Virtual Reality As a Training Tool to Treat Physical Inactivity in Children

Time to act on childhood obesity: the use of technology

Childhood obesity is rapidly increasing worldwide and there is an urgent need to implement treatment and prevention programs. Over the last decade, in addition to increasing rates of childhood obesity, we have also observed rapid technological and digital development. The Covid-19 pandemic has largely contributed to both expansions but has also allowed an opening towards a broader vision of medicine, through new therapeutic opportunities such as mobile healthcare. The digital and technological delivery of obesity prevention and treatment programs can represent an innovative tool to support children and families to overcome some limitations and barriers such as the accessibility of programs that prevent them from adopting healthy lifestyle changes. This review aimed to summarize the impact of different digital interventions for children and adolescent affected by obesity.

Harnessing technological solutions for childhood obesity prevention and treatment: a systematic review and meta-analysis of current applications

BACKGROUND

Technology holds promise for delivery of accessible, individualized, and destigmatized obesity prevention and treatment to youth.

OBJECTIVES

This review examined the efficacy of recent technology-based interventions on weight outcomes.

METHODS

Seven databases were searched in April 2020 following PRISMA guidelines. Inclusion criteria were: participants aged 1-18 y, use of technology in a prevention/treatment intervention for overweight/obesity; weight outcome; randomized controlled trial (RCT); and published after January 2014. Random effects models with inverse variance weighting estimated pooled mean effect sizes separately for treatment and prevention interventions. Meta-regressions examined the effect of technology type (telemedicine or technology-based), technology purpose (stand-alone or adjunct), comparator (active or no-contact control), delivery (to parent, child, or both), study type (pilot or not), child age, and intervention duration.

FINDINGS

In total, 3406 records were screened for inclusion; 55 studies representing 54 unique RCTs met inclusion criteria. Most (89%) included articles were of high or moderate quality. Thirty studies relied mostly or solely on technology for intervention delivery. Meta-analyses of the 20 prevention RCTs did not show a significant effect of prevention interventions on weight outcomes (d = 0.05, p = 0.52). The pooled mean effect size of n = 32 treatment RCTs showed a small, significant effect on weight outcomes (d = ‒0.13, p = 0.001), although 27 of 33 treatment studies (79%) did not find significant differences between treatment and comparators. There were significantly greater treatment effects on outcomes for pilot interventions, interventions delivered to the child compared to parent-delivered interventions, and as child age increased and intervention duration decreased. No other subgroup analyses were significant.

CONCLUSIONS

Recent technology-based interventions for the treatment of pediatric obesity show small effects on weight; however, evidence is inconclusive on the efficacy of technology based prevention interventions. Research is needed to determine the comparative effectiveness of technology-based interventions to gold-standard interventions and elucidate the potential for mHealth/eHealth to increase scalability and reduce costs while maximizing impact.

Antifragility in Climbing: Determining Optimal Stress Loads for Athletic Performance Training

In the past decades, much research has examined the negative effects of stressors on the performance of athletes. However, according to evolutionary biology, organisms may exhibit growth under stress, a phenomenon called antifragility. For both coaches and their athletes, a key question is how to design training conditions to help athletes develop the kinds of physical, physiological, and behavioral adaptations underlying antifragility. An answer to this important question requires a better understanding of how individual athletes respond to stress or loads in the context of relevant sports tasks. In order to contribute to such understanding, the present study leverages a theoretical and methodological approach to generate individualized load-response profiles in the context of a climbing task. Climbers (n = 37) were asked to complete different bouldering (climbing) routes with increasing loading (i.e. difficulty). We quantified the behavioral responses of each individual athlete by mathematically combining two measures obtained for each route: (a) maximal performance (i.e. the percentage of the route that was completed) and (b) number of attempts required to achieve maximal performance. We mapped this composite response variable as a function of route difficulty. This procedure resulted in load-response curves that captured each athlete's adaptability to stress, termed phenotypic plasticity (PP), specifically operationalized as the area under the generated curves. The results indicate individual load-response profiles (and by extension PP) for athletes who perform at similar maximum levels. We discuss how these profiles might be used by coaches to systematically select stress loads that may be ideally featured in performance training.