ArticleYonsei medical journal2022
Real-Time Exercise Feedback through a Convolutional Neural Network: A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application.
Article in Yonsei medical journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Using artificial intelligence for physical exercise and healthy ageing among older adults: a scoping review.BMC geriatrics · 2026Article
- Strategies to Increase Patients' Adherence to Digital Therapeutics for Musculoskeletal Diseases: A Narrative Review.Yonsei medical journal · 2026Review
- Artificial intelligence in spine care: A scoping review of treatment applications.North American Spine Society journal · 2026Review
- Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review.JMIR human factors · 2024Article
- A scoping review of methodologies for applying artificial intelligence to physical activity interventions.Journal of sport and health science · 2024Article
- Feasibility of Mobile Health App-Based Home Aerobic Exercise for Physical Performance in Healthy Young Adults.Annals of rehabilitation medicine · 2024Article
- Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review.Journal of medical Internet research · 2023Article
- Mobile Apps to Improve Brace-Wearing Compliance in Patients with Idiopathic Scoliosis: A Quality Analysis, Functionality Review and Future Directions.Journal of clinical medicine · 2023Article
- Promoting Adherence to Joint Exercise Using the Donation Model: Proof via a Motion-Detecting Mobile Exercise Coaching Application.Yonsei medical journal · 2022Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMobile applications are widely used in the healthcare market. This study aimed to determine whether exercise using a machine learning-based motion-detecting mobile exercise coaching application (MDMECA) is superior to video streaming-based exercise for improving quality of life and decreasing lower back pain. MATERIALS AND
methodsThe same 14-day daily workout program consisting of five exercises was performed by 104 participants using the MDMECA and another 72 participants using video streaming. The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) and lower back pain scores were assess as pre- and post-workout measurements. Scores for the treatment-satisfaction subscale of the visual analog scale (TS-VAS), intention to use a disease-oriented exercise program, intention to recommend the program to others, and available expenses for a disease-oriented exercise program were determined after the workout.
resultsThe MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09;
conclusionThe MDMECA is more effective than video streaming-based exercise in increasing exercise adherence, improving QoL, and reducing lower back pain. MDMECAs could be promising tools of use to achieve better medical outcomes and higher treatment satisfaction.
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Registered trials
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