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In recent years, efforts have been made to implement virtual reality (VR) to support the delivery of poststroke upper extremity motor rehabilitation exercises. Therefore, it is important to review and analyze the existing research evidence of its effectiveness. Through a systematic review and meta-analysis of randomized controlled trials, this study examined the effectiveness of using VR-supported exercise therapy for upper extremity motor rehabilitation in patients with stroke. This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The CINAHL Plus, MEDLINE, Web of Science, Embase, and Cochrane Library databases were searched on December 31, 2021. Changes in outcomes related to impairments in upper extremity functions and structures, activity limitations, and participation restrictions in life situations from baseline to after intervention, after intervention to follow-up assessment, and baseline to follow-up assessment were examined. Standardized mean differences (SMDs) were calculated using a random-effects model. Subgroup analyses were performed to determine whether the differences in treatment outcomes depended on age, stroke recovery stage, VR program type, therapy delivery format, similarities in intervention duration between study groups, intervention duration in VR groups, and trial length. A total of 42 publications representing 43 trials (aggregated sample size=1893) were analyzed. Compared with the control groups that used either conventional therapy or no therapy, the intervention groups that used VR to support exercise therapy showed significant improvements in upper extremity motor function (Fugl-Meyer Assessment-Upper Extremity; SMD 0.45, 95% CI 0.21-0.68; P<.001), range of motion (goniometer; SMD 1.01, 95% CI 0.50-1.52; P<.001), muscle strength (Manual Muscle Testing; SMD 0.79, 95% CI 0.28-1.30; P=.002), and independence in day-to-day activities (Functional Independence Measure; SMD 0.23, 95% CI 0.06-0.40; P=.01, and modified Rankin Scale; SMD 0.57, 95% CI 0.01-1.12; P=.046). Significant subgroup differences were observed in hand dexterity (Box and Block Test), spasticity (Ashworth Scale or modified Ashworth Scale), arm and hand motor ability (Wolf Motor Function Test and Manual Function Test), hand motor ability (Jebsen Hand Function Test), and quality of life (Stroke Impact Scale). There was no evidence that the benefits of VR-supported exercise therapy were maintained after the intervention ended. VR-supported upper extremity exercise therapy can be effective in improving motor rehabilitation results. Our review showed that of the 12 rehabilitation outcomes examined during the course of VR-based therapy, significant improvements were detected in 2 (upper extremity motor function and range of motion), and both significant and nonsignificant improvements were observed in another 2 (muscle strength and independence in day-to-day activities), depending on the measurement tools or methods used. PROSPERO CRD42021256826; https://tinyurl.com/2uarftbh.

Background Patients with coexisting type 2 diabetes and hypertension generally exhibit poor adherence to self-management, which adversely affects their disease control. Therefore, identification of the factors related to patient adherence is warranted. In this study, we aimed to examine (i) the socio-demographic correlates of patient adherence to a set of self-management behaviors relevant to type 2 diabetes and hypertension, namely, medication therapy, diet therapy, exercise, tobacco and alcohol avoidance, stress reduction, and self-monitoring/self-care, and (ii) whether health attitudes and self-efficacy in performing self-management mediated the associations between socio-demographic characteristics and adherence. Methods We performed a secondary analysis of data collected in a randomized controlled trial. The sample comprised 148 patients with coexisting type 2 diabetes mellitus and hypertension. Data were collected by a questionnaire and analyzed using logistic regression. Results Female patients were found to be less likely to exercise regularly (odds ratio [OR] = 0.49, P  = 0.03) and more likely to avoid tobacco and alcohol (OR = 9.87, P  < 0.001) than male patients. Older patients were found to be more likely to adhere to diet therapy (OR = 2.21, P  = 0.01) and self-monitoring/self-care (OR = 2.17, P  = 0.02). Patients living with family or others (e.g., caregivers) were found to be more likely to exercise regularly (OR = 3.44, P  = 0.02) and less likely to avoid tobacco and alcohol (OR = 0.10, P  = 0.04) than those living alone. Patients with better perceived health status were found to be more likely to adhere to medication therapy (OR = 2.02, P  = 0.03). Patients with longer diabetes duration (OR = 2.33, P  = 0.01) were found to be more likely to adhere to self-monitoring/self-care. Self-efficacy was found to mediate the association between older age and better adherence to diet therapy, while no significant mediating effects were found for health attitudes. Conclusions Adherence to self-management was found to be associated with socio-demographic characteristics (sex, age, living status, perceived health status, and diabetes duration). Self-efficacy was an important mediator in some of these associations, suggesting that patient adherence may be improved by increasing patients’ self-management efficacy, such as by patient empowerment, collaborative care, or enhanced patient–physician interactions.

