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Published on in Vol 10, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36377, first published .
Quality Evaluation of Free-living Validation Studies for the Assessment of 24-Hour Physical Behavior in Adults via Wearables: Systematic Review

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

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

Authors of this article:

Marco Giurgiu1, 2 Author Orcid Image ;   Irina Timm1 Author Orcid Image ;   Marlissa Becker3 Author Orcid Image ;   Steffen Schmidt1 Author Orcid Image ;   Kathrin Wunsch1 Author Orcid Image ;   Rebecca Nissen1 Author Orcid Image ;   Denis Davidovski1 Author Orcid Image ;   Johannes B J Bussmann4 Author Orcid Image ;   Claudio R Nigg5 Author Orcid Image ;   Markus Reichert1, 2, 6 Author Orcid Image ;   Ulrich W Ebner-Priemer1, 2 Author Orcid Image ;   Alexander Woll1 Author Orcid Image ;   Birte von Haaren-Mack7 Author Orcid Image
Article Authors Cited by (14) Tweetations (42) Metrics

    Review

    1Department of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

    2Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany

    3Unit Physiotherapy, Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands

    4Department of Rehabilitation Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands

    5Health Science Department, Institute of Sport Science, University of Bern, Bern, Switzerland

    6Department of eHealth and Sports Analytics, Faculty of Sport Science, Ruhr-University Bochum, Bochum, Germany

    7Department of Health and Social Psychology, Institute of Psychology, German Sport University, Cologne, Germany

    Corresponding Author:

    Marco Giurgiu, PhD

    Department of Sports and Sports Science, Karlsruhe Institute of Technology

    Hertzstr. 16

    Karlsruhe, 76187

    Germany

    Phone: 49 017620763557

    Email: marco.giurgiu@kit.edu


    Background: Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.

    Objective: This study aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.

    Methods: Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).

    Results: Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.

    Conclusions: Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.

    JMIR Mhealth Uhealth 2022;10(6):e36377

    doi:10.2196/36377

    Keywords

    wearables; validation; sedentary behavior; physical activity; sleep 


    24-Hour Physical Behavior

    Although physical activity (PA) has been commonly assessed using self-report measures in the past decades, device-based measurement of PA research has been on the rise for several years. This method has also developed since its implementation and moved forward from assessing single parameters such as steps and counts, over the assessment of sedentary behavior (SB) and PA in parallel, to an integrated perspective of different movement and nonmovement patterns—the so-called 24-hour activity cycle (24-HAC) [1,2]. Studies have shown that these different parameters independently contribute to health. Hence, the current World Health Organization guidelines [3] encourage adults and older adults to increase the time spent on PA while simultaneously limiting the amount of sedentary time. Positive implications can be expected for overall physical and mental health throughout the life span, including having a healthy sleep pattern to this recommendation.

    This apparent shift from investigating a single behavior such as PA to a multi-perspective focus on 24-hour physical behavior (ie, including sleep, SB, and PA) has also been theoretically addressed. Rosenberger et al [1] introduced the 24-HAC model as a new paradigm for PA, and Trembley et al [2] provided a conceptual model of movement-based terminology around the 24-hour cycle. This approach was further extended by the Prospective Physical Activity, Sitting, and Sleep consortium, which suggested a subdivision of the 24-hour physical behavior construct into 3 behaviors by applying different dimensions [4], meaning that each behavior covers aspects of biological (ie, sleep or awake), postural (eg, lying, sitting, and upright), and intensity (eg, light, moderate, and vigorous) dimensions. Therefore, the differentiation among PA, SB, and sleep in terms of the 24-HAC model requires valid and simultaneous assessments of all 3 dimensions (ie, biological state, posture or activity type, and intensity; Multimedia Appendix 1, Table S1 [5-226]) under real-life conditions. Rigorous validation studies performed in a free-living environment are necessary to accurately predict the performance of a device and algorithm under real-life conditions.

    Wearable Technology and Validation

    As technical opportunities have evolved rapidly during the past decades, wearables (ie, body-worn devices such as accelerometers, smartwatches, pedometers, or fitness trackers) have become a leading fitness trend, with an estimated US $95 billion industry [227], which is still growing. Moreover, a considerable number of research studies integrated device-based methods to capture physical behavior data, and first discussions have already come up on whether it is prime time for wearables with scientific validation to be a global physical behavior surveillance methodology [228,229]. However, the application of wearables in studies that assess health-related questions presents several methodological and practical challenges. For example, strategies for data processing, monitoring protocols, assessment limitations (eg, muscle-strengthening exercises), and quality criteria such as validity need to be taken into account [230].

    An important test quality criterion is the concept of validity, which represents a fundamental criterion for evaluating the quality of an instrument, referring to the degree to which it truly measures the construct it targets [231]. Regarding the 24-hour physical behavior cycle, researchers are commonly interested in criterion-referenced validity as their assessed outcome parameters are highly objective [232]. Although (or because of) the number of validation studies has increased over the past years, there is high heterogeneity across published protocols and used measurement methods, which severely limits valid device comparisons [233]. Thus, suggestions for standardized validation procedures have received increasing attention in the scientific community [233-235].

    Standardized Protocols and Validation Framework

    There have been several attempts in this direction, as collaborations such as the INTERLIVE network have already started developing standardized protocols to validate consumer wearables for steps [233] and heart rate [236]. Furthermore, Keadle et al [234] introduced a stage process framework of validity to facilitate the development and validation of processing methods for assessing physical behavior using wearables. This framework contains 5 validation phases with increasing levels, starting from device manufacturing and culminating with application in health studies. Validation studies should be implemented following mechanical testing (phase 0) and calibration testing (phase 1). Here, a fixed and semistructured evaluation under laboratory conditions (phase 2) should be applied, followed by an evaluation under real-life conditions (phase 3) [234]. The validation of devices should pass through all these stages before the respective device can be used in health research studies (phase 4). As there is a nonnegligible difference in error rates between laboratory and real-life conditions [233], a wide array of activities of daily living should be captured and compared under real-life conditions. It also needs to be taken into account that participants are instructed to perform specific activities under laboratory conditions, which may result in unnaturally performed activities (eg, the Hawthorne effect) [237]. Hence, the quantification of measurement error is essential to be defined in an unconstrained free-living environment, and wearables’ outcomes should be compared with a reference measure such as video recordings or the doubly labeled water method, depending on the outcome parameter of choice. Overall, the aim should be the realization of standardized validation protocols to be embedded in a framework [233,234], which may have positive implications for all stakeholders such as manufacturers, scientists, and consumers. The results of validation studies are helpful to disabuse consumers and can assist researchers in study design when selecting an appropriate wearable device for the respective question or questions to be answered [238,239].

    Objectives

    Although validated devices are a prerequisite for proper research and validation frameworks have been proposed, to the best of our knowledge, no previous review has systematically evaluated the characteristics and quality of free-living validation studies. This review focuses on the following purposes. First, as our main purpose, we aimed to raise researchers’ and consumers’ attention to the quality of published validation protocols while aiming to identify and compare specific consistencies and inconsistencies between validation protocols. To evaluate the quality of the studies, we followed core principles, recommendations, and expert statements [232-234,240] with published quality criteria (eg, study duration, number of included participants, selection of criterion measures, and data synchronization). Second, we aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.


    This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines (Multimedia Appendix 1, Table S2 [5-226]) [241].

    Search Strategy and Study Selection

    Three separate search strings were combined: terms for validation analyses, 24-hour physical behavior constructs, and wearables. An a priori pilot search was conducted to optimize the final term (Multimedia Appendix 1, Table S3 [5-226]). Publications from 1970 to December 2020 were searched using the following databases: EBSCOhost, IEEE Xplore, PubMed, Scopus, and Web of Science. We reran the search in July 2021 to check for updates and checked the reference lists of included studies for publications that met the inclusion criteria.

    All articles were imported to the Citavi library (Citavi 6.8; Swiss Academic Software GmbH). After removing all duplicates, the study selection process included 3 screening phases for eligibility. In the first phase, 2 reviewers (MG and RN) independently screened the titles of the publications. Articles were excluded only if both reviewers categorized them as not eligible for review purposes. In the second phase, 2 reviewers (MG and RN) independently screened and reviewed the abstracts of the publications. Discrepancies in screening were resolved by consulting with a third reviewer (BvHM). Finally, in the third phase, the full texts of the remaining articles were assessed for eligibility by 7 members of the author’s team (MG, RN, DD, KS, SS, IT, and BvHM). Each article was independently screened by at least two reviewers. Discrepancies in screening were resolved through discussion until a consensus was reached. The reviewers were not blinded to the author or journal information.

    Inclusion and Exclusion Criteria

    On the basis of the population, intervention, comparison, and outcome principle [242], we included peer-reviewed English-language publications that met the criteria described in the following sections.

    Population

    Participants were adults and older adults aged ≥18 years, regardless of health conditions. Studies that specifically targeted adults and populations of older adults (aged ≥18 years) were excluded.

    Intervention

    Any wearable validation study in which at least one part of the study was conducted under free-living (naturalistic or real-life) conditions (eg, at participants’ homes or schools and without instructions on when to start or stop a particular activity) was included. Studies in which the protocol was conducted under laboratory conditions were excluded.

    Control or Comparison

    We included only studies where a criterion measure was described (eg, observation or wearable devices) and excluded all studies where no criterion measure was described (eg, comparison between 2 devices without indicating a criterion measure).

    Outcomes

    Studies were included in which the wearable outcome or outcomes could be classified into at least one dimension of the 24-hour physical behavior construct (ie, biological state, posture or activity type, or intensity [4]; Multimedia Appendix 1, Table S1 [5-226]). We excluded studies in which the outcome did not distinguish among sleep-wake states; intensities; or posture or activity types such as sleep quality, gait analyses, or heart rate parameters.

    Data Extraction and Synthesis

    Data extraction and synthesis were conducted independently by 2 authors (MG, RN, DD, KW, SS, or IT). Discrepancies were discussed until a consensus was reached. The following study details were extracted: author, year, location, population information (sample size, mean age of participants, percentage of females, and ethnicity), measurement period, validated wearable device (wearing position, software, epoch length, and cutoff point or algorithm), dimensions of the 24-hour physical behavior construct (biological state, intensity, and posture or activity type), validated outcome, criterion measure, statistical analyses for validation purposes, conclusion, and funding information. Given the wide range of study protocols in terms of varying conditions (eg, wear location, measurement duration, sample size, statistical analyses, or criterion measure), we conducted a narrative synthesis based on the reported results or conclusions. The data synthesis focused on the purpose (ie, whether the included wearables showed promise for use in further studies). In particular, we classified the studies as moderate to strong validity, mixed results, and poor or weak validity.

    Quality Assessment

    The risk of bias for each article was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool [240]. The tool comprises 4 different domains (ie, patient selection, index measure, criterion measure, and flow and timing). Following the QUADAS-2 guidelines, we selected a set of signaling questions for each domain and added questions modified from the QUADAS-2 background document based on core principles, recommendations, and expert statements for validation studies [232-234,240]. The risk of bias assessment was independently conducted by at least two authors. Discrepancies were discussed until a consensus was reached. The study quality was evaluated at the domain level; that is, if all signaling questions for a domain were answered yes, then the risk of bias was deemed to be low. If any signaling question was answered no, then the risk of bias was deemed to be high. The unclear category was only used when insufficient data were reported for evaluation. On the basis of domain-level ratings, we created a decision tree to evaluate the overall study quality as low risk, some concerns, or high risk (Multimedia Appendix 1, Figure S1 [5-226]).


    Overview

    The search resulted in 13,285 unique records, with 222 (1.67%) publications being included [5-226] (Figure 1). Most studies (208/222, 93.7%) validated an outcome from one dimension, whereas few (14/222, 6.3%) studies validated outcomes from 2 different dimensions (ie, intensity and posture or activity type or intensity and biological state) at the same time during a study protocol. Only 0.5% (1/222) of studies included outcomes from all the 3 dimensions. In particular, of all the 237 identified outcomes, 153 (64.6%) were classified into the intensity dimension, 38 (16%) were classified into the posture or activity type dimension, and 47 (21.2%) were classified into the biological state dimension.

