Objectives The use of natural environments and nature activities as elements in the treatment and rehabilitation of mental health challenges is gaining international attention. The objective of the present review was to summarise the knowledge on the effects of nature-based health interventions (NBHIs) targeting individuals diagnosed with anxiety, depression and/or experiencing stress. Design Systematic review and meta-analyses. The quality and certainty of evidence were assessed using the SIGN and GRADE. Data sources Searches were performed in Embase, MEDLINE, PsycINFO, CINAHL, Cochrane and Web of Science. Eligibility criteria (1) NBHIs, (2) Individuals with a diagnosis of mild to moderate anxiety, depression and/or experiencing stress, (3) Age of participating individuals: 18–84 years, (4) Study designs: randomised controlled trials, cohort studies, case-control studies and case-series studies and (5) Publication date: 2000–2024. Data extraction and synthesis Screening, quality appraisal and certainty of evidence, assessed using SIGN and GRADE, were performed by two independent reviewers, except title screening. Meta-analyses were performed using random-effect models. Results Nineteen articles were included, of which 14 were included in the meta-analyses. The articles showed substantial variation in design, interventions, settings and risk of bias, limiting the certainty of evidence according to GRADE. Participating in NBHIs led to a small to large effect in mental health with standardised mean changes of −0.80 (95% CI= (−1.56; −0.04)), −0.87 (95% CI= (−1.18; −0.56)), −0.32 (95% CI= (−0.74; 0.09)) and 0.58 (95% CI= (0.39; 0.77)) for anxiety, depression and stress scores and overall mental health scores, respectively. Conclusions This is the first systematic review examining the effect of NBHIs exclusively on individuals diagnosed with anxiety, depression and/or experiencing stress. Our findings suggest small to large improvements after participating in NBHIs. However, methodological limitations to the included articles necessitate cautious interpretation. PROSPERO registration number CRD42024516270.

Throwing speed is likely a key determinant of shoulder-specific load. However, it is difficult to estimate the speed of throws in handball in field-based settings with many players due to limitations in current technology. Therefore, the purpose of this study was to develop a novel method to estimate throwing speed in handball using a low-cost accelerometer-based device. Nineteen experienced handball players each performed 25 throws of varying types while we measured the acceleration of the wrist using the accelerometer and the throwing speed using 3D motion capture. Using cross-validation, we developed four prediction models using combinations of the logarithm of the peak total acceleration, sex and throwing type as the predictor and the throwing speed as the outcome. We found that all models were well-calibrated (mean calibration of all models: 0.0 m/s, calibration slope of all models: 1.00) and precise (R2 = 0.71–0.86, mean absolute error = 1.30–1.82 m/s). We conclude that the developed method provides practitioners and researchers with a feasible and cheap method to estimate throwing speed in handball from segments of wrist acceleration signals containing only a single throw.

Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. Methods: We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. Results: Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. Conclusions: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.