Impact of weekdays versus weekend days on accelerometer measured physical behavior among children and adolescents: results from the MoMo study = Einfluss des Wochentages auf das mit Beschleunigungsmessern erfasste Bewegungsverhalten von Kindern und Jugendlichen: Ergebnisse der MoMo-Studie

Structured activities, in which children participate for example at school, are consistent and limited in scope. After-school or weekend activities, by contrast, involve a wider range of behaviors. Studies have shown that physical activity (PA), as measured by accelerometers, is lower on weekends compared to weekdays or school days, whereas PA does not differ between weekdays. In the present study, we examined accelerometer data of children and adolescents living in Germany for the different weekdays and weekend days. The current analysis used cross-sectional data of participants (n = 2743) aged 6–17 years collected between 2014 and 2017. The final valid sample consisted of 2278 children and adolescents divided into three age groups (6–10 years, n = 713; 11–13 years, n = 706; 14–17 years, n = 859) and two gender groups (1072 boys, 1206 girls). Physical behavior, including sedentary behavior, as well as light, moderate, vigorous PA, and wear time were analyzed. Absolute and percentage intensity distributions were evaluated daily. The average wear time was 807 min daily from Monday–Thursday with significant deviations from the mean on Friday (+38 min), Saturday (−76 min), and Sunday (−141 min). Absolute moderate to vigorous PA times were lower on weekends than during the week. However, the percentage intensity distribution remained constant over all days. Girls were less physically active and more sedentary than boys (F$_{1,2272}$ = 38.3; p < 0.01) and adolescents were significantly less active than younger children (F$_{2,2272}$ = 138.6; p < 0.01). Waking times increased with age (F$_{2,2272}$ = 138.6; p < 0.01). Shorter awake periods limit possible active times on weekends, resulting in lower PA and sedentary behavior compared to weekdays. The percentage distributions of the different physical behavior intensity categories are similar over all weekdays and weekend days. We could not find a justification for specific weekend interventions. Instead, interventions should generally try to shift activity away from sedentary behavior towards a more active lifestyle

How specific combinations of epoch length, non-wear time and cut-points influence physical activity – Processing accelerometer data from children and adolescents in the nationwide MoMo study = Einfluss spezifischer Kombinationen von Epochenlänge, Nichttragezeit und Cut-off-Werten auf die körperliche Aktivität – Signalverarbeitung von Akzelerometerdaten bei Kindern und Jugendlichen in der bundesweiten MoMo-Studie

This study assesses three factors that influence the quantification of children’s and adolescents’ physical activity (PA) using accelerometers: selection of (1) non-wear algorithm, (2) epoch length and (3) cut-points. A total of 1525 participants from MoMo wave 3 (2018–2022), aged 6–17 years, wore GT3X accelerometers (ActiGraph, LLC, Pensacola, FL, USA) during waking hours. Acceleration counts were reintegrated into lengths of 1, 5, 15, 30, and 60 s epochs. Two non-wear time algorithms and two sets of cut-points were applied to each epoch length. Differences were found in both the comparison of the non-wear time algorithms and the comparison of the cut-points when the different epoch lengths were considered. This may result in large differences in estimated sedentary behavior and PA values. We propose to pool the data by merging and combining multiple accelerometer datasets from different studies and evaluate them in a harmonized way in the future. In addition to the need for future validation studies using short epoch lengths for young children, we also propose to conduct meta-analyses. This allows the use of data from multiple studies to validate cut-points and to propose a consensual set of cut-points that can be used in different settings and projects. The high discrepancy between results when comparing different epoch lengths has to be considered when interpreting accelerometer data and is regarded a confounding variable when comparing levels of PA between studies

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

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

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

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

Ambulatory assessment for physical activity research. State of the science, best practices and future directions

Technological and digital progress benefits physical activity (PA) research. Here we compiled expert knowledge on how Ambulatory Assessment (AA) is utilized to advance PA research, i.e., we present results of the 2nd International CAPA Workshop 2019 "Physical Activity Assessment - State of the Science, Best Practices, Future Directions" where invited researchers with experience in PA assessment, evaluation, technology and application participated. First, we provide readers with the state of the AA science, then we give best practice recommendations on how to measure PA via AA and shed light on methodological frontiers, and we furthermore discuss future directions. AA encompasses a class of methods that allows the study of PA and its behavioral, biological and physiological correlates as they unfold in everyday life. AA includes monitoring of movement (e.g., via accelerometry), physiological function (e.g., via mobile electrocardiogram), contextual information (e.g., via geolocation-tracking), and ecological momentary assessment (EMA; e.g., electronic diaries) to capture self-reported information. The strengths of AA are data assessment that near real-time, which minimizes retrospective biases in real-world settings, consequentially enabling ecological valid findings. Importantly, AA enables multiple assessments across time within subjects resulting in intensive longitudinal data (ILD), which allows unraveling within-person determinants of PA in everyday life. In this paper, we show how AA methods such as triggered e-diaries and geolocation-tracking can be used to measure PA and its correlates, and furthermore how these findings may translate into real-life interventions. In sum, AA provides numerous possibilities for PA research, especially the opportunity to tackle within-subject antecedents, concomitants, and consequences of PA as they unfold in everyday life. In-depth insights on determinants of PA could help us design and deliver impactful interventions in real-world contexts, thus enabling us to solve critical health issues in the 21st century such as insufficient PA and high levels of sedentary behavior. (DIPF/Orig.

Ambulatory assessment for physical activity research: State of the science, best practices and future directions

Technological and digital progress benefits physical activity (PA) research. Here we compiled expert knowledge on how Ambulatory Assessment (AA) is utilized to advance PA research, i.e., we present results of the 2nd International CAPA Workshop 2019 “Physical Activity Assessment – State of the Science, Best Practices, Future Directions” where invited researchers with experience in PA assessment, evaluation, technology and application participated. First, we provide readers with the state of the AA science, then we give best practice recommendations on how to measure PA via AA and shed light on methodological frontiers, and we furthermore discuss future directions. AA encompasses a class of methods that allows the study of PA and its behavioral, biological and physiological correlates as they unfold in everyday life. AA includes monitoring of movement (e.g., via accelerometry), physiological function (e.g., via mobile electrocardiogram), contextual information (e.g., via geolocation-tracking), and ecological momentary assessment (EMA; e.g., electronic diaries) to capture self-reported information. The strengths of AA are data assessments near real-time, which minimize retrospective biases in real-world settings, consequentially enabling ecological valid findings. Importantly, AA enables multiple assessments across time within subjects resulting in intensive longitudinal data (ILD), which allows unraveling within-person determinants of PA in everyday life. In this paper, we show how AA methods such as triggered e-diaries and geolocation-tracking can be used to measure PA and its correlates, and furthermore how these findings may translate into real-life interventions. In sum, AA provides numerous possibilities for PA research, especially the opportunity to tackle within-subject antecedents, concomitants, and consequences of PA as they unfold in everyday life. In-depth insights on determinants of PA could help us design and deliver impactful interventions in real-world contexts, thus enabling us to solve critical health issues in the 21st century such as insufficient PA and high levels of sedentary behavior