BACKGROUND: Among the numerous factors contributing to health care providers\' engagement with mobile apps, including user characteristics (eg, dexterity, anatomy, and attitude) and mobile features (eg, screen and button size), usability and quality of apps have been introduced as the most influential factors.
OBJECTIVE: This study aims to investigate the usability and quality of the Head Computed Tomography Scan Appropriateness Criteria (HAC) mobile app for physicians\' computed tomography scan ordering.
METHODS: Our study design was primarily based on methodological triangulation by using mixed methods research involving quantitative and qualitative think-aloud usability testing, quantitative analysis of the Mobile Apps Rating Scale (MARS) for quality assessment, and debriefing across 3 phases. In total, 16 medical interns participated in quality assessment and testing usability characteristics, including efficiency, effectiveness, learnability, errors, and satisfaction with the HAC app.
RESULTS: The efficiency and effectiveness of the HAC app were deemed satisfactory, with ratings of 97.8% and 96.9%, respectively. MARS assessment scale indicated the overall favorable quality score of the HAC app (82 out of 100). Scoring 4 MARS subscales, Information (73.37 out of 100) and Engagement (73.48 out of 100) had the lowest scores, while Aesthetics had the highest score (87.86 out of 100). Analysis of the items in each MARS subscale revealed that in the Engagement subscale, the lowest score of the HAC app was \"customization\" (63.6 out of 100). In the Functionality subscale, the HAC app\'s lowest value was \"performance\" (67.4 out of 100). Qualitative think-aloud usability testing of the HAC app found notable usability issues grouped into 8 main categories: lack of finger-friendly touch targets, poor search capabilities, input problems, inefficient data presentation and information control, unclear control and confirmation, lack of predictive capabilities, poor assistance and support, and unclear navigation logic.
CONCLUSIONS: Evaluating the quality and usability of mobile apps using a mixed methods approach provides valuable information about their functionality and disadvantages. It is highly recommended to embrace a more holistic and mixed methods strategy when evaluating mobile apps, because results from a single method imperfectly reflect trustworthy and reliable information regarding the usability and quality of apps.

UNASSIGNED: Controlling saturated fat and cholesterol intake is important for the prevention of cardiovascular diseases. Although the use of mobile diet-tracking apps has been increasing, the reliability of nutrition apps in tracking saturated fats and cholesterol across different nations remains underexplored.
UNASSIGNED: This study aimed to examine the reliability and consistency of nutrition apps focusing on saturated fat and cholesterol intake across different national contexts. The study focused on 3 key concerns: data omission, inconsistency (variability) of saturated fat and cholesterol values within an app, and the reliability of commercial apps across different national contexts.
UNASSIGNED: Nutrient data from 4 consumer-grade apps (COFIT, MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!) and an academic app (Formosa FoodApp) were compared against 2 national reference databases (US Department of Agriculture [USDA]-Food and Nutrient Database for Dietary Studies [FNDDS] and Taiwan Food Composition Database [FCD]). Percentages of missing nutrients were recorded, and coefficients of variation were used to compute data inconsistencies. One-way ANOVAs were used to examine differences among apps, and paired 2-tailed t tests were used to compare the apps to national reference data. The reliability across different national contexts was investigated by comparing the Chinese and English versions of MyFitnessPal with the USDA-FNDDS and Taiwan FCD.
UNASSIGNED: Across the 5 apps, 836 food codes from 42 items were analyzed. Four apps, including COFIT, MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!, significantly underestimated saturated fats, with errors ranging from -13.8% to -40.3% (all P<.05). All apps underestimated cholesterol, with errors ranging from -26.3% to -60.3% (all P<.05). COFIT omitted 47% of saturated fat data, and MyFitnessPal-Chinese missed 62% of cholesterol data. The coefficients of variation of beef, chicken, and seafood ranged from 78% to 145%, from 74% to 112%, and from 97% to 124% across MyFitnessPal-Chinese, MyFitnessPal-English, and LoseIt!, respectively, indicating a high variability in saturated fats across different food groups. Similarly, cholesterol variability was consistently high in dairy (71%-118%) and prepackaged foods (84%-118%) across all selected apps. When examining the reliability of MyFitnessPal across different national contexts, errors in MyFitnessPal were consistent across different national FCDs (USDA-FNDSS and Taiwan FCD). Regardless of the FCDs used as a reference, these errors persisted to be statistically significant, indicating that the app\'s core database is the source of the problems rather than just mismatches or variances in external FCDs.
UNASSIGNED: The findings reveal substantial inaccuracies and inconsistencies in diet-tracking apps\' reporting of saturated fats and cholesterol. These issues raise concerns for the effectiveness of using consumer-grade nutrition apps in cardiovascular disease prevention across different national contexts and within the apps themselves.

