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Background: Social exclusion usually contributes to an increased vulnerability to mental health problems and risky health behaviors. This study aims to identify the role of health behavior in the increased risk of depressive symptoms among adolescents during the coronavirus pandemic in Hungary.Methods: A total of 705 high school students participated in our study (M = 15.9 years; SD = 1.19). The self-administered questionnaire included items about sociodemographics, eating habits, physical activity, sedentary behavior, and substance use. Depressive symptoms were measured using the short version of the Child Depression Inventory. Descriptive statistics and binary logistic regression were used to analyze our results.Results: Daily fruit and vegetable consumption was reported by 21.7% and 22.4% of respondents, respectively. The proportion of the respondents reporting daily sweets consumption stood at 13.2%, daily soft drinks consumption was 12.3%, and daily energy drink consumption tallied to 4.5%. More than one-third of the sample (35.5%) reported having breakfast every school day, which rose to 68.1% of the sample reporting breakfast on both weekend days. The rate of students engaged in daily physical activity was 6.5%, while 86.1% of them reported more than four hours screen time in a day. In addition, despite the mandatory confinement, a notable percentage of adolescents engaged in substance use. Consistent with previous studies, girls had a higher risk of depression. Low levels of physical activity and high levels of screen time – as well as alcohol and drug use – were associated with a high risk of depression.Conclusions: We believe our study provided useful information on adolescent health behaviors that can lead to adolescents’ depression, and that maintaining physical activity can prevent it even in these unusual circumstances.

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

Chronic diseases are a leading cause of adult mortality, accounting for 41 million deaths globally each year. Low levels of physical activity and sedentary behavior are major risk factors for adults to develop a chronic disease. Physical activity interventions can help support patients in clinical care to be more active. Commercial activity trackers that can measure daily steps, physical activity intensity, sedentary behavior, and distance moved are being more frequently used within health-related interventions. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is a planning and evaluation approach to explore the reach, effectiveness, adoption, implementation, and maintenance of interventions. The objective of this study is to conduct an integrative systematic review and report the 5 main RE-AIM dimensions in interventions that used activity trackers in clinical care to improve physical activity or reduce sedentary behavior in adults diagnosed with chronic diseases. A search strategy and study protocol were developed and registered on the PROSPERO platform. Inclusion criteria included adults (18 years and older) diagnosed with a chronic disease and have used an activity tracker within their clinical care. Searches of 10 databases and gray literature were conducted, and qualitative, quantitative, and mixed methods studies were included. Screening was undertaken by more than 1 researcher to reduce the risk of bias. After screening, the final studies were analyzed using a RE-AIM framework data extraction evaluation tool. This tool assisted in identifying the 28 RE-AIM indicators within the studies and linked them to the 5 main RE-AIM dimensions. The initial search identified 4585 potential studies. After a title and abstract review followed by full-text screening, 15 studies were identified for data extraction. The analysis of the extracted data found that the RE-AIM dimensions of adoption (n=1, 7% of studies) and maintenance (n=2, 13% of studies) were underreported. The use of qualitative thematic analysis to understand the individual RE-AIM dimensions was also underreported and only used in 3 of the studies. Two studies used qualitative analysis to explore the effectiveness of the project, while 1 study used thematic analysis to understand the implementation of an intervention. Further research is required in the use of activity trackers to support patients to lead a more active lifestyle. Such studies should consider using the RE-AIM framework at the planning stage with a greater focus on the dimensions of adoption and maintenance and using qualitative methods to understand the main RE-AIM dimensions within their design. These results should form the basis for establishing long-term interventions in clinical care. PROSPERO CRD42022319635; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=319635.

Background Physical activity occurs across various domains including leisure/recreation, for transportation, or for work or household reasons. Rural and urban active living environments are characterized by different opportunities for physical activity within each domain which may translate into different patterns of behavior. The aim of this study was to compare rural–urban differences in physical activity across different domains, and explore interactions between sociodemographic factors, physical activity domains, and rurality. Methods We used self-reported data collected across three physical activity domains (active transportation, recreation, occupational/household) and relevant sociodemographic variables from the Canadian Community Health Survey. Adjusting for sociodemographic factors, we did two separate cross-sectional analyses: 1) binary logistic regression to determine the odds of reporting any activity in each domain, and 2) ordinary least squares regression using the sub-samples reporting > 0 min per week of activity to compare how much activity was reported in each domain. Results Our final survey weighted sample of Canadian adults (mean age 47.4 years) was n  = 25,669,018 (unweighted n  = 47,266). Rural residents were less likely to report any active transportation (OR = 0.59, 95% CI [0.51, 0.67], p  < .0001). For recreational physical activity, rural males had lower odds (OR = 0.75, 95% CI [0.67, 0.83], p  < .0001) and rural females had higher odds (OR = 1.19, 95% CI [1.08, 1.30], p  = .0002) of reporting any participation compared to urban residents. Rural males (OR = 1.90, 95% CI [1.74, 2.07], p  < .0001) and females (OR = 1.33, 95% CI [1.21, 1.46], p  < .0001) had higher odds of reporting any occupational or household physical activity. Conclusions Urban residents tend to participate in more active transportation, while rural residents participate in more occupational or household physical activity. Location-based differences in physical activity are best understood by examining multiple domains and must include appropriate sociodemographic interactions, such as income and sex/gender.