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart illustrating the literature search and screening process.
    View this figure

    Participant and Study Characteristics

    Of the included studies, 84.2% (187/222) were published within the past decade (in or after 2011), indicating the increasing use of wearable technologies for physical behavior measurement (Table 1); 93.2% (207/222) were conducted in wealthier high-income countries in North America, Europe, or Australia and Oceania. The number of participants ranged between 1 and 3752, although most studies (113/222, 50.9%) recruited between 20 and 50 participants. The mean age of the participant samples ranged between young (18.0, SD 0.6 years) and older adults (86.4, SD 6.0 years). In most studies, the mean age of the sample was between 18 and 64 years, and the proportion of female participants ranged from 26% to 74% (144/222, 64.8%). Healthy participants were recruited in 79.7% (177/222) of all studies, whereas 20.7% (46/222) of all studies included participants with different physical and mental health restrictions such as cardiometabolic diseases or chronic heart failure in 2.7% (6/222), chronic obstructive pulmonary disease in 2.7% (6/222), stroke in 2.3% (5/222), insomnia in 2.3% (5/222), or intellectual and visual disabilities in 0.9% (2/222). Information about the participants’ ethnicity was reported in 14.9% (33/222) of all studies. The conceptualization of study protocols regarding measurement duration varied between approximately 30 minutes and up to several weeks. The study duration of ≤1 day was predominantly for studies that focused on posture or activity-type outcomes (ie, in 18/36, 50% of the included studies). Most studies (205/222, 92.3%) conducted statistical analyses at the person or study level (eg, correlations, 2-tailed t tests, and repeated-measures ANOVA). Some studies (21/222, 9.5%) conducted both person or study-level analyses and epoch-by-epoch comparisons (eg, sensitivity and specificity). Approximately 10.4% (23/222) of studies reported that the manufacturer was involved in the study funding or provided devices for validation purposes. No funding information was reported by 16.2% (36/222) of the studies, whereas the remaining studies (164/222, 73.9%) indicated that funding was independent of manufacturer companies. Detailed data extraction is reported as a supplement (Multimedia Appendix 1, Table S4 [5-226]).

    Table 1. Summary of data extraction: participant and study characteristics (N=237).
    CategoryTotal, n (%)Biological state (n=47), n (%)Posture or activity type (n=37), n (%)Intensity (n=153), n (%)
    Publication year

    		Before or in 19997 (3)1 (2.1)3 (8.1)3 (2)

    		2000-201028 (11.8)4 (8.5)3 (8.1)21 (13.7)

    		After or in 2011187 (78.9)42 (89.4)31 (83.8)129 (84.3)
    Study locationa

    		Africa1 (0.4)—b—1 (0.7)

    		Asia17 (7.2)5 (10.6)2 (5.4)10 (6.5)

    		Europe95 (40.1)14 (29.8)21 (57)69 (45.1)

    		North America92 (38.8)21 (44.7)10 (27)65 (42.5)

    		Australia or Oceania16 (6.8)7 (14.9)4 (10.8)7 (4.6)
    Number of participants

    		≤1971 (30)12 (25.5)22 (59.5)40 (26.1)

    		20-50113 (47.7)23 (48.9)13 (35.1)86 (56.2)

    		≥5138 (16)12 (25.5)2 (5.4)27 (17.6)
    Age (years; mean age)c

    		18-64174 (73.4)36 (76.6)28 (75.7)122 (79.7)

    		≥6544 (18.6)10 (21.3)7 (18.9)31 (20.3)
    Sex (female; %)d

    		0-2533 (13.9)6 (12.8)6 (16.2)22 (14.4)

    		26-74144 (60.8)36 (76.6)22 (59.5)99 (64.7)

    		75-10035 (14.8)5 (10.6)8 (21.6)23 (15)
    Measurement duration (days)e

    		≤169 (29.1)17 (36.2)19 (51.4)39 (25.5)

    		2-650 (21.1)15 (31.9)7 (18.9)33 (21.6)

    		≥7101 (42.6)15 (31.9)9 (24.3)80 (52.3)
    Criterion measure

    		Doubly labeled water42 (17.7)——42 (27.5)

    		Heart telemetry————

    		Indirect calorimetry4 (1.7)——4 (2.6)

    		Observation (direct)7 (3)—4 (10.8)3 (2)

    		Observation (images)2 (0.8)—2 (5.4)—

    		Observation (video)14 (5.9)—11 (29.7)5 (3.3)

    		Polysomnography24 (10.1)24 (51.1)——

    		Questionnaire or diary16 (6.8)6 (12.8)4 (10.8)6 (3.9)

    		Wearable113 (47.7)14 (29.8)16 (43.2)94 (61.4)

    		EEGf or Zmachine4 (1.7)3 (6.4)——
    Statistical analyses

    		Epoch-by-epoch33 (13.9)13 (27.7)15 (40.5)8 (5.2)

    		Person or study level208 (87.8)44 (93.6)28 (75.7)150 (98)

    aOne study did not report the study location.

    bNot available.

    cA total of 5 studies were not included in the summary statistics because of the lack of age information. One study was counted twice as it included 2 different age groups.

    dA total of 10 studies was not included in the summary statistics because of the lack of sex information.

    eA total of 2 studies was not included in the summary statistics because of the lack of measurement duration information.

    fEEG: electroencephalogram.

    Wearables

    We identified 163 different wearables from 82 companies, of which 61 (37.4%) were classified as research-grade devices and 102 (62.6%) were classified as commercial-grade devices. The types of wearables varied across uniaxial, biaxial, or triaxial accelerometers and pedometers. On the basis of our narrative data synthesis, we ranked 26.4% (122/463 of results or conclusions as moderate to strong validity, 48.8% (226/463) as mixed validity, and 25% (115/463) as poor or weak validity (Multimedia Appendix, Table S5 [5-226]). In relation to other types of wearables, triaxial accelerometers were used in 70.5% (167/237) of all included studies. Detailed technical information for each wearable device is available as a supplement (Multimedia Appendix, Table S6 [5-226]). Of the 163 different wearables, 96 (58.9%) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12%), and ActivPAL (12/163, 7.4%) were used most often in the included validation studies. Of all the 222 reviewed articles, 78 (35.1%) studies included different types of wearables, and 26 (11.7%) studies included different wearing positions to enable inter- and intradevice comparisons (Table 2). The variation of different sensor models within a study protocol ranged from 1 to 12 different wearables. In particular, 64.9% (144/222) of all studies included one model of wearable, 20.3% (45/222) included 2 different models of wearables, and 14.9% (33/222) included ≥3 different models of wearables. We identified 11 different validated outcomes. Of all reported outcomes, 51.3% (164/320) represented continuous parameters such as steps, energy expenditure, or counts, whereas 48.8% (156/320) represented categorical outcomes such as sleep time, time spent in light PA, or time spent in moderate to vigorous PA. Approximately 18% (40/222) of studies validated 2 different outcomes such as steps and energy expenditure during a study protocol. More than half of the studies (125/237, 52.7%) validated one type of wearable device in a single wearing position. We identified 13 different wearing positions. The wrist and hip or waist positions were used most often for validation purposes. In 50% (111/222) of all studies, the authors provided information about the software application used for data preprocessing.

    Table 2. Summary of data extraction: wearables (N=237).
    CategoryTotal, n (%)Biological state (n=47), n (%)Posture or activity type (n=37), n (%)Intensity (n=153), n (%)
    Type

    		Uniaxial accelerometer44 (10.8)13 (14.4)3 (6.4)29 (9.2)

    		Biaxial accelerometer28 (6.9)1 (1.1)2 (4.3)26 (8.3)

    		Triaxial accelerometer286 (70.1)72 (80)40 (85.1)212 (67.5)

    		Pedometer30 (7.4)2 (2.2)—a30 (9.6)

    		Unclear20 (4.9)2 (2.2)2 (4.3)17 (5.4)
    Outcome

    		Sleep time42 (13.1)42 (89.4)——

    		Sleep-wake metrics5 (1.6)5 (10.6)——

    		Different postures or types29 (9.1)—30 (73.2)—

    		Sit-to-stand transitions5 (1.6)—5 (12.2)—

    		Time in sedentary behavior32 (10)—6 (14.6)26 (11.2)

    		Time in light physical activity14 (4.4)——14 (6)

    		Time in moderate to vigorous physical activity33 (10.3)——33 (14.2)

    		Time in physical activity6 (1.9)——6 (2.6)

    		Energy expenditure72 (22.5)——72 (30.9)

    		Steps75 (23.4)——75 (32.2)

    		Counts7 (2.2)——7 (3)
    Wear positionb

    		Ankle11 (2.4)1 (1)1 (1.6)9 (2.6)

    		Backpack, pocket, and bra20 (4.3)—2 (3.1)19 (5.6)

    		Chest15 (3.3)1 (1)3 (4.7)11 (3.2)

    		Foot3 (0.7)1 (1)—2 (0.6)

    		Hip and waist148 (32.2)7 (7.1)23 (35.9)124 (36.4)

    		Leg3 (0.7)—3 (4.7)—

    		Lower back12 (2.6)1 (1)3 (4.7)10 (2.9)

    		Neck3 (0.7)——3 (0.9)

    		Thigh28 (6.1)2 (2)15 (23.4)13 (3.8)

    		Torso1 (0.2)——1 (0.3)

    		Trunk3 (0.7)—3 (4.7)—

    		Upper arm12 (2.6)1 (1)1 (1.6)10 (2.9)

    		Wrist201 (43.7)83 (84.7)10 (15.6)139 (40.8)

    aNot available.

    bOne study did not report any information about the sensor wearing position and one study did not specify the information about the sensor wearing position. If studies included multiple devices or different wearing positions, we counted each device and wearing position separately.

    Study Quality

    In total, we included 9 signaling questions as quality criteria to evaluate the risk of bias. The percentage of studies that met the criteria ranged from 38.7% (92/238) to 92% (219/238; Table 3). On average, 5.2 (SD 1.41) of 9 questions were answered with yes (ie, meeting the criteria). Studies validating a biological state, intensity, or posture or activity type outcome met on average 4.6 (SD 1.23), 5.5 (SD 1.36), and 4.9 (SD 1.51) out of 9 questions with yes (ie, no risk of bias), respectively. We evaluated whether the reference standard was the appropriate gold standard as a central criterion for evaluating overall study quality. In 38.4% (91/237) of all studies, the reference standard was equivalent to the suggested criterion measures [234]. Wearables were the most frequently selected reference criterion in 50.5% (112/222) of the studies. On the basis of our classification tree to evaluate the overall study quality (Multimedia Appendix 1, Figure S1 and Table S7 [5-226]), 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk. To provide an overview of the study quality, Figure 2 illustrates the overall study quality on a study level, separated by each dimension of the 24-hour physical behavior construct.

    Table 3. Criteria for the risk of bias assessment and the percentage of studies meeting these criteria (N=237).
    Criteria itemsStudies meeting criterion, n (%)

    		TotalBiological state (n=47)Posture or activity type (n=37)Intensity (n=153)
    Domain 1: patient selection or study design

    		Was the study conducted in different free-living settings (eg, work or home)?174 (91.6)aN/Ab28 (75.7)146 (95.4)

    		Did the study take place for at least 2 days?156 (65.8)28 (59.6)16 (43.2)112 (73.2)

    		Did the study provide any information about the inclusion and exclusion criteria of the recruiting process?165 (69.6)34 (72.3)25 (67.6)106 (69.3)

    		Did the study include a sample of at least 20 participants?163 (68.8)36 (76.6)15 (40.5)112 (73.2)
    Domain 2: index measure

    		Was the algorithm of the validated outcome reported (ie, formula) or at least further information cited?97 (40.9)23 (48.9)18 (48.6)56 (36.6)

    		Did the participants wear the wearable for at least 8 hours per day?107 (56.3)aN/A15 (40.5)92 (60.1)
    Domain 3: criterion measure

    		Is the selected reference the gold standard?91 (38.4)23 (48.9)14 (37.8)54 (35.3)
    Domain 4: flow and timing

    		Did the authors provide any information about data synchronization?75 (41.4)c24 (51.1)22 (59.5)29 (19)

    		Were all participants included in the analyses or were any exclusion reasons provided?218 (92.4)d45 (95.7)31 (83.8)142 (92.8)

    aOnly relevant for 190 studies.

    bN/A: not applicable.

    cOnly relevant for 181 studies.

    dOnly relevant for 236 studies.

    ‎
    Figure 2. The overall risk of bias classification separated by different dimensions of the 24-hour physical behavior construct. The number within the circles represents the study number, as listed in the full data extraction. Studies with a green circle were evaluated as low risk, the orange circle represents the quality with some concerns, and red circles represent high risk. LPA: light physical activity; MVPA: moderate to vigorous physical activity.
    View this figure

    Principal Findings

    We evaluated the characteristics and quality of free-living validation studies in which at least one dimension of the 24-hour physical behavior construct (ie, biological state, posture or activity type, and intensity [1,4]) was assessed using wearables and validated against a criterion measure. In summary, the validation of biological state and posture or activity-type outcomes was rare, and almost all of the 163 different types of research- and commercial-grade wearables were validated for only one aspect of the 24-hour physical behavior construct (ie, intensity outcomes). Compared with the selected quality criteria for studies under free-living conditions that are in line with published core principles, recommendations, and expert statements [234-236], most of the reviewed protocols failed to meet the criteria; however, only a few of the evaluated studies were overall ranked with low risk of bias or with some concerns. Therefore, more high-quality validation studies with adults and older adults under real-life conditions are needed. According to the framework of wearable validation studies [234], the aim of phase 3 studies is to validate device outcomes under real-life conditions against appropriate reference measures.

    Criterion Measure

    Our evaluation of the most central category physical behavior criterion measure followed Keadle et al [234]; for example, physiological outcomes (eg, activity energy expenditure) are recommended to be validated against indirect calorimetry or doubly labeled water, behavioral criterion measures (eg, step count and postures) are recommended to be validated against video-recorded direct observation [234], and the recommended criterion measure for differentiation between sleep and wake patterns is polysomnography [243,244]. Notably, only 40.9% (91/222) of the reviewed studies used the recommended gold standard. Primarily, research-grade devices served as criterion measures, which is highly critical as there is no evidence that wearables can serve as a basis for validating other wearables and offer a high risk of bias regarding criterion validity [233,245,246].

