Background: Hypertension has become an important global public health challenge. Mobile health (mHealth) intervention is a viable strategy to improve outcomes for patients with hypertension. However, evidence on the effect of mHealth app interventions on self-management in patients with hypertension is yet to be updated, and the active ingredients promoting behavior change in interventions remain unclear. Objective: We aimed to evaluate the effect of mHealth app self-management interventions on blood pressure (BP) management and investigate the use of behavior change techniques (BCTs) in mHealth app interventions. Methods: We conducted a literature search in 6 electronic databases from January 2009 to October 2023 for studies reporting the application of mHealth apps in self-management interventions. The Cochrane Risk of Bias (version 2) tool for randomized controlled trials was used to assess the quality of the studies. BCTs were coded according to the Taxonomy of BCTs (version 1). The extracted data were analyzed using RevMan5.4 software (Cochrane Collaboration). Results: We reviewed 20 studies, of which 16 were included in the meta-analysis. In total, 21 different BCTs (mean 8.7, SD 3.8 BCTs) from 12 BCT categories were reported in mHealth app interventions. The most common BCTs were self-monitoring of outcomes of behavior, feedback on outcomes of behavior, instruction on how to perform the behavior, and pharmacological support. The mHealth app interventions resulted in a –5.78 mm Hg (95% CI –7.97 mm Hg to –3.59 mm Hg; P<.001) reduction in systolic BP and a –3.28 mm Hg (95% CI –4.39 mm Hg to –2.17 mm Hg; P<.001) reduction in diastolic BP. The effect of interventions on BP reduction was associated with risk factors, such as hypertension, that were addressed by the mHealth app intervention (multiple risk factors vs a single risk factor: –6.50 mm Hg, 95% CI –9.00 mm Hg to –3.99 mm Hg vs –1.54 mm Hg, 95% CI –4.15 mm Hg to 1.06 mm Hg; P=.007); the presence of a theoretical foundation (with vs without behavior change theory: –10.06 mm Hg, 95% CI –16.42 mm Hg to –3.70 mm Hg vs –4.13 mm Hg, 95% CI –5.50 to –2.75 mm Hg; P=.07); intervention duration (3 vs ≥6 months: –8.87 mm Hg, 95% CI –10.90 mm Hg to –6.83 mm Hg vs –5.76 mm Hg, 95% CI –8.74 mm Hg to –2.77 mm Hg; P=.09); and the number of BCTs (≥11 vs <11 BCTs: –9.68 mm Hg, 95% CI –13.49 mm Hg to –5.87 mm Hg vs –2.88 mm Hg, 95% CI –3.90 mm Hg to –1.86 mm Hg; P<.001). Conclusions: The self-management interventions based on mHealth apps were effective strategies for lowering BP in patients with hypertension. The effect of interventions was influenced by factors related to the study’s intervention design and BCT.

Background: Physical inactivity is defined to be an activity level insufficient to meet recommendations. Exergame, which refers to a combination of exercise and video games, has the potential to promote physical activity (PA). Behavior change techniques (BCTs), the minimal, replicable components of an intervention, are widely used to identify components used in health behavior promotion. Objective: A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to examine the overall effects of exergame-based interventions for promoting PA and their influencing factors. BCTs were also identified and discussed in this review. Methods: We searched for relevant RCTs across 6 databases from their inception to March 21, 2024. Meta-analyses using random-effects models assessed the effects on PA, moderate to vigorous physical activity (MVPA), light physical activity, moderate physical activity, vigorous physical activity, sedentary time, step count, and BMI. Subgroup analyses of PA were conducted to explore the influencing factors of exergame-based behavior change interventions. Review Manager software (version 5.3; Cochrane Collaboration) and Stata software (version 16; StataCorp) were used to analyze data. Results: A total of 20 RCTs targeting populations with various medical conditions (aged between 7.5 and 79 years; 1073/2211, 48.5% female) were included in this review, with sample sizes ranging from 16 to 1112. Exergame-based behavior change interventions significantly increased PA (standard mean difference [SMD] 0.19, 95% CI 0.05-0.33), MVPA (SMD 0.48, 95% CI 0.12-0.85), and step counts (SMD 0.54, 95% CI 0.13-0.94). Furthermore, subgroup analyses showed that intervention implementer (research assistants vs other implementers), game console (Microsoft Xbox vs Sony PlayStation vs Nintendo Wii), game participation type (individual game vs nonindividual game), measurement method (subjective vs objective), and the number of BCTs (n<7 vs 7≤n<10 vs n≥10) used significantly influenced the effectiveness of these interventions. The most frequently used BCTs included “1.4 action planning” (n=15), “1.1 goal setting” (n=13), “12.5 adding objects to the environment” (n=13), “2.3 self-monitoring of behavior” (n=11), and “4.1 instruction on how to perform the behavior” (n=11). Conclusions: Our review has demonstrated that exergame-based interventions are effective in promoting PA. Future trials are needed to further validate the insights proposed in our studies and assess the long-term effects on PA.

