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Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. We evaluate the proposed framework against prevailing fall-prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on large-scale data sets. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel IT artifacts for healthcare applications. Whereas modern medicine has enabled humans to live longer and more robust lives, recent years have seen a significant increase in chronic care costs. The prevention of threats to mobility, such as falls, freezing of gait, and others, is critical for chronic disease management. Researchers and physicians often analyze data from wearable motion sensor–based information systems (IS) to prevent falls because of their convenience, low cost, and user privacy protection. However, prior studies on fall prevention often achieve suboptimal performance because of their limited capacities in modeling data distributions. In this study, we adopt the computational design science paradigm to develop a novel fall prevention framework, which includes the hidden Markov model with generative adversarial network (HMM-GAN) that extracts temporal and sequential patterns from sensor signals and recognizes snippet states, and a logistic regression that utilizes the snippet states and determines whether and when to trigger protective devices to prevent fall injuries. Drawing upon the HMM, deep learning, and a new expectation-maximization instantiation, the proposed framework addresses limitations of existing methods by automatically extracting features from motion sensor data, accounting for both independent and sequential information in data snippets, operating on sensor signals with varying distributions and sharp peaks and valleys, allowing lead times, and being applicable in both semisupervised and supervised modes. We evaluate the proposed fall prevention framework against prevailing fall prevention models and the HMM-GAN component against state-of-the-art sensor analytics models on selected large-scale ground truth data sets containing thousands of falls and normal activities. Through an in-depth case study, we demonstrate how the proposed framework can lead to significantly reduced potentially catastrophic falls by senior citizens and produce more than $33 million of economic benefits over competing models. Besides practical health information technology contributions, HMM-GAN offers methodological contributions to the IS knowledge base for scholars designing novel information technology artifacts for healthcare applications. History: Olivia Sheng, Senior Editor; Huimin Zhao, Associate Editor. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72293581, 91846201, 72293580, 72188101, 72101079, 71771131, and 72110107003], the Division of Industrial Innovation and Partnerships [Grant 1622788], and the Division of Computer and Network Systems [Grant 1850362] of the National Science Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1203. [ABSTRACT FROM AUTHOR]

Aim: To explore the effect of a fall prevention strategy on older patients based on the Patient Engagement Framework. Design: A longitudinal quasi‐experimental quantitative design. Methods: Older patients who met the inclusion criteria were recruited from geriatric, oncology, neurology and cardiology departments of a teaching general hospital in China. Development of a fall prevention intervention strategy for older patients was based on the Patient Engagement Framework. Patients in the intervention group were given this fall prevention strategy (N = 58), and those in the control group were given conventional measures (N = 58). The following indicators were compared between the two groups after intervention: (a) number of falls; (b) Knowledge‐Attitude‐Practice (KAP) score; (c) Modified Fall Efficacy Scale score. Results: After the implementation of an intervention strategy in older patients, the number of falls decreased from 3 to 0; the score of KAP and Modified Fall Efficacy Scale was promoted (p <.05). [ABSTRACT FROM AUTHOR]

Background: Falls are one of the main reasons for mortality and morbidity in older adults. However, despite adoption of prevention strategies, the number of falls in older adults has not declined. This study aimed to develop a questionnaire to assess fall prevention knowledge and explore associated factors among Chinese community-dwelling older adults. Methods: The Home-Based Fall Prevention Knowledge (HFPK) questionnaire was developed by Delphi expert consultation. We tested the internal consistency, reliability, and content validity of the HFPK. A total of 374 community-dwelling older adults participated in this study. The HFPK was used to assess their fall prevention knowledge. Results: After being evaluated by 15 experts, the item content validity index ranged from 0.867 to 1, and the scale content validity index was 0.985, which met the criterion for content validity. Cronbach's α coefficient was 0.933, which satisfied the reliability criterion. Stepwise linear regression analysis showed that fall prevention knowledge was significantly associated with having higher education, being female, having a higher monthly income, people who were public officials before retirement, and having fewer children (p < 0.05). Conclusion: Fall prevention knowledge should be improved among older males and those with lower education, lower monthly income, people who were not public officials before retirement, and more children. [ABSTRACT FROM AUTHOR]

