied settings when validating wearables. Researchers should seek to incorporate multiple intensities, populations, and modalities into their study designs while utilizing appropriate analytical techniques to measure and determine validity and reliability.

Zur Reliabilität von Wearable Devices am Beispiel einer Premium Multisport-Smartwatch

Wearable Devices versprechen durch ihre motivierende Wirkung einen wichtigen Beitrag zur Bindung des Individuums an körperlich-sportliche Aktivitäten und somit zum Aufbau und Erhalt von Gesundheit und Leistungsfähigkeit in Zeiten des digitalen gesellschaftlichen Wandels. Übergeordnetes Ziel der vorliegenden Untersuchung war die Beurteilung der Testgüte von Wearable Devices anhand eines marktrelevanten Gerätes, der Garmin fēnix® 5. Als Forschungsdesiderat wurde der Reliabilitätsaspekt der Methodenkonkordanz identifiziert. Zur Überprüfung der Methodenkonkordanz wurden das Stresslevel bei kognitiver Stressinduktion, der Kalorienverbrauch bei moderater Ausdauerlaufbelastung sowie die maximale Sauerstoffaufnahme bei Laufausbelastung von 30 männlichen Probanden (Alter: 23,13 ± 2,5 Jahre; BMI: 24,95 ± 2,45 kg/m2) mit der Garmin fēnix® 5 bestimmt und die Ergebnisse mit denen im Feld gängigen Referenzmethoden Elektrokardiographie, Indirekte Kalorimetrie bzw. Spiroergometrie verglichen. Zur rechnerischen Überprüfung der Methodenkonkordanz diente Lin’s Konkordanzkorrelationskoeffizient (CCCLin). Die Ergebnisse zeigen eine hohe Präzision der Garmin fēnix® 5 im Vergleich mit der Referenzmethode Elektrokardiographie hinsichtlich der Messung des notwendigerweise z-standardisierten Stressparameters (p = 0,89) sowie eine gerade mittlere exakte intrainidividuelle Konkordanz mit der Referenzmethode Indirekte Kalorimetrie bzw. Spiroergometrie hinsichtlich der Messung des Parameters Kalorienverbrauch (CCCLin = 0,43 [p = 0,52, Cb = 0,82]) bzw. maximale Sauerstoffaufnahme (CCCLin = 0,50 [p = 0,77, Cb = 0,66]). Die Garmin fēnix® 5 kann somit zumindest bei erstmaliger Verwendung nicht als hinreichend konkordante Alternative zu den gängigen aktivitäts- und leistungsbezogenen Referenzmethoden empfohlen werden.

Precision Health: The Role of the Social and Behavioral Sciences in Advancing the Vision

BACKGROUND

In 2015, Collins and Varmus articulated a vision for precision medicine emphasizing molecular characterization of illness to identify actionable biomarkers to support individualized treatment. Researchers have argued for a broader conceptualization, precision health. Precision health is an ambitious conceptualization of health, which includes dynamic linkages between research and practice as well as medicine, population health, and public health. The goal is a unified approach to match a full range of promotion, prevention, diagnostic, and treatment interventions to fundamental and actionable determinants of health; to not just address symptoms, but to directly target genetic, biological, environmental, and social and behavioral determinants of health.

PURPOSE

The purpose of this paper is to elucidate the role of social and behavioral sciences within precision health.

MAIN BODY

Recent technologies, research frameworks, and methods are enabling new approaches to measure, intervene, and conduct social and behavioral science research. These approaches support three opportunities in precision health that the social and behavioral sciences could colead including: (a) developing interventions that continuously "tune" to each person's evolving needs; (b) enhancing and accelerating links between research and practice; and (c) studying mechanisms of change in real-world contexts. There are three challenges for precision health: (a) methods of knowledge organization and curation; (b) ethical conduct of research; and (c) equitable implementation of precision health.

CONCLUSIONS

Precision health requires active coleadership from social and behavioral scientists. Prior work and evidence firmly demonstrate why the social and behavioral sciences should colead with regard to three opportunity and three challenge areas.

