“Wearables on vogue”: a scoping review on wearables on physical activity and sedentary behavior during COVID-19 pandemic

Background Wearables are intriguing way to promote physical activity and reduce sedentary behavior in populations with and without chronic diseases. However, the contemporary evidence demonstrating the effectiveness of wearables on physical health during the COVID-19 pandemic has yet to be explored. Aim The present review aims to provide the readers with a broader knowledge of the impact of wearables on physical health during the pandemic. Methods Five electronic databases (Web of Science, Scopus, Ovid Medline, Cumulative Index to Nursing and Allied Health Literature and Embase) were searched. The eligibility criteria of the studies to be included were based on PICOT criteria: population (adults, children and elderly), intervention (wearable, smartphones), comparison (any behavioral intervention), outcome (physical activity or sedentary behavior levels) and time frame (between December 1st, 2019 and November 19th, 2021). The present scoping review was framed as per the guidelines of the Arksey and O’Malley framework. Results Of 469 citations initially screened, 17 articles were deemed eligible for inclusion and potential scoping was done. Smartphone-based applications with inbuilt accelerometers were commonly used, while a few studies employed smart bands, smartwatches for physical health monitoring. Most of the studies observed the increased use of wearables in healthy adults followed by elderly, children and pregnant women. Considerable reduction (almost—50%) in physical activity during the pandemic: daily step count (− 2812 steps/min), standing (− 32.7%) and walking (− 52.2%) time was found. Conclusion Wearables appears to be impending means of improving physical activity and reducing sedentary behavior remotely during the COVID-19 pandemic.

Influence of fat-free mass and resting metabolic rate on increased food reinforcement after exercise training

Purpose Models of appetite control have been largely based on negative feedback from gut and adipose signaling to central appetite centers. However, contemporary models posit that fat-free mass (FFM) or the energy demand of FFM [i.e., resting metabolic rate (RMR)] may play a primary role in the motivational drive for food intake (i.e., food reinforcement). The relative reinforcing value of food (RRVfood) is associated with energy intake (EI) and increases with an acute energy deficit. Chronic exercise-induced energy deficits lead to alterations in fat mass (FM), FFM, and RMR and provide an opportunity to test whether change in (∆) FM, ∆FFM, ∆usual EI, or ∆RMR are associated with ∆RRVfood. Methods Participants (n = 29, BMI = 25–35 kg/m2) engaged in aerobic exercise expending 300 or 600 kcal, 5 days/weeks for 12 weeks. The reinforcing value of food (PMaxfood) was measured via a computer-based operant responding task and RRVfood was calculated as the reinforcing value of food relative to non-eating sedentary behaviors. RMR was determined by indirect calorimetry and body composition by DXA. Results Post-training FFM correlated with usual post-training EI (rs = 0.41, p < 0.05), PMaxfood (rs=0.52, p < 0.01), and RMR (rs = 0.85, p < 0.0001). ∆RMR negatively correlated with ∆PMaxfood (rs = − 0.38, p < 0.05) and with ∆RRVfood (rs = − 0.37, p < 0.05). ∆PMaxfood and ∆RRVfood were not associated with ∆FFM (p = 0.71, p = 0.57, respectively). Conclusions Reductions in RMR with weight loss may increase food reinforcement as means of restoring FFM and RMR to pre-weight loss amounts. Limiting reductions in RMR during weight loss may benefit weight maintenance by restricting increases in food reinforcement after weight loss.

Maturational effect on physical capacities and anabolic hormones in under-16 elite footballers: a cross-sectional study

Purpose The purposes of this study were to describe the fitness and hormonal levels according to playing time (PT) (i.e., PT during season less (PT1) or more (PT2) than 50% of the total time) and maturation level (ML) (i.e., normal (ML1) and early maturity levels (ML2)), and to analyze the differences between groups for the measures of aerobic capacity, anaerobic power, power performance, and hormonal concentrations. Methods Twenty-four youth footballers of a U16 team participated in this study. Anthropometric measures, maturity status, growth hormone, insulin-like growth factor (IGF-1), maximal oxygen uptake, fatigue index, and countermovement jump were collected. Results Significant differences were found between both PT and ML groups for maturational status, aerobic capacity, power performance, and IGF1 concentrations. The interaction of PT and ML revealed significant differences for maturity offset and power performance. When using the skeletal age as a covariant, the previously significant differences found were reduced only to the fatigue index measure. Conclusions The response variables analyzed in the present study seem to be influenced by PT and ML. This must be considered when planning training, and coaches must be sensible to these effects as they may assume a preponderant role in PT.