Background: Preparing for aging with personalized technology is crucial due to the growing elderly population. Artificial Intelligence (AI), notably AI chatbots in healthcare, has transformed technology by simulating human-like conversations. Research on middle-aged adults’ acceptance of AI chatbots is limited. Assessing middle-aged individuals’ intentions to use AI is vital for enhancing AI competency among the elderly and guiding future interventions. Objective: This study aims to explore the acceptance of middle-aged individuals toward AI chatbots and influencing factors and verify the usability of Technology Acceptance Model 2 (TAM2) in the use of AI technology in middle-aged people, also to inspire the design of future intelligent systems and online interventions for improving the health and well-being of the aging population. Methods: A cross-sectional design and snowball sampling method were utilized to conduct an online questionnaire survey among middle-aged adults. The questionnaire was compiled based on TAM2 and was created using the online survey platform. SPSS 26.0 software was used for statistical analysis. Results: A total of 259 valid questionnaires were included in the final data analysis. The study reported the Cronbach's α of 0.94 for the questionnaire. We found that perceived ease of use, subjective norm, and user image significantly influence users’ intention to use AI chatbots. Notably, perceived usefulness emerged as a complete mediator in the relationship between subjective norm and intention to use, highlighting its central role in shaping user perceptions. The study also revealed a moderate acceptance level among middle-aged adults, emphasizing the need for targeted interventions. Conclusions: This study emphasized the importance of customizing AI technology to improve its adoption among middle-aged adults, providing valuable guidance for developers and policymakers. The findings indicated the need for effective aging preparation that includes technological competency, suggesting that future planning should encompass comprehensive preparations for aging to enhance AI competency among the middle-aged population.

Background: Since the release of ChatGPT in November 2022, this emerging technology has garnered a lot of attention in various fields, and nursing is no exception. However, to date, no study has comprehensively summarized the status and opinions of using ChatGPT across different nursing fields. Objective: We aim to synthesize the status and opinions of using ChatGPT according to different nursing fields, as well as assess ChatGPT’s strengths, weaknesses, and the potential impacts it may cause. Methods: This scoping review was conducted following the framework of Arksey and O’Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive literature research was conducted in 4 web-based databases (PubMed, Embase, Web of Science, and CINHAL) to identify studies reporting the opinions of using ChatGPT in nursing fields from 2022 to September 3, 2023. The references of the included studies were screened manually to further identify relevant studies. Two authors conducted studies screening, eligibility assessments, and data extraction independently. Results: A total of 30 studies were included. The United States (7 studies), Canada (5 studies), and China (4 studies) were countries with the most publications. In terms of fields of concern, studies mainly focused on “ChatGPT and nursing education” (20 studies), “ChatGPT and nursing practice” (10 studies), and “ChatGPT and nursing research, writing, and examination” (6 studies). Six studies addressed the use of ChatGPT in multiple nursing fields. Conclusions: As an emerging artificial intelligence technology, ChatGPT has great potential to revolutionize nursing education, nursing practice, and nursing research. However, researchers, institutions, and administrations still need to critically examine its accuracy, safety, and privacy, as well as academic misconduct and potential ethical issues that it may lead to before applying ChatGPT to practice.