Validity of a Local Positioning System during Outdoor and Indoor Conditions for Team Sports

This study aimed to compare the validity of a local positioning system (LPS) during outdoor and indoor conditions for team sports. The impact of different filtering techniques was also investigated. Five male team sport athletes (age: 27 ± 2 years; maximum oxygen uptake: 48.4 ± 5.1 mL/min/kg) performed 10 trials on a team sport-specific circuit on an artificial turf and in a sports hall. During the circuit, athletes wore two devices of a recent 20-Hz LPS. From the reported raw and differently filtered velocity data, distances covered during different walking, jogging, and sprinting sections within the circuit were computed for which the circuit was equipped with double-light timing gates as criterion measures. The validity was determined by comparing the known and measured distances via the relative typical error of estimate (TEE). The LPS validity for measuring distances covered was good to moderate during both environments (TEE: 0.9–7.1%), whereby the outdoor validity (TEE: 0.9–6.4%) was superior than indoor validity (TEE: 1.2–7.1%). During both environments, validity outcomes of an unknown manufacturer filter were superior (TEE: 0.9–6.2%) compared to those of a standard Butterworth filter (TEE: 0.9–6.4%) and to unprocessed raw data (TEE: 1.0–7.1%). Our findings show that the evaluated LPS can be considered as a good to moderately valid tracking technology to assess running-based movement patterns in team sports during outdoor and indoor conditions. However, outdoor was superior to indoor validity, and also impacted by the applied filtering technique. Our outcomes should be considered for practical purposes like match and training analyses in team sport environments.

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models.

Establishing a Global Standard for Wearable Devices in Sport and Fitness: Perspectives from the New England Chapter of the American College of Sports Medicine Members

The recent explosion of wearable technology and the associated concerns prompted the International Federation of Sports Medicine (FIMS) to create a quality assurance standard for wearable devices, which provides commissioned testing of marketing claims and endorsement of commercial wearables that test favorably. An open forum as announced in the conference advertising was held at the Annual Meeting of the New England Regional Chapter of the American College of Sports Medicine (NEACSM) November 7 to 8, 2019, in Providence, Rhode Island, USA for attending NEACSM members to voice their input on the process. Herein, we report the proceedings. The round table participants perceived the quality assurance standard to be important, but identified some practical process challenges that included the broad scope and complexity of the device universe, the need for a multiphase testing pathway, and the associated fees for product evaluation. The participants also supported the evaluation of device data analysis, behavioral influences, and user experience in the overall evaluation. Looking forward, the FIMS quality assurance standard faces the challenge of balancing these broader perspectives with practical constraints of budget, facilities, time, and human resources.

Blood Pressure Sensors: Materials, Fabrication Methods, Performance Evaluations and Future Perspectives

Advancements in materials science and fabrication techniques have contributed to the significant growing attention to a wide variety of sensors for digital healthcare. While the progress in this area is tremendously impressive, few wearable sensors with the capability of real-time blood pressure monitoring are approved for clinical use. One of the key obstacles in the further development of wearable sensors for medical applications is the lack of comprehensive technical evaluation of sensor materials against the expected clinical performance. Here, we present an extensive review and critical analysis of various materials applied in the design and fabrication of wearable sensors. In our unique transdisciplinary approach, we studied the fundamentals of blood pressure and examined its measuring modalities while focusing on their clinical use and sensing principles to identify material functionalities. Then, we carefully reviewed various categories of functional materials utilized in sensor building blocks allowing for comparative analysis of the performance of a wide range of materials throughout the sensor operational-life cycle. Not only this provides essential data to enhance the materials' properties and optimize their performance, but also, it highlights new perspectives and provides suggestions to develop the next generation pressure sensors for clinical use.

The use of wearable technology to measure and support abilities, disabilities and functional skills in autistic youth: a scoping review

Background:

Wearable technology (WT) to measure and support social and non-social functioning in Autism Spectrum Disorder (ASD) has been a growing interest of researchers over the past decade. There is however limited understanding of the WTs currently available for autistic individuals, and how they measure functioning in this population.

Objective:

This scoping review explored the use of WTs for measuring and supporting abilities, disabilities and functional skills in autistic youth.

Method:

Four electronic databases were searched to identify literature investigating the use of WT in autistic youth, resulting in a total of 33 studies being reviewed. Descriptive and content analysis was conducted, with studies subsequently mapped to the ASD International Classification of Functioning, Disability and Health Core-sets and the ICF Child and Youth Version (ICF-CY).

