Background: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors–based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning–based FDSs using manual feature extraction, and deep learning (DL)–based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. Objective: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Methods: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. Results: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). Conclusions: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Background: Physical activity in early life has positive health effects, but few children meet the physical activity recommendations. Fundamental motor skills (FMS) are related to physical activity and according to the theory, physical activity drives them in the early years and vice versa. However, no study has conducted a meta-analysis of the association between moderate-to-vigorous intensity physical activity (MVPA) and early FMS. This meta-analysis examined the bidirectional correlation between MVPA and domain-specific FMS in preschool children. Methods: We searched PubMed, Embase, and Cochrane Library databases for articles published up to August 2023. Cross-sectional and longitudinal studies were included if they targeted children (3–6 years old) as the study population. The association between objectively measured MVPA and FMS was evaluated. Results: We found 445 titles and abstracts. A total of ten studies (eleven datasets) and 2514 children met the inclusion criteria, including eight cross-sectional studies and three longitudinal studies. When using MVPA as the exposure, no associations were found with locomotor skills (β = 0.83, 95% CI: −0.08, 1.74, p = 0.07) and gross motor skills (β = 2.72, 95% CI: −0.28, 5.72, p = 0.08), but an association with object management skills was found (β = 0.18, 95% CI: 0.06, 0.30, p = 0.001). When MVPA was used as the outcome, no associations were observed between locomotor skills (β = 0.06, 95% CI: −0.35, 0.47, p = 0.79), but associations with object management skills (β = 0.15, 95% CI: 0.02, 0.27, p = 0.02) and gross motor skill were found (β = 0.56, 95% CI: 0.38, 0.75, p = 0.001). The sensitivity analysis showed that the results must be treated with caution. Conclusion: We found that gross motor skill (exposure) was positively associated with MVPA (outcome) in preschoolers. Object management skills were positively associated with MVPA (exposure) and MVPA (outcome) in preschoolers. In contrast, MVPA as an exposure was not associated with locomotor skills and gross motor skills. The results may suggest that promoting FMS is important for preschool children’s MVPA.

This study examined how different sequences of concurrent training impacted physical activity (PA), body composition, and physical fitness in young obese males. We also investigated whether the effectiveness of these interventions in reducing body fat percentage (BF%) was influenced by PA levels. A 12-week randomized controlled trial involving a cohort of 45 obese young males (mean age: 22.42 ± 1.96 years, mean BMI: 29.78 ± 3.37) was conducted. Participants were randomly assigned to three groups: the CRE group (Resistance Training (RT) followed by Endurance Training (ET)), the CER group (ET followed by RT), and the control group (Con). The training sessions were held three times a week. Measurements, including PA level, body composition, bone density, VO2max, and muscle strength, were assessed before and after the intervention. Compared to those at baseline, following the intervention, both the CRE and CER groups showed significant improvements in various parameters, including PA level, body composition, bone density, VO_2max, and muscle strength (p < 0.05), whereas no significant changes were observed in the Con group (p > 0.05). Specifically, the CRE group demonstrated remarkable progress, as evidenced by an increase in MVPA level (η²_p = 0.37, p < 0.001), a reduction in fat mass (η²_p = 0.28, p < 0.001), BF% (η²_p = 0.28, p < 0.001), android fat (%) (η²_p = 0.21, p < 0.001), gynoid fat (%) (η²_p = 0.30, p < 0.001), and various physical fitness indices, such as maximum strength (η²_p = 0.20, p = 0.008), explosive strength (η²_p = 0.38, p < 0.001), and muscular endurance (η²_p = 0.55, p < 0.001), surpassing the improvements observed in the CER and Con groups. Changes in PA levels during the intervention influence the efficacy of CT in reducing BF%. CT, particularly when RT precedes ET, had the potential to improve PA levels, overall physical fitness, body composition, and bone health in obese young males. Moreover, changes in PA levels during the intervention impacted the effectiveness of CT in reducing BF%. ChiCTR, ChiCTR2200063892.

