Background: The effectiveness of timely medication, physical activity (PA), a healthy diet, and blood pressure (BP) monitoring for promoting health outcomes and behavioral changes among patients with hypertension is supported by a substantial amount of literature, with “adherence” playing a pivotal role. Nevertheless, there is a lack of consistent evidence regarding whether digital interventions can improve adherence to healthy behaviors among individuals with hypertension. Objective: The aim was to develop a health behavioral digital intervention for hypertensive patients (HBDIHP) based on an intelligent health promotion system and WeChat following the behavior change wheel (BCW) theory and digital micro-intervention care (DMIC) model and assess its efficacy in controlling BP and improving healthy behavior adherence. Methods: A 2-arm, randomized trial design was used. We randomly assigned 68 individuals aged >60 years with hypertension in a 1:1 ratio to either the control or experimental group. The digital intervention was established through the following steps: (1) developing digital health education materials focused on adherence to exercise prescriptions, Dietary Approaches to Stop Hypertension (DASH), prescribed medication, and monitoring of BP; (2) using the BCW theory to select behavior change techniques; (3) constructing the intervention's logic following the guidelines of the DMIC model; (4) creating an intervention manual including the aforementioned elements. Prior to the experiment, participants underwent physical examinations at the community health service center's intelligent health cabin and received intelligent personalized health recommendations. The experimental group underwent a 12-week behavior intervention via WeChat, while the control group received routine health education and a self-management manual. The primary outcomes included BP and adherence indicators. Data analysis was performed using SPSS, with independent sample t tests, chi-square tests, paired t tests, and McNemar tests. A P value <.05 was considered statistically significant. Results: The final analysis included 54 participants with a mean age of 67.24 (SD 4.19) years (n=23 experimental group, n=31 control group). The experimental group had improvements in systolic BP (–7.36 mm Hg, P=.002), exercise time (856.35 metabolic equivalent [MET]-min/week, P<.001), medication adherence (0.56, P=.001), BP monitoring frequency (P=.02), and learning performance (3.23, P<.001). Both groups experienced weight reduction (experimental: 1.2 kg, P=.002; control: 1.11 kg, P=.009) after the intervention. The diet types and quantities for both groups (P<.001) as well as the subendocardial viability ratio (0.16, P=.01) showed significant improvement. However, there were no statistically significant changes in other health outcomes. Conclusions: The observations suggest our program may have enhanced specific health outcomes and adherence to health behaviors in older adults with hypertension. However, a longer-term, larger-scale trial is necessary to validate the effectiveness. Trial Registration: Chinese Clinical Trial Registry ChiCTR2200062643; https://www.chictr.org.cn/showprojEN.html?proj=172782 International Registered Report Identifier (IRRID): RR2-10.2196/46883

Background: A scientific, personalized, and quantitative exercise prescription that has the potential to be an important therapeutic agent for all ages in the prevention of chronic disease is highly recommended. However, it is often poorly implemented, as clinicians lack the necessary knowledge and skills while participants have low adherence due to design defects (eg, prescriptions fail to take individual willingness, the appeal of exercise, and complex physical conditions into account). Intelligent personalized prescription is thus worth exploring. Objective: The aim of this study was to investigate whether a year-long cloud platform–based and intelligent personalized exercise prescription intervention could improve Chinese middle-aged and older adult community dwellers’ health outcomes. Methods: A total of 177 participants (aged 52-85 years; mean 67.93, SD 7.05) were recruited from 2 Chinese community health service centers in Anhui Province, China. The exercise intervention was delivered over 12 months with a single-group pretest–posttest design. After being assessed in terms of physical activity, health-related lifestyle, history of chronic diseases and drug use, family history of disease and cardiovascular function, body composition, bone mineral density, and physical fitness through an eHealth promotion system, participants with relative contraindications for exercise were personally prescribed the health care exercise mode by an intelligent system, while those without relative contraindication and who had a regular exercise habit were prescribed the scientific fitness mode. Paired t tests were used for the analysis. Results: A total of 97 participants were classified into the health care mode, and the remaining 80 participants were assigned to the scientific fitness mode. Significant changes in heart rate (mean difference [MD] 2.97; 95% CI 1.1-4.84; P=.002), subendocardial viability ratio (MD –0.13; CI: –1.19 to –0.63; P<.001), weight (MD 0.99; CI 0.29-1.69; P=.006), BMI (MD 0.38; CI 0.11-0.64; P=.006), body fat rate (MD 0.88; CI 0.24-1.51; P=.007), fat mass (MD 0.92; CI 0.33-1.53; P=.003), and brachial-ankle pulse wave velocity (MD: –0.72; CI –1.17 to –0.27; P=.002) were observed among participants with the health care mode exercise prescriptions at the 12-month postintervention versus the baseline assessment, while no changes in systolic blood pressure, diastolic blood pressure, muscle mass, bone mineral density, t value, z value, balance, or ability were discerned. The results showed a functional decline in the physical fitness of both groups, including in handgrip strength (healthcare mode: MD 4.41; scientific fitness mode: MD 3.11), vital capacity (healthcare mode: MD 261.99; scientific fitness mode: MD 250.78), and agility (healthcare mode MD=–0.35; scientific fitness mode: MD=–0.39) with all P values <.001, except handgrip strength in the scientific fitness mode (P=.002). There were no significant differences in other parameters among participants with scientific fitness mode exercise prescriptions. Conclusions: The observations suggest that our exercise prescription intervention program might promote certain health outcomes in Chinese middle-aged and older adult community dwellers, yet we are unable to recommend such a program given the existing limitations. Future randomized controlled trials with diverse samples are warranted to confirm our findings.

