Real-Time Exercise Feedback through a Convolutional Neural Network: A Machine Learning-Based Motion-Detecting Mobile Exercise Coaching Application
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Purpose: Mobile 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 streamingbased exercise for improving quality of life and decreasing lower back pain. Materials and Methods: The 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. Results: The MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09; p<0.01) and a greater reduction in lower back pain score (MDMECA, -0.96; control, -0.26; p<0.01). Scores for TS-VAS, intention to use a disease-oriented exercise program, and intention to recommend the program to others were all higher (p<0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups. Conclusion: The 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.
Abstract
Purpose: Mobile 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 streamingbased exercise for improving quality of life and decreasing lower back pain. Materials and Methods: The 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. Results: The MDMECA group showed a higher increase in SF-36 score (MDMECA, 9.10; control, 1.09; p<0.01) and a greater reduction in lower back pain score (MDMECA, -0.96; control, -0.26; p<0.01). Scores for TS-VAS, intention to use a disease-oriented exercise program, and intention to recommend the program to others were all higher (p<0.01) in the MDMECA group. However, the available expenses for a disease-oriented program were not significantly different between the two groups. Conclusion: The 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.
참고문헌 (19)
- Jette AM, "A home-based exercise program for nondisabled older adults", J Am Geriatr Soc, 44, pp.644~649 (1996)
- Miller JS, "Motivating patients with shoulder and back pain to self-care : can a videotape of exercise support physiotherapy?", Physiotherapy, 95, pp.29~35 (2009)
- Sweeney S, "The effect of a home based exercise intervention package on outcome in ankylosing spondylitis : a randomized controlled trial", J Rheumatol, 29, pp.763~766 (2002)
- Stockwell K, "Feasibility of parent communication training with remote coaching using smartphone apps", Int J Lang Commun Disord, 54, pp.265~280 (2019)
- Wijnen A, "Effectiveness of a home-based rehabilitation program after total hip arthroplasty driven by a tablet app and remote coaching : nonrandomized controlled trial combining a single-arm intervention cohort with historical controls", JMIR Rehabil Assist Technol, 7, pp.e14139~ (2020)
- Ricciardi C, "Using gait analysis’ parameters to classify Parkinsonism : a data mining approach", Comput Methods Programs Biomed, 180, pp.105033~ (2019)
- Cook RE, "Gait analysis alters decision-making in cerebral palsy", J Pediatr Orthop, 23, pp.292~295 (2003)
- Balaban B, "Gait disturbances in patients with stroke", PM R, 6, pp.635~642 (2014)
- Romano A, "Evaluation of gait in Duchenne Muscular Dystrophy : relation of 3D gait analysis to clinical assessment", Neuromuscul Disord, 29, pp.920~929 (2019)
- Barbara Pellegrini, "Biomechanical analysis of the “running” vs. “conventional” diagonal stride uphill techniques as performed by elite cross-country skiers", Journal of Sport and Health Science, 11(1), pp.30~39 (2020), Elsevier BV[DOI]
- Jandacka D, "Footfall patterns of a runner with an Achilles tendon rupture", J Sport Health Sci, 6, pp.498~502 (2017)
- Oyama S, "Baseball pitching kinematics, joint loads, and injury prevention", J Sport Health Sci, 1, pp.80~91 (2012)
- Wang JJ, "Determine an effective golf swing by swing speed and impact precision tests", J Sport Health Sci, 4, pp.244~249 (2015)
- Rincón D, "Wristbands containing accelerometers for objective arm swing analysis in patients with Parkinson’s disease", Sensors(Basel), 20, pp.4339~ (2020)
- Lin TY, "Lecture notes in computer science, vol 8693", pp.740~755 (2014), Springer International Publishing
- Brazier JE, "Validating the SF-36 health survey questionnaire : new outcome measure for primary care", BMJ, 305, pp.160~164 (1992)
- Morris PE, "Standardized rehabilitation and hospital length of stay among patients with acute respiratory failure : a randomized clinical trial", JAMA, 315, pp.2694~2702 (2016)
- McDermott MM, "Effect of a home-based exercise intervention of wearable technology and telephone coaching on walking performance in peripheral artery disease : the HONOR randomized clinical trial", JAMA, 319, pp.1665~1676 (2018)
- 김성규, "Self-Reported Physical Activity Is Associated with Lupus Nephritis in Systemic Lupus Erythematosus: Data from KORean Lupus Network (KORNET) Registry", Yonsei Medical Journal, 59(7), pp.857~864 (2018), 연세대학교의과대학[DOI]