Background: Interventions aimed at promoting physical activity (PA) behavior through habit formation pathways are gaining popularity, as they differ from conventional interventions that rely on intention pathways. Past research has established a positive correlation between PA habits and behavior. However, the efficacy of current interventions designed to form PA habits and improve PA automaticity is not yet fully ascertained. Additionally, the intervention components that significantly impact the effectiveness of these interventions are yet to be determined. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We conducted a search of three databases (PubMed, Embase, and Cochrane Library) from January 2000 to December 2022, with a focus on interventions for developing PA habits. Two independent authors conducted paper selection, quality assessment, data extraction, and coding of behavior change techniques (BCTs). The effect size of interventions was calculated using standardized mean difference. Subgroup analyses were carried out based on follow-up duration, delivery method, sample characteristics, and theory. Furthermore, we employed meta-regression to investigate the association between BCTs and PA habits. Results: Ten eligible studies with relatively high quality were included in the final data set. Characteristics of studies varied in intervention sample and delivery way. The habit formation interventions significantly increased PA habit (SMD = 0.31, 95% CI 0.14—0.48, P < .001) compared to the control groups. Subgroup analysis demonstrated that the duration of follow-up ≤ 12 weeks have a higher effect size on PA habit than the duration > 12 weeks. Meta-regression revealed that problem solving has a significant positive association with effectiveness improvement (β = 0.36, 95% CI 0.17–0.55), while social reward is linked with a reduction in effectiveness (β = -0.40, 95% CI -0.74–0.06). Conclusions: Our findings reveal that habit formation interventions are effective in fostering PA habit. Future studies could leverage the insights form this study to optimize the intervention design and achieve better effectiveness.

Background: Lack of time for exercise is common among office workers given their busy lives. Because of occupational restrictions and difficulty in taking time off, it is necessary to suggest effective ways for workers to exercise regularly. Sustaining lifestyle habits that increase nonexercise activity in daily life can solve the issue of lack of exercise time. Healthy Lifestyle Coaching Chatbot is a messenger app based on the habit formation model that can be used as a tool to provide a health behavior intervention that emphasizes the importance of sustainability and involvement. Objective: This study aimed to assess the efficacy of the Healthy Lifestyle Coaching Chatbot intervention presented via a messenger app aimed at stair-climbing habit formation for office workers. Methods: From February 1, 2018, to April 30, 2018, a total of 106 people participated in the trial after online recruitment. Participants were randomly assigned to the intervention group (n=57) or the control group (n=49). The intervention group received cues and intrinsic and extrinsic rewards for the entire 12 weeks. However, the control group did not receive intrinsic rewards for the first 4 weeks and only received all rewards as in the intervention group from the fifth to twelfth week. The Self-Report Habit Index (SRHI) of participants was evaluated every week, and the level of physical activity was measured at the beginning and end of the trial. SPSS Statistics version 21 (IBM Corp) was used for statistical analysis. Results: After 4 weeks of intervention without providing the intrinsic rewards in the control group, the change in SRHI scores was 13.54 (SD 14.99) in the intervention group and 6.42 (SD 9.42) in the control group, indicating a significant difference between the groups (P=.04). When all rewards were given to both groups, from the fifth to twelfth week, the change in SRHI scores of the intervention and control groups was comparable at 12.08 (SD 10.87) and 15.88 (SD 13.29), respectively (P=.21). However, the level of physical activity showed a significant difference between the groups after 12 weeks of intervention (P=.045). Conclusions: This study provides evidence that intrinsic rewards are important to enhance the sustainability and effectiveness of an intervention. The Healthy Lifestyle Coaching Chatbot program can be a cost-effective method for healthy habit formation. Trial Registration: Clinical Research Information Service KCT0004009; https://tinyurl.com/w4oo7md

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: 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: 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.