Background: Overweight and obesity have become a public health issue. Lifestyle modifications delivered through mobile devices, especially mobile phones, present an opportunity to support weight loss efforts. However, evidence regarding the effects of mobile apps on other outcomes, such as blood pressure and physical activity (PA), remains limited. Recent studies on this topic require a systematic review and updating, and the active elements that promote behavior change remain unclear. Objective: The meta-analysis aimed to explore the effects of mobile phone apps on weight-related outcomes (weight, BMI, waist circumference [WC], fat mass, fat mass percentage), behavioral outcomes (moderate-to-vigorous physical activity [MVPA], energy intake), and metabolic outcomes (systolic blood pressure [SBP], diastolic blood pressure [DBP], triglycerides, hemoglobin A_1c [HbA_1c]) among adults with overweight and obesity. Behavior change techniques (BCTs), the smallest replicable intervention elements, were also identified to clarify the components used in current studies, along with associated resources, including facilitating, boosting, and nudging. In addition, factors influencing the effectiveness of these interventions were explored. Methods: Six databases (PubMed, Embase, CENTRAL, Web of Science, PsycINFO, and CINAHL) were searched for relevant randomized controlled trials (RCTs) published in English from inception to May 20, 2024. Two independent authors conducted study selection, data extraction, and quality assessment. The effect size of interventions was calculated using the mean difference (MD), and a random-effects model was applied for data analysis. Subgroup and sensitivity analyses were conducted to explore potential influencing factors and identify possible sources of heterogeneity. Results: A total of 29 studies were included. The results indicated that mobile phone app interventions significantly reduced weight (MD=−1.45 kg, 95% CI −2.01 to −0.89; P<.001), BMI (MD=−0.35 kg/m², 95% CI −0.57 to −0.13; P=.002), WC (MD=−1.98 cm, 95% CI −3.42 to −0.55; P=.007), fat mass (MD=−1.32 kg, 95% CI −1.94 to −0.69; P<.001), DBP (MD=−1.76 mm Hg, 95% CI −3.47 to −0.04; P=.04), and HbA_1c (MD=−0.13%, 95% CI −0.22 to −0.04; P=.005). However, nonsignificant effects were observed for other outcomes. The most frequently used BCTs included 2.3 “self-monitoring of behavior” (n=25), 4.1 “instruction on how to perform the behavior” (n=24), 2.2 “feedback on behavior” (n=20), 1.1 “goal setting (behavior)” (n=19), and 1.4 “action planning” (n=15). Fifty-nine percent of included studies used 3 resource types (ie, facilitating, boosting, and nudging). Subgroup analyses identified combined diet and PA interventions, medium-term intervention duration, and the use of ≥8 BCTs as potential reference interventions for improving outcomes. Conclusions: This meta-analysis demonstrates that mobile phone app interventions significantly reduce weight, BMI, WC, fat mass, DBP, and HbA_1c in adults with overweight and obesity. However, future studies should explore ways to optimize app interventions by incorporating behavior change strategies and resources to further enhance their overall effectiveness.

The meta-analysis aimed to explore the effects of mobile phone applications on weight-related, behavior, and metabolic outcomes among adults with overweight and obesity. Six databases were searched for relevant randomized controlled trials (RCTs) published between January 1, 2010 and November 7, 2023 in English. Two independent authors conducted study selection, data extraction, quality assessment. The effect size of interventions was calculated using mean difference. A random-effects model was applied for data analysis. A total of 27 studies were included. The results indicated that mobile phone application intervention reduced weight (MD=-1.38 kg, P<0.001, 95% CI -1.97 to -0.80), BMI (MD=-0.44 kg/m2, P<0.001, 95% CI -0.57 to -0.30), WC (MD=-2.13 cm, P=0.004, 95% CI -3.57 to -0.69), fat mass, and DBP (MD=-2.04 mmHg, P=0.01, 95% CI -3.65 to 0.44) with statistical significance. Future studies could consider how to optimize app interventions through behavior change strategies to enhance their overall effectiveness.