Sedentary behavior (SB) may lead not only to reduced athletic performance but also to impaired self-rated health (SRH) in college students. Although the beneficial effects of physical activity (PA) on SRH have been demonstrated, the intrinsic factors contributing to PA remain to be elucidated. This study examined physical activity atmosphere (PAA), physical activity capability (PAC), physical activity motivation (PAM), and physical activity behavior (PAB), and analyzed their effect sizes and pathways in relation to SRH among sedentary college students. A total of 966 university students (F = 387, M = 579; mean age = 20.36 ± 1.78 years) were selected from eight colleges and universities in Beijing and cross-sectionally assessed using the Self-Rated Health Measurement Scale (SRHMS), the Self-Description Questionnaire (SDQ), the Behavioral Regulation in Physical Education Questionnaire (BRPEQ), and the Physical Activity Atmosphere Questionnaire (PAAQ). Correlation, mediation, and effect size analyses were performed using Pearson correlation, Structural Equation Modeling (SEM), and Bootstrap methods. Correlation results showed significant positive correlations for all indicators of PAA, PAB, PAC, PAM, and SRH. The SEM results showed that the following pathways existed in this study: with PAC as the independent variable, PAC→PAB (Effect size = 0.584), PAC→SRH (Effect size = 0.142), PAC→PAM (Effect size = 0.628), PAC→PAB→SRH (Effect size = 0.237), PAC→PAM→PAB→SRH (Effect size = 0.184). When PAA is the independent variable, only one direction exists: PAA→PAM (Effect size = 0.497). With PAM as the independent variable, PAM→PAB (Effect size = 0.294). With PAB as the independent variable, PAB→SRH (Effect size = 0.375). Meanwhile, the model fit indices were: χ 2 /df = 4.218, p  < 0.001, GFI = 0.908, NFI = 0.936, CFI = 0.912, IFI = 0.968, RFI = 0.936, TLI = 0.964, RMSEA = 0.911. (1) PAC has a positive and direct association with SRH in sedentary students; thus, creating a better exercise environment may help college students reduce sedentariness and improve SRH. (2) PAM and PAB exert positive mediating effects on SRH, suggesting that enhancing both will strengthen the mediating effect of PAC on SRH. (3) Promoting SRH among sedentary students requires addressing both motivational and environmental factors. Schools should provide optimal sporting environments to stimulate students’ motivation, behavior, and capability in PA, thereby reducing SB and improving mental health.

This study aimed to examine the associations and reciprocal predictive relationships between parental exercise support and adolescents’ physical activity (PA) intentions and behaviors. A total of 1482 adolescents from central and southern China were assessed at two time points over one year using the Parental Exercise Support Scale, PA Intention Scale, and PA Rating Scale. Cross-lagged panel analyses were conducted using Mplus 8.7. (1) Significant synchronous and longitudinal correlations were observed among parental exercise support, PA intentions, and PA behaviors; (2) Parental exercise support at Time 1 (T1) positively predicted adolescents’ PA intentions and behaviors at Time 2 (T2), and T1 PA behavior also positively predicted T2 parental exercise support; (3) A bidirectional predictive relationship was found between PA intentions and PA behaviors. Parental exercise support is a key external factor influencing adolescents’ PA intentions and behaviors. It not only positively predicts future PA motivation and engagement but is also reflects by adolescents’ actual participation in PA.

Annotating accelerometer-based physical activity data remains a challenging task, limiting the creation of robust supervised machine learning models due to the scarcity of large, labeled, free-living human activity recognition (HAR) datasets. Researchers are exploring self-supervised learning (SSL) as an alternative to relying solely on labeled data approaches. However, there has been limited exploration of the impact of large-scale, unlabeled datasets for SSL pre-training on downstream HAR performance, particularly utilizing more than one accelerometer. To address this gap, a transformer encoder network is pre-trained on various amounts of unlabeled, dual-accelerometer data from the HUNT4 dataset: 10, 100, 1k, 10k, and 100k hours. The objective is to reconstruct masked segments of signal spectrograms. This pre-trained model, termed SelfPAB, serves as a feature extractor for downstream supervised HAR training across five datasets (HARTH, HAR70+, PAMAP2, Opportunity, and RealWorld). SelfPAB outperforms purely supervised baselines and other SSL methods, demonstrating notable enhancements, especially for activities with limited training data. Results show that more pre-training data improves downstream HAR performance, with the 100k-hour model exhibiting the highest performance. It surpasses purely supervised baselines by absolute F1-score improvements of 7.1% (HARTH), 14% (HAR70+), and an average of 11.26% across the PAMAP2, Opportunity, and RealWorld datasets. Compared to related SSL methods, SelfPAB displays absolute F1-score enhancements of 10.4% (HARTH), 18.8% (HAR70+), and 16% (average across PAMAP2, Opportunity, RealWorld).

