Weekend physical activity profiles and their relationship with quality of life: The SOPHYA cohort of Swiss children and adolescents.

INTRODUCTION: Quality of life (QoL) is an important health indicator among children and adolescents. Evidence on the effect of physical activity (PA)-related behaviors on QoL among youth remains inconsistent. Conventional accelerometer-derived PA metrics and guidelines with a focus on whole weeks may not adequately characterize QoL relevant PA behavior. OBJECTIVE: This study aims to a) identify clusters of accelerometer-derived PA profiles during weekend days among children and adolescents living in Switzerland, b) assess their cross-sectional and predictive association with overall QoL and its dimensions, and c) investigate whether the associations of QoL with the newly identified clusters persist upon adjustment for the commonly used PA metrics moderate-to-vigorous physical activity (MVPA) and time spent in sedentary behavior (SB). METHODS: The population-based Swiss children's Objectively measured PHYsical Activity (SOPHYA) cohort among children and adolescents aged 6 to 16 years was initiated at baseline in 2013. PA and QoL information was obtained twice over a five-year follow-up period. The primary endpoint is the overall QoL score and its six dimension scores obtained by KINDL® questionnaire. The primary predictor is the cluster membership of accelerometer-derived weekend PA profile. Clusters were obtained by applying the k-medoid algorithm to the distance matrix of profiles obtained by pairwise alignments of PA time series using the Dynamic Time Warping (DTW) algorithm. Secondary predictors are accelerometer-derived conventional PA metrics MVPA and SB from two combined weekend days. Linear regression models were applied to assess a) the cross-sectional association between PA cluster membership and QoL at baseline and b) the predictive association between PA cluster membership at baseline and QoL at follow-up, adjusting for baseline QoL. RESULTS: The study sample for deriving PA profile clusters consisted of 51.4% girls and had an average age of 10.9 [SD 2.5] years). The elbow and silhouette methods indicated that weekend PA profiles are best classified in two or four clusters. The most differentiating characteristic for the two-clusters classification ("lower activity" and "high activity"), and the four-clusters classification ("inactive", "low activity", "medium activity", and "high activity"), respectively was the participant's mean counts per 15-seconds epoch. Participants assigned to high activity clusters were younger and more often male. Neither the clustered PA profiles nor MVPA or SB were cross-sectionally or predictively associated with overall QoL. The only association of a conventional PA metrics with QoL while adjusting for cluster membership was observed between MVPA during the weekend days and social well-being with a mean score difference of 2.4 (95%CI: 0.3 to 4.5; p = 0.025). CONCLUSION: The absence of strong associations of PA metrics for the weekend with QoL, except for the positive association between MVPA during the weekend days and social well-being, is in line with results from two randomized studies not showing efficacy of PA interventions on youth QoL. But because PA decreases with age, its promotion and relevance to QoL remain important research topics. Larger longitudinal study samples with more than two follow-up time points of children and adolescents are needed to derive new novel accelerometer-derived PA profiles and to associate them with QoL dimensions.

Cross-Sectional but Not Prospective Association of Accelerometry-Derived Physical Activity With Quality of Life in Children and Adolescents.

Objectives: This study aims to quantify the cross-sectional and prospective associations between quality of life (QoL) and moderate-to-vigorous physical activity (MVPA). Methods: This study was based on the Swiss children's Objectively measured PHYsical Activity cohort. The primary endpoint is the overall QoL score and its six dimensions. The main predictor is the average time spent in MVPA per day. Linear mixed effects and linear regression models respectively were used to investigate the cross-sectional and prospective associations between MVPA and QoL. Results: There were 352 participants in the study with complete data from baseline (2013-2015) and follow-up (2019). MVPA was positively associated with overall QoL and physical wellbeing (p = 0.023 and 0.002 respectively). The between-subject MVPA was positively associated with the overall QoL, physical wellbeing, and social wellbeing (p = 0.030, 0.017, and 0.028 respectively). Within-subject MVPA was positively associated with physical wellbeing and functioning at school (p = 0.039 and 0.013 respectively). Baseline MVPA was not associated with QoL 5 years later. Conclusion: Future longitudinal studies should employ shorter follow-up times and repeat measurements to assess the PA and QoL association.