Purpose: Exercise is a vital adjunct therapy for patients with hypertension comorbidities. However, medical personnel and patients face significant obstacles in implementing exercise prescription recommendations. AI has been developed as a beneficial tool in the healthcare field. The performance of intelligent tools such as ChatGPT 4.0 and Intelligent Health Promotion Systems (IHPS) in issuing exercise prescriptions for patients with hypertension comorbidities remains to be verified. Patients and Methods: After collecting patient information through IHPS hardware and questionnaire systems, the data were input into the software terminals of ChatGPT 4.0 and IHPS according to the five stages of the Transtheoretical Model, resulting in exercise prescriptions. Subsequently, experts from various fields scored the accuracy, comprehensiveness, and applicability of each prescription, along with providing professional recommendations based on their expertise. By comparing the performance of both systems, their capability to serve this specific group was evaluated. Results: In most cases, ChatGPT scored significantly higher than IHPS in terms of accuracy, comprehensiveness, and applicability. However, when patients exhibited certain functional movement disorders, GPT’s exercise prescriptions involved higher health risks, whereas the more conservative approach of IHPS was advantageous. Conclusion: The path of generating exercise prescriptions using artificial intelligence, whether via ChatGPT or IHPS, cannot achieve a completely satisfactory state.But can serve as a supplementary tool for professionals issuing exercise prescriptions to patients with hypertension comorbidities, especially in alleviating the financial burden of consulting costs. Future research could further explore the performance of AI in issuing exercise prescriptions, harmonize it with physiological indicators and phased feedback, and develop an interactive user experience.

Background: The internet has become one of the most important channels for residents to seek health information, particularly in remote rural areas in China. Objective: In this study, we aimed to explore the gap between self-rated health information literacy and internet health information seeking ability for patients with chronic diseases in rural communities and to preliminarily evaluate their barriers when seeking health information via the internet. Methods: Residents from rural communities near Bengbu City and with chronic diseases were included in this study. A self-rated questionnaire was used to evaluate their health information literacy, 3 behavioral competency tasks were designed to preliminarily evaluate their ability to seek health information on the internet and semistructured interviews were used to investigate their barriers to obtaining health information via the internet. A small audiorecorder was used to record the interview content, and screen-recording software was used to record the participants’ behavior during the web-based operational tasks. Results: A total of 70 respondents completed the self-rated health information literacy questionnaire and the behavioral competence test, and 56 respondents participated in the semistructured interviews. Self-rated health information literacy (score out of 70: mean 46.21, SD 4.90) of the 70 respondents were moderate. Although 91% (64/70) of the respondents could find health websites, and 93% (65/70) of the respondents could find information on treatment that they thought was the best, 35% (23/65) of respondents did not know how to save the results they had found. The operational tasks indicated that most articles selected by the respondents came from websites with encyclopedic knowledge or answers from people based on their own experiences rather than authoritative health information websites. After combining the results of the semistructured interviews with the DISCERN scale test results, we found that most interviewees had difficulty obtaining high-quality health information via the internet. Conclusions: Although the health information literacy level of patients with rural chronic disease was moderate, they lack the ability to access high-quality health information via the internet. The vast majority of respondents recognized the importance of accessing health information but were not very proactive in accessing such information.

Mental health literacy (MHL) is vital for raising mental health awareness, reducing stigma, and improving health-related decision making, which may have lifelong consequences for patients with ment...

Objective: The purposes of this study were to explore the associations of activities of daily living (ADL) and depression among older adults with family caregivers’ quality of life and provide evidence for improving family caregivers’ quality of life. Methods: Older adults ( n =395) and their family caregivers ( n =395) were selected as participants. The ADL scale and Geriatric Depression Scale were used to assess ADL and depression among older adults, and the 36-Item Short Form Health Questionnaire (SF-36) was used to assess family caregivers’ quality of life. Descriptive statistics and multiple linear regression were used to analyze the data. Results: The older adults’ ADL and depression scores were 21±7 and 11±6, respectively. Approximately 69.9% of older adults had declining or severely impaired ADL, and 47.1% had mild or moderate-to-severe depression. Family caregivers’ mean quality of life score was 529±100. There was a negative correlation of older adults’ ADL and depression with caregivers’ quality of life. The correlation coefficient between ADL and the SF-36 mental component summary score was stronger than it was with the SF-36 physical component summary score. Conclusions: The ADL and depression of older adults influenced family caregivers’ quality of life. Psychological health deserves closer attention, especially that of caregivers of disabled older adults.