Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Giurgiu, Marco; Timm, Irina; Becker, Marlissa; Schmidt, Steffen; Wunsch, Kathrin; Nissen, Rebecca; Davidovsky, Denis; Bussmann, Johannes B. J.; Nigg, Claudio R.; Reichert, Markus; Ebner‐Priemer, Ulrich W.; Wöll, Alexander; Haaren‐Mack, Birte von

doi:10.2196/36377

Cited by 12 publications

(8 citation statements)

References 259 publications

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“…Our study advances this aspect of open science in the measurement of sedentary time by including our analysis program for researchers to derive detailed sedentary postures. As described in detail within existing reviews on the topic [ 30 , 31 , 32 ], the validity of monitors to detect sedentary positions is important and well documented, but researchers may be interested in sitting time specifically. Our provided analysis program helps address this gap and provides researchers with the capacity to isolate sitting time from lying time, which may be useful for developing activity monitoring devices and/or validating sitting-time questionnaires.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions

O’Brien

Daley

Schwartz

et al. 2023

Sensors

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Objective monitors such as the activPAL characterize time when the thigh is horizontal as sedentary time. However, there are physiological differences between lying, bent-legged sitting, and straight-legged sitting. We introduce a three-monitor configuration to assess detailed sedentary postures and demonstrate its use in characterizing such positions in free-living conditions. We explored time spent in each sedentary posture between prolonged (>1 h) versus non-prolonged (<1 h) sedentary bouts. In total, 35 healthy adults (16♀, 24 ± 3 years; 24 h/day for 6.8 ± 1.0 days) wore an activPAL accelerometer on their thigh, torso, and shin. Hip and knee joint flexion angle estimates were determined during sedentary bouts using the dot-product method between the torso–thigh and thigh–shin, respectively. Compared to lying (69 ± 60 min/day) or straight-legged sitting (113 ± 100 min/day), most time was spent in bent-legged sitting (439 ± 101 min/day, p < 0.001). Most of the bent-legged sitting time was accumulated in non-prolonged bouts (328 ± 83 vs. 112 ± 63 min/day, p < 0.001). In contrast, similar time was spent in straight-legged sitting and lying between prolonged/non-prolonged bouts (both, p > 0.26). We document that a considerable amount of waking time is accumulated in lying or straight-legged sitting. This methodological approach equips researchers with a means of characterizing detailed sedentary postures in uncontrolled conditions and may help answer novel research questions on sedentariness.

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Section: Discussionmentioning

confidence: 99%

Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions

O’Brien

Daley

Schwartz

et al. 2023

Sensors

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“…In line with our previous reviews [ 47 – 49 ] about the quality of validation studies, we identified a large number of different research and commercial-grade wearables that were validated under laboratory conditions. The quality of a validation study is a highly critical criterion to enable both researchers and consumers to make a guided decision about which studies to rely on and which device to use.…”

Section: Discussionmentioning

confidence: 99%

Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions

Giurgiu,

Ketelhut,

Kubica

et al. 2023

Int J Behav Nutr Phys Act

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Background Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing. Methods We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions. Results Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as “low risk”, while 16.5% (N = 90) were classified as “some concerns” and 79.1% (N = 431) as “high risk”. Conclusion Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.

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“…The high BMI (30.57 +/− 6.32 kg*m −2 ) was particularly noticeable. The quantity and quality of sleep can change profoundly across the lifespan [ 49 , 94 ]. Older people commonly have difficulty falling asleep and staying asleep.…”

Section: Discussionmentioning

confidence: 99%

“…Results in the literature show a variable accuracy (moderate to good) of the wrist-worn activity trackers. Most of the research has been carried out on healthy young subjects [ 6 , 48 , 49 ]. A study of middle-aged to older sleep patients, patients with an average BMI in the obesity range, and patients with sleep disorders or sleep apnea and the isolated usage of nocturnal breathing aids, such as Continuous Positive Airway Pressure (CPAP) devices, can provide an extended view of the accuracy under these conditions.…”

Section: Introductionmentioning

confidence: 99%

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

Budig

Stoohs

Keiner

2022

Sensors

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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.

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Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

Cited by 12 publications

References 259 publications

Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions

Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions

Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions

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

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