Background: Falls are one of the main reasons for mortality and morbidity in older adults. However, despite adoption of prevention strategies, the number of falls in older adults has not declined. This study aimed to develop a questionnaire to assess fall prevention knowledge and explore associated factors among Chinese community-dwelling older adults. Methods: The Home-Based Fall Prevention Knowledge (HFPK) questionnaire was developed by Delphi expert consultation. We tested the internal consistency, reliability, and content validity of the HFPK. A total of 374 community-dwelling older adults participated in this study. The HFPK was used to assess their fall prevention knowledge. Results: After being evaluated by 15 experts, the item content validity index ranged from 0.867 to 1, and the scale content validity index was 0.985, which met the criterion for content validity. Cronbach's α coefficient was 0.933, which satisfied the reliability criterion. Stepwise linear regression analysis showed that fall prevention knowledge was significantly associated with having higher education, being female, having a higher monthly income, people who were public officials before retirement, and having fewer children (p < 0.05). Conclusion: Fall prevention knowledge should be improved among older males and those with lower education, lower monthly income, people who were not public officials before retirement, and more children. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore Chinese older adults' experiences of using digital health interventions and their perspectives of a digital falls prevention program (KOKU), to explore acceptability for its use in their daily life. Participants' needs and preferences will be incorporated into the existing KOKU digital program to improve user-experience, engagement and digital inclusion of Chinese older adults living in the UK. Conducting interviews with Chinese older adults in community settings provide a better understanding of cultural factors when co-designing digital health applications.

Background: In China, as population aging accelerates, senior care facilities have gradually become a mainstream option. According to the World Health Organization (WHO), the annual rate of falls has increased from 30 to 50% in senior care facilities. A study found that older adults who live in senior care facilities are three times more likely to fall than those who live in the community. The quality of care is highly related to the occurrence of falls. Therefore, exploring paid caregivers' experiences is very important to prevent falls in senior care facilities., Objective: The aim of this study was to explore paid caregivers' experiences of fall prevention and care in China's senior care facilities. Furthermore, we discussed the situation and provided suggestions., Design: This is a phenomenological study using face-to-face, in-depth, semi-structured interviews., Setting: The study was conducted at four senior care facilities in Changsha, Hunan, China., Participants: Fourteen paid caregivers in four senior care facilities, including nursing assistants and senior nurses, participated in this study., Methods: A purposive sample method was used to select 14 nursing assistants and senior nurses from four different senior care facilities in Changsha from March to April 2022. Every participant individually completed a face-to-face, in-depth, semi-structured interview. Based on the phenomenological research methodology, the thematic analysis method and the Colaizzi analysis method were used for data analysis and theme extraction., Results: Based on interview data, a total of seven themes were distilled: (1) paid caregivers' professional requirements; (2) paid caregivers' attitude toward falls; (3) paid caregivers' fall training and education; (4) paid caregivers' knowledge about falls; (5) paid caregivers' fall risk assessment; (6) paid caregivers' fall prevention; and (7) paid caregivers' fall treatment., Conclusion: In China's senior care facilities, paid caregivers to need to be responsible and pay appropriate attention to older adults. First, senior nurses and nursing assistants need to enhance communication and cooperation. Second, they must learn about deficiencies in fall risk assessment and try their best to improve their capability. Third, they must adopt appropriate education methods to improve fall prevention capability. Finally, the protection of privacy should be taken seriously., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Luo, Ran, Deng, Li, Zhang and Zhao.)

The December 2019 COVID-19 outbreak in China has led to worldwide quarantine, as recommended by local governments and the World Health Organization. Particularly affected are older adults (i.e., those aged ≥ 65 years) who are at elevated risk for various adverse health outcomes, including declines in motor ability and physical activity (PA) participation, increased obesity, impaired cognition, and various psychological disorders. Thus, given the secular increases in the older adult population, novel and effective intervention strategies are necessary to improve physical activity behaviors and health in this population. Virtual reality (VR)-integrated exercise is a promising intervention strategy, which has been utilized in healthcare fields like stroke rehabilitation and psychotherapy. Therefore, the purpose of this editorial is to synthesize recent research examining the efficacy and effectiveness of VR exercise in the promotion of favorable health outcomes among the older adults. Results indicate the application of VR exercise to facilitate improved physical outcomes (e.g., enhanced motor ability, reduced obesity), cognition and psychological outcomes. VR exercise has also been observed to be an effective intervention strategy for fall prevention in this population. Future research should employ more rigorous research designs to allow for a more robust quantitative synthesis of the effect of VR exercise on the preceding outcomes to elucidate which type(s) of VR-based PA interventions are most effective in promoting improved health outcomes among older adults. Findings from this study will better inform the development of technology-savvy PA programs for wellness promotion in older adults who practice social distancing and exercise from home under the unprecedented global health crisis. [ABSTRACT FROM AUTHOR]