Assessment of Osteogenic Exercise Efficacy via Bone Turnover Markers in Premenopausal Women: A Randomized Controlled Trial.

Assessing bone's response to physical activity interventions is challenging. This randomized controlled trial investigates if changes in bone turnover markers can offer an early evaluation of a physical activity intervention's effectiveness in improving bone mineral density (BMD) in premenopausal women. Participants in the intervention group (n = 27, with 24 completing the trial) were instructed to walk at least 10,000 steps every day on a brisk walk and to execute 60 jumps daily, each surpassing 4g of acceleration, using an accelerometer-based wearable device. Meanwhile, the control group (n = 26, with 18 completing the trial) continued with their usual lifestyle. Bone turnover markers, comprising of C-terminal telopeptide of Type I collagen, procollagen Type 1 N-terminal propeptide, and total osteocalcin (carboxylated and undercarboxylated) were measured at baseline and midway through the intervention (3 months). Dual-energy X-ray absorptiometry scans of the hip and lumbar spine were conducted at baseline and the end of the intervention (6 months) to estimate BMD. Analysis of covariance exhibited significant differences between groups in procollagen Type 1 N-terminal propeptide (-6.74 µg/L, p = .023) and C-terminal telopeptide of Type I collagen (-83 ng/L, p = .043) after 3 months, and in femoral neck BMD (+0.024 g/cm2, p = .016), total hip BMD (+0.036 g/cm2, p = .004), and lumbar spine BMD (+0.026 g/cm2, p = .020) after 6 months. A significant correlation (r = -.73; p < .001) was detected between reductions in C-terminal telopeptide of Type I collagen and increases in femoral neck BMD. In conclusion, this intervention improved BMD in premenopausal women, with bone turnover markers potentially useful for early intervention assessment, though further research is needed.

A randomized controlled mHealth trial that evaluates social comparison-oriented gamification to improve physical activity, sleep quantity, and quality of life in young adults.

INTRODUCTION: The integration of gamification in mHealth interventions presents a novel approach to enhance user engagement and health outcomes. This study aims to evaluate whether comparison-oriented gamification can effectively improve various aspects of health and well-being, including physical activity, sedentary behavior, sleep, and overall quality of life among young adults. METHODS: Potential 107 young adults (from 19 to 28 years old) participated in an 8-week trial. Participants were assigned to either a gamified mHealth intervention (LevantApp) with daily leaderboards and progress bars (n = 53, 26 % dropped-out), or a control condition without gamification (n = 52, 29 % dropped-out). Physical activity (number of steps, moderate and moderate-to-vigorous physical activity -MVPA-) and sleep quantity were measured objectively via accelerometry and subjectively using the International Physical Activity Questionnaire(IPAQ), Pittsburgh Sleep Quality Index(PSQI), Sedentary Behavior Questionnaire(SBQ), and Short Form Health Survey(SF-36). RESULTS: This mHealth intervention with social comparison-oriented gamification significantly improved moderate physical activity to a greater extent than the control group. Additionally, the intervention group showed improvements in the number of steps, moderate physical activity, sedentary time, emotional wellbeing, and social functioning. However, no significant group by time interaction was observed. No significant differences were observed in sleep quality or quantity. CONCLUSION: s: The LevantApp gamified mHealth intervention was effective in improving moderate physical activity, physical functioning, and role-emotional in young adults. No significant effects were found on step counts, MVPA or sleep, suggesting that while gamification can enhance specific aspects of physical activity and quality of life, its impact may vary across different outcomes.

Effects of an mHealth physical activity intervention to prevent osteoporosis in premenopausal women. A randomized controlled trial.

OBJECTIVES: It is critical to develop prevention strategies for osteoporosis that reduce the burden fractures place on individuals and health systems. This study evaluated the effects of an mHealth intervention that delivered and monitored a non-supervised exercise program on bone mineral density (BMD). DESIGN: Randomized controlled trial. METHODS: 60 premenopausal women aged 35-50 years were divided into an intervention group (IG) and a control group (CG). The IG followed a 6-month intervention aimed at increasing osteogenic physical activity, guided by two daily goals: walking at least 10,000 steps and completing 60 impacts over 4 g of acceleration. These goals were monitored using a wearable accelerometer linked to an mHealth app. The CG maintained their regular lifestyle and wore the accelerometer without feedback. BMD was estimated using dual-energy X-ray absorptiometry (DXA) scans at baseline and after 6 months, with group-by-time analyses conducted using ANCOVA. The intervention's impact on physical fitness and activity habits was also evaluated. RESULTS: 46 participants completed the study (IG = 24; CG = 22). The IG showed significant improvements compared to the CG in femoral neck BMD (IG: +0.003 ±â€¯0.029 g/cm2 vs CG: -0.027 ±â€¯0.031 g/cm2), trochanter BMD (IG: +0.004 ±â€¯0.023 g/cm2 vs CG: -0.026 ±â€¯0.030 g/cm2), and total hip BMD (IG: +0.006 ±â€¯0.043 g/cm2 vs CG: -0.040 ±â€¯0.048 g/cm2). The IG also demonstrated significant improvements in physical fitness measures, including peak torque and power at various speeds and positions. No adverse events related to the intervention were reported. CONCLUSIONS: This non-supervised physical activity intervention delivered by wearable-technology and an mHealth app was effective in improving BMD, suggesting its potential for osteoporosis prevention.