COVID-19 pandemic and health related quality of life in primary school children in Switzerland: a repeated cross-sectional study.

BACKGROUND: The COVID-19 pandemic and the resulting containment measures had and still have a profound impact on everyday life. Both the fear of infection and the imposed restrictions can have biopsychosocial consequences. The aim of the present study was to analyze whether there is a difference in the health-related quality of life (HRQoL) of primary school children in 2014/15 compared to in 2020, the first year of the COVID-19 pandemic. METHODS: The present study included 1,712 children aged 5 to 11 years who either participated in the baseline assessment of the SOPHYA cohort study in 2014/15 or were newly recruited during follow-up of the cohort in 2020. In both surveys, the children invited for participation were identified based on registry data. HRQoL was assessed with the validated KINDL-R questionnaire, which scores HRQoL along six different dimensions. RESULTS: The overall scores (82.4 [81.8; 83.0] vs. 79.6 [79,1; 80.2]), and in particular the emotional well-being scores (85.6 [84.6; 86.6] vs. 83.3 [82.4; 84.2]), were lower during the year of the pandemic (2020) compared to the survey year 2014/15. The highest decrease between 2014/15 and 2020 in the adjusted models was seen for the youngest age group (-3.9 points), followed by children from families with a high income (-3.2 points), girls (-3.1 points), Swiss citizens (-3.1 points) and children from the German-speaking part of Switzerland (-3.1 points). HRQoL was particularly low during periods with restrictions and at the height of the COVID-19 waves in 2020. CONCLUSION: The SOPHYA-study showed that HRQoL, and especially emotional well-being, was lower in 5 to 11-year-old children in Switzerland during the first year of the pandemic compared to the results from the survey conducted in 2014/15. In the year of the pandemic, the scores were lowest at the height of the COVID-19 waves and their associated restrictions. As it cannot be distinguished whether fear of the disease itself or the restrictions caused this decrease in HRQoL, containment policies should keep COVID-19 infections as low as possible, but still enable children to profit from protective factors such as leisure activities, physical activity and social contact.

Sedentary Behavior in Children by Wearable Cameras: Development of an Annotation Protocol.

INTRODUCTION: There is increasing evidence that not all types of sedentary behavior have the same harmful effects on children's health. Hence, there has been a growing interest in the use of wearable cameras. The aim of this study is to develop a protocol to categorize children's wearable camera data into sedentary behavior components. METHODS: Wearable camera data were collected in 3 different samples of children in 2014. A development sample (3 children aged 4-8 years) was used to design the annotation protocol. A training sample (4 children aged 10 years) was used to train 3 different coders. The independent reliability sample (14 children aged 9-11 years) was used for independent coding of wearable camera images and to estimate inter-rater agreement. Data were analyzed in 2018. Cohen's κ was calculated for every rater pair on a per-participant basis. Means and SDs were then calculated across per-participant κ scores. RESULTS: A total of 41,651 images from 14 participants were considered for analysis. Inter-rater agreement over all raters over all the sedentary behavior components was almost perfect (mean κ=0.85, 95% CI=0.83, 0.87). Inter-rater reliability for screen-based sedentary behavior (mean κ=0.72, 95% CI=0.62, 0.82) and nonscreen sedentary behavior (κ=0.69, 95% CI=0.65, 0.72) showed substantial agreement. Inter-rater reliability for location (κ=0.91, 95% CI=0.88, 0.93) showed almost perfect agreement. CONCLUSIONS: A reliable annotation protocol to categorize wearable camera data of children into sedentary behavior components was developed. Once applied to larger samples in children, this protocol can ultimately help to better understand the potential harms of screen time and sedentary behavior in children.

Reactivity to accelerometer measurement of children and adolescents.

