A Systematic Review of Fitness Apps and Their Potential Clinical and Sports Utility for Objective and Remote Assessment of Cardiorespiratory Fitness
Notice bibliographique
Résumé
BACKGROUND: Cardiorespiratory fitness (CRF) assessment provides key information regarding general health status that has high clinical utility. In addition, in the sports setting, CRF testing is needed to establish a baseline level, prescribe an individualized training program and monitor improvement in athletic performance. As such, the assessment of CRF has both clinical and sports utility. Technological advancements have led to increased digitization within healthcare and athletics. Nevertheless, further investigation is needed to enhance the validity and reliability of existing fitness apps for CRF assessment in both contexts. OBJECTIVES: The present review aimed to (1) systematically review the scientific literature, examining the validity and reliability of apps designed for CRF assessment; and (2) systematically review and qualitatively score available fitness apps in the two main app markets. Lastly, this systematic review outlines evidence-based practical recommendations for developing future apps that measure CRF. DATA SOURCES: , ScopusTM, and SPORTDiscus, and data was also found within app markets (Google Play and the App Store). STUDY ELIGIBILITY CRITERIA: Eligible scientific studies examined the validity and/or reliability of apps for assessing CRF through a field-based fitness test. Criteria for the app markets involved apps that estimated CRF. STUDY APPRAISAL AND SYNTHESIS METHODS: The scientific literature search included four major electronic databases and the timeframe was set between 01 January 2000 and 31 October 2018. A total of 2796 articles were identified using a set of fitness-related terms, of which five articles were finally selected and included in this review. The app market search was undertaken by introducing keywords into the search engine of each app market without specified search categories. A total of 691 apps were identified using a set of fitness-related terms, of which 88 apps were finally included in the quantitative and qualitative synthesis. RESULTS: Five studies focused on the scientific validity of fitness tests with apps, while only two of these focused on reliability. Four studies used a sub-maximal fitness test via apps. Out of the scientific apps reviewed, the SA-6MWTapp showed the best validity against a criterion measure (r = 0.88), whilst the InterWalk app showed the highest test-retest reliability (ICC range 0.85-0.86). LIMITATIONS: Levels of evidence based on scientific validity/reliability of apps and on commercial apps could not be robustly determined due to the limited number of studies identified in the literature and the low-to-moderate quality of commercial apps. CONCLUSIONS: The results from this scientific review showed that few apps have been empirically tested, and among those that have, not all were valid or reliable. In addition, commercial apps were of low-to-moderate quality, suggesting that their potential for assessing CRF has yet to be realized. Lastly, this manuscript has identified evidence-based practical recommendations that apps might potentially offer to objectively and remotely assess CRF as a complementary tool to traditional methods in the clinical and sports settings.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,007 |
| Bibliométrie | 0,020 | 0,016 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».