Mobile health apps have changed the way people obtain health information and services and advance their understanding and management of their health. Although many health apps are available, little is known about the prevalence of their use for different purposes, whether such use is associated with demographic characteristics, and the impacts of their use on health knowledge and management. The main objectives of this study were to examine the prevalence, extent, and demographic correlates of health app use and the perceived impacts of health app use on increased health knowledge and improved health condition management. We conducted a cross-sectional questionnaire survey of 633 Chinese adults randomly drawn from the general population in Hong Kong. Of the 633 participants, 612 (96.7%) reported using mobile devices. Of them, 235 (38.4%) reported using multiple types of health apps. The most-used type of health app was about healthy living information (197/612, 32.2%), followed by measuring/recording vital signs (80/612, 13.1%), health and medical reminders (64/612, 10.5%), recovery and rehabilitation information (42/612, 6.9%), diagnosis assistance (28/612, 4.6%), emergency services (16/612, 2.6%), telehealth (11/612, 1.8%), and “other” (19/612, 3.1%). Multivariate logistic regression analysis found that health app users were more likely to be women (odds ratio [OR] 1.68, 95% CI 1.14-2.48, P=.01) of a higher self-rated social class (OR 3.66, 95% CI 1.11-12.11, P=.03). Participants who worked in education/culture/academia (OR 2.31, 95% CI 1.16-4.59, P=.02) or disciplinary forces (OR 5.07, 95% CI 1.25-20.62, p=.02) were more likely to believe that using health apps could increase their health knowledge; participants working in education/culture/academia were also more likely to believe that using health apps could improve the effectiveness of health condition management (OR 2.18, 95% CI 1.10-4.34, P=.03). Effort should be made to promote health app use, especially to demographic groups that are currently less likely to use health apps (eg, males, individuals from lower social classes). From the public health perspective, guidelines could be developed to help individuals identify quality health apps that meet their needs. Moreover, app developers could improve the usability of health apps to promote health app use.

The issue of long patient waits has attracted increasing public attention due to the negative effects of waiting on patients’ satisfaction with health care. The present study examined the associations between actual waiting time, perceived acceptability of waiting time, actual service time, perceived acceptability of service time, actual visit duration, and the level of patient satisfaction with care. We conducted a cross-sectional time study and questionnaire survey of endocrinology outpatients visiting a major teaching hospital in China. Our results show that actual waiting time was negatively associated with patient satisfaction regarding several aspects of the care they received. Also, patients who were less satisfied with the sociocultural atmosphere and the identity-oriented approach to their care tended to perceive the amounts of time they spent waiting and receiving care as less acceptable. It is not always possible to prevent dissatisfaction with waiting, or to actually reduce waiting times by increasing resources such as increased staffing. However, several improvements in care services can be considered. Our suggestions include providing clearer, more transparent information to keep patients informed about the health care services that they may receive, and the health care professionals who are responsible for those services. We also suggest that care providers are encouraged to continue to show empathy and respect for patients, that patients are provided with private areas where they can talk with health professionals and no one can overhear, and that hospital staff treat the family members or friends who accompany patients in a courteous and friendly way.

Usability can influence patients' acceptance and adoption of a health information technology. However, little research has been conducted to study the usability of a self-management health care system, especially one geared toward elderly patients. This usability study evaluated a new computer-based self-management system interface for older adults with chronic diseases, using a paper prototype approach. Fifty older adults with different chronic diseases participated. Two usability evaluation methods were involved: (1) a heuristics evaluation and (2) end-user testing with a think-aloud testing method, audio recording, videotaping, and interviewing. A set of usability metrics was employed to determine the overall system usability, including task incompletion rate, task completion time, frequency of error, frequency of help, satisfaction, perceived usefulness, and perceived ease of use. Interviews were used to elicit participants' comments on the system design. The quantitative data were analyzed using descriptive statistics and the qualitative data were analyzed for content. The participants were able to perform the predesigned self-management tasks with the current system design and they expressed mostly positive responses about the perceived usability measures regarding the system interface. However, the heuristics evaluation, performance measures, and interviews revealed a number of usability problems related to system navigation, information search and interpretation, information presentation, and readability. Design recommendations for further system interface modifications were discussed. This study verified the usability of the self-management system developed for older adults with chronic diseases. Also, we demonstrated that our usability evaluation approach could be used to quickly and effectively identify usability problems in a health care information system at an early stage of the system development process using a paper prototype. Conducting a usability evaluation is an essential step in system development to ensure that the system features match the users' true needs, expectations, and characteristics, and also to minimize the likelihood of the users committing user errors and having difficulties using the system.