PURPOSE: Awareness of being monitored can influence participants' habitual physical activity (PA) behavior. This reactivity effect may threaten the validity of PA assessment. Reports on reactivity when measuring the PA of children and adolescents have been inconsistent. The aim of this study was to investigate whether PA outcomes measured by accelerometer devices differ from measurement day to measurement day and whether the day of the week and the day on which measurement started influence these differences. METHODS: Accelerometer data (counts per minute [cpm]) of children and adolescents (n = 2081) pooled from eight studies in Switzerland with at least 10 h of daily valid recording were investigated for effects of measurement day, day of the week, and start day using mixed linear regression. RESULTS: The first measurement day was the most active day. Counts per minute were significantly higher than on the second to the sixth day, but not on the seventh day. Differences in the age-adjusted means between the first and consecutive days ranged from 23 to 45 cpm (3.6%-7.1%). In preschoolchildren, the differences almost reached 10%. The start day significantly influenced PA outcome measures. CONCLUSIONS: Reactivity to accelerometer measurement of PA is likely to be present to an extent of approximately 5% on the first day and may introduce a relevant bias to accelerometer-based studies. In preschoolchildren, the effects are larger than those in elementary and secondary schoolchildren. As the day of the week and the start day significantly influence PA estimates, researchers should plan for at least one familiarization day in school-age children and randomly assign start days.

Neural network versus activity-specific prediction equations for energy expenditure estimation in children.

The aim of this study was to compare the energy expenditure (EE) estimations of activity-specific prediction equations (ASPE) and of an artificial neural network (ANNEE) based on accelerometry with measured EE. Forty-three children (age: 9.8 ± 2.4 yr) performed eight different activities. They were equipped with one tri-axial accelerometer that collected data in 1-s epochs and a portable gas analyzer. The ASPE and the ANNEE were trained to estimate the EE by including accelerometry, age, gender, and weight of the participants. To provide the activity-specific information, a decision tree was trained to recognize the type of activity through accelerometer data. The ASPE were applied to the activity-type-specific data recognized by the tree (Tree-ASPE). The Tree-ASPE precisely estimated the EE of all activities except cycling [bias: -1.13 ± 1.33 metabolic equivalent (MET)] and walking (bias: 0.29 ± 0.64 MET; P < 0.05). The ANNEE overestimated the EE of stationary activities (bias: 0.31 ± 0.47 MET) and walking (bias: 0.61 ± 0.72 MET) and underestimated the EE of cycling (bias: -0.90 ± 1.18 MET; P < 0.05). Biases of EE in stationary activities (ANNEE: 0.31 ± 0.47 MET, Tree-ASPE: 0.08 ± 0.21 MET) and walking (ANNEE 0.61 ± 0.72 MET, Tree-ASPE: 0.29 ± 0.64 MET) were significantly smaller in the Tree-ASPE than in the ANNEE (P < 0.05). The Tree-ASPE was more precise in estimating the EE than the ANNEE. The use of activity-type-specific information for subsequent EE prediction equations might be a promising approach for future studies.

Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph.

OBJECTIVES: The GT1M ActiGraph has been replaced by the triaxial GT3X which includes an inclinometer to detect postures. The purpose of this study was to investigate comparability of the GT3X to the GT1M and to develop activity intensity cut-points for the vector magnitude of the GT3X (VM(GT3X)) in children. Additionally, the study aimed to assess the validity of the GT3X inclinometer for detecting postures. DESIGN: Forty-nine children aged 10-15 were tested during semi-structured activities in a laboratory setting (lying, sitting, standing, Nintendo Wii boxing, walking and running). METHODS: Expired respiratory gases were measured continuously using the Cosmed K4b(2) portable metabolic system. Simultaneously, ActiGraph counts by a GT3X and a GT1M were recorded. RESULTS: Significantly higher counts were found for GT3X vertical mean counts per second (vcps) and antero-posterior counts per second (apcps) during running, relative to the GT1M. Cut-points for the VM(GT3X), developed using Receiver Operator Characteristics (ROC) curves (development group N=32, validation group N=17), were <3 counts s⁻¹ for sedentary (cross-validation: 87% correctly classified), 3-56 counts s⁻¹ for light (cross-validation: 70% correctly classified) and >56 counts s⁻¹ for moderate to vigorous (mvpa) (cross-validation: 86% correctly classified). The inclinometer correctly classified standing 20%, lying 15%, sitting 94% and the off position 45% of the time. CONCLUSIONS: The vcps from the two monitors differ for certain activities. Cross-validated cut-points for the classification of VM(GT3X) counts into sedentary, light and mvpa are presented. Posture classification by the GT3X should be interpreted with care, as misclassifications are common.