Effects of lifestyle behaviours and depressed mood on sleep quality in young adults. A machine learning approach.

BACKGROUND: Modern lifestyles may lead to high stress levels, frequently associated with mood disorders (e.g. depressed mood) and sleep disturbance. The objective of this study was to develop a machine learning model aimed at identifying risk factors for developing poor sleep quality in young adults. MATERIAL AND METHODS: The sample consisted of 383 college-aged students (mean age ± SD: 21 ± 1 years; 61% males). Sleep quality, mood state, physical activity, number of sitting hours, and smartphone use were measured. RESULTS: A decision tree algorithm distinguished participants' sleep quality with 74% accuracy using a combination of four features: depressed mood, physical activity, sitting time, and vigour. Together with depressed mood, both physical activity (>6432 metabolic equivalent tasks -METs- per week) and sedentary behaviour (sitting time greater than 7 h/day) were the primary features that could differentiate those with poor sleep quality from those with good sleep quality. CONCLUSIONS: We provided a decision tree model with a sensitivity of 90.7% and a specificity of 54.3%, with an AUC of 0.725. These findings could promote improvements in prevention strategies and contribute to the development of meaningful and evidence-based intervention programs.

Supervised and Non-Supervised Exercise Programs for the Management of Cancer-Related Fatigue in Women with Breast Cancer: A Systematic Review and Meta-Analysis.

Physical exercise is considered to be a non-pharmacological strategy for reducing symptoms of cancer-related fatigue (CRF) in women with breast cancer (BC). This systematic review and meta-analysis aims to assess the effects of non-supervised exercise programs in comparison with the effects of supervised exercise interventions for CRF in BC patients. Randomized controlled trials that investigated the effect of exercise on CRF in women were searched for until 29 June 2022. Inclusion criteria comprised women diagnosed with BC; exercise-based interventions; trials comparing at least one exercise group vs. a control group; trials that assessed exercise effects on CRF. Thirty-one studies met the inclusion criteria (n = 2964). Both non-supervised and supervised exercise programs significantly reduced CRF (standard mean difference (SMD) = −0.46, confidence interval (CI) = (−0.64, −0.28), p < 0.0001 and SMD = −0.74, CI = (−0.99, −0.48), p < 0.0001, respectively), without statistical difference (p = 0.09). However, a short-term training program subgroup analyses showed significant differences between supervised and non-supervised training programs (p = 0.01), showing that supervised training programs have a greater effect (SMD = −1.33, CI = (−1.92, −0.73), p < 0.0001) than non-supervised ones (SMD = −0.44, CI = (−0.78, −0.11), p = 0.009). Both supervised and non-supervised exercise programs may reduce CRF in BC patients; however, in the short-term, supervised exercise may have a greater effect on CRF in BC patients.

Effects of Aging and Fitness on Hopping Biomechanics.

Physical exercise promotes healthy aging and is associated with greater functionality and quality of life. Muscle strength and power are established factors in the ability to perform daily tasks and live independently. Stiffness, for mechanical reasons, is another important constituent of running performance and locomotion. This study aims to analyze the impact of age and training status on one-legged hopping biomechanics and to evaluate whether age-related power decline can be reduced with regular physical exercise. Forty-three male subjects were recruited according to their suitability for one of four groups (young athletes, senior athletes, young controls and senior controls) according to their age (young between 21 and 35, vs. older between 59 and 75) and training status (competing athletes vs. non-physically active). The impact of age and training status on one-legged hopping biomechanics were evaluated using the two-way analysis of variance (ANOVA) method. Significant differences among groups were found for hopping height (p < 0.05), ground contact time (p < 0.05), peak ground reaction force (p < 0.05) and peak power (p < 0.01). No differences among groups were found in ground-phase vertical displacement and vertical stiffness (p > 0.05). Young athletes and older non-physically active people achieved the best and worst performance, respectively. Interestingly, there were not any differences found between young non-physically active people and senior athletes, suggesting that chronic training can contribute to partly offset effects that are normally associated with aging.