Mobile Health (mHealth) Apps in Sport Training: A Scoping Review

Mobile health (mHealth) apps increasingly capture the physiological, biomechanical, and psychological variables involved in sport training, but the evidence remains fragmented across single-domain reviews, leaving practitioners without a consolidated basis for selecting and deploying these tools across the training process. This scoping review aimed to identify and characterize research on mHealth apps in sport training, focusing on their performance testing, training load and recovery monitoring, technical and skill development, injury screening and prevention, and athlete self-management. It also synthesized evidence regarding their applications, intended purposes, technical characteristics, and the evidence supporting their effectiveness. Five databases (PubMed, Scopus, Web of Science, SPORTDiscus, Embase) were searched from inception to July 2026 for journal articles reporting original empirical data on app research on the sport training process in athletes. Studies involving only the promotion of physical activity or lacking human-subject testing, including commercially available apps without supporting research on their effectiveness, were excluded. Findings were synthesized narratively, and methodological quality was appraised with the Mixed Methods Appraisal Tool. Of 9476 records identified, 111 studies met the inclusion criteria and were inductively classified into ten application categories: sport skill training (n = 26), performance measurement (n = 20), vertical jump measurement (n = 18), self-reported monitoring (n = 12), physiological measurement (n = 12), musculoskeletal screening (n = 10), psychological intervention (n = 5), nutrition (n = 3), anthropometric and maturation screening (n = 3), and tactical and match analysis (n = 2). Most apps relied on built-in smartphone sensors or no sensing at all and used manual or deterministic computation; processing location went unreported in 74.8% of studies, which reflects a reporting gap rather than an architectural profile of the field, and reported that AI or machine learning labels did not track with actual method disclosure. Validation and reliability designs dominated the evidence base (52%), while randomized or controlled effectiveness trials were rare (10%). Apps generally showed good relative validity but limited absolute accuracy against criterion instruments, and wherever apps were deployed longitudinally, adherence rather than accuracy determined their real-world value. mHealth apps now support nearly every stage of sport training and can substitute for laboratory instruments in select, validated use cases, including video-based sprint and jump timing and chest-strap-paired heart-rate variability monitoring. However, the field remains organized around demonstrating measurement accuracy rather than showing that app-guided decisions improve athlete outcomes. A successful pathway for mHealth app development should progress from technical validity, through measurement reliability and responsiveness, to decision rules, and then to practitioner adoption by coaches and athletes, ultimately yielding better athlete outcomes.