The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review

Straiton, Nicola; Alharbi, Muaddi; Bauman, Adrian; Neubeck, Lis; Gullick, Janice; Bhindi, Ravinay; Gallagher, Robyn

doi:10.1016/j.maturitas.2018.03.016

Cited by 131 publications

(145 citation statements)

References 25 publications

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“…Pedometers are less accurate when the sensor is attached to the wrist or hip, compared to ankle-based pedometers. Similarly, slower walking speed, unsteady and uneven gaits influence accuracy [33][34][35][36]. In the case of accelerometers, Nelson et al [6] found that wrist-worn accelerometers are more accurate than hip-worn sensors.…”

Section: The Tracker and Sensor Typesmentioning

confidence: 99%

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Mahloko

Adebesin

2020

Lecture Notes in Computer Science

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The use of consumer wearable health device (CWHD) for fitness tracing has seen an upward trend worldwide. CWHDs support individuals in taking ownership of their personal well-being and keeping track of their fitness goals. However, there are genuine concerns over the accuracy of the data collected by these devices. In this study, we investigated the factors that influence the accuracy of the data collected by CWHDs for heart rate measurement, physical activity (PA), and sleep monitoring using a systematic literature review. Forty-seven papers were analyzed from five electronic databases based on specific inclusion and exclusion criteria. All 47 papers that we analyzed were published by authors from developed countries. Using thematic analysis, we classified the factors that influence the accuracy of the data collected by CWHDs into three main groups, namely (i) the tracker and sensor type, (ii) the algorithm used in the device, and (iii) the limitation in the design, energy consumption, and processing capability of the device. The research results point to a dearth of studies that focus on the accuracy of the data collected by CWHDs by researchers from developing countries. Keywords: Consumer wearable health device Á CWHD Á Wearable health device Á Wearable health device data accuracy Á Systematic literature review © IFIP International Federation for Information Processing 2020

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Section: The Tracker and Sensor Typesmentioning

confidence: 99%

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Mahloko

Adebesin

2020

Lecture Notes in Computer Science

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“…Activity EE variables were calculated using "Freedson VM3 Combination '11" (Sasaki 2011 [13] + Williams Work-Energy), and converted to TEE by adding resting energy expenditure (using the Scho eld equation [33]) and 10% of TEE to account for dietary induced thermogenesis.…”

Section: Instrumentsmentioning

confidence: 99%

Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: A mixed methods approach

Henriksen

Sand

Deraas³

et al. 2020

Preprint

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Background: Lack of physical activity (PA) is a risk factor for death and non-communicable disease. Despite this, more than one fourth of adults worldwide do not follow PA guidelines. As part of a feasibility study to test a complex intervention for increasing PA, we included a consumer-based activity tracker (AT) as a tool to measure PA outcomes and to track heart rate during exercise sessions. The aim of the present study was to identify factors that increase wear time when using a consumer-based AT for monitoring of participants in clinical research. Methods: Sixteen participants aged 55-74 years, with obesity, sedentary lifestyle, and elevated cardiovascular risk were recruited to a 12-month feasibility study. Participants wore a Polar M430 AT to collect continuous PA data during a six-month intervention followed by six months of follow-up. We performed quantitative wear time analysis, tested the validity of the AT, and completed two rounds of qualitative interviews to investigate how individual wear-time was linked to participant responses. Results: From one year of tracking, mean number of valid wear days were 292 (SD=86), i.e. 80%. The Polar M430 provides acceptable measurements for total energy expenditure. Motivations for increased wear time were that participants were asked to wear it and the ability to track PA progress. Perceived usefulness included time keeping, heart rate- and sleep tracking, becoming more conscious about day-to-day activity, and improved understanding of which activity types were more effective for energy expenditure. Sources of AT annoyance were measurement inaccuracies and limited instruction for use. Suggestions for improvement were that the AT was big, unattractive, and complicated to use.Conclusions: Adherence to wearing a consumer-based AT was high. Results indicate that it is feasible to use a consumer-based AT to measure PA over a longer period. Potential success factors for increased wear time includes adequate instruction for AT use, allowing participants to choose different AT designs, and using trackers with accurate measurements. To identify accurate trackers, AT validation studies in the target cohort may be needed. Trial registration: U.S. National Library of Medicine, Clinical Trial registry: NCT03807323; Registered 16 September 2019 – Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03807323

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“…Extant validation studies typically limit their assessment to one of these two categories, either focusing exclusively on user-experience (e.g., Beaukenhorst et al 2020) or on signal quality. Additionally, those that focus on signal quality tend to emphasize either qualitative measures (e.g., McCarthy et al, 2016) or quantitative measures (e.g., Kasos et al, 2019;Straiton et al, 2018;van Lier et al, 2019;Weippert et al, 2010). While each of these categories of validation are informative and useful in their own right, variability in approaches can be intimidating for newcomers interested in utilizing ambulatory measurement in their research.…”

Section: Framework For Selecting and Benchmarking Mobile Devices In Pmentioning

confidence: 99%

Framework for Selecting and Benchmarking Mobile Devices in Psychophysiological Research

Kleckner¹,

Feldman²,

Goodwin³

et al. 2019

Preprint

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Commercially available consumer electronics (smartwatches and wearable biosensors) are increasingly enabling acquisition of peripheral physiological and physical activity data inside and outside of laboratory settings. However, there is scant literature available for selecting and assessing the suitability of these novel devices for scientific use. To overcome this limitation, the current paper offers a framework to aid researchers in choosing and evaluating wearable technologies for use in empirical research. Our seven-step framework includes: (1) identifying signals of interest; (2) characterizing intended use cases; (3) identifying study-specific pragmatic needs; (4) selecting devices for evaluation; (5) establishing an assessment procedure; (6) performing qualitative and quantitative analyses on resulting data; and, if desired, (7) conducting power analyses to determine sample size needed to more rigorously compare performance across devices. We illustrate the application of the framework by comparing electrodermal, cardiovascular, and accelerometry data from a variety of commercial wireless sensors (Affectiva Q, Empatica E3, Empatica E4, Actiwave Cardio, Shimmer) relative to a well-validated, wired Mindware laboratory system. Our evaluations are performed in two studies (N=10, N=11) involving psychometrically sound, standardized tasks that include physical activity and affect induction. After applying our framework to this data, we conclude that only some commercially available consumer devices for physiological measurement are capable of wirelessly measuring peripheral physiological and physical activity data of sufficient quality for scientific use cases. Thus, the framework appears to be beneficial at suggesting steps for conducting more systematic, transparent, and rigorous evaluations of mobile physiological devices prior to deployment in studies.

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The validity and reliability of consumer-grade activity trackers in older, community-dwelling adults: A systematic review

Cited by 131 publications

References 25 publications

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

A Systematic Literature Review of the Factors that Influence the Accuracy of Consumer Wearable Health Device Data

Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: A mixed methods approach

Framework for Selecting and Benchmarking Mobile Devices in Psychophysiological Research

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