    Study Duration

    Optimally, study protocols take place over a 24-hour period over multiple days, thus covering a wide range of representative habitual activities [233,234]. This recommended criterion was covered by 2 signaling questions. First, we evaluated whether data collection was not restricted to one particular setting (eg, at work or at home), which was met by nearly all the reviewed studies. Second, as it is almost not feasible to collect data over several days for criterion measures such as video recording [233,234], we specified at least 2 days for a low-risk classification. Two-thirds of the reviewed studies met this criterion. However, we identified a considerable number of studies (69/222, 31.1%) that collected data over a short period (≤1 day). The risk of bias might have been present as the setting was restricted to a specific environment (eg, at work), thus limiting the ability to capture a wide range of habitual behaviors. Moreover, reactivity is a serious issue that reveals a potential error source when collecting data from wearables. Researchers expected that reactivity would be a time issue, implying that participants may change their behavior at the beginning of the monitoring period but return to a more stable pattern later [247,248]. Similar effects have been observed in sleep laboratories using polysomnographic monitoring [249].

    Study Population

    Ideally, the validity of wearables can be generalized to a wide range of diverse samples (eg, age, sex, ethnicity, and health condition) [234,250]. While focusing on adults and older adults (aged ≥18 years), this review revealed that 78.4% (174/222) of the studies included samples between 18 and 64 years of age, whereas there was a lack of studies that included older adults. Critically, most devices (99/163, 60.7%) were validated only once. According to the recommended principle, validation study protocols should include either a variety of cohorts within a single study or a series of studies with different participant characteristics [233,234,251]. For example, we could only identify 20.3% (45/222) of studies that included samples with restricted health conditions. As a practical implication, a given wearable device might be valid for healthy adults and older adults but not for those with health restrictions [232]. A solution might be to recruit a larger sample size, which would enable a higher intersubject variability, or to conduct a series of validation studies with varying participant characteristics. Optimally, sample size calculations ensure adequate power for validation purposes [233,252]. Finally, although challenging because of data protection guidelines, we recommend reporting information about ethnicity (reported in 32/222, 14.4% of studies) whenever possible and providing detailed information about inclusion and exclusion criteria regarding the recruiting process and for statistical analyses.

    Wearing Position and Types of Wearables

    To enable a comparison between different wearables or wearing positions, researchers may simultaneously collect data from multiple sensors or different wearing positions [234,251]. Most of the reviewed studies did not include multiple wearables (eg, research and commercial grade) and did not capture data from the validated devices at different wearing positions (eg, hip or waist, wrist, and thigh). Depending on the primary outcome of interest, the recommendations of where to place the wearable device may vary. For example, to assess sleep-wake patterns, wrist-worn devices may optimize the recording of small movements that occur at the distal extremities when an individual is supine [245,253]. For example, Fairclough et al [254] reported that wrist placement promotes superior compliance than that of the hip position. In contrast, if researchers are interested in differentiating between body postures (eg, sitting vs standing), the thigh might be the position of choice because of the option of wearing the device under clothing to accurately assess intensity and posture or activity types [4]. However, only 1.8% (4/222) of studies validated posture or activity-type outcomes using thigh-worn devices. Future validation studies are needed with multiple devices and different wearing positions to increase comparability and to inform end users of which device to use and where to place it [250]. In addition, future signal analytical research purposes might be valuable in terms of extracting different outcomes from a single wearing position. Moreover, different types of wearables (ie, pedometers and uniaxial, biaxial, and triaxial accelerometers) have been validated. Researchers should be aware that the different types of devices have different technical requirements. For example, uniaxial accelerometers measure acceleration in 1 direction, whereas triaxial accelerometers measure acceleration in 3 directions. Thus, triaxial accelerometers provide more information, which might be helpful in developing further algorithms.

    Synchronization, Transparency, and Statistical Analyses

    We evaluated whether the studies reported data synchronization, wear time, the algorithm of the validated outcome, and data analyses. As less than half (75/222, 33.8%) of all included studies reported information about the synchronization process between index and criterion measures, potentially introducing errors and biasing results, we suggest future research endeavors to apply time-stamped solutions such as asking participants to perform 3 vertical jumps at the beginning and the end of the measurement [233]. Following practical consideration when applying wearables [255,256], a large number of studies defined a valid day if ≥10 hours of wear time during waking hours were captured. We set the quality criterion to ≥8 hours per day, revealing that 56% (107/190) of studies considered the wear time criteria for a valid day. Capturing shorter periods may increase the risk of bias as less time is available to assess the data in different settings (eg, at home or at work).

    A critical aspect from the perspective of transparency is the presentation of algorithms. Only 43.7% (97/222) of studies reported the algorithm or at least cited further information on the validated outcomes. In particular, no information about the used algorithms was provided in studies in which a commercial-grade device was validated. At this point, researchers often do not have access to the raw data of commercial-grade wearables or the black box algorithms. Moreover, companies can update wearables’ firmware or algorithms at any time, which hinders comparability [257]. In addition, the pace at which technology is evolving in optimizing algorithms far exceeds the pace of published validation research [238]. Open-source methods that are more flexible in using algorithms for different devices are needed [233,234].

    A quality criterion for the used statistical analyses was not set because of the lack of consistent statistical guidelines for reporting the validity of an activity monitor. Most of the reviewed studies used traditional statistical tests of differences such as t tests or ANOVAs. Optimally and in line with recently published suggestions, researchers should integrate different analytical approaches such as combining traditional analyses with equivalence testing and including epoch-by-epoch comparisons whenever possible [234,258].

    Limitations

    Some points merit further discussion. First, the evaluation of study quality was based on self-selected criteria. In particular, we selected the QUADAS-2 [240] tool and added further signaling questions in line with core principles, recommendations, and expert statements [232-234]. However, as we are not aware of any further quality tools and signaling questions for wearable validation purposes, our selected criteria can serve as a starting point for future systematic reviews that focus on the study quality of wearable technology under free-living conditions. Second, our included validation studies were published between 1987 and 2021. Given the rapid development of wearable technologies and the increasing availability of different research and commercial-grade devices, quality standards have been developed. Thus, when interpreting the study protocols, the time during which the study was conducted should be considered. Third, our review focused on the quality of study protocols. However, we did not take into account further important considerations when using wearables, such as wear or nonwear time algorithms, costs of the monitor, or time of data processing [35,250]. Fourth, our findings were limited to our search strategy; thus, we may have missed further validation studies. However, we applied backward and forward citation searches through the reference lists of the included studies to identify articles that may not have appeared in our search. Finally, this systematic review was limited to articles published in the English language.

    Conclusions

    Currently, there is a wealth of research on commercial-grade wearables; however, the quality of published validation protocols in adults had not been assessed thus far. However, this is a critical step to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. To this end, our review unraveled that most validation studies did not meet the recommended quality principles [233,250]. Primarily, there is a lack of validation studies with gold standard reference measures such as video recording, polysomnography, or the doubly labeled water method. Moreover, most devices were validated only once and focused predominantly on intensity measure outcomes. Given the rising interest in the 24-hour physical behavior construct in health research, the next generation of validation studies should consider the validity of >1 aspect of the 24-hour physical behavior construct during a study protocol or to conduct a series of studies. Thus, we conclude that standardized protocols for free-living validation embedded in a framework [234] are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in (1) selecting wearables for self-tracking purposes, (2) applying wearables in health studies, and (3) fostering innovation to achieve improved validity.

    Authors' Contributions

    MG, UWEP, MR, and BvHM contributed to the conception and design of the study. MG, RN, DD, and MB contributed to the development of the search strategy. MG, SS, KW, RN, MB, DD, IT, and BvHM conducted the systematic review. MG, KW, SS, IT, DD, MB, and RN performed the data extraction. All authors assisted with the interpretation. MG, UEWP, HB, CRN, BvHM, MR, and KW were the principal authors of the manuscript. All authors contributed to the drafting and revision of the final manuscript. All authors approved the final version of the manuscript.

    Conflicts of Interest

    UWEP works as a consult for Boehringer-Ingelheim. The authors have no other conflicts to declare.

    ‎
    Multimedia Appendix 1

    Characteristics of 24-hour physical behavior, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist, search terms used in databases, classification tree for the judgment of overall study quality, data extraction, the validity of wearables, an overview of wearables used in validation studies, and risk of bias for the included studies.

