Background It is well known that regular physical exercise has associated benefits; yet, participation remains suboptimal. Mobile health (mHealth) has become an indispensable medium to deliver behavior change interventions, and there is a growing interest in the gamification apps in mHealth to promote physical activity (PA) participation. Gamification could use game design elements (such as points, leaderboards, and progress bars), and it has the potential to increase motivation for PA and engagement. However, mHealth-based gamification interventions are still emerging, and little is known about the application status and efficacy of such interventions. Objective This systematic review aims to investigate gamification apps in mHealth for improving PA levels and simultaneously summarize the impact of gamification interventions on PA participation. Methods We searched PubMed, Scopus, Web of Science, Embase, CINAHL (EBSCO host), and IEEE Xplore from inception to December 20, 2020. Original empirical research exploring the effects of gamification interventions on PA participation was included. The papers described at least one outcome regarding exercise or PA participation, which could be subjective self-report or objective indicator measurement. Of note, we excluded studies about serious games or full-fledged games. Results Of 2944 studies identified from the database search, 50 (1.69%) were included, and the information was synthesized. The review revealed that gamification of PA had been applied to various population groups and broadly distributed among young people but less distributed among older adults and patients with a disease. Most of the studies (30/50, 60%) combined gamification with wearable devices to improve PA behavior change, and 50% (25/50) of the studies used theories or principles for designing gamified PA interventions. The most frequently used game elements were goal-setting, followed by progress bars, rewards, points, and feedback. This review demonstrated that gamification interventions could increase PA participation; however, the results were mixed, and modest changes were attained, which could be attributed to the heterogeneity across studies. Conclusions Overall, this study provides an overview of the existing empirical research in PA gamification interventions and provides evidence for the efficacy of gamification in enhancing PA participation. High-quality empirical studies are needed in the future to assess the efficacy of a combination of gamification and wearable activity devices to promote PA, and further exploration is needed to investigate the optimal implementation of these features of game elements and theories to enhance PA participation.

Background The incidence of depression is increasing worldwide. Depression can lead to poor physical health and even suicide. However, in high-income countries, only about 50% of the people with depression receive appropriate therapy, and the detection rate of depression in low- and middle-income countries is relatively lower. Web-based self-management enables remote treatment and solves the problem of insufficient psychological treatment resources. Many past studies have evaluated the effectiveness of web-based self-management of depression, but there has been no synthesis of evidence. Therefore, this study conducted a meta-analysis of the effectiveness of web-based self-management for depressive symptoms. Method Six electronic databases (Cochrane Central Register of Controlled Trials, PubMed, Web of Science, Embase, CINAHL, and PsycINFO) were searched in September 2020. All literature referring to the effects of web-based self-management on depression were shortlisted by performing the medical subject headings (MeSH) search combined with a text word search. Results A total of 18 eligible randomized controlled trials were identified, and the results from 3055 participants were consolidated. The web-based self-management group exhibited a greater reduction in depressive symptoms than the control group (g = − 0.46; 95% CI: 0.62,0.30), and there was no evidence of publication bias. Subgroup analysis revealed that patients with moderate-to-severe depression benefited from web-based self-management interventions. In terms of interventions, those based on cognitive behavioral therapy (CBT) were highly effective. We noted that the longer the intervention time, the better was the improvement in the status of depression. Furthermore, it was established that participants who communicated with therapists and showed greater adherence to the intervention experienced significant improvement in their symptoms. The results of the intervention group were better than those of the waiting-list, treatment-as-usual, and online psychoeducation groups. Conclusions Web-based self-management is a promising therapy for depression. Future research should aim to refine these aspects of the intervention to achieve a beneficial impact.

ObjectiveThe effectiveness of integrating message framing into educational interventions to promote the health behaviour of patients with chronic diseases is still being debated in nursing research. The objective of this study was to assess the impact of educational interventions based on gain and loss frames on the health behaviours and beliefs of patients with chronic diseases and to identify the frame that achieves better outcomes.DesignThe systematic review was based on PRISMA guidelines for comprehensively searching, appraising and synthesising research evidence.Data sourcesWe searched the PubMed, Web of Science, PsycINFO and CINAHL databases for reports published from database inception until 26 March 2021.Eligibility criteriaIntervention studies, published in English, with adult patients with chronic disease conditions, and with intervention contents involved in the implementation of message framing, were considered. The outcomes were health behaviours or beliefs, such as knowledge, self-efficacy, intention or attitudes.Data extraction and synthesisData extraction and entry were performed using a predesigned data extraction form and assessed independently by two reviewers using the Cochrane Collaboration Risk of Bias I.ResultsA total of 11 intervention studies were included. We found that educational intervention based on both gain and loss frames could enhance the positive effects of communication, and promote healthy behaviours and beliefs in patients with chronic disease. Many of the studies we included here showed the advantage of loss framing messages. Due to the limited number of articles included and without quantitative analysis, this result should be interpreted cautiously.ConclusionsIntegrating message framing into health education might be a promising strategy to motivate patients with chronic disease to improve their health behaviours and beliefs. More extensive and well-designed trials are needed to support the conclusions and discuss the effective framing, moderators and mediators of framing.PROSPERO registration numberCRD42021250931.

Background Gestational diabetes mellitus (GDM) is a common pregnancy complication that negatively impacts the health of both the mother and child. Early prediction of the risk of GDM may permit prompt and effective interventions. This systematic review and meta-analysis aimed to summarize the study characteristics, methodological quality, and model performance of first-trimester prediction model studies for GDM. Methods Five electronic databases, one clinical trial register, and gray literature were searched from the inception date to March 19, 2022. Studies developing or validating a first-trimester prediction model for GDM were included. Two reviewers independently extracted data according to an established checklist and assessed the risk of bias by the Prediction Model Risk of Bias Assessment Tool (PROBAST). We used a random-effects model to perform a quantitative meta-analysis of the predictive power of models that were externally validated at least three times. Results We identified 43 model development studies, six model development and external validation studies, and five external validation-only studies. Body mass index, maternal age, and fasting plasma glucose were the most commonly included predictors across all models. Multiple estimates of performance measures were available for eight of the models. Summary estimates range from 0.68 to 0.78 (I2 ranged from 0% to 97%). Conclusion Most studies were assessed as having a high overall risk of bias. Only eight prediction models for GDM have been externally validated at least three times. Future research needs to focus on updating and externally validating existing models.

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