Background Regular physical activity is crucial for the health of older adults. While existing tools can accurately measure activity levels, they fail to reveal the underlying psychological motivations behind this behavior. Based on the Theory of Planned Behavior, this study developed and validated a scale to evaluate the psychological factors influencing physical activity among older Chinese adults. Methods The initial scale was developed based on the Theory of Planned Behavior through a literature analysis, consultation with experts, and a preliminary survey. A cross-sectional study was conducted with 451 Chinese older adults who were selected using convenience sampling. Item analysis and exploratory factor analysis (EFA) were performed on data from 152 participants to refine the scale. Then, confirmatory factor analysis (CFA) was conducted with the remaining 299 participants to validate the scale’s structure. The scale’s reliability was evaluated by measuring its internal consistency and test-retest reliability. Results The final scale comprises 19 items across four dimensions: behavioral attitude (6 items), subjective norm (3 items), perceived behavioral control (5 items), and behavioral intention (5 items). Exploratory factor analysis (EFA) revealed the Kaiser-Meyer-Olkin (KMO) measure was 0.837, and Bartlett’s test of sphericity was significant ( χ² = 1559.016, df  = 171, p  < 0.001), with the cumulative variance explained reaching 69.26%. Confirmatory factor analysis (CFA) confirmed a good model fit ( χ²/df  = 1.713, RMSEA = 0.049, SRMR = 0.039, TLI = 0.963, and CFI = 0.968). For reliability, the overall Cronbach’s α was 0.939, with dimension values ranging from 0.872 to 0.884, and the test-retest reliability was 0.940. The scale demonstrated excellent content validity (S-CVI/Ave = 0.915). In conclusion, the scale demonstrates good reliability and validity for use in related research and practice. Conclusion The Physical Activity Behavior Scale for Older Adults is a reliable and valid tool for assessing the psychological determinants of physical activity in this population.

The study aims to examine the mediation effects of physical literacy and physical activity behavior in a relationship between psychological distress and life satisfaction among Chinese college students during the real-life Coronavirus disease pandemic (COVID-19) circumstance. This study implemented a cross-sectional design, and 1,516 participants from 12 universities participated in this study. Structural equation modeling was used to examine a hypothesized model. The findings indicated an acceptable model fit (X2[61] = 508.2, Comparative Fit Index [CFI] = 0.958, Tucker Lewis Index [TLI] = 0.946, Root Mean Square Error of Approximation [RMSEA] = 0.076, 90% CI [0.070, 0.082], Standardized Root Mean Square Residual [SRMR] = 0.047). The results indicated that college students with low participation in physical activity could experience less than healthy living conditions. The findings offered empirical support to the theory that physical literacy could advance individuals’ healthy living by promoting physical activity participation. The study suggested that educational institutions and physical activity programs should cultivate individuals’ physical literacy in order to promote lifelong healthy living.

Esports players are often referred to as sedentary athletes, as gaming requires prolonged sedentary screen exposure. As sedentary behavior and physical inactivity are major causes of noncommunicable diseases and premature death, esports players may be at an increased risk for health implications. Prior research has established esports players as having higher levels of body fat and lower levels of lean body mass versus age-matched controls, suggesting the need to assess further health and fitness outcomes of this demographic. However, while research interest is undoubtedly increasing, the majority of studies has focused on subjective self-report data and has lacked relevant objective health and fitness measurements. This study aimed to assess the health and fitness status of a group of competitive esports players in relation to an age- and sex-matched comparison group. In total, 51 competitive esports players (mean 23, SD 3 years, 2 female) and 51 nonesports players (mean 24, SD 3 years, 2 female) were enrolled in this cross-sectional laboratory study. The esports players and the nonesports players completed a questionnaire assessing demographic data and self-reported physical activity levels. Furthermore, physical parameters including BMI, waist-to-height ratio, body fat percentage, systolic blood pressure, diastolic blood pressure, pulse wave velocity, maximal grip strength, and maximal oxygen consumption were assessed. There were no significant differences in BMI (t =1.54; P=.13; d=0.30), waist-to-height ratio (t =1.44; P=.16; d=0.28), body fat percentage (t =-0.48; P=.63; d=-0.09), systolic blood pressure (t =-0.06; P=.93; d=-0.01), diastolic blood pressure (t =0.37; P=.71; d=0.07), pulse wave velocity (t =-2.08; P=.15; d=-0.43), maximal grip strength (t =-.08; P=.94; d=-0.02), maximal oxygen consumption (t =-0.11; P=.92; d=-0.02), and physical activity (PA) levels (t =2.17; P=.08; d=0.46) between the groups. While the health narrative directed toward esports players has been mainly negative, this laboratory-based study indicated that esports players are not less healthy or fit compared to their peers. However, it seems that esports players are very heterogeneous and seem to span across the whole range of the fitness and health spectrum. Thus, the generalized statements of the esports athlete as an obese and unhealthy individual may need to be reconsidered.