    PDF File (Adobe PDF File), 1635 KB
    1. Rosenberger ME, Fulton JE, Buman MP, Troiano RP, Grandner MA, Buchner DM, et al. The 24-hour activity cycle: a new paradigm for physical activity. Med Sci Sports Exerc 2019 Mar;51(3):454-464 [FREE Full text] [CrossRef] [Medline]
    2. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, SBRN Terminology Consensus Project Participants. Sedentary Behavior Research Network (SBRN) - Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act 2017 Jun 10;14(1):75 [FREE Full text] [CrossRef] [Medline]
    3. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med 2020 Dec;54(24):1451-1462 [FREE Full text] [CrossRef] [Medline]
    4. Stevens ML, Gupta N, Inan Eroglu E, Crowley PJ, Eroglu B, Bauman A, et al. Thigh-worn accelerometry for measuring movement and posture across the 24-hour cycle: a scoping review and expert statement. BMJ Open Sport Exerc Med 2020 Dec 24;6(1):e000874 [FREE Full text] [CrossRef] [Medline]
    5. Agogo GO, van der Voet H, Hulshof PJ, Trijsburg L, van Eeuwijk FA, Boshuizen HC. Validation of accelerometer for measuring physical activity in free-living individuals. Balt J Health Phys Act 2018 Mar 31;10(1):7-21. [CrossRef]
    6. Albright C, Jerome GJ. The accuracy of talking pedometers when used during free-living: a comparison of four devices. J Vis Impair Blind 2011 May;105(5):299-304. [CrossRef]
    7. Alharbi M, Bauman A, Neubeck L, Gallagher R. Validation of Fitbit-Flex as a measure of free-living physical activity in a community-based phase III cardiac rehabilitation population. Eur J Prev Cardiol 2016 Sep;23(14):1476-1485. [CrossRef] [Medline]
    8. Ameen MS, Cheung LM, Hauser T, Hahn MA, Schabus M. About the accuracy and problems of consumer devices in the assessment of sleep. Sensors (Basel) 2019 Sep 25;19(19):4160 [FREE Full text] [CrossRef] [Medline]
    9. An HS, Jones GC, Kang SK, Welk GJ, Lee JM. How valid are wearable physical activity trackers for measuring steps? Eur J Sport Sci 2017 Apr;17(3):360-368. [CrossRef] [Medline]
    10. Ancoli-Israel S, Clopton P, Klauber MR, Fell R, Mason W. Use of wrist activity for monitoring sleep/wake in demented nursing-home patients. Sleep 1997 Jan;20(1):24-27 [FREE Full text] [CrossRef] [Medline]
    11. Annegarn J, Spruit MA, Uszko-Lencer NH, Vanbelle S, Savelberg HH, Schols AM, et al. Objective physical activity assessment in patients with chronic organ failure: a validation study of a new single-unit activity monitor. Arch Phys Med Rehabil 2011 Nov;92(11):1852-7.e1. [CrossRef] [Medline]
    12. Assah FK, Ekelund U, Brage S, Wright A, Mbanya JC, Wareham NJ. Accuracy and validity of a combined heart rate and motion sensor for the measurement of free-living physical activity energy expenditure in adults in Cameroon. Int J Epidemiol 2011 Feb;40(1):112-120. [CrossRef] [Medline]
    13. Au-Yeung WT, Kaye JA, Beattie Z. Step count standardization: validation of step counts from the Withings Activite using PiezoRxD and wGT3X-BT. Annu Int Conf IEEE Eng Med Biol Soc 2020 Jul;2020:4608-4611 [FREE Full text] [CrossRef] [Medline]
    14. Baandrup L, Jennum PJ. A validation of wrist actigraphy against polysomnography in patients with schizophrenia or bipolar disorder. Neuropsychiatr Dis Treat 2015 Aug 28;11:2271-2277 [FREE Full text] [CrossRef] [Medline]
    15. Bai Y, Tompkins C, Gell N, Dione D, Zhang T, Byun W. Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting. PLoS One 2021 May 26;16(5):e0251975 [FREE Full text] [CrossRef] [Medline]
    16. Barkley JE, Glickman E, Fennell C, Kobak M, Frank M, Farnell G. The validity of the commercially-available, low-cost, wrist-worn Movband accelerometer during treadmill exercise and free-living physical activity. J Sports Sci 2019 Apr;37(7):735-740. [CrossRef] [Medline]
    17. Barone Gibbs B, Paley JL, Jones MA, Whitaker KM, Connolly CP, Catov JM. Validity of self-reported and objectively measured sedentary behavior in pregnancy. BMC Pregnancy Childbirth 2020 Feb 11;20(1):99 [FREE Full text] [CrossRef] [Medline]
    18. Barreira TV, Zderic TW, Schuna Jr JM, Hamilton MT, Tudor-Locke C. Free-living activity counts-derived breaks in sedentary time: are they real transitions from sitting to standing? Gait Posture 2015 Jun;42(1):70-72. [CrossRef] [Medline]
    19. Bartholdy C, Gudbergsen H, Bliddal H, Kjærgaard M, Lykkegaard KL, Henriksen M. Reliability and construct validity of the SENS Motion® activity measurement system as a tool to detect sedentary behaviour in patients with knee osteoarthritis. Arthritis 2018 Mar 1;2018:6596278 [FREE Full text] [CrossRef] [Medline]
    20. Beattie Z, Oyang Y, Statan A, Ghoreyshi A, Pantelopoulos A, Russell A, et al. Estimation of sleep stages in a healthy adult population from optical plethysmography and accelerometer signals. Physiol Meas 2017 Oct 31;38(11):1968-1979. [CrossRef] [Medline]
    21. Berendsen BA, Hendriks MR, Meijer K, Plasqui G, Schaper NC, Savelberg HH. Which activity monitor to use? Validity, reproducibility and user friendliness of three activity monitors. BMC Public Health 2014 Jul 24;14:749 [FREE Full text] [CrossRef] [Medline]
    22. Berninger NM, Ten Hoor GA, Plasqui G. Validation of the VitaBit sit-stand tracker: detecting sitting, standing, and activity patterns. Sensors (Basel) 2018 Mar 15;18(3):877 [FREE Full text] [CrossRef] [Medline]
    23. Berryhill S, Morton CJ, Dean A, Berryhill A, Provencio-Dean N, Patel SI, et al. Effect of wearables on sleep in healthy individuals: a randomized crossover trial and validation study. J Clin Sleep Med 2020 May 15;16(5):775-783 [FREE Full text] [CrossRef] [Medline]
    24. Blackwell T, Ancoli-Israel S, Redline S, Stone KL, Osteoporotic Fractures in Men (MrOS) Study Group. Factors that may influence the classification of sleep-wake by wrist actigraphy: the MrOS Sleep Study. J Clin Sleep Med 2011 Aug 15;7(4):357-367 [FREE Full text] [CrossRef] [Medline]
    25. Blackwell T, Redline S, Ancoli-Israel S, Schneider JL, Surovec S, Johnson NL, Study of Osteoporotic Fractures Research Group. Comparison of sleep parameters from actigraphy and polysomnography in older women: the SOF study. Sleep 2008 Feb;31(2):283-291 [FREE Full text] [CrossRef] [Medline]
    26. Block VJ, Zhao C, Hollenbach JA, Olgin JE, Marcus GM, Pletcher MJ, et al. Validation of a consumer-grade activity monitor for continuous daily activity monitoring in individuals with multiple sclerosis. Mult Scler J Exp Transl Clin 2019 Nov 21;5(4):2055217319888660 [FREE Full text] [CrossRef] [Medline]
    27. Blondeel A, Demeyer H, Janssens W, Troosters T. Accuracy of consumer-based activity trackers as measuring tool and coaching device in patients with COPD and healthy controls. PLoS One 2020 Aug 4;15(8):e0236676 [FREE Full text] [CrossRef] [Medline]
    28. Boeselt T, Spielmanns M, Nell C, Storre JH, Windisch W, Magerhans L, et al. Validity and usability of physical activity monitoring in patients with Chronic Obstructive Pulmonary Disease (COPD). PLoS One 2016 Jun 15;11(6):e0157229 [FREE Full text] [CrossRef] [Medline]
    29. Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Estimation of free-living energy expenditure using a novel activity monitor designed to minimize obtrusiveness. Obesity (Silver Spring) 2010 Sep;18(9):1845-1851 [FREE Full text] [CrossRef] [Medline]
    30. Bonomi AG, Plasqui G, Goris AH, Westerterp KR. Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer. J Appl Physiol (1985) 2009 Sep;107(3):655-661 [FREE Full text] [CrossRef] [Medline]
    31. Bourke AK, Ihlen EA, Helbostad JL. Validation of the activPAL3 in free-living and laboratory scenarios for the measurement of physical activity, stepping, and transitions in older adults. J Meas Phys Behav 2019 Jun;2(2):58-65. [CrossRef]
    32. Bourke AK, Ihlen EA, Van de Ven P, Nelson J, Helbostad JL. Video analysis validation of a real-time physical activity detection algorithm based on a single waist mounted tri-axial accelerometer sensor. Annu Int Conf IEEE Eng Med Biol Soc 2016 Aug;2016:4881-4884. [CrossRef] [Medline]
    33. Brage S, Westgate K, Franks PW, Stegle O, Wright A, Ekelund U, et al. Estimation of free-living energy expenditure by heart rate and movement sensing: a doubly-labelled water study. PLoS One 2015 Sep 8;10(9):e0137206 [FREE Full text] [CrossRef] [Medline]
    34. Brazeau AS, Beaudoin N, Bélisle V, Messier V, Karelis AD, Rabasa-Lhoret R. Validation and reliability of two activity monitors for energy expenditure assessment. J Sci Med Sport 2016 Jan;19(1):46-50. [CrossRef] [Medline]
    35. Brewer W, Swanson BT, Ortiz A. Validity of Fitbit's active minutes as compared with a research-grade accelerometer and self-reported measures. BMJ Open Sport Exerc Med 2017;3(1):e000254 [FREE Full text] [CrossRef] [Medline]
    36. Breteler MJ, Janssen JH, Spiering W, Kalkman CJ, van Solinge WW, Dohmen DA. Measuring free-living physical activity with three commercially available activity monitors for telemonitoring purposes: Validation study. JMIR Form Res 2019 Apr 24;3(2):e11489 [FREE Full text] [CrossRef] [Medline]
    37. Brooke SM, An HS, Kang SK, Noble JM, Berg KE, Lee JM. Concurrent validity of wearable activity trackers under free-living conditions. J Strength Cond Res 2017 Apr;31(4):1097-1106. [CrossRef] [Medline]
    38. Brugniaux JV, Niva A, Pulkkinen I, Laukkanen RM, Richalet JP, Pichon AP. Polar Activity Watch 200: a new device to accurately assess energy expenditure. Br J Sports Med 2010 Mar;44(4):245-249. [CrossRef] [Medline]
    39. Busse ME, van Deursen RW, Wiles CM. Real-life step and activity measurement: reliability and validity. J Med Eng Technol 2009;33(1):33-41. [CrossRef] [Medline]
    40. Bussmann JB, van de Laar YM, Neeleman MP, Stam HJ. Ambulatory accelerometry to quantify motor behaviour in patients after failed back surgery: a validation study. Pain 1998 Feb;74(2-3):153-161. [CrossRef] [Medline]
    41. Cabanas-Sánchez V, Higueras-Fresnillo S, De la Cámara MÁ, Veiga OL, Martinez-Gomez D. Automated algorithms for detecting sleep period time using a multi-sensor pattern-recognition activity monitor from 24 h free-living data in older adults. Physiol Meas 2018 May 16;39(5):055002. [CrossRef] [Medline]
    42. Calabro MA, Kim Y, Franke WD, Stewart JM, Welk GJ. Objective and subjective measurement of energy expenditure in older adults: a doubly labeled water study. Eur J Clin Nutr 2015 Jul;69(7):850-855 [FREE Full text] [CrossRef] [Medline]
    43. Carpenter C, Yang CH, West D. A comparison of sedentary behavior as measured by the Fitbit and ActivPAL in college students. Int J Environ Res Public Health 2021 Apr 08;18(8):3914 [FREE Full text] [CrossRef] [Medline]
    44. Campos C, DePaul VG, Knorr S, Wong JS, Mansfield A, Patterson KK. Validity of the ActiGraph activity monitor for individuals who walk slowly post-stroke. Top Stroke Rehabil 2018 May;25(4):295-304. [CrossRef] [Medline]
    45. Carter J, Wilkinson D, Blacker S, Rayson M, Bilzon J, Izard R, et al. An investigation of a novel three-dimensional activity monitor to predict free-living energy expenditure. J Sports Sci 2008 Apr;26(6):553-561. [CrossRef] [Medline]
    46. Castner J, Mammen MJ, Jungquist CR, Licata O, Pender JJ, Wilding GE, et al. Validation of fitness tracker for sleep measures in women with asthma. J Asthma 2019 Jul;56(7):719-730 [FREE Full text] [CrossRef] [Medline]
    47. Cellini N, McDevitt EA, Mednick SC, Buman MP. Free-living cross-comparison of two wearable monitors for sleep and physical activity in healthy young adults. Physiol Behav 2016 Apr 01;157:79-86 [FREE Full text] [CrossRef] [Medline]
    48. Chakravarthy A, Resnick B. Reliability and validity testing of the MotionWatch 8 in older adults. J Nurs Meas 2017 Dec 01;25(3):549-558. [CrossRef] [Medline]
    49. Choquette S, Chuin A, Lalancette DA, Brochu M, Dionne IJ. Predicting energy expenditure in elders with the metabolic cost of activities. Med Sci Sports Exerc 2009 Oct;41(10):1915-1920. [CrossRef] [Medline]
    50. Chowdhury EA, Western MJ, Nightingale TE, Peacock OJ, Thompson D. Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors. PLoS One 2017 Feb 24;12(2):e0171720 [FREE Full text] [CrossRef] [Medline]
