HQ5 : Do Intervention and Evaluation Using Digital Health Services Prevent Sarcopenia in High‐Risk Non‐Older Adults? English Translation of the Japanese Guideline for Digital Health for the Prevention and Improvement of Sarcopenia and Frailty

Counseling and intervention services for the prevention of sarcopenia, including exercise therapy, have been provided in institutions. In recent years, with the development of information and communication technology and various devices, a situation has emerged in which information, devices, people, and organizations can be connected and used as digital health services. Indeed, technologies and methods utilizing digital health services, such as sensors for physical and mental status, smartphone and web-based interventions, and the use of games, have been developed and may contribute to the prevention of sarcopenia. However, there is no consensus on whether interventions and assessments using these digital health services are effective in preventing sarcopenia in high-risk non-older adults. We defined this as a healthcare question (HQ), and the purpose of this study was to conduct a meta-analysis of the HQ and to use the results as a guideline. A systematic review was conducted by searching articles in three databases (Web of Science, MEDLINE, and CENTRAL). A total of 11 673 articles were identified, and after excluding duplicates, primary screening (abstract survey) and full-text screening were conducted. Ultimately, 36 articles were extracted as eligible articles (Figure 1) 1. A meta-analysis was performed on 29 studies 2-30, excluding those that could not be integrated because the data were described only in terms of percentage change or lacked post-intervention measurements. Interventions and evaluations using a variety of digital health services were conducted in individuals with diverse disease backgrounds, including stroke, multiple sclerosis, cancer, and knee osteoarthritis; outcomes examined included grip strength, lower extremity muscle strength, chair stand-up test, lean body mass, gait speed, Dynamic Gait Index (DGI), Timed Up and Go Test (TUG), and number of dropouts. Digital health services used included monitoring with wearable devices and sensors, exercise management using a smartphone application, remote exercise instruction using a web system, games, and VR. The intervention duration ranged from 2 weeks to 6 months. Forest plots showed significant intervention effects on grip strength, chair stand-up test, gait speed, DGI, and TUG. Compared with the control group, the intervention group was 2.24 kg higher in grip strength (p = 0.02) (Figure 2) 1, 3.98 times/30s more in chair stand-up test (p < 0.001) (Figure 3) 1, 0.10 m/s faster in gait speed (p = 0.01) (Figure 4) 1, 3.54 higher in DGI (p < 0.001) (Figure 5) 1 and 1.47 s shorter in TUG (p < 0.001) (Figure 6) 1. The number of dropouts was lower in the intervention group than in the control group (p = 0.03) (Figure 7) 1. On the other hand, there was no significant intervention effect on lower extremity muscle strength or lean body mass. The subjects, intervention methods, and durations, as well as outcomes and intervention effects, in the studies included in the meta-analysis are summarized in Table 1 1. Since it was difficult to analyze the effects of individual digital health services due to the small number of studies, we analyzed the effects of digital health services by classifying them into two categories: “asynchronous intervention,” in which the subject alone performs the intervention using digital devices such as apps and wearable devices, and “synchronous intervention,” in which people other than the subject are involved through guidance and other means using VR and videoconferencing. The results showed that grip strength, chair stand-up test, gait speed, DGI, and TUG, which showed significant intervention effects when not classified into synchronous and asynchronous interventions, lost significant effects in asynchronous interventions (no study cases for DGI) and continued to show significant intervention effects in synchronous interventions (Figures 2-6) 1. For example, 8 weeks of balance training with VR for subacute stroke patients significantly improved DGI and TUG compared with a control group with vestibular organ rehabilitation (DGI: 19.20 ± 2.11 vs. 15.66 ± 2.69, TUG: 15 ± 4.90 vs. 19 ± 2.45 s) 14 (Figures 5 and 6) 1. There was no difference in the number of dropouts between the asynchronous and synchronous interventions (Figure 7) 1. No significant intervention effects were observed for lower extremity muscle strength or lean body mass for either asynchronous or synchronous intervention. No significant intervention effects were found for lower extremity muscle strength and lean body mass; however, significant intervention effects were observed for gait speed, chair stand-up test, DGI, and TUG. These results