Background: Chronic diseases are putting huge pressure on health care systems. Nurses are widely recognized as one of the competent health care providers who offer comprehensive care to patients during rehabilitation after hospitalization. In recent years, telerehabilitation has opened a new pathway for nurses to manage chronic diseases at a distance; however, it remains unclear which chronic disease patients benefit the most from this innovative delivery mode. Objective: This study aims to summarize current components of community-based, nurse-led telerehabilitation programs using the chronic care model; evaluate the effectiveness of nurse-led telerehabilitation programs compared with traditional face-to-face rehabilitation programs; and compare the effects of telerehabilitation on patients with different chronic diseases. Methods: A systematic review and meta-analysis were performed using 6 databases for articles published from 2015 to 2021. Studies comparing the effectiveness of telehealth rehabilitation with face-to-face rehabilitation for people with hypertension, cardiac diseases, chronic respiratory diseases, diabetes, cancer, or stroke were included. Quality of life was the primary outcome. Secondary outcomes included physical indicators, self-care, psychological impacts, and health-resource use. The revised Cochrane risk of bias tool for randomized trials was employed to assess the methodological quality of the included studies. A meta-analysis was conducted using a random-effects model and illustrated with forest plots. Results: A total of 26 studies were included in the meta-analysis. Telephone follow-ups were the most commonly used telerehabilitation delivery approach. Chronic care model components, such as nurses-patient communication, self-management support, and regular follow-up, were involved in all telerehabilitation programs. Compared with traditional face-to-face rehabilitation groups, statistically significant improvements in quality of life (cardiac diseases: standard mean difference [SMD] 0.45; 95% CI 0.09 to 0.81; P=.01; heterogeneity: X21=1.9; I2=48%; P=.16; chronic respiratory diseases: SMD 0.18; 95% CI 0.05 to 0.31; P=.007; heterogeneity: X22=1.7; I2=0%; P=.43) and self-care (cardiac diseases: MD 5.49; 95% CI 2.95 to 8.03; P<.001; heterogeneity: X25=6.5; I2=23%; P=.26; diabetes: SMD 1.20; 95% CI 0.55 to 1.84; P<.001; heterogeneity: X24=46.3; I2=91%; P<.001) were observed in the groups that used telerehabilitation. For patients with any of the 6 targeted chronic diseases, those with hypertension and diabetes experienced significant improvements in their blood pressure (systolic blood pressure: MD 10.48; 95% CI 2.68 to 18.28; P=.008; heterogeneity: X21=2.2; I2=54%; P=0.14; diastolic blood pressure: MD 1.52; 95% CI –10.08 to 13.11, P=.80; heterogeneity: X21=11.5; I2=91%; P<.001), and hemoglobin A1c (MD 0.19; 95% CI –0.19 to 0.57 P=.32; heterogeneity: X24=12.4; I2=68%; P=.01) levels. Despite these positive findings, telerehabilitation was found to have no statistically significant effect on improving patients’ anxiety level, depression level, or hospital admission rate. Conclusions: This review showed that telerehabilitation programs could be beneficial to patients with chronic disease in the community. However, better designed nurse-led telerehabilitation programs are needed, such as those involving the transfer of nurse-patient clinical data. The heterogeneity between studies was moderate to high. Future research could integrate the chronic care model with telerehabilitation to maximize its benefits for community-dwelling patients with chronic diseases. Trial Registration: International Prospective Register of Systematic Reviews CRD42022324676; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=324676

Background: Artificial intelligence (AI) is increasingly being applied in various health care services due to its enhanced efficiency and accuracy. As the population ages, AI-based health technologies could be a potent tool in older adults’ health care to address growing, complex, and challenging health needs. This study aimed to investigate perspectives on and acceptability of the use of AI-led health technologies among older adults and the potential challenges that they face in adopting them. The findings from this inquiry could inform the designing of more acceptable and user-friendly AI-based health technologies. Objective: The objectives of the study were (1) to investigate the attitudes and perceptions of older adults toward the use of AI-based health technologies; (2) to identify potential facilitators, barriers, and challenges influencing older adults’ preferences toward AI-based health technologies; and (3) to inform strategies that can promote and facilitate the use of AI-based health technologies among older adults. Methods: This study adopted a qualitative descriptive design. A total of 27 community-dwelling older adults were recruited from a local community center. Three sessions of semistructured interviews were conducted, each lasting 1 hour. The sessions covered five key areas: (1) general impressions of AI-based health technologies; (2) previous experiences with AI-based health technologies; (3) perceptions and attitudes toward AI-based health technologies; (4) anticipated difficulties in using AI-based health technologies and underlying reasons; and (5) willingness, preferences, and motivations for accepting AI-based health technologies. Thematic analysis was applied for data analysis. The Theoretical Domains Framework and the Capability, Opportunity, Motivation, and Behavior (COM-B) model behavior change wheel were integrated into the analysis. Identified theoretical domains were mapped directly to the COM-B model to determine corresponding strategies for enhancing the acceptability of AI-based health technologies among older adults. Results: The analysis identified 9 of the 14 Theoretical Domains Framework domains—knowledge, skills, social influences, environmental context and resources, beliefs about capabilities, beliefs about consequences, intentions, goals, and emotion. These domains were mapped to 6 components of the COM-B model. While most participants acknowledged the potential benefits of AI-based health technologies, they emphasized the irreplaceable role of human expertise and interaction. Participants expressed concerns about the usability of AI technologies, highlighting the need for user-friendly and tailored AI solutions. Privacy concerns and the importance of robust security measures were also emphasized as critical factors affecting their willingness to adopt AI-based health technologies. Conclusions: Integrating AI as a supportive tool alongside health care providers, rather than regarding it as a replacement, was highlighted as a key strategy for promoting acceptance. Government support and clear guidelines are needed to promote ethical AI implementation in health care. These measures can improve health outcomes in the older adult population by encouraging the adoption of AI-driven health technologies.

