Background: Both athletes and recreational exercisers often perform relatively high volumes of aerobic and strength training simultaneously. However, the compatibility of these two distinct training modes remains unclear. Objective: This systematic review assessed the compatibility of concurrent aerobic and strength training compared with strength training alone, in terms of adaptations in muscle function (maximal and explosive strength) and muscle mass. Subgroup analyses were conducted to examine the influence of training modality, training type, exercise order, training frequency, age, and training status. Methods: A systematic literature search was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed/MEDLINE, ISI Web of Science, Embase, CINAHL, SPORTDiscus, and Scopus were systematically searched (12 August 2020, updated on 15 March 2021). Eligibility criteria were as follows. Population: healthy adults of any sex and age; Intervention: supervised concurrent aerobic and strength training for at least 4 weeks; Comparison: identical strength training prescription, with no aerobic training; Outcome: maximal strength, explosive strength, and muscle hypertrophy. Results: A total of 43 studies were included. The estimated standardised mean differences (SMD) based on the random-effects model were − 0.06 (95% confidence interval [CI] − 0.20 to 0.09; p = 0.446), − 0.28 (95% CI − 0.48 to − 0.08; p = 0.007), and − 0.01 (95% CI − 0.16 to 0.18; p = 0.919) for maximal strength, explosive strength, and muscle hypertrophy, respectively. Attenuation of explosive strength was more pronounced when concurrent training was performed within the same session (p = 0.043) than when sessions were separated by at least 3 h (p > 0.05). No significant effects were found for the other moderators, i.e. type of aerobic training (cycling vs. running), frequency of concurrent training (> 5 vs. < 5 weekly sessions), training status (untrained vs. active), and mean age (< 40 vs. > 40 years). Conclusion: Concurrent aerobic and strength training does not compromise muscle hypertrophy and maximal strength development. However, explosive strength gains may be attenuated, especially when aerobic and strength training are performed in the same session. These results appeared to be independent of the type of aerobic training, frequency of concurrent training, training status, and age. PROSPERO: CRD42020203777.

Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.

Consumer wearable and smartphone devices provide an accessible means to objectively measure physical activity (PA) through step counts. With the increasing proliferation of this technology, consumers, practitioners and researchers are interested in leveraging these devices as a means to track and facilitate PA behavioural change. However, while the acceptance of these devices is increasing, the validity of many consumer devices have not been rigorously and transparently evaluated. The Towards Intelligent Health and Well-Being Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives to develop best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice consumer wearable and smartphone step counter validation protocol. A two-step process was used to aggregate data and form a scientific foundation for the development of an optimal and feasible validation protocol: (1) a systematic literature review and (2) additional searches of the wider literature pertaining to factors that may introduce bias during the validation of these devices. The systematic literature review process identified 2897 potential articles, with 85 articles deemed eligible for the final dataset. From the synthesised data, we identified a set of six key domains to be considered during design and reporting of validation studies: target population, criterion measure, index measure, validation conditions, data processing and statistical analysis. Based on these six domains, a set of key variables of interest were identified and a ‘basic’ and ‘advanced’ multistage protocol for the validation of consumer wearable and smartphone step counters was developed. The INTERLIVE consortium recommends that the proposed protocol is used when considering the validation of any consumer wearable or smartphone step counter. Checklists have been provided to guide validation protocol development and reporting. The network also provide guidance for future research activities, highlighting the imminent need for the development of feasible alternative ‘gold-standard’ criterion measures for free-living validation. Adherence to these validation and reporting standards will help ensure methodological and reporting consistency, facilitating comparison between consumer devices. Ultimately, this will ensure that as these devices are integrated into standard medical care, consumers, practitioners, industry and researchers can use this technology safely and to its full potential.

The effects of short sprint interval training (sSIT) with efforts of ≤10 s on maximal oxygen consumption (V̇O₂max), aerobic and anaerobic performances remain unknown. To verify the effectiveness of sSIT in physically active adults and athletes, a systematic literature search was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA). The databases PubMed/MEDLINE, ISI Web of Science, and SPORTDiscus were systematically searched on May 9, 2020, and updated on September 14, 2021. Inclusion criteria were based on PICO and included healthy athletes and active adults of any sex (≤40 years), performing supervised sSIT (≤10 s of “all‐out” and non‐“all‐out” efforts) of at least 2 weeks, with a minimum of 6 sessions. As a comparator, a non‐sSIT control group, another high‐intensity interval training (HIIT) group, or a continuous training (CT) group were required. A total of 18 studies were deemed eligible. The estimated SMDs based on the random‐effects model were −0.56 (95% CI: −0.79, −0.33, p < 0.001) for V̇O₂max, −0.43 (95% CI: −0.67, −0.20, p < 0.001) for aerobic performance, and −0.44 (95% CI: −0.70, −0.18, p < 0.001) for anaerobic performance after sSIT vs. no exercise/usual training. However, there were no significant differences (p > 0.05) for all outcomes when comparing sSIT vs. HIIT/CT. Our findings indicate a very high effectiveness of sSIT protocols in different exercise modes (e.g., cycling, running, paddling, and punching) to improve V̇O₂max, aerobic, and anaerobic performances in physically active young healthy adults and athletes.

The primary aim of this study was to examine the associations of normal weight obesity (NWO) with physical fitness in Chinese university students. As a secondary aim, we assessed whether possible differences in physical fitness between students classified as NWO and normal weight non-obese (NWNO) were mediated by skeletal muscles mass. A total of 383 students (205 males and 178 females, aged 18–24 years) from two universities volunteered to participate in this study. Body height and weight were measured by standard procedures and body composition was assessed by bio-impedance analysis (InBody 720). NWO was defined by a BMI of 18.5–23.9 kg/m2 and a body fat percentage of > 20% or > 30% in male and female students, respectively. Physical fitness was measured using a 10-min intermittent endurance running test (Andersen test), countermovement jumps (CMJ) and a 5 × 5 m shuttle run test (5mSR). The level of leisure time physical activity (PA) was assessed by a questionnaire. 13.7% of male and 27.5% of female students were classified as NWO. Compared to NWNO, students classified as NWO showed a significantly poorer performance in the Andersen test (males: 1146 ± 70 m vs. 1046 ± 95 m, females: 968 ± 61 m vs. 907 ± 67 m, p < 0.001), CMJ (males: 55.0 ± 7.6 cm vs. 44.9 ± 7.5 cm, females: 39.8 ± 8.0 cm vs. 33.7 ± 5.9 cm, p < 0.001) and 5mSR (males: 18.7 ± 1.0 s vs. 20.0 ± 0.9 s, females: 21.1 ± 1.1 s vs. 22.4 ± 1.3 s, p < 0.001), respectively. The lower levels of physical fitness in NWO were partially explained by lower skeletal muscle mass (p < 0.001) both in male and female students. NWO was associated with poorer physical fitness and the relationship was partially mediated by lower skeletal muscle mass. The study indicated that attention should be paid for the potential hidden health risk in university students with normal body mass index but excessive fat mass.