    51. Chu AH, Ng SH, Paknezhad M, Gauterin A, Koh D, Brown MS, et al. Comparison of wrist-worn Fitbit Flex and waist-worn ActiGraph for measuring steps in free-living adults. PLoS One 2017 Feb 24;12(2):e0172535 [FREE Full text] [CrossRef] [Medline]
    52. Clemes SA, O'Connell S, Rogan LM, Griffiths PL. Evaluation of a commercially available pedometer used to promote physical activity as part of a national programme. Br J Sports Med 2010 Dec;44(16):1178-1183. [CrossRef] [Medline]
    53. Colbert LH, Matthews CE, Havighurst TC, Kim K, Schoeller DA. Comparative validity of physical activity measures in older adults. Med Sci Sports Exerc 2011 May;43(5):867-876 [FREE Full text] [CrossRef] [Medline]
    54. De Cocker KA, De Meyer J, De Bourdeaudhuij IM, Cardon GM. Non-traditional wearing positions of pedometers: validity and reliability of the Omron HJ-203-ED pedometer under controlled and free-living conditions. J Sci Med Sport 2012 Sep;15(5):418-424. [CrossRef] [Medline]
    55. Collins JE, Yang HY, Trentadue TP, Gong Y, Losina E. Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions. PLoS One 2019 Jan 30;14(1):e0211231 [FREE Full text] [CrossRef] [Medline]
    56. Connolly CP, Dahmen J, Catena RD, Campbell N, Montoye AH. Physical activity monitor accuracy for overground walking and free-living conditions among pregnant women. J Meas Phys Behav 2020 Feb 2;3(2):100-109. [CrossRef]
    57. Correa JB, Apolzan JW, Shepard DN, Heil DP, Rood JC, Martin CK. Evaluation of the ability of three physical activity monitors to predict weight change and estimate energy expenditure. Appl Physiol Nutr Metab 2016 Jul;41(7):758-766 [FREE Full text] [CrossRef] [Medline]
    58. Cuberek R, El Ansari W, Frömel K, Skalik K, Sigmund E. A comparison of two motion sensors for the assessment of free-living physical activity of adolescents. Int J Environ Res Public Health 2010 Apr;7(4):1558-1576 [FREE Full text] [CrossRef] [Medline]
    59. Culhane KM, Lyons GM, Hilton D, Grace PA, Lyons D. Long-term mobility monitoring of older adults using accelerometers in a clinical environment. Clin Rehabil 2004 May;18(3):335-343. [CrossRef] [Medline]
    60. Davidson L, McNeill G, Haggarty P, Smith JS, Franklin MF. Free-living energy expenditure of adult men assessed by continuous heart-rate monitoring and doubly-labelled water. Br J Nutr 1997 Nov;78(5):695-708. [CrossRef] [Medline]
    61. Degroote L, De Bourdeaudhuij I, Verloigne M, Poppe L, Crombez G. The accuracy of smart devices for measuring physical activity in daily life: validation study. JMIR Mhealth Uhealth 2018 Dec 13;6(12):e10972 [FREE Full text] [CrossRef] [Medline]
    62. Degroote L, Hamerlinck G, Poels K, Maher C, Crombez G, De Bourdeaudhuij I, et al. Low-Cost consumer-based trackers to measure physical activity and sleep duration among adults in free-living conditions: validation study. JMIR Mhealth Uhealth 2020 May 19;8(5):e16674 [FREE Full text] [CrossRef] [Medline]
    63. DeShaw KJ, Ellingson L, Bai Y, Lansing J, Perez M, Welk G. Methods for activity monitor validation studies: an example with the Fitbit charge. J Meas Phys Behav 2018;1(3):130-135. [CrossRef]
    64. Dickinson DL, Cazier J, Cech T. A practical validation study of a commercial accelerometer using good and poor sleepers. Health Psychol Open 2016 Nov 29;3(2):2055102916679012 [FREE Full text] [CrossRef] [Medline]
    65. Dominick GM, Winfree KN, Pohlig RT, Papas MA. Physical activity assessment between consumer- and research-grade accelerometers: a comparative study in free-living conditions. JMIR Mhealth Uhealth 2016 Sep 19;4(3):e110 [FREE Full text] [CrossRef] [Medline]
    66. Dieu O, Mikulovic J, Fardy PS, Bui-Xuan G, Béghin L, Vanhelst J. Physical activity using wrist-worn accelerometers: comparison of dominant and non-dominant wrist. Clin Physiol Funct Imaging 2017 Sep;37(5):525-529. [CrossRef] [Medline]
    67. Donahoe K, Macdonald DJ, Tremblay MS, Saunders TJ. Validation of PiezoRx pedometer derived sedentary time. Int J Exerc Sci 2018 Jun 1;11(7):552-560 [FREE Full text] [Medline]
    68. Dondzila CJ, Lewis C, Lopez JR, Parker T. Congruent accuracy of wrist-worn activity trackers during controlled and free-living conditions. Int J Exerc Sci 2018;11(7):575-584.
    69. Dondzila CJ, Swartz AM, Miller NE, Lenz EK, Strath SJ. Accuracy of uploadable pedometers in laboratory, overground, and free-living conditions in young and older adults. Int J Behav Nutr Phys Act 2012 Dec 11;9:143 [FREE Full text] [CrossRef] [Medline]
    70. Durkalec-Michalski K, Woźniewicz M, Bajerska J, Jeszka J. Comparison of accuracy of various non-calorimetric methods measuring energy expenditure at different intensities. Human Movement 2013;14(2):161-167. [CrossRef]
    71. Edwardson CL, Davies M, Khunti K, Yates T, Rowlands AV. Steps per day measured by consumer activity trackers worn at the non-dominant and dominant wrist relative to a waist-worn pedometer. J Meas Phys Behav 2018 Mar;1(1):2-8. [CrossRef]
    72. Ekelund U, Yngve A, Sjöström M, Westerterp K. Field evaluation of the Computer Science and Application's Inc. Activity monitor during running and skating training in adolescent athletes. Int J Sports Med 2000 Nov;21(8):586-592. [CrossRef] [Medline]
    73. Farina N, Lowry RG. The validity of consumer-level activity monitors in healthy older adults in free-living conditions. J Aging Phys Act 2018 Jan 01;26(1):128-135. [CrossRef] [Medline]
    74. Feito Y, Bassett DR, Thompson DL. Evaluation of activity monitors in controlled and free-living environments. Med Sci Sports Exerc 2012 Apr;44(4):733-741. [CrossRef] [Medline]
    75. Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. Int J Behav Nutr Phys Act 2015 Mar 27;12:42 [FREE Full text] [CrossRef] [Medline]
    76. Feito Y, Garner HR, Bassett DR. Evaluation of ActiGraph's low-frequency filter in laboratory and free-living environments. Med Sci Sports Exerc 2015 Jan;47(1):211-217. [CrossRef] [Medline]
    77. Fokkenrood HJ, Verhofstad N, van den Houten MM, Lauret GJ, Wittens C, Scheltinga MR, et al. Physical activity monitoring in patients with peripheral arterial disease: validation of an activity monitor. Eur J Vasc Endovasc Surg 2014 Aug;48(2):194-200 [FREE Full text] [CrossRef] [Medline]
    78. Full KM, Kerr J, Grandner MA, Malhotra A, Moran K, Godoble S, et al. Validation of a physical activity accelerometer device worn on the hip and wrist against polysomnography. Sleep Health 2018 Apr;4(2):209-216 [FREE Full text] [CrossRef] [Medline]
    79. Fuller KL, Juliff L, Gore CJ, Peiffer JJ, Halson SL. Software thresholds alter the bias of actigraphy for monitoring sleep in team-sport athletes. J Sci Med Sport 2017 Aug;20(8):756-760. [CrossRef] [Medline]
    80. Fullerton E, Heller B, Munoz-Organero M. Recognizing human activity in free-living using multiple body-worn accelerometers. IEEE Sensors J 2017 Aug 15;17(16):5290-5297 [FREE Full text] [CrossRef]
    81. Gardner AW, Poehlman ET. Assessment of free-living daily physical activity in older claudicants: validation against the doubly labeled water technique. J Gerontol A Biol Sci Med Sci 1998 Jul;53(4):M275-M280. [CrossRef] [Medline]
    82. Garnotel M, Bastian T, Romero-Ugalde HM, Maire A, Dugas J, Zahariev A, et al. Prior automatic posture and activity identification improves physical activity energy expenditure prediction from hip-worn triaxial accelerometry. J Appl Physiol (1985) 2018 Mar 01;124(3):780-790 [FREE Full text] [CrossRef] [Medline]
    83. Gill JM, Hawari NS, Maxwell DJ, Louden D, Mourselas N, Bunn C, et al. Validation of a novel device to measure and provide feedback on sedentary behavior. Med Sci Sports Exerc 2018 Mar;50(3):525-532 [FREE Full text] [CrossRef] [Medline]
    84. Godfrey A, Culhane KM, Lyons GM. Comparison of the performance of the activPAL Professional physical activity logger to a discrete accelerometer-based activity monitor. Med Eng Phys 2007 Oct;29(8):930-934. [CrossRef] [Medline]
    85. Gomersall SR, Ng N, Burton NW, Pavey TG, Gilson ND, Brown WJ. Estimating physical activity and sedentary behavior in a free-living context: a pragmatic comparison of consumer-based activity trackers and actigraph accelerometry. J Med Internet Res 2016 Sep 07;18(9):e239 [FREE Full text] [CrossRef] [Medline]
    86. Gruwez A, Libert W, Ameye L, Bruyneel M. Reliability of commercially available sleep and activity trackers with manual switch-to-sleep mode activation in free-living healthy individuals. Int J Med Inform 2017 Jun;102:87-92. [CrossRef] [Medline]
    87. Haghayegh S, Khoshnevis S, Smolensky MH, Diller KR, Castriotta RJ. Performance assessment of new-generation Fitbit technology in deriving sleep parameters and stages. Chronobiol Int 2020 Jan;37(1):47-59. [CrossRef] [Medline]
    88. Hargens TA, Deyarmin KN, Snyder KM, Mihalik AG, Sharpe LE. Comparison of wrist-worn and hip-worn activity monitors under free living conditions. J Med Eng Technol 2017 Apr;41(3):200-207. [CrossRef] [Medline]
    89. Hamill K, Jumabhoy R, Kahawage P, de Zambotti M, Walters EM, Drummond SP. Validity, potential clinical utility and comparison of a consumer activity tracker and a research-grade activity tracker in insomnia disorder II: outside the laboratory. J Sleep Res 2020 Feb;29(1):e12944 [FREE Full text] [CrossRef] [Medline]
    90. Henriksen A, Grimsgaard S, Horsch A, Hartvigsen G, Hopstock L. Validity of the polar M430 activity monitor in free-living conditions: validation study. JMIR Form Res 2019 Aug 16;3(3):e14438 [FREE Full text] [CrossRef] [Medline]
    91. Härtel S, Gnam JP, Löffler S, Bös K. Estimation of energy expenditure using accelerometers and activity-based energy models—validation of a new device. Eur Rev Aging Phys Act 2011;8(2):109-114. [CrossRef]
    92. Hernández-Vicente A, Santos-Lozano A, De Cocker K, Garatachea N. Validation study of Polar V800 accelerometer. Ann Transl Med 2016 Aug;4(15):278 [FREE Full text] [CrossRef] [Medline]
    93. Herrmann SD, Hart TL, Lee CD, Ainsworth BE. Evaluation of the MyWellness Key accelerometer. Br J Sports Med 2011 Feb;45(2):109-113. [CrossRef] [Medline]
    94. Hickey A, Del Din S, Rochester L, Godfrey A. Detecting free-living steps and walking bouts: validating an algorithm for macro gait analysis. Physiol Meas 2017 Jan;38(1):N1-15. [CrossRef] [Medline]
    95. Hickey A, John D, Sasaki JE, Mavilia M, Freedson P. Validity of activity monitor step detection is related to movement patterns. J Phys Act Health 2016 Feb;13(2):145-153. [CrossRef] [Medline]
    96. Höchsmann C, Knaier R, Infanger D, Schmidt-Trucksäss A. Validity of smartphones and activity trackers to measure steps in a free-living setting over three consecutive days. Physiol Meas 2020 Jan 30;41(1):015001 [FREE Full text] [CrossRef] [Medline]
    97. Hui J, Heyden R, Bao T, Accettone N, McBay C, Richardson J, et al. Validity of the Fitbit One for measuring activity in community-dwelling stroke survivors. Physiother Can 2018;70(1):81-89 [FREE Full text] [CrossRef] [Medline]
    98. Hollewand AM, Spijkerman AG, Bilo HJ, Kleefstra N, Kamsma Y, van Hateren KJ. Validity of an accelerometer-based activity monitor system for measuring physical activity in frail older adults. J Aging Phys Act 2016 Oct;24(4):555-558. [CrossRef] [Medline]
    99. Jean-Louis G, Kripke DF, Cole RJ, Assmus JD, Langer RD. Sleep detection with an accelerometer actigraph: comparisons with polysomnography. Physiol Behav 2001 Jan;72(1-2):21-28. [CrossRef] [Medline]
    100. Jenkins CA, Tiley LC, Lay I, Hartmann JA, Chan JK, Nicholas CL. Comparing GENEActiv against Actiwatch-2 over seven nights using a common sleep scoring algorithm and device-specific wake thresholds. Behav Sleep Med (forthcoming) 2021 Jun 05:1-11. [CrossRef] [Medline]
    101. Johannsen DL, Calabro MA, Stewart J, Franke W, Rood JC, Welk GJ. Accuracy of armband monitors for measuring daily energy expenditure in healthy adults. Med Sci Sports Exerc 2010 Nov;42(11):2134-2140. [CrossRef] [Medline]
    102. Patrik Johansson H, Rossander-Hulthén L, Slinde F, Ekblom B. Accelerometry combined with heart rate telemetry in the assessment of total energy expenditure. Br J Nutr 2006 Mar;95(3):631-639. [CrossRef] [Medline]
