Low Quality of Free Coaching Apps With Respect to the American College of Sports Medicine Guidelines: A Review of Current Mobile Apps
Bibliographic record
Abstract
BACKGROUND: Low physical activity level is a significant contributor to chronic disease, weight dysregulation, and mortality. Nearly 70% of the American population is overweight, and 35% is obese. Obesity costs an estimated US$ 147 billion annually in health care, and as many as 95 million years of life. Although poor nutritional habits remain the major culprit, lack of physical activity significantly contributes to the obesity epidemic and related lifestyle diseases. OBJECTIVE: Over the past 10 years, mobile devices have become ubiquitous, and there is an ever-increasing number of mobile apps that are being developed to facilitate physical activity, particularly for active people. However, no systematic assessment has been performed about their quality with respect to following the parameters of sound fitness principles and scientific evidence, or suitability for a variety of fitness levels. The aim of this paper is to fill this gap and assess the quality of mobile coaching apps on iOS mobile devices. METHODS: A set of 30 popular mobile apps pertaining to physical activity programming was identified and reviewed on an iPhone device. These apps met the inclusion criteria and provided specific prescriptive fitness and exercise programming content. The content of these apps was compared against the current guidelines and fitness principles established by the American College of Sports Medicine (ACSM). A weighted scoring method based on the recommendations of the ACSM was developed to generate subscores for quality of programming content for aerobic (0-6 scale), resistance (0-6 scale), and flexibility (0-2 scale) components using the frequency, intensity, time, and type (FITT) principle. An overall score (0-14 scale) was generated from the subscores to represent the overall quality of a fitness coaching app. RESULTS: Only 3 apps scored above 50% on the aerobic component (mean 0.7514, SD 1.2150, maximum 4.1636), 4 scored above 50% on the resistance/strength component (mean 1.4525, SD 1.2101, maximum 4.1094), and no app scored above 50% on the flexibility component (mean 0.1118, SD 0.2679, maximum 0.9816). Finally, only 1 app had an overall score (64.3%) above 50% (mean 2.3158, SD 1.911, maximum 9.0072). CONCLUSIONS: There are over 100,000 health-related apps. When looking at popular free apps related to physical activity, we observe that very few of them are evidence based, and respect the guidelines for aerobic activity, strength/resistance training, and flexibility, set forth by the ACSM. Users should exercise caution when adopting a new app for physical activity purposes. This study also clearly identifies a gap in evidence-based apps that can be used safely and effectively to start a physical routine program, develop fitness, and lose weight. App developers have an exciting opportunity to improve mobile coaching app quality by addressing these gaps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".