BACKGROUND: The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic (\"COVID-19 mitigation apps\"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people\'s acceptance of various COVID-19 mitigation apps deployed throughout the pandemic.
OBJECTIVE: This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people\'s contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people\'s acceptance of data sharing and data retention practices in various social contexts.
METHODS: A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents\' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people\'s acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results.
RESULTS: Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents\' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios.
CONCLUSIONS: This study showed that people\'s acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.

BACKGROUND: Dietary behaviors significantly influence health outcomes across populations. Unhealthy diets are linked to serious diseases and substantial economic burdens, contributing to approximately 11 million deaths and significant disability-adjusted life years annually. Digital dietary interventions offer accessible solutions to improve dietary behaviors. However, attrition, defined as participant dropout before intervention completion, is a major challenge, with rates as high as 75%-99%. High attrition compromises intervention validity and reliability and exacerbates health disparities, highlighting the need to understand and address its causes.
OBJECTIVE: This study systematically reviews the literature on attrition in digital dietary interventions to identify the underlying causes, propose potential solutions, and integrate these findings with behavior theory concepts to develop a comprehensive theoretical framework. This framework aims to elucidate the behavioral mechanisms behind attrition and guide the design and implementation of more effective digital dietary interventions, ultimately reducing attrition rates and mitigating health inequalities.
METHODS: We conducted a systematic review, meta-analysis, and thematic synthesis. A comprehensive search across 7 electronic databases (PubMed, MEDLINE, Embase, CENTRAL, Web of Science, CINAHL Plus, and Academic Search Complete) was performed for studies published between 2013 and 2023. Eligibility criteria included original research exploring attrition in digital dietary interventions. Data extraction focused on study characteristics, sample demographics, attrition rates, reasons for attrition, and potential solutions. We followed ENTREQ (Enhancing the Transparency in Reporting the Synthesis of Qualitative Research) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and used RStudio (Posit) for meta-analysis and NVivo for thematic synthesis.
RESULTS: Out of the 442 identified studies, 21 met the inclusion criteria. The meta-analysis showed mean attrition rates of 35% for control groups, 38% for intervention groups, and 40% for observational studies, with high heterogeneity (I²=94%-99%) indicating diverse influencing factors. Thematic synthesis identified 15 interconnected themes that align with behavior theory concepts. Based on these themes, the force-resource model was developed to explore the underlying causes of attrition and guide the design and implementation of future interventions from a behavior theory perspective.
CONCLUSIONS: High attrition rates are a significant issue in digital dietary interventions. The developed framework conceptualizes attrition through the interaction between the driving force system and the supporting resource system, providing a nuanced understanding of participant attrition, summarized as insufficient motivation and inadequate or poorly matched resources. It underscores the critical necessity for digital dietary interventions to balance motivational components with available resources dynamically. Key recommendations include user-friendly design, behavior-factor activation, literacy training, force-resource matching, social support, personalized adaptation, and dynamic follow-up. Expanding these strategies to a population level can enhance digital health equity. Further empirical validation of the framework is necessary, alongside the development of behavior theory-guided guidelines for digital dietary interventions.
BACKGROUND: PROSPERO CRD42024512902; https://tinyurl.com/3rjt2df9.