    103. Júdice PB, Santos DA, Hamilton MT, Sardinha LB, Silva AM. Validity of GT3X and Actiheart to estimate sedentary time and breaks using ActivPAL as the reference in free-living conditions. Gait Posture 2015 May;41(4):917-922. [CrossRef] [Medline]
    104. Júdice PB, Teixeira L, Silva AM, Sardinha LB. Accuracy of Actigraph inclinometer to classify free-living postures and motion in adults with overweight and obesity. J Sports Sci 2019 Aug;37(15):1708-1716. [CrossRef] [Medline]
    105. Kanda M, Minakata Y, Matsunaga K, Sugiura H, Hirano T, Koarai A, et al. Validation of the triaxial accelerometer for the evaluation of physical activity in Japanese patients with COPD. Intern Med 2012;51(4):369-375 [FREE Full text] [CrossRef] [Medline]
    106. Kang SG, Kang JM, Ko KP, Park SC, Mariani S, Weng J. Validity of a commercial wearable sleep tracker in adult insomnia disorder patients and good sleepers. J Psychosom Res 2017 Jun;97:38-44. [CrossRef] [Medline]
    107. Kawada T. Agreement rates for sleep/wake judgments obtained via accelerometer and sleep diary: a comparison. Behav Res Methods 2008 Nov;40(4):1026-1029. [CrossRef] [Medline]
    108. Keating XD, Liu J, Liu X, Shangguan R, Guan J, Chen L. Validity of Fitbit Charge 2 in controlled college physical education settings. ICHPER-SD J Res 2018;9(2):28-35.
    109. Kerr J, Carlson J, Godbole S, Cadmus-Bertram L, Bellettiere J, Hartman S. Improving hip-worn accelerometer estimates of sitting using machine learning methods. Med Sci Sports Exerc 2018 Jul;50(7):1518-1524 [FREE Full text] [CrossRef] [Medline]
    110. Kerr J, Marshall SJ, Godbole S, Chen J, Legge A, Doherty AR, et al. Using the SenseCam to improve classifications of sedentary behavior in free-living settings. Am J Prev Med 2013 Mar;44(3):290-296. [CrossRef] [Medline]
    111. Kim Y, Barry VW, Kang M. Validation of the ActiGraph GT3X and activPAL accelerometers for the assessment of sedentary behavior. Meas Phys Educ Exerc Sci 2015 Aug 19;19(3):125-137. [CrossRef]
    112. Kinnunen H, Häkkinen K, Schumann M, Karavirta L, Westerterp KR, Kyröläinen H. Training-induced changes in daily energy expenditure: methodological evaluation using wrist-worn accelerometer, heart rate monitor, and doubly labeled water technique. PLoS One 2019 Jul 10;14(7):e0219563 [FREE Full text] [CrossRef] [Medline]
    113. Kinnunen H, Tanskanen M, Kyröläinen H, Westerterp KR. Wrist-worn accelerometers in assessment of energy expenditure during intensive training. Physiol Meas 2012 Nov;33(11):1841-1854. [CrossRef] [Medline]
    114. Koehler K, Braun H, de Marées M, Fusch G, Fusch C, Schaenzer W. Assessing energy expenditure in male endurance athletes: validity of the SenseWear Armband. Med Sci Sports Exerc 2011 Jul;43(7):1328-1333. [CrossRef] [Medline]
    115. Koenders N, Seeger JP, van der Giessen T, van den Hurk TJ, Smits IG, Tankink AM, et al. Validation of a wireless patch sensor to monitor mobility tested in both an experimental and a hospital setup: a cross-sectional study. PLoS One 2018 Oct 25;13(10):e0206304 [FREE Full text] [CrossRef] [Medline]
    116. Kogure T, Shirakawa S, Shimokawa M, Hosokawa Y. Automatic sleep/wake scoring from body motion in bed: validation of a newly developed sensor placed under a mattress. J Physiol Anthropol 2011;30(3):103-109 [FREE Full text] [CrossRef] [Medline]
    117. Kooiman TJ, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil 2015 Oct 12;7:24 [FREE Full text] [CrossRef] [Medline]
    118. Kozey-Keadle S, Libertine A, Lyden K, Staudenmayer J, Freedson PS. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc 2011 Aug;43(8):1561-1567. [CrossRef] [Medline]
    119. Kubala AG, Barone Gibbs B, Buysse DJ, Patel SR, Hall MH, Kline CE. Field-based measurement of sleep: agreement between six commercial activity monitors and a validated accelerometer. Behav Sleep Med 2020;18(5):637-652 [FREE Full text] [CrossRef] [Medline]
    120. Kumahara H, Ayabe M, Ichibakase M, Tashima A, Chiwata M, Takashi T. Validity of activity monitors worn at multiple nontraditional locations under controlled and free-living conditions in young adult women. Appl Physiol Nutr Metab 2015 May;40(5):448-456. [CrossRef] [Medline]
    121. Kurita S, Yano S, Ishii K, Shibata A, Sasai H, Nakata Y, et al. Comparability of activity monitors used in Asian and Western-country studies for assessing free-living sedentary behaviour. PLoS One 2017 Oct 18;12(10):e0186523 [FREE Full text] [CrossRef] [Medline]
    122. Kwon S, Wan N, Burns RD, Brusseau TA, Kim Y, Kumar S, et al. The validity of MotionSense HRV in estimating sedentary behavior and physical activity under free-living and simulated activity settings. Sensors (Basel) 2021 Feb 18;21(4):1411 [FREE Full text] [CrossRef] [Medline]
    123. Latshang TD, Mueller DJ, Lo Cascio CM, Stöwhas AC, Stadelmann K, Tesler N, et al. Actigraphy of wrist and ankle for measuring sleep duration in altitude travelers. High Alt Med Biol 2016 Sep;17(3):194-202. [CrossRef] [Medline]
    124. Le Masurier GC, Lee SM, Tudor-Locke C. Motion sensor accuracy under controlled and free-living conditions. Med Sci Sports Exerc 2004 May;36(5):905-910. [CrossRef] [Medline]
    125. Lebleu J, Detrembleur C, Guebels C, Hamblenne P, Valet M. Concurrent validity of Nokia Go activity tracker in walking and free-living conditions. J Eval Clin Pract 2020 Feb;26(1):223-228. [CrossRef] [Medline]
    126. Lee JA, Laurson KR. Validity of the SenseWear armband step count measure during controlled and free-living conditions. J Exerc Sci Fit 2015 Jun;13(1):16-23 [FREE Full text] [CrossRef] [Medline]
    127. Lee JA, Williams SM, Brown DD, Laurson KR. Concurrent validation of the Actigraph gt3x+, Polar Active accelerometer, Omron HJ-720 and Yamax Digiwalker SW-701 pedometer step counts in lab-based and free-living settings. J Sports Sci 2015;33(10):991-1000. [CrossRef] [Medline]
    128. Lee JM, Byun W, Keill A, Dinkel D, Seo Y. Comparison of wearable trackers' ability to estimate sleep. Int J Environ Res Public Health 2018 Jun 15;15(6):1265 [FREE Full text] [CrossRef] [Medline]
    129. Lee JY, Kwon S, Kim WS, Hahn SJ, Park J, Paik NJ. Feasibility, reliability, and validity of using accelerometers to measure physical activities of patients with stroke during inpatient rehabilitation. PLoS One 2018 Dec 31;13(12):e0209607 [FREE Full text] [CrossRef] [Medline]
    130. Lee PH, Suen LK. The convergent validity of Actiwatch 2 and ActiGraph Link accelerometers in measuring total sleeping period, wake after sleep onset, and sleep efficiency in free-living condition. Sleep Breath 2017 Mar;21(1):209-215. [CrossRef] [Medline]
    131. Leenders NY, Sherman WM, Nagaraja HN. Energy expenditure estimated by accelerometry and doubly labeled water: do they agree? Med Sci Sports Exerc 2006 Dec;38(12):2165-2172. [CrossRef] [Medline]
    132. Li X, Zhang Y, Jiang F, Zhao H. A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy. Chronobiol Int 2020 Jul;37(7):1002-1015. [CrossRef] [Medline]
    133. Liu S, Brooks D, Thomas S, Eysenbach G, Nolan RP. Lifesource XL-18 pedometer for measuring steps under controlled and free-living conditions. J Sports Sci 2015;33(10):1001-1006. [CrossRef] [Medline]
    134. Löf M, Henriksson H, Forsum E. Evaluations of Actiheart, IDEEA® and RT3 monitors for estimating activity energy expenditure in free-living women. J Nutr Sci 2013 Sep 6;2:e31 [FREE Full text] [CrossRef] [Medline]
    135. Lyden K, Keadle SK, Staudenmayer J, Freedson PS. The activPALTM accurately classifies activity intensity categories in healthy adults. Med Sci Sports Exerc 2017 May;49(5):1022-1028 [FREE Full text] [CrossRef] [Medline]
    136. Lyden K, Kozey Keadle SL, Staudenmayer JW, Freedson PS. Validity of two wearable monitors to estimate breaks from sedentary time. Med Sci Sports Exerc 2012 Nov;44(11):2243-2252 [FREE Full text] [CrossRef] [Medline]
    137. Mackey DC, Manini TM, Schoeller DA, Koster A, Glynn NW, Goodpaster BH, Health‚ Aging‚ and Body Composition Study. Validation of an armband to measure daily energy expenditure in older adults. J Gerontol A Biol Sci Med Sci 2011 Oct;66(10):1108-1113 [FREE Full text] [CrossRef] [Medline]
    138. Madrid-Navarro CJ, Puertas Cuesta FJ, Escamilla-Sevilla F, Campos M, Ruiz Abellán F, Rol MA, et al. Validation of a device for the ambulatory monitoring of sleep patterns: a pilot study on Parkinson's disease. Front Neurol 2019 Apr 11;10:356 [FREE Full text] [CrossRef] [Medline]
    139. Mantua J, Gravel N, Spencer RM. Reliability of sleep measures from four personal health monitoring devices compared to research-based actigraphy and polysomnography. Sensors (Basel) 2016 May 05;16(5):646 [FREE Full text] [CrossRef] [Medline]
    140. Marcotte RT, Petrucci Jr GJ, Cox MF, Freedson PS, Staudenmayer JW, Sirard JR. Estimating sedentary time from a hip- and wrist-worn accelerometer. Med Sci Sports Exerc 2020 Jan;52(1):225-232. [CrossRef] [Medline]
    141. Matthews CE, Kozey Keadle S, Moore SC, Schoeller DS, Carroll RJ, Troiano RP, et al. Measurement of active and sedentary behavior in context of large epidemiologic studies. Med Sci Sports Exerc 2018 Feb;50(2):266-276 [FREE Full text] [CrossRef] [Medline]
    142. McDevitt B, Moore L, Akhtar N, Connolly J, Doherty R, Scott W. Validity of a novel research-grade physical activity and sleep monitor for continuous remote patient monitoring. Sensors (Basel) 2021 Mar 13;21(6):2034 [FREE Full text] [CrossRef] [Medline]
    143. McGinley SK, Armstrong MJ, Khandwala F, Zanuso S, Sigal RJ. Assessment of the MyWellness Key accelerometer in people with type 2 diabetes. Appl Physiol Nutr Metab 2015 Nov;40(11):1193-1198. [CrossRef] [Medline]
    144. McVeigh JA, Ellis J, Ross C, Tang K, Wan P, Halse RE, et al. Convergent validity of the Fitbit Charge 2 to measure sedentary behavior and physical activity in overweight and obese adults. J Meas Phys Behav 2021;4(1):39-46. [CrossRef]
    145. Middelweerd A, Van der Ploeg HP, Van Halteren A, Twisk JW, Brug J, Te Velde SJ. A validation study of the Fitbit one in daily life using different time intervals. Med Sci Sports Exerc 2017 Jun;49(6):1270-1279. [CrossRef] [Medline]
    146. Mikkelsen ML, Berg-Beckhoff G, Frederiksen P, Horgan G, O'Driscoll R, Palmeira AL, et al. Estimating physical activity and sedentary behaviour in a free-living environment: a comparative study between Fitbit Charge 2 and Actigraph GT3X. PLoS One 2020 Jun 11;15(6):e0234426 [FREE Full text] [CrossRef] [Medline]
    147. Miyamoto S, Minakata Y, Azuma Y, Kawabe K, Ono H, Yanagimoto R, et al. Verification of a motion sensor for evaluating physical activity in COPD patients. Can Respir J 2018 Apr 23;2018:8343705 [FREE Full text] [CrossRef] [Medline]
    148. Moore SA, Hallsworth K, Bluck LJ, Ford GA, Rochester L, Trenell MI. Measuring energy expenditure after stroke: validation of a portable device. Stroke 2012 Jun;43(6):1660-1662. [CrossRef] [Medline]
    149. Mouritzen NJ, Larsen LH, Lauritzen MH, Kjær TW. Assessing the performance of a commercial multisensory sleep tracker. PLoS One 2020 Dec 11;15(12):e0243214 [FREE Full text] [CrossRef] [Medline]
    150. Murakami H, Kawakami R, Nakae S, Yamada Y, Nakata Y, Ohkawara K, et al. Accuracy of 12 wearable devices for estimating physical activity energy expenditure using a metabolic chamber and the doubly labeled water method: validation study. JMIR Mhealth Uhealth 2019 Aug 02;7(8):e13938 [FREE Full text] [CrossRef] [Medline]
    151. Narayanan A, Stewart T, Mackay L. A dual-accelerometer system for detecting human movement in a free-living environment. Med Sci Sports Exerc 2020 Jan;52(1):252-258. [CrossRef] [Medline]
    152. Myers J, Dupain M, Vu A, Jaffe A, Smith K, Fonda H, et al. Agreement between activity-monitoring devices during home rehabilitation: a substudy of the AAA STOP trial. J Aging Phys Act 2014 Jan;22(1):87-95. [CrossRef] [Medline]
    153. Nguyen DM, Lecoultre V, Sunami Y, Schutz Y. Assessment of physical activity and energy expenditure by GPS combined with accelerometry in real-life conditions. J Phys Act Health 2013 Aug;10(6):880-888. [CrossRef] [Medline]
    154. O'Brien CM, Duda JL, Kitas GD, Veldhuijzen van Zanten JJ, Metsios GS, Fenton SA. Measurement of sedentary time and physical activity in rheumatoid arthritis: an ActiGraph and activPAL™ validation study. Rheumatol Int 2020 Sep;40(9):1509-1518 [FREE Full text] [CrossRef] [Medline]