suggest that the intervention using digital health services may not have a significant effect on muscle strength and muscle mass, but may have an effect on overall lower limb function, including balance ability. Additionally, synchronous interventions involving individuals other than the subject may be more effective when utilizing digital health services. However, it is challenging to draw a conclusion due to the limited number of studies. The fact that fewer participants dropped out of the intervention group compared with the control group may be due to several factors, including the possibility that participants enjoyed playing games and other activities, as well as the ease of participation through online intervention. Further research is expected to develop not only intervention methods that lead to effective outcomes, but also methods that can be sustained. To assess the quality of evidence for the systematic review, each outcome was assessed using the GRADE criteria. The results showed that the certainty of evidence was “moderate” for gait speed and number of dropouts, “low” for grip strength and lean body mass, and “very low” for lower limb muscle strength, chair rise test, DGI, and TUG. Based on these results, we concluded that interventions using digital health services are suggested to prevent sarcopenia in non-older adults at risk for sarcopenia, although more research is needed. No adverse events were reported in the included studies. Due to the intervention effects on outcomes related to sarcopenia (grip strength, chair stand test, gait speed, DGI, and TUG), there may be more benefit than risk. Citizen representatives were included in the HQ development meetings and in the voting to determine the recommendations, considering their intentions as much as possible. Considering that the household ownership rate of smartphones is approximately 90% and the individual ownership rate is ~70% by 2020 (2021 White Paper on Information and Communications, Ministry of Internal Affairs and Communications), the barriers for using smartphone applications and other interventions are expected to be relatively low. On the other hand, there may be cases where the purchase of new wearable devices is necessary. As evidence on the effectiveness of digital health services accumulates, cost-effectiveness is expected to become clearer. In developing this guideline, we concluded that the recommendation is weak and the certainty of the evidence is low due to the limited number of studies that met the criteria for a systematic review. It is hoped that future studies will be conducted to examine the effects of interventions utilizing digital health services. The present study suggests that synchronous interventions (e.g., VR, videoconferencing) may be more effective than asynchronous interventions (e.g., interventions conducted by the subject alone using applications, wearable devices), in which non-subjects are involved through instruction, etc. In the future, studies should be conducted to verify the effectiveness of digital health services using digital devices alone and in combination with both human and digital devices, with the goal of establishing more effective digital health services. In recent years, the technological evolution of generative AI, utilizing large-scale language models (LLM), has been remarkable, enabling the production of natural, human-like responses and sentences. Future research progress on digital health services using LLM, as well as human-digital device combinations, is expected. In addition to verifying the effectiveness of digital health services, it is expected that research and development will be conducted to compare the effectiveness of digital health services with the use of facilities, to examine intervention methods that lead to continued use, and to research and develop intervention methods that are tailored to individual characteristics, such as the user's environment and personality. This document is the official English translation of the Japanese version published by the Japanese Association for Sarcopenia and Frailty and the National Center for Geriatrics and Gerontology in March 2025 (1). This work was supported by the Japan Agency for Medical Research and Development (AMED) under the project “Project for Establishing Research and Development Infrastructure for the Social Implementation of Prevention and Health Promotion/Healthcare Social Implementation Infrastructure Development Project” (Management No. JP22rea522005). M.A. has disclosed financial relationships with Daiichi Sankyo Co. Ltd., MSD K.K. Toa Eiyo Co. Ltd., Towa Pharmaceutical Co. Ltd., Eisai Co. Ltd., Kracie Pharmaceutical Co. Ltd., Tsumura Pharmaceutical Co. Ltd., Tanabe Mitsubishi Pharma Corporation, Chugai Pharmaceutical Co. Ltd., Ono Pharmaceutical Co. Ltd., Takeda Pharmaceutical Co. Ltd., Astellas Pharma Inc., Bayer Yakuhin Ltd.