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: 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.

Acute pancreatitis (AP) is the most common pancreatic disease. Predicting the severity of AP is critical for making preventive decisions. However, the performance of existing scoring systems in pre...

Aims and objectives: To summarize the use of machine learning (ML) for hospital‐acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high‐quality ML predictive models.Background: As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study.Design: Systematic review.Methods: Relevant articles published between 2010–2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta‐analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool.Results: Twenty‐three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149–75353; the prevalence of pressure injuries ranged from 0.5%–49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre‐processing and model validation.Conclusions: ML, as a powerful decision‐making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre‐processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice.Relevance to Clinical Practice: This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre‐processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision‐making tool. A complete and rigorous model construction process should be followed in future studies to develop high‐quality ML models that can be applied in clinical 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.