Mobile app-assisted self-care interventions are emerging promising tools to support self-care of patients with chronic diseases such as type 2 diabetes and hypertension. The effectiveness of such interventions requires further exploration for more supporting evidence. A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to examine the effectiveness of mobile app-assisted self-care interventions developed for type 2 diabetes and/or hypertension in improving patient outcomes. We followed the Cochrane Collaboration guidelines and searched MEDLINE, Cochrane Library, EMBASE, and CINAHL Plus for relevant studies published between January 2007 and January 2019. Primary outcomes included changes in hemoglobin A (HbA ) levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). Changes in other clinical-, behavioral-, knowledge-, and psychosocial-related outcomes were included as secondary outcomes. Primary outcomes and objective secondary outcomes that were reported in at least two trials were meta-analyzed; otherwise, a narrative synthesis was used for data analysis. A total of 27 trials were identified and analyzed. For primary outcomes, the use of mobile app-assisted self-care interventions was associated with significant reductions in HbA levels (standardized mean difference [SMD] -0.44, 95% CI -0.59 to -0.29; P<.001), SBP (SMD -0.17, 95% CI -0.31 to -0.03, P=.02), and DBP (SMD -0.17, 95% CI -0.30 to -0.03, P=.02). Subgroup analyses for primary outcomes showed that several intervention features were supportive of self-management, including blood glucose, blood pressure, and medication monitoring, communication with health care providers, automated feedback, personalized goal setting, reminders, education materials, and data visualization. In addition, 8 objective secondary outcomes were meta-analyzed, which showed that the interventions had significant lowering effects on fasting blood glucose levels and waist circumference. A total of 42 secondary outcomes were narratively synthesized, and mixed results were found. Mobile app-assisted self-care interventions can be effective tools for managing blood glucose and blood pressure, likely because their use facilitates remote management of health issues and data, provision of personalized self-care recommendations, patient-care provider communication, and decision making. More studies are required to further determine which combinations of intervention features are most effective in improving the control of the diseases. Moreover, evidence regarding the effects of these interventions on the behavioral, knowledge, and psychosocial outcomes of patients is still scarce, which warrants further examination.

Background Medication administration is a fundamental component of nursing practices, with direct implications for patient safety, health outcomes, and healthcare quality. Despite its importance, however, the literature lacks a comprehensive understanding of how individual and systemic factors are associated in medication administration, thus prompting this study. Methods A web-based survey was administered to 3,829 nursing staff members across seven hospitals in a Hong Kong cluster. The survey aimed to examine the associations between hospital context, professional ranking, and working experience on nurses’ knowledge, attitudes, and role perception in medication administration. The final count of usable responses was 1,393, yielding a response rate of 36.4%. Results The study’s findings suggest that nurses’ knowledge, attitudes, and role perception in the medication administration process vary significantly according to the hospital context and the nurses’ work experience and professional rank. Limitations This study relies on self-reported data and a sample limited to Hong Kong, which may affect generalizability. The lack of formal psychometric validation is also an important methodological consideration. Conclusions This study highlights the importance of considering individual and systemic factors, including hospital context and nurses’ professional ranking and nursing experience, in strategies aimed at enhancing medication administration and reducing errors. The findings also reveal the need for further research to untangle the complex relationships between these factors and their influence on patient safety and healthcare quality.

Objective To facilitate the older adults with knee pain to perform exercises and improve knee health, we proposed the design of a machine learning-based system for lower-limb exercise training that features three main components: video demonstration of exercises, real-time movement feedback, and tracking of exercise progress. At this early stage of design, we aimed to examine the perceptions of a paper-based prototype among older adults with knee pain and investigate the factors that may influence their perceptions of the system. Methods A cross-sectional survey of the participants’ (N = 94) perceptions of the system was conducted using a questionnaire, which assessed their perceived effects of the system, perceived ease of use of the system, attitude toward the system, and intention to use the system. Ordinal logistic regression was conducted to examine whether the participants’ perceptions of the system were influenced by their demographic and clinical characteristics, physical activity level, and exercise experience. Results The participants’ responses to the perception statements exhibited consensus agreement (≥ 75%). Age, gender, duration of knee pain, knee pain intensity, experience with exercise therapy, and experience with technology-supported exercise programs were significantly associated with the participants’ perceptions of the system. Conclusions Our results demonstrate that the system appears promising for use by older adults to manage their knee pain. Therefore, it is needed to develop a computer-based system and further investigate its usability, acceptance, and clinical effectiveness.

