The aim of this meta-analysis was to quantify the change in sedentary time during the COVID-19 pandemic and its effect on health outcomes in the general population. One thousand six hundred and one articles published after 2019 were retrieved from five databases, of which 64 and 40 were included in the systematic review and meta-analysis, respectively. Studies were grouped according to population: children (65 years). Average sedentary time was calculated, with sub-analyses performed by country, behaviour type and health outcomes. Children were most affected, increasing their sedentary time by 159.5 ± 142.6 min day⁻¹, followed by adults (+126.9 ± 42.2 min day⁻¹) and older adults (+46.9 ± 22.0 min day⁻¹). There were no sex differences in any age group. Screen time was the only consistently measured behaviour and accounted for 46.8% and 57.2% of total sedentary time in children and adults, respectively. Increases in sedentary time were negatively correlated with global mental health, depression, anxiety and quality of life, irrespective of age. Whilst lockdown negatively affected all age groups, children were more negatively affected than adults or older adults, highlighting this population as a key intervention target. As lockdowns ease worldwide, strategies should be employed to reduce time spent sedentary. Trial registration: PROSPERO (CRD42020208909).

Introduction: Exercise is widely accepted to improve health, reducing the risk of premature mortality, cardiovascular disease (CVD) and cancer. However, several epidemiological studies suggest that the exercise-longevity relationship may be ‘J’ shaped; with elite athlete’s likely training above these intensity and volume thresholds. Therefore, the aim of this meta-analysis was to examine this relationship in former elite athletes. Methods: 38,047 English language articles were retrieved from Web of Science, PubMed and SportDiscus databases published after 1970, of which 44 and 24 were included in the systematic review and meta-analysis, respectively. Athletes were split into three groups depending on primary sport: Endurance (END), Mixed/Team, or power (POW). Standard mortality ratio’s (SMR) and standard proportionate mortality ratio (SPMR) were obtained, or calculated, and combined for the meta-analysis. Results: Athletes lived significantly longer than the general population (male SMR 0.69 [95% CI 0.61–0.78]; female SMR 0.51 [95% CI 0.40–0.65]; both p < 0.01). There was no survival benefit for male POW athletes compared to the general population (SMR 1.04 [95% CI 0.91–1.12]). Although male athlete’s CVD (SMR 0.73 [95% CI 0.62–0.85]) and cancer mortality (SMR 0.75 [95% CI 0.63–0.89]), were significantly reduced compared to the general population, there was no risk-reduction for POW athletes CVD mortality (SMR 1.10 [0.86–1.40]) or END athletes cancer mortality (SMR 0.73 [0.50–1.07]). There was insufficient data to calculate female sport-specific SMR’s. Discussion: Overall, athletes live longer and have a reduced incidence of both CVD and cancer mortality compared to the general population, refuting the ‘J’ shape hypothesis. However, different health risks may be apparent according to sports classification, and between sexes, warranting further investigation. Trial registration PROSPERO (registration number: CRD42019130688).

Objectives: To summarise the evidence regarding the prevalence of anxiety and depression in former elite athletes compared with the general population. Design: Systematic review and meta-analysis. Data sources: Five electronic databases (Web of Science, PubMed, MEDLINE, Scopus and SPORTDiscus) were searched from 1970 to 2023. Eligibility criteria: Studies were included in the meta-analysis if they: (1) were written in English; (2) included male or female elite athletes that had been retired ≥1 year; (3) included a control group or population reference values; (4) reported the time-point prevalence of anxiety and/or depression; and (5) were of a retrospective, longitudinal or prospective, methodological design. A modified version of the Downs and Black tool was used to determine risk of bias. Results: 37 unique studies including 24 732 former athletes (2% female) were included in the meta-analysis. The time-point prevalence of anxiety (prevalence ratio (PR): 2.08 (95% CI 1.57 to 2.60)) and depression (PR: 2.58 (95% CI 2.04 to 3.12)) in former athletes was over twice that of the general population. Subgroup analyses revealed former American football players and jockeys had the highest time-point prevalence of both anxiety and depression (PR: 2.24–2.88), whereas the time-point prevalence of depression and anxiety was not significantly different to the general population for former rugby players (PR: 1.13–1.30). Conclusion: Our meta-analyses demonstrated the time-point prevalence of anxiety and depression in former elite athletes could be over twice that of the general population, with sport-specific differences evident. PROSPERO registration number: CRD42022347359.

Population-level physical activity (PA) and sedentary time/behaviour estimates represent a significant public health issue exacerbated by restrictions enforced to control COVID-19. This integrative review interrogated available literature to explore the pandemic’s impact on correlates of such behaviours in adults (≥18 years). Five electronic databases were systematically searched in January 2021. Data extracted from 64 articles were assessed for risk-of-bias using the Mixed Methods Assessment Tool, with correlates identified, coded, and themed via thematic analysis. A socioecological model of during-pandemic PA was conceptualized and mapped to the Capability, Opportunity, Motivation, and Behaviour (COM-B) model of behaviour change mechanisms, which illustrates influences over five levels: Individual (biological)—general health; Individual (psychological)—mental health, cognition, motivation, and behaviour; Social—domestic situation, sociodemographic factors, support, and lifestyle choices; Environmental—resources and area of residence; and Policy—COVID-19-related rules. For sedentary time/behaviour, individual level factors, namely general and mental health, may be important correlates. Neither age or sex were clearly correlated with either behaviour. As we transition into a new normal, understanding which behaviour mechanisms could effectively challenge physical inactivity is essential. Targeting capability on a psychological level may facilitate PA and limit sedentary time/behaviour, whereas, on a physical level, maximizing PA opportunities could be crucial.

Background: Movement quality is typically assessed by drawing comparisons against predetermined movement standards. Movements are often discretely scored or labelled against pre-set criteria, though movement quality can also be evaluated using motion-related measurements (e.g., spatio-temporal parameters and kinematic variables). Wearable technology has the potential to measure and assess movement quality and offer valuable, practical feedback. Objectives: A systematic approach was taken to examine the benefits associated with multi-sensor and multiple wearable-device usage, compared with unimodal applications, when assessing movement quality. Consequently, this review considers the additional variables and features that could be obtained through multi-sensor devices for use in movement analyses. Processing methods and applications of the various configurations were also explored. Methods: Articles were included within this review if they were written in English, specifically studied the use of wearable sensors to assess movement quality, and were published between January 2010 and December 2022. Of the 62,635 articles initially identified, 27 papers were included in this review. The quality of included studies was determined using a modified Downs and Black checklist, with 24/27 high quality. Results: Fifteen of the 27 included studies used a classification approach, 11 used a measurement approach, and one used both methods. Accelerometers featured in all 27 studies, in isolation (n = 5), with a gyroscope (n = 9), or with both a gyroscope and a magnetometer (n = 13). Sampling frequencies across all studies ranged from 50 to 200 Hz. The most common classification methods were traditional feature-based classifiers (n = 5) and support vector machines (SVM; n = 5). Sensor fusion featured in six of the 16 classification studies and nine of the 12 measurement studies, with the Madgwick algorithm most prevalent (n = 7). Conclusions: This systematic review highlights the differences between the applications and processing methods associated with the use of unimodal and multi-sensor wearable devices when assessing movement quality. Further, the use of multiple devices appears to increase the feasibility of effectively assessing holistic movements, while multi-sensor devices offer the ability to obtain more output metrics.