    155. O'Neill B, McDonough SM, Wilson JJ, Bradbury I, Hayes K, Kirk A, et al. Comparing accelerometer, pedometer and a questionnaire for measuring physical activity in bronchiectasis: a validity and feasibility study? Respir Res 2017 Jan 14;18(1):16 [FREE Full text] [CrossRef] [Medline]
    156. O'Brien MW, Wojcik WR, D'Entremont L, Fowles JR. Validation of the PiezoRx® step count and moderate to vigorous physical activity times in free living conditions in adults: a pilot study. Int J Exerc Sci 2018 Jan 2;11(7):541-551 [FREE Full text] [Medline]
    157. Paul SS, Tiedemann A, Hassett LM, Ramsay E, Kirkham C, Chagpar S, et al. Validity of the Fitbit activity tracker for measuring steps in community-dwelling older adults. BMJ Open Sport Exerc Med 2015 Jul 8;1(1):e000013 [FREE Full text] [CrossRef] [Medline]
    158. Pavey TG, Gomersall SR, Clark BK, Brown WJ. The validity of the GENEActiv wrist-worn accelerometer for measuring adult sedentary time in free living. J Sci Med Sport 2016 May;19(5):395-399. [CrossRef] [Medline]
    159. Pomeroy J, Brage S, Curtis JM, Swan PD, Knowler WC, Franks PW. Between-monitor differences in step counts are related to body size: implications for objective physical activity measurement. PLoS One 2011 Apr 27;6(4):e18942 [FREE Full text] [CrossRef] [Medline]
    160. Quante M, Kaplan ER, Cailler M, Rueschman M, Wang R, Weng J, et al. Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms. Nat Sci Sleep 2018 Jan 18;10:13-20 [FREE Full text] [CrossRef] [Medline]
    161. Rabinovich RA, Louvaris Z, Raste Y, Langer D, Van Remoortel H, Giavedoni S, PROactive Consortium. Validity of physical activity monitors during daily life in patients with COPD. Eur Respir J 2013 Nov;42(5):1205-1215 [FREE Full text] [CrossRef] [Medline]
    162. Rafamantanantsoa HH, Ebine N, Yoshioka M, Higuchi H, Yoshitake Y, Tanaka H, et al. Validation of three alternative methods to measure total energy expenditure against the doubly labeled water method for older Japanese men. J Nutr Sci Vitaminol (Tokyo) 2002 Dec;48(6):517-523. [CrossRef] [Medline]
    163. Redenius N, Kim Y, Byun W. Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity. BMC Med Res Methodol 2019 Feb 07;19(1):29 [FREE Full text] [CrossRef] [Medline]
    164. Regalia G, Gerboni G, Migliorini M, Lai M, Pham J, Puri N, et al. Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults. Chronobiol Int 2021 Mar;38(3):400-414. [CrossRef] [Medline]
    165. Reid RE, Insogna JA, Carver TE, Comptour AM, Bewski NA, Sciortino C, et al. Validity and reliability of Fitbit activity monitors compared to ActiGraph GT3X+ with female adults in a free-living environment. J Sci Med Sport 2017 Jun;20(6):578-582. [CrossRef] [Medline]
    166. Rosenberger ME, Buman MP, Haskell WL, McConnell MV, Carstensen LL. Twenty-four hours of sleep, sedentary behavior, and physical activity with nine wearable devices. Med Sci Sports Exerc 2016 Mar;48(3):457-465 [FREE Full text] [CrossRef] [Medline]
    167. Rothney MP, Brychta RJ, Meade NN, Chen KY, Buchowski MS. Validation of the ActiGraph two-regression model for predicting energy expenditure. Med Sci Sports Exerc 2010 Sep;42(9):1785-1792 [FREE Full text] [CrossRef] [Medline]
    168. Rousset S, Fardet A, Lacomme P, Normand S, Montaurier C, Boirie Y, et al. Comparison of total energy expenditure assessed by two devices in controlled and free-living conditions. Eur J Sport Sci 2015;15(5):391-399. [CrossRef] [Medline]
    169. Rozanski GM, Aqui A, Sivakumaran S, Mansfield A. Consumer wearable devices for activity monitoring among individuals after a stroke: a prospective comparison. JMIR Cardio 2018 Jan 04;2(1):e1 [FREE Full text] [CrossRef] [Medline]
    170. Rutgers CJ, Klijn MJ, Deurenberg P. The assessment of 24-hour energy expenditure in elderly women by minute-by-minute heart rate monitoring. Ann Nutr Metab 1997;41(2):83-88. [CrossRef] [Medline]
    171. Sánchez-Ortuño MM, Edinger JD, Means MK, Almirall D. Home is where sleep is: an ecological approach to test the validity of actigraphy for the assessment of insomnia. J Clin Sleep Med 2010 Feb 15;6(1):21-29 [FREE Full text] [Medline]
    172. Sargent C, Lastella M, Halson SL, Roach GD. The validity of activity monitors for measuring sleep in elite athletes. J Sci Med Sport 2016 Oct;19(10):848-853. [CrossRef] [Medline]
    173. Sasaki S, Ukawa S, Okada E, Wenjing Z, Kishi T, Sakamoto A, et al. Comparison of a new wrist-worn accelerometer with a commonly used triaxial accelerometer under free-living conditions. BMC Res Notes 2018 Oct 20;11(1):746 [FREE Full text] [CrossRef] [Medline]
    174. Schmal H, Holsgaard-Larsen A, Izadpanah K, Brønd JC, Madsen CF, Lauritsen J. Validation of activity tracking procedures in elderly patients after operative treatment of proximal femur fractures. Rehabil Res Pract 2018 Jun 19;2018:3521271 [FREE Full text] [CrossRef] [Medline]
    175. Schneider PL, Crouter SE, Bassett DR. Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc 2004 Feb;36(2):331-335. [CrossRef] [Medline]
    176. Scott J, Grierson A, Gehue L, Kallestad H, MacMillan I, Hickie I. Can consumer grade activity devices replace research grade actiwatches in youth mental health settings? Sleep Biol Rhythms 2019 Jan 17;17(2):223-232. [CrossRef]
    177. Semanik P, Lee J, Pellegrini CA, Song J, Dunlop DD, Chang RW. Comparison of physical activity measures derived from the Fitbit Flex and the ActiGraph GT3X+ in an employee population with chronic knee symptoms. ACR Open Rheumatol 2020 Jan;2(1):48-52 [FREE Full text] [CrossRef] [Medline]
    178. Siddall AG, Powell SD, Needham-Beck SC, Edwards VC, Thompson JE, Kefyalew SS, et al. Validity of energy expenditure estimation methods during 10 days of military training. Scand J Med Sci Sports 2019 Sep;29(9):1313-1321. [CrossRef] [Medline]
    179. Silcott NA, Bassett Jr DR, Thompson DL, Fitzhugh EC, Steeves JA. Evaluation of the Omron HJ-720ITC pedometer under free-living conditions. Med Sci Sports Exerc 2011 Sep;43(9):1791-1797. [CrossRef] [Medline]
    180. Silva AM, Santos DA, Matias CN, Júdice PB, Magalhães JP, Ekelund U, et al. Accuracy of a combined heart rate and motion sensor for assessing energy expenditure in free-living adults during a double-blind crossover caffeine trial using doubly labeled water as the reference method. Eur J Clin Nutr 2015 Jan;69(1):20-27. [CrossRef] [Medline]
    181. Silva GS, Yang H, Collins JE, Losina E. Validating Fitbit for evaluation of physical activity in patients with knee osteoarthritis: do thresholds matter? ACR Open Rheumatol 2019 Nov;1(9):585-592 [FREE Full text] [CrossRef] [Medline]
    182. Šimůnek A, Dygrýn J, Gába A, Jakubec L, Stelzer J, Chmelík F. Validity of Garmin Vívofit and polar loop for measuring daily step counts in free-living conditions in adults. Acta Gymnica 2016 Sep 30;46(3):129-135. [CrossRef]
    183. Sjöberg V, Westergren J, Monnier A, Lo Martire R, Hagströmer M, Äng BO, et al. Wrist-worn activity trackers in laboratory and free-living settings for patients with chronic pain: criterion validity study. JMIR Mhealth Uhealth 2021 Jan 12;9(1):e24806 [FREE Full text] [CrossRef] [Medline]
    184. Skipworth RJ, Stene GB, Dahele M, Hendry PO, Small AC, Blum D, European Palliative Care Research Collaborative (EPCRC). Patient-focused endpoints in advanced cancer: criterion-based validation of accelerometer-based activity monitoring. Clin Nutr 2011 Dec;30(6):812-821. [CrossRef] [Medline]
    185. Skotte J, Korshøj M, Kristiansen J, Hanisch C, Holtermann A. Detection of physical activity types using triaxial accelerometers. J Phys Act Health 2014 Jan;11(1):76-84. [CrossRef] [Medline]
    186. Slinde F, Bertz F, Winkvist A, Ellegård L, Olausson H, Brekke HK. Energy expenditure by multisensor armband in overweight and obese lactating women validated by doubly labeled water. Obesity (Silver Spring) 2013 Nov;21(11):2231-2235 [FREE Full text] [CrossRef] [Medline]
    187. Smits EJ, Winkler EA, Healy GN, Dall PM, Granat MH, Hodges PW. Comparison of single- and dual-monitor approaches to differentiate sitting from lying in free-living conditions. Scand J Med Sci Sports 2018 Aug;28(8):1888-1896. [CrossRef] [Medline]
    188. Stein AD, Rivera JM, Pivarnik JM. Measuring energy expenditure in habitually active and sedentary pregnant women. Med Sci Sports Exerc 2003 Aug;35(8):1441-1446. [CrossRef] [Medline]
    189. St-Laurent A, Mony MM, Mathieu MÈ, Ruchat SM. Validation of the Fitbit Zip and Fitbit Flex with pregnant women in free-living conditions. J Med Eng Technol 2018 May;42(4):259-264. [CrossRef] [Medline]
    190. St-Onge M, Mignault D, Allison DB, Rabasa-Lhoret R. Evaluation of a portable device to measure daily energy expenditure in free-living adults. Am J Clin Nutr 2007 Mar;85(3):742-749. [CrossRef] [Medline]
    191. Strath SJ, Brage S, Ekelund U. Integration of physiological and accelerometer data to improve physical activity assessment. Med Sci Sports Exerc 2005 Nov;37(11 Suppl):S563-S571. [CrossRef] [Medline]
    192. Sugino A, Minakata Y, Kanda M, Akamatsu K, Koarai A, Hirano T, et al. Validation of a compact motion sensor for the measurement of physical activity in patients with chronic obstructive pulmonary disease. Respiration 2012;83(4):300-307 [FREE Full text] [CrossRef] [Medline]
    193. Sushames A, Edwards A, Thompson F, McDermott R, Gebel K. Validity and reliability of Fitbit Flex for step count, moderate to vigorous physical activity and activity energy expenditure. PLoS One 2016 Sep 2;11(9):e0161224 [FREE Full text] [CrossRef] [Medline]
    194. Svensson T, Chung UI, Tokuno S, Nakamura M, Svensson AK. A validation study of a consumer wearable sleep tracker compared to a portable EEG system in naturalistic conditions. J Psychosom Res 2019 Nov;126:109822. [CrossRef] [Medline]
    195. te Lindert BH, Van Someren EJ. Sleep estimates using microelectromechanical systems (MEMS). Sleep 2013 May 01;36(5):781-789 [FREE Full text] [CrossRef] [Medline]
    196. Tedesco S, Sica M, Ancillao A, Timmons S, Barton J, O'Flynn B. Validity evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in free-living environments in an older adult cohort. JMIR Mhealth Uhealth 2019 Jun 19;7(6):e13084 [FREE Full text] [CrossRef] [Medline]
    197. Toth LP, Park S, Springer CM, Feyerabend MD, Steeves JA, Bassett DR. Video-recorded validation of wearable step counters under free-living conditions. Med Sci Sports Exerc 2018 Jun;50(6):1315-1322. [CrossRef] [Medline]
    198. Thorup CB, Andreasen JJ, Sørensen EE, Grønkjær M, Dinesen BI, Hansen J. Accuracy of a step counter during treadmill and daily life walking by healthy adults and patients with cardiac disease. BMJ Open 2017 Mar 31;7(3):e011742 [FREE Full text] [CrossRef] [Medline]
    199. Tudor-Locke C, Sisson SB, Lee SM, Craig CL, Plotnikoff RC, Bauman A. Evaluation of quality of commercial pedometers. Can J Public Health 2006;97 Suppl 1:S10-S16 [FREE Full text] [CrossRef] [Medline]
    200. Tully MA, McBride C, Heron L, Hunter RF. The validation of Fibit Zip™ physical activity monitor as a measure of free-living physical activity. BMC Res Notes 2014 Dec 23;7:952 [FREE Full text] [CrossRef] [Medline]
    201. Uiterwaal M, Glerum EB, Busser HJ, van Lummel RC. Ambulatory monitoring of physical activity in working situations, a validation study. J Med Eng Technol 1998;22(4):168-172. [CrossRef] [Medline]
    202. Vähä-Ypyä H, Husu P, Suni J, Vasankari T, Sievänen H. Reliable recognition of lying, sitting, and standing with a hip-worn accelerometer. Scand J Med Sci Sports 2018 Mar;28(3):1092-1102. [CrossRef] [Medline]
    203. Valenti G, Camps SG, Verhoef SP, Bonomi AG, Westerterp KR. Validating measures of free-living physical activity in overweight and obese subjects using an accelerometer. Int J Obes (Lond) 2014 Jul;38(7):1011-1014. [CrossRef] [Medline]
    204. van Alphen HJ, Waninge A, Minnaert AE, Post WJ, van der Putten AA. Construct validity of the Actiwatch-2 for assessing movement in people with profound intellectual and multiple disabilities. J Appl Res Intellect Disabil 2021 Jan;34(1):99-110 [FREE Full text] [CrossRef] [Medline]
