mHealth Apps Assessment among Postpartum Women with Obesity and Depression

Effectiveness of internet-based and mobile-based interventions for adults with overweight or obesity experiencing symptoms of depression: a systematic review protocol

INTRODUCTION

Internet-based and mobile-based interventions (IMIs) provide innovative low-threshold and cost-effective prevention and self-management options for mental health problems complementary to standard treatment. The objective of this systematic review is to summarise the effectiveness and to critically evaluate studies on IMIs addressing comorbid depressive symptoms in adults with overweight or obesity.

METHODS AND ANALYSIS

The study authors will systematically search the databases MEDLINE, Cochrane Library, PsycINFO, Web of Science, Embase and Google Scholar (for grey literature) for randomised controlled trials (RCTs) of IMIs for individuals with overweight or obesity and comorbid depressive symptoms without restrictions on publication date (planned inception 1 June 2023 to 1 December 2023). Two reviewers will independently extract and evaluate data from studies eligible for inclusion by assessing quality of evidence and qualitatively synthesising results. Preferred Reporting Items for Systematic reviews Meta-Analyses (PRISMA) standards and the revised Cochrane Risk of Bias tool in RCTs (RoB 2) will be applied.

ETHICS AND DISSEMINATION

Ethical approval is not required as no primary data will be collected. Study results will be disseminated through publication in a peer-reviewed journal and presentations on conferences.

PROSPERO REGISTRATION NUMBER

CRD42023361771.

Evaluation of depression and obesity indices based on applications of ANOVA, regression, structural equation modeling and Taguchi algorithm process

Introduction

Depression and obesity are the main threat among women which have been considered by many research scholars in psychology studies. In their analysis for measuring and estimating obesity and depression they were involving statistical functions.

Methods

Regression, Analysis of Variance (ANOVA), and in the last two decades Structural Equation Modeling are the most familiar statistical methods among research scholars. Taguchi algorism process is one the statistical methods which mostly have been applying in engineering studies. In this study we are looking at two main objectives. The first one is to introduce Taguchi algorism process and apply it in a case study in psychology area. The second objective is challenging among four statistical techniques include ANOVA, regression, SEM, and Taguchi technique in a same data. To achieve those aims we involved depression and obesity indices with other familiar indicators contain socioeconomic, screen time, sleep time, and usage fitness and nutrition mobile applications.

Results and discussion

Outputs proved that Taguchi technique is able to analyze some correlations which are not achieved by applying ANOVA, regression, and SEM. Moreover, SEM has a special capability to estimate some hidden correlations which are not possible to evaluate them by using ANOVA, regression, and even Taguchi method. In the last, we found that some correlations are significant by SEM, however, in the same data with regression those correlation were not significant. This paper could be a warning for psychology research scholars to be more careful with involving statistical methods for measuring and estimating of their research variables.

Factors Influencing Continued Usage Behavior on Mobile Health Applications

(1) Background: As people pay more attention to health, mobile health applications (mHealth apps) are becoming popular. These apps offer health services that run on mobile devices to help improve users’ health behaviors. However, few studies explore what motivates users to continue to use these apps. This study proposes antecedents influencing users’ electronic satisfaction (e-satisfaction) and their continued behaviors of using mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), this study constructs a research model including perceived reliability and online review to predict the continued usage behavior on mHealth apps in China; (2) Methods: We conduct an online survey to collect data from participants who have used mHealth apps. This study receives 327 valid responses and tests the research model using the partial least squares structural equation model approach; (3) Results: Our results find that antecedents positively affect continued usage intention through the mediation role of e-satisfaction with mHealth apps. Interestingly, this study reveals that habit positively affects the continued usage behavior and moderates the effect of e-satisfaction and continued intention of using mHealth apps; (4) Conclusions: This study presents theoretical implications on the extended UTAUT2 and provides practical implications understanding of managing mHealth apps in China.

Determinants Impacting User Behavior towards Emergency Use Intentions of m-Health Services in Taiwan

Emergency usage intention and behavior are crucial to business service success for m-Health providers and patient healthcare service. This study aimed to identify the factors that influence m-Health acceptance and the effect of emergency use intentions on usage behavior among Taiwanese m-Health users by adopting and extending the Unified Theory of Acceptance and Use of Technology (UTAUT). This study also examines the moderating role of gender and age in the effects of the independent variables on satisfaction with m-Health services. An online questionnaire was used to collect data from 371 participants. The results revealed that performance expectancy, facilitating conditions, and trust had positive effects on user satisfaction. Additionally, m-Health knowledge and user satisfaction had positive effects on emergency use intentions. However, social influence and effort expectancy did not have a significant effect on satisfaction. Moreover, age and gender significantly moderated the effects of some predictors.