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Mobile Health Applications and Software for the Detection and Management of Sarcopenia Among Older Adults: A Scoping
Meghna Suresh Prabhu1, Sucheta V Kolekar2, Girish Nandakumar1
1Department of Physiotherapy, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Background And Purpose:
Sarcopenia, a condition characterized by reduced muscle strength, mass, and physical performance, is commonly diagnosed using tools such as hand grip strength tests, dual-energy X-ray absorptiometry, bioelectrical impedance analysis, and gait speed measurements. Despite their reliability, these methods are often inaccessible due to high costs and low availability in many settings. Mobile Health (mHealth) and artificial intelligence tools can improve early disease detection. While some mHealth technologies have been developed for sarcopenia detection and management, a comprehensive review of their details is lacking. This review aims to identify and summarize the applications and software available for detecting and managing sarcopenia.
Methods:
A systematic search was conducted across six databases: Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, PubMed, Scopus, and Web of Science, using a predefined set of validated keywords. Data extracted includes the names of applications and software, diagnostic criteria utilized, required sensors and external devices, operating systems, availability, cost, appraised components, and diagnostic precision. The methodological quality of the studies was assessed using the Joanna Briggs Institute checklist and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS 2).
Results:
Thirteen studies were selected from the initial 2700 based on the specified inclusion criteria. Five applications and two software tools employing one or more surrogate measures, with/without external devices, were identified, and a few have adopted the diagnostic algorithm. Most studies did not report diagnostic accuracy, the available ones ranged from 76% to 94.9%. Three applications involving resistance exercise and remote monitoring for management were identified.
Conclusion:
This review identified five applications and two software tools for sarcopenia detection, and three applications for its management. Given the subpar methodological quality of the evidence, the review's findings should be interpreted cautiously. These results signify the need for the development of a comprehensive app to detect and manage sarcopenia based on a standard diagnostic algorithm and a tailor-made rehabilitation program based on the severity of sarcopenia.