Objective This study aimed to examine the performance of machine learning models in predicting the progression of knee pain, functional decline, and incidence of knee osteoarthritis (OA) in high-risk individuals, with automated machine learning (AutoML) being used to automate the prediction process. Design There were four stages in the process of our AutoML-integrated prediction. Stage 1—Data preparation: The data of 3200 eligible individuals in the Osteoarthritis Initiative (OAI) study who were considered at high risk of knee OA at the baseline visit were extracted and used. Specifically, 1094 variables from the OAI study were used to predict the changes in knee pain, physical function, and incidence of knee OA (i.e. the first occurrence of frequent knee symptoms and definite tibial osteophytes (Kellgren and Lawrence grade ≥2)) over a 9-year period. Stage 2—Model training: The AutoML approach was used to automatically train nine widely used machine learning (ML) models. Stage 3—Model testing: The AutoML approach was used to automatically test the performance of the ML models. Stage 4—Selection of important input variables: The AutoML approach automated the process of computing the importance scores of all input variables and identifying the most important ones, using the technique of permutation feature importance. Results Using the AutoML approach, the weighted ensemble model and the CatBoost model showed the best performance among all nine ML models. For the prediction of each outcome in each year, the five most important input variables were identified, most of which were obtained from self-reported questionnaire surveys and radiographic imaging reports. Conclusion The AutoML approach has shown potential in automating the process of using ML models to predict long-term changes in knee OA-related outcomes. Its use could support the deployment of ML solutions, facilitating the provision of personalized interventions to prevent the deterioration of knee health and incident knee OA.

Despite the great potential of eHealth, substantial costs are involved in its implementation, and it is essential to know whether these costs can be justified by its benefits. Such needs have led to an increased interest in measuring the benefits of eHealth, especially using the willingness to pay (WTP) metric as an accurate proxy for consumers' perceived benefits of eHealth. This offered us an opportunity to systematically review and synthesize evidence from the literature to better understand WTP for eHealth and its influencing factors. This study aimed to provide a systematic review of WTP for eHealth and its influencing factors. This study was performed and reported as per the Cochrane Collaboration and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, CINAHL Plus, Cochrane Library, EconLit, and PsycINFO databases were searched from their inception to April 19, 2022. We conducted random-effects meta-analyses to calculate WTP values for eHealth (at 2021 US dollar rates) and meta-regression analyses to examine the factors affecting WTP. A total of 30 articles representing 35 studies were included in the review. We found that WTP for eHealth varied across studies; when expressed as a 1-time payment, it ranged from US $0.88 to US $191.84, and when expressed as a monthly payment, it ranged from US $5.25 to US $45.64. Meta-regression analyses showed that WTP for eHealth was negatively associated with the percentages of women (β=-.76; P<.001) and positively associated with the percentages of college-educated respondents (β=.63; P<.001) and a country's gross domestic product per capita (multiples of US $1000; β=.03; P<.001). Compared with eHealth provided through websites, people reported a lower WTP for eHealth provided through asynchronous communication (β=-1.43; P<.001) and a higher WTP for eHealth provided through medical devices (β=.66; P<.001), health apps (β=.25; P=.01), and synchronous communication (β=.58; P<.001). As for the methods used to measure WTP, single-bounded dichotomous choice (β=2.13; P<.001), double-bounded dichotomous choice (β=2.20; P<.001), and payment scale (β=1.11; P<.001) were shown to obtain higher WTP values than the open-ended format. Compared with ex ante evaluations, ex post evaluations were shown to obtain lower WTP values (β=-.37; P<.001). WTP for eHealth varied significantly depending on the study population, modality used to provide eHealth, and methods used to measure it. WTP for eHealth was lower among certain population segments, suggesting that these segments may be at a disadvantage in terms of accessing and benefiting from eHealth. We also identified the modalities of eHealth that were highly valued by consumers and offered suggestions for the design of eHealth interventions. In addition, we found that different methods of measuring WTP led to significantly different WTP estimates, highlighting the need to undertake further methodological explorations of approaches to elicit WTP values.