    205. Van Blarigan EL, Kenfield SA, Tantum L, Cadmus-Bertram LA, Carroll PR, Chan JM. The Fitbit One physical activity tracker in men with prostate cancer: validation study. JMIR Cancer 2017 Apr 18;3(1):e5 [FREE Full text] [CrossRef] [Medline]
    206. van de Wouw E, Evenhuis HM, Echteld MA. Comparison of two types of Actiwatch with polysomnography in older adults with intellectual disability: a pilot study. J Intellect Dev Disabil 2013 Sep;38(3):265-273. [CrossRef] [Medline]
    207. van den Berg-Emons HJ, Bussmann JB, Balk AH, Stam HJ. Validity of ambulatory accelerometry to quantify physical activity in heart failure. Scand J Rehabil Med 2000 Dec;32(4):187-192. [CrossRef] [Medline]
    208. van der Weegen S, Essers H, Spreeuwenberg M, Verwey R, Tange H, de Witte L, et al. Concurrent validity of the MOX activity monitor compared to the ActiGraph GT3X. Telemed J E Health 2015 Apr;21(4):259-266. [CrossRef] [Medline]
    209. van Hees VT, Renström F, Wright A, Gradmark A, Catt M, Chen KY, et al. Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. PLoS One 2011;6(7):e22922 [FREE Full text] [CrossRef] [Medline]
    210. van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimäki M, et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep 2018 Aug 28;8(1):12975 [FREE Full text] [CrossRef] [Medline]
    211. van Nassau F, Chau JY, Lakerveld J, Bauman AE, van der Ploeg HP. Validity and responsiveness of four measures of occupational sitting and standing. Int J Behav Nutr Phys Act 2015 Nov 25;12:144 [FREE Full text] [CrossRef] [Medline]
    212. Vanhelst J, Mikulovic J, Bui-Xuan G, Dieu O, Blondeau T, Fardy P, et al. Comparison of two ActiGraph accelerometer generations in the assessment of physical activity in free living conditions. BMC Res Notes 2012 Apr 25;5:187 [FREE Full text] [CrossRef] [Medline]
    213. Varela Mato V, Yates T, Stensel D, Biddle S, Clemes SA. Concurrent validity of Actigraph-determined sedentary time against the Activpal under free-living conditions in a sample of bus drivers. Meas Phys Educ Exerc Sci 2017 Jun 22;21(4):212-222. [CrossRef]
    214. Vetrovsky T, Siranec M, Marencakova J, Tufano JJ, Capek V, Bunc V, et al. Validity of six consumer-level activity monitors for measuring steps in patients with chronic heart failure. PLoS One 2019 Sep 13;14(9):e0222569 [FREE Full text] [CrossRef] [Medline]
    215. Villars C, Bergouignan A, Dugas J, Antoun E, Schoeller DA, Roth H, et al. Validity of combining heart rate and uniaxial acceleration to measure free-living physical activity energy expenditure in young men. J Appl Physiol (1985) 2012 Dec 01;113(11):1763-1771 [FREE Full text] [CrossRef] [Medline]
    216. Washburn RA, Janney CA, Fenster JR. The validity of objective physical activity monitoring in older individuals. Res Q Exerc Sport 1990 Mar;61(1):114-117. [CrossRef] [Medline]
    217. Webber SC, St John PD. Comparison of ActiGraph GT3X+ and StepWatch step count accuracy in geriatric rehabilitation patients. J Aging Phys Act 2016 Jul;24(3):451-458. [CrossRef] [Medline]
    218. Welk GJ, McClain JJ, Eisenmann JC, Wickel EE. Field validation of the MTI Actigraph and BodyMedia armband monitor using the IDEEA monitor. Obesity (Silver Spring) 2007 Apr;15(4):918-928 [FREE Full text] [CrossRef] [Medline]
    219. Welk G, Kim Y, Shook RP, Ellingson L, Lobelo RL. Validation of a noninvasive, disposable activity monitor for clinical applications. J Phys Act Health 2017 Jul;14(7):546-551. [CrossRef] [Medline]
    220. White T, Westgate K, Hollidge S, Venables M, Olivier P, Wareham N, et al. Estimating energy expenditure from wrist and thigh accelerometry in free-living adults: a doubly labelled water study. Int J Obes (Lond) 2019 Nov;43(11):2333-2342 [FREE Full text] [CrossRef] [Medline]
    221. Whybrow S, Ritz P, Horgan GW, Stubbs RJ. An evaluation of the IDEEA™ activity monitor for estimating energy expenditure. Br J Nutr 2013 Jan 14;109(1):173-183. [CrossRef] [Medline]
    222. Williams JM, Taylor DJ, Slavish DC, Gardner CE, Zimmerman MR, Patel K, et al. Validity of actigraphy in young adults with insomnia. Behav Sleep Med 2020;18(1):91-106. [CrossRef] [Medline]
    223. Winkler EA, Bodicoat DH, Healy GN, Bakrania K, Yates T, Owen N, et al. Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol. Physiol Meas 2016 Oct;37(10):1653-1668. [CrossRef] [Medline]
    224. Wu Y, Johns JA, Poitras J, Kimmerly DS, O'Brien MW. Improving the criterion validity of the activPAL in determining physical activity intensity during laboratory and free-living conditions. J Sports Sci 2021 Apr;39(7):826-834. [CrossRef] [Medline]
    225. Yoshida A, Ishikawa-Takata K, Tanaka S, Suzuki N, Nakae S, Murata H, et al. Validity of combination use of activity record and accelerometry to measure free-living total energy expenditure in female endurance runners. J Strength Cond Res 2019 Nov;33(11):2962-2970. [CrossRef] [Medline]
    226. Yamada Y, Hashii-Arishima Y, Yokoyama K, Itoi A, Adachi T, Kimura M. Validity of a triaxial accelerometer and simplified physical activity record in older adults aged 64-96 years: a doubly labeled water study. Eur J Appl Physiol 2018 Oct;118(10):2133-2146. [CrossRef] [Medline]
    227. Thompson WR. Worldwide survey of fitness trends for 2022. ACSMs Health Fit J 2022;26(1):11-20. [CrossRef]
    228. Troiano RP, Stamatakis E, Bull FC. How can global physical activity surveillance adapt to evolving physical activity guidelines? Needs, challenges and future directions. Br J Sports Med 2020 Dec;54(24):1468-1473 [FREE Full text] [CrossRef] [Medline]
    229. Trost SG. Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time? Int J Behav Nutr Phys Act 2020 Mar 18;17(1):28 [FREE Full text] [CrossRef] [Medline]
    230. Burchartz A, Anedda B, Auerswald T, Giurgiu M, Hill H, Ketelhut S, et al. Assessing physical behavior through accelerometry – state of the science, best practices and future directions. Psychol Sport Exerc 2020 Jul;49:101703 [FREE Full text] [CrossRef]
    231. Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res 2010 May;19(4):539-549 [FREE Full text] [CrossRef] [Medline]
    232. Bassett Jr DR, Rowlands A, Trost SG. Calibration and validation of wearable monitors. Med Sci Sports Exerc 2012 Jan;44(1 Suppl 1):S32-S38 [FREE Full text] [CrossRef] [Medline]
    233. Johnston W, Judice PB, Molina García P, Mühlen JM, Lykke Skovgaard E, Stang J, et al. Recommendations for determining the validity of consumer wearable and smartphone step count: expert statement and checklist of the INTERLIVE network. Br J Sports Med 2021 Jul;55(14):780-793 [FREE Full text] [CrossRef] [Medline]
    234. Keadle SK, Lyden KA, Strath SJ, Staudenmayer JW, Freedson PS. A framework to evaluate devices that assess physical behavior. Exerc Sport Sci Rev 2019 Oct;47(4):206-214. [CrossRef] [Medline]
    235. Sperlich B, Holmberg HC. Wearable, yes, but able…?: it is time for evidence-based marketing claims!. Br J Sports Med 2017 Aug;51(16):1240 [FREE Full text] [CrossRef] [Medline]
    236. Mühlen JM, Stang J, Lykke Skovgaard E, Judice PB, Molina-Garcia P, Johnston W, et al. Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network. Br J Sports Med 2021 Jul;55(14):767-779 [FREE Full text] [CrossRef] [Medline]
    237. McCarney R, Warner J, Iliffe S, van Haselen R, Griffin M, Fisher P. The Hawthorne Effect: a randomised, controlled trial. BMC Med Res Methodol 2007 Jul 03;7:30 [FREE Full text] [CrossRef] [Medline]
    238. Fuller D, Colwell E, Low J, Orychock K, Tobin MA, Simango B, et al. Reliability and validity of commercially available wearable devices for measuring steps, energy expenditure, and heart rate: systematic review. JMIR Mhealth Uhealth 2020 Sep 08;8(9):e18694 [FREE Full text] [CrossRef] [Medline]
    239. Moore CC, McCullough AK, Aguiar EJ, Ducharme SW, Tudor-Locke C. Toward harmonized treadmill-based validation of step-counting wearable technologies: a scoping review. J Phys Act Health 2020 Jul 11;17(8):840-852 [FREE Full text] [CrossRef] [Medline]
    240. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, QUADAS-2 Group. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011 Oct 18;155(8):529-536 [FREE Full text] [CrossRef] [Medline]
    241. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021 Mar 29;372:n71 [FREE Full text] [CrossRef] [Medline]
    242. Schardt C, Adams MB, Owens T, Keitz S, Fontelo P. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC Med Inform Decis Mak 2007 Jun 15;7:16 [FREE Full text] [CrossRef] [Medline]
    243. Grandner MA, Rosenberger ME. Actigraphic sleep tracking and wearables: Historical context, scientific applications and guidelines, limitations, and considerations for commercial sleep devices. In: Grandner MA, editor. Sleep and Health. Cambridge, MA, USA: Academic Press; 2019:147-157.
    244. Ancoli-Israel S, Martin JL, Blackwell T, Buenaver L, Liu L, Meltzer LJ, et al. The SBSM guide to actigraphy monitoring: clinical and research applications. Behav Sleep Med 2015;13 Suppl 1:S4-38. [CrossRef] [Medline]
    245. Umemneku Chikere CM, Wilson K, Graziadio S, Vale L, Allen AJ. Diagnostic test evaluation methodology: a systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard - an update. PLoS One 2019 Oct 11;14(10):e0223832 [FREE Full text] [CrossRef] [Medline]
    246. Welk GJ, Bai Y, Lee JM, Godino J, Saint-Maurice PF, Carr L. Standardizing analytic methods and reporting in activity monitor validation studies. Med Sci Sports Exerc 2019 Aug;51(8):1767-1780 [FREE Full text] [CrossRef] [Medline]
    247. Rowe DA, Mahar MT, Raedeke TD, Lore J. Measuring physical activity in children with pedometers: reliability, reactivity, and replacement of missing data. Pediatr Exerc Sci 2004 Nov;16(4):343-354. [CrossRef]
    248. Driller MW, Dunican IC. No familiarization or 'first-night effect' evident when monitoring sleep using wrist actigraphy. J Sleep Res 2021 Aug;30(4):e13246 [FREE Full text] [CrossRef] [Medline]
    249. Freedson P, Bowles HR, Troiano R, Haskell W. Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. Med Sci Sports Exerc 2012 Jan;44(1 Suppl 1):S1-S4 [FREE Full text] [CrossRef] [Medline]
    250. Clemes SA, Matchett N, Wane SL. Reactivity: an issue for short-term pedometer studies? Br J Sports Med 2008 Jan;42(1):68-70. [CrossRef] [Medline]
    251. Staudenmayer J, Zhu W, Catellier DJ. Statistical considerations in the analysis of accelerometry-based activity monitor data. Med Sci Sports Exerc 2012 Jan;44(1 Suppl 1):S61-S67. [CrossRef] [Medline]
    252. Quante M, Kaplan ER, Rueschman M, Cailler M, Buxton OM, Redline S. Practical considerations in using accelerometers to assess physical activity, sedentary behavior, and sleep. Sleep Health 2015 Dec;1(4):275-284. [CrossRef] [Medline]
    253. Fairclough SJ, Noonan R, Rowlands AV, Van Hees V, Knowles Z, Boddy LM. Wear compliance and activity in children wearing wrist- and hip-mounted accelerometers. Med Sci Sports Exerc 2016 Feb;48(2):245-253. [CrossRef] [Medline]
    254. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nyström C, Mora-Gonzalez J, Löf M, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med 2017 Sep;47(9):1821-1845 [FREE Full text] [CrossRef] [Medline]
    255. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008 Jan;40(1):181-188. [CrossRef] [Medline]
    256. Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR Mhealth Uhealth 2018 Aug 09;6(8):e10527 [FREE Full text] [CrossRef] [Medline]
    257. Dixon PM, Saint-Maurice PF, Kim Y, Hibbing P, Bai Y, Welk GJ. A primer on the use of equivalence testing for evaluating measurement agreement. Med Sci Sports Exerc 2018 Apr;50(4):837-845 [FREE Full text] [CrossRef] [Medline]
    258. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc 2011 Feb;43(2):357-364 [FREE Full text] [CrossRef] [Medline]

    ‎
    24-HAC: 24-hour activity cycle
    PA: physical activity
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    QUADAS-2: Quality Assessment of Diagnostic Accuracy Studies-2
    SB: sedentary behavior

    Edited by L Buis; submitted 12.01.22; peer-reviewed by A Naseri Jahfari, K Fadahunsi; comments to author 03.03.22; revised version received 27.04.22; accepted 29.04.22; published 09.06.22

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