Background: In recent years, telehealth has become a common channel for health care professionals to use to promote health and provide distance care. COVID-19 has further fostered the widespread use of this new technology, which can improve access to care while protecting the community from exposure to infection by direct personal contact, and reduce the time and cost of traveling for both health care users and providers. This is especially true for community-dwelling older adults who have multiple chronic diseases and require frequent hospital visits. Nurses are globally recognized as health care professionals who provide effective community-based care to older adults, facilitating their desire to age in place. However, to date, it is unclear whether the use of telehealth can facilitate their work of promoting self-care to community-dwelling older adults. Objective: This review aims to summarize findings from randomized controlled trials on the effect of nurse-led telehealth self-care promotion programs compared with the usual on-site or face-to-face services on the quality of life (QoL), self-efficacy, depression, and hospital admissions among community-dwelling older adults. Methods: A search of 6 major databases was undertaken of relevant studies published from May 2011 to April 2021. Standardized mean differences (SMDs) and their 95% CIs were calculated from postintervention outcomes for continuous data, while the odds ratio was obtained for dichotomous data using the Mantel–Haenszel test. Results: From 1173 possible publications, 13 trials involving a total of 4097 participants were included in this meta-analysis. Compared with the control groups, the intervention groups of community-dwelling older adults significantly improved in overall QoL (SMD 0.12; 95% CI 0.03 to 0.20; P=.006; I2=21%), self-efficacy (SMD 0.19; 95% CI 0.08 to 0.30; P<.001; I2=0%), and depression level (SMD –0.22; 95% CI –0.36 to –0.08; P=.003; I2=89%). Conclusions: This meta-analysis suggests that employing telehealth in nurse-led self-care promotion programs may have a positive impact on older adults, although more studies are needed to strengthen the evidence base, particularly regarding organization and delivery. Trial Registration: PROSPERO (Prospective International Register of Systematic Reviews) CRD42021257299; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=257299

Importance: Mobile health (mHealth) smartphone apps are becoming increasingly popular among older adults, although the reactive care approach of these apps has limited their usability.Objective: To evaluate the effects of an interactive mHealth program supported by a health-social partnership team on quality of life (QOL) among community-dwelling older adults in Hong Kong.Design, Setting, and Participants: This was a 3-group, randomized clinical trial conducted in 5 community centers in Hong Kong from December 1, 2020, to April 30, 2022, with a last follow-up date of January 31, 2022. Participants included older adults aged at least 60 years who were living within the service area, used a smartphone, and had at least 1 of the following problems: chronic pain, hypertension, or diabetes. Data were analyzed from May 1 to 10, 2022.Interventions: Participants were randomly assigned to the mHealth with interactivity (mHealth+I) group, mHealth group, or control group. Participants in the mHealth+I group received the mHealth app and nurse case management supported by a health-social partnership team. The mHealth group received the mHealth app only. The control group received no mHealth app or health-social care services.Main Outcomes and Measures: The primary outcome was the change in QOL from baseline to 3 months after completion of the intervention.Results: Among 221 participants (mean [SD] age 76.6 [8.0] years; 185 [83.7%] women), 76 were randomized to the control group, 71 were randomized to the mHealth group, and 74 were randomized to the mHealth+I group. The most common chronic diseases or problems were hypertension (147 participants [66.5%]), pain (144 participants [65.2%]), cataracts (72 participants [32.6%]), and diabetes (61 participants [27.6%]). At 3 months after the intervention and compared with the intervention group, there were no statistically significant differences in either the physical component summary (mHealth+I: β = −1.01 [95% CI, −4.13 to 2.11]; P = .53; mHealth: β = 0.22 [95% CI, −3.07 to 3.50]; P = .90) or the mental component summary (mHealth+I: β = −0.87 [95% CI, −4.42 to 2.69]; P = .63; mHealth: β = 1.73 [95% CI, −1.89 to 5.34]; P = .35) QOL scores. Only secondary outcomes, including self-efficacy (β = −2.31 [95% CI, −4.26 to −0.36]; P = .02), systolic blood pressure (β = −2.30 [95% CI, −5.00 to −0.13]; P = .04), pain levels (β = 1.18 [95% CI, 0.52 to 2.00]; P = .02), and health services utilization (β = 0.98 [95% CI, 0.32 to 2.09]; P = .048) improved in the mHealth+I group compared with the control group.Conclusions and Relevance: This randomized clinical trial found no difference in the primary outcome between the mHealth+I group and the control group confirming that there were no incremental benefits to adding interactivity in mHealth programs for older adults with chronic diseases.Trial Registration: Clinicaltrials.gov Identifier: NCT03878212

The number of older adults who are homebound is growing substantially owing to a global aging population and advanced health care technology. A current report from the United States shows that approximately 3 million community-dwelling older adults are in a chronic homebound state,¹ that is, confined to their home and normally unable to go outdoors more than once per week because of physical and functional impairments.² Older adults who are homebound encounter a range of physical ailments, such as chronic pain and muscle weakness, which prevent them from leaving their homes.^3,4 It is therefore unsurprising that older adults who are homebound have a higher prevalence of polypharmacy and greater health care service utilization than their counterparts who are not homebound.⁵