Results:

Studies were predominately pilot studies for novel devices. WTs measured a range of physiological and behavioural functions to objectively measure stereotypical motor movements, social function, communication, and emotion regulation in autistic youth in the context of a range of environments and activities.

Conclusions:

While this review raises promising prospects for the use of WTs for autistic youth, the current evidence is limited and requires further investigation.

A Discussion on Different Approaches for Prescribing Physical Interventions - Four Roads Lead to Rome, but Which One Should We Choose?

It is well recognized that regular physical exercise has positive effects on physical and mental health. To use the beneficial health effects of physical exercise, there are worldwide movements encouraging health care providers to include physical exercise in their care and treatments strategies. However, a crucial point in administering the "exercise polypill" is the dosing and, in turn, the prescription of the physical intervention (PI). In this perspective article, we discuss the advantages and disadvantages of different approaches to prescribe PI. In this context, we also highlight outstanding questions and potential areas of opportunity for further investigations.

Accuracy of Distance Recordings in Eight Positioning-Enabled Sport Watches: Instrument Validation Study

Background

Elite athletes and recreational runners rely on the accuracy of global navigation satellite system (GNSS)–enabled sport watches to monitor and regulate training activities. However, there is a lack of scientific evidence regarding the accuracy of such sport watches.

Objective

The aim was to investigate the accuracy of the recorded distances obtained by eight commercially available sport watches by Apple, Coros, Garmin, Polar, and Suunto when assessed in different areas and at different speeds. Furthermore, potential parameters that affect the measurement quality were evaluated.

Methods

Altogether, 3 × 12 measurements in urban, forest, and track and field areas were obtained while walking, running, and cycling under various outdoor conditions.

Results

The selected reference distances ranged from 404.0 m to 4296.9 m. For all the measurement areas combined, the recorded systematic errors (±limits of agreements) ranged between 3.7 (±195.6) m and –101.0 (±231.3) m, and the mean absolute percentage errors ranged from 3.2% to 6.1%. Only the GNSS receivers from Polar showed overall errors <5%. Generally, the recorded distances were significantly underestimated (all P values <.04) and less accurate in the urban and forest areas, whereas they were overestimated but with good accuracy in 75% (6/8) of the sport watches in the track and field area. Furthermore, the data assessed during running showed significantly higher error rates in most devices compared with the walking and cycling activities.

Conclusions

The recorded distances might be underestimated by up to 9%. However, the use of all investigated sport watches can be recommended, especially for distance recordings in open areas.

Review of Validity and Reliability of Garmin Activity Trackers

Purpose

A systematic review to summarize the validity and reliability of steps, distance, energy expenditure, speed, elevation, heart rate, and sleep assessed by Garmin activity trackers.

Methods

Searches included studies published through December 31, 2018. Correlation coefficients (CC) were assessed as low (<0.60), moderate (0.60-<0.75), good (0.75-<0.90), or excellent (>=0.90). Mean absolute percentage errors (MAPE) were assessed as acceptable at <5% in controlled conditions and <10% for free-living.

Results

Overall, 32 studies of adults documented validity. Four of these studies also documented reliability. The sample size ranged from 1 to 95 for validity and 4 to 31 for reliability testing. Step inter- and intra-reliability was good-to-excellent and speed intra-reliability was excellent. No other features were explored for reliability. Step validity, across 16 studies, generally indicated good-to-excellent CC and acceptable MAPE. Distance validity, tested in three studies, generally indicated poor CC and MAPE that exceeded acceptable limits, with both over and underestimation. Energy expenditure validity, across 12 studies, generally indicated wide variability in CC and MAPE that exceeded acceptable limits. Heart rate validity in five studies had low-to-excellent CC and all MAPE exceeded acceptable limits. Speed, elevation, and sleep validity were assessed in only one or two studies each; for sleep, the criterion relied on self-report rather than polysomnography.

Conclusion

This systematic review of Garmin activity trackers among adults indicated higher validity of steps; few studies on speed, elevation, and sleep; and lower validity for distance, energy expenditure, and heart rate. Intra- and inter-device feature reliability needs further testing.