Enhancing Athlete Performance with Mobile Health Applications: Benefits and Challenges
Bibliographic record
Abstract
Mobile health applications have gained significant attention in recent years due to their ability to enhance athletic performance, prevent injuries, and support overall health management.By integrating advanced technologies, these applications provide athletes with personalized exercise plans, real-time monitoring of physiological and psychoemotional indicators, and immediate feedback.However, challenges such as user adherence, personalization, and privacy concerns persist, requiring further exploration.This study employed a systematic literature review (SLR) using the PRISMA framework to identify, screen, and synthesize peer-reviewed studies published between 2013 and 2023.Databases including PubMed, Scopus, and Web of Science were searched using terms such as "mobile health applications," "athlete performance," and "privacy compliance."Eligible studies focused on mobile health applications specifically designed for athletes, addressing features like personalized plans, monitoring, and data security.Quality assessments were performed using the Cochrane Risk of Bias Tool and the Newcastle-Ottawa Scale.The review identified that mobile health applications significantly improve athletic performance, injury prevention, and motivation through personalized exercise plans and real-time feedback.However, persistent challenges were noted, including difficulties in personalizing plans for diverse athletes, ensuring user adherence, and addressing privacy and security concerns related to data handling.The findings underscore the need for robust privacy mechanisms and interdisciplinary collaboration to optimize these applications.Mobile health applications play a vital role in enhancing athletes' physical and mental well-being.However, addressing challenges such as personalization, adherence, and data security is essential to their continued success.Future research should focus on advancing AI-driven personalization, improving user engagement, and strengthening privacy safeguards to maximize the potential of these applications in athletic health management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.070 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".