Objective: This study aimed to compare the effects of two concurrent training (CT) protocols on the physical fitness of middle school students. Method: A 12-week quasi-experimental pre-test/post-test study was conducted with 157 middle school students (age = 12.48 ± 0.34, n = 90 females) divided into three groups: CT group A (CT-0h) received combined resistance training (RT) and aerobic training (AT) in each physical education session, CT group B (CT-48h) received RT and AT across two separate physical education classes 48 h apart, and a control group (Con) received no training. Training occurred twice a week. Test indicators included cardiorespiratory fitness (CRF) measured by estimated VO₂max and 20 m shuttle run (laps), as well as muscle strength assessed through long jump, vertical jump, and handgrip strength. Results: The intervention groups exhibited significant increases in estimated VO₂max and muscle strength compared to their baseline values (p < 0.05). Both CT-0h and CT-48h groups demonstrated significant improvements in 20 m shuttle run (laps) (mean difference: 8.88 laps, p < 0.01; mean difference: 4.81 laps, p < 0.01, respectively), standing long jump (mean difference: 6.20 cm, p < 0.01; mean difference: 3.68 cm, p < 0.01, respectively), vertical jump (mean difference: 4.95 cm, p < 0.01; mean difference: 4.04 cm, p < 0.01, respectively), and handgrip strength (mean difference: 11.17 kg, p < 0.01; mean difference: 6.99 kg, p < 0.01, respectively). CT-0h group exhibited significantly increased estimated VO₂max (mean difference: 1.47 ml/kg/min, p < 0.01) compared to the CT-48h group. Conclusion: Both CT programs effectively improved adolescents’ physical fitness indicators. However, the program that integrated RT and AT within the same physical education class demonstrated superior enhancement in adolescents’ CRF.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Obesity has become one of today's global health challenges. According to the World Health Organisation, in 2022, 2.5 billion adults aged 18 years and older will be overweight, including more than 890 million adults with obesity. Objective: Exercise interventions based on mobile health technology are widely available, but the effectiveness and feasibility of interventions using mobile health apps and exercise watches to improve the physical health of overweight and obese male college students are unknown, and this study compares the effects of online interventions carried out by mobile health technology and offline interventions guided by physical trainers on the physical health of overweight and obese male college students. Methods: This study used a randomised controlled trial with a pre-test post-test design, and participants were randomly divided into an online group, an offline group and a control group. The online group exercised online through the fitness APP, and the offline group was instructed by a professional trainer to exercise offline, and both groups wore sports watches to monitor their activities, and the training content was the same. The control group did not carry out any intervention. Results: At the end of the intervention, the BMI of the online and offline groups decreased by 1.5 kg/m² and 1.6 kg/m², respectively (P<0.01), and the body fat rate decreased by 1.69% in the online group and 1.72% in the offline group (P<0.01). Several physical fitness indicators, such as muscle mass, increased significantly by an average of 1,115.23g in the online group and 1,120.56g in the offline group (P<0.05), and lung capacity improved by 5.2% and 5.5% in the online and offline groups, respectively (P0.05). Additionally, a dose-effect relationship was found between the amount of physical activity and the rate of change in body fat, with a 6.9 per cent increase in the rate of change in body fat for every 1 MET*h increase in moderate-intensity physical activity, and a 5.4 per cent increase in the rate of change in body fat for every 1 MET*h increase in high-intensity physical activity. Conclusions: Exercise intervention based on mobile health technology effectively improves the physical fitness of overweight and obese male college students, and the effect is consistent with offline intervention. There was a significant dose-effect relationship between the amount of physical activity and the rate of change in body fat, which provided a scientific basis for the development of personalised training programmes. Clinical Trial: The study was approved by the Ethics Committee of the Capital Institute of Sports (Code: 2024A063, Approval Date: 2017/12/26) and registered in the Chinese Clinical Trial Registry (Registration No.: ChiCTR2200063892).