This systematic review explores machine learning (ML) applications in surgical motion analysis using non-optical motion tracking systems (NOMTS), alone or with optical methods. It investigates objectives, experimental designs, model effectiveness, and future research directions. From 3632 records, 84 studies were included, with Artificial Neural Networks (38%) and Support Vector Machines (11%) being the most common ML models. Skill assessment was the primary objective (38%). NOMTS used included internal device kinematics (56%), electromagnetic (17%), inertial (15%), mechanical (11%), and electromyography (1%) sensors. Surgical settings were robotic (60%), laparoscopic (18%), open (16%), and others (6%). Procedures focused on bench-top tasks (67%), clinical models (17%), clinical simulations (9%), and non-clinical simulations (7%). Over 90% accuracy was achieved in 36% of studies. Literature shows NOMTS and ML can enhance surgical precision, assessment, and training. Future research should advance ML in surgical environments, ensure model interpretability and reproducibility, and use larger datasets for accurate evaluation.

This systematic review primarily aims to provide a summary of the game mechanics implemented in eHealth tools supporting young people’s self-management of their chronic diseases. This review secondarily investigates the rationale for implementing game mechanics and the effects of these tools. A systematic search was conducted in Embase, Medline, PsycINFO, and Web of Science, from inception until August 30, 2022. Studies were eligible if focus was on the utilization of gamification in eHealth self-management interventions for young people (age = 10-25 years) with chronic diseases. Primary quantitative, qualitative, and mixed-method studies written in English were included. We identified 34 eHealth tools, of which 20 (59%) were gamified tools and 14 (41%) were serious games. We found that 55 unique game mechanics were implemented. The most commonly used were rewards (50%), score (44%), creative control (41%), and social interaction (32%). In comparison with gamified tools, the number and diversity of game mechanics applied were higher in serious games. For most tools (85%), a general rationale was provided for utilizing gamification, which often was to promote engaging experiences. A rationale for using specific game mechanics was less commonly provided (only for 45% of the game mechanics). The limited availability of experimental research precludes to test the effectiveness of using gamification in eHealth to support self-management in young people with chronic diseases. In this study, we highlight the importance of reporting the rationale for utilizing specific game mechanics in eHealth tools to ensure a proper alignment with evidence-based practice and the need of conducting experimental research. PROSPERO: CRD42021293037.

INTRODUCTION: Young people (aged 10-25 years) with chronic diseases are vulnerable to have reduced social participation and quality of life. It is important to empower young people to engage in their chronic diseases self-management. In comparison with traditional face-to-face care, interventions delivered through the internet and related technologies (eHealth) are less stigmatising and more accessible. Gamified eHealth self-management interventions may be particularly promising for young people. This systematic review aims at identifying (1) the game mechanics that have been implemented in eHealth interventions to support young people's self-management of their chronic (somatic or psychiatric) diseases, (2) the investigators' rationale for implementing such game mechanics and, if possible, (3) the effects of these interventions. METHODS AND ANALYSIS: The Preferred Reporting Items for Systematic reviews and Meta-Analysis statement guidelines will be followed. A systematic search of the literature will be conducted in Embase, Psycinfo and Web of Science from inception until 30 August 2022. Studies will be eligible if focused on (1) young people (aged 10-25 years) with chronic diseases and (2) describing gamified eHealth self-management interventions. When possible, the effects of the gamified interventions will be compared with non-gamified interventions or care-as-usual. Primary quantitative, qualitative or mixed-method studies written in English will be included. Two independent reviewers will (1) select studies, (2) extract and summarise the implemented game mechanics as well as the characteristics of the intervention and study, (3) evaluate their methodological quality and (4) synthesise the evidence. The reviewers will reach a consensus through discussion, and if required, a third researcher will be consulted. ETHICS AND DISSEMINATION: As systematic reviews use publicly available data, no formal ethical review and approval are needed. Findings will be published in peer-reviewed journals, presented at conferences and communicated to relevant stakeholders including patient organisations via the eHealth Junior Consortium. PROSPERO REGISTRATION NUMBER: CRD42021293037.

Objective: To summarize available evidence on the association between hip shape as quantified by statistical shape modeling (SSM) and the incidence or progression of hip osteoarthritis. Design: We conducted a systematic search of five electronic databases, based on a registered protocol (available: PROSPERO CRD42020145411). Articles presenting original data on the longitudinal relationship between radiographic hip shape (quantified by SSM) and hip OA were eligible. Quantitative meta-analysis was precluded because of the use of different SSM models across studies. We used the Newcastle–Ottawa Scale (NOS) for risk of bias assessment. Results: Nine studies (6,483 hips analyzed with SSM) were included in this review. The SSM models used to describe hip shape ranged from 16 points on the femoral head to 85 points on the proximal femur and hemipelvis. Multiple hip shape features and combinations thereof were associated with incident or progressive hip OA. Shape variants that seemed to be consistently associated with hip OA across studies were acetabular dysplasia, cam morphology, and deviations in acetabular version (either excessive anteversion or retroversion). Conclusions: Various radiographic, SSM-defined hip shape features are associated with hip OA. Some hip shape features only seem to increase the risk for hip OA when combined together. The heterogeneity of the used SSM models across studies precludes the estimation of pooled effect sizes. Further studies using the same SSM model and definition of hip OA are needed to allow for the comparison of outcomes across studies, and to validate the found associations.