BACKGROUND: The aim of the paper is to establish the requirements and methodology for the development and implementation of a recommender system for mental health apps to support patients in self-managing their mental health while awaiting formal treatment.
METHODS: The system was developed using an algorithm-based approach, including: (1) user needs assessment through literature review and interviews with various stakeholders, (2) software modelling and prototype creation, and (3) bench testing of the prototype with health experts and users.
RESULTS: Based on initial exploration of users\' requirements, relevant standards and regulations, a library of trusted mental health apps was compiled and a recommendation engine was built to generate accurate user profiles and deliver personalised health recommendations, which will be further tested to ensure quality.
CONCLUSIONS: Developing a constructive mental health recommendation system requires the establishment of clear and comprehensive requirements, as well as a robust methodology adressing concerns related to data security, confidentiality, safety, and reliability. Subsequent research may compare various indicators of mental health outcomes at the start and end of patients\' waiting period to gain more insights into how the recommender system could be further improved to enhance user experience and their overall well-being.

BACKGROUND: Previous systematic reviews of digital eating disorder interventions have demonstrated effectiveness at improving symptoms of eating disorders; however, our understanding of how these interventions work and what contributes to their effectiveness is limited. Understanding the behavior change techniques (BCTs) that are most commonly included within effective interventions may provide valuable information for researchers and developers. Establishing whether these techniques have been informed by theory will identify whether they target those mechanisms of action that have been identified as core to changing eating disorder behaviors. It will also evaluate the importance of a theoretical approach to digital intervention design.
OBJECTIVE: This study aims to define the BCTs within digital self-management interventions or minimally guided self-help interventions for adults with eating disorders that have been evaluated within randomized controlled trials. It also assessed which of the digital interventions were grounded in theory and the range of modes of delivery included.
METHODS: A literature search identified randomized controlled trials of digital intervention for the treatment of adults with eating disorders with minimal therapist support. Each digital intervention was coded for BCTs using the established BCT Taxonomy v1; for the application of theory using an adapted version of the theory coding scheme (TCS); and for modes of delivery using the Mode of Delivery Ontology. A meta-analysis evaluated the evidence that any individual BCT moderated effect size or that other potential factors such as the application of theory or number of modes of delivery had an effect on eating disorder outcomes.
RESULTS: Digital interventions included an average of 14 (SD 2.6; range 9-18) BCTs. Self-monitoring of behavior was included in all effective interventions, with Problem-solving, Information about antecedents, Feedback on behavior, Self-monitoring of outcomes of behavior, and Action planning identified in >75% (13/17) of effective interventions. Social support and Information about health consequences were more evident in effective interventions at follow-up compared with postintervention measurement. The mean number of modes of delivery was 4 (SD 1.6; range 2-7) out of 12 possible modes, with most interventions (15/17, 88%) being web based. Digital interventions that had a higher score on the TCS had a greater effect size than those with a lower TCS score (subgroup differences: χ21=9.7; P=.002; I²=89.7%) within the meta-analysis. No other subgroup analyses had statistically significant results.
CONCLUSIONS: There was a high level of consistency in terms of the most common BCTs within effective interventions; however, there was no evidence that any specific BCT contributed to intervention efficacy. The interventions that were more strongly informed by theory demonstrated greater improvements in eating disorder outcomes compared to waitlist or treatment-as-usual controls. These results can be used to inform the development of future digital eating disorder interventions.
BACKGROUND: PROSPERO CRD42023410060; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=410060.

A digital diary in the form of a mobile messenger service offers a novel method for data collection in cancer research. Little is known about the things to consider when using this data collection method in clinical research for patients with cancer. In this Viewpoint paper, we discuss the lessons we learned from using a qualitative digital diary method via a mobile messenger service for data collection in oncology care. The lessons learned focus on three main topics: (1) data quality, (2) practical aspects, and (3) data protection. We hope to provide useful information to other researchers who consider this method for their research with patients. First, in this paper, we argue that the interactive nature of a digital diary via a messenger service is very well suited for the phenomenological approach and produces high-quality data. Second, we discuss practical issues of data collection with a mobile messenger service, including participant and researcher interaction. Third, we highlight corresponding aspects around technicalities, particularly those regarding data security. Our views on data privacy and information security are summarized in a comprehensive checklist to inform fellow researchers on the selection of a suitable messenger service for different scenarios. In our opinion, a digital diary via a mobile messenger service can provide high-quality data almost in real time and from participants\' daily lives. However, some considerations must be made to ensure that patient data are sufficiently protected. The lessons we learned can guide future qualitative research using this relatively novel method for data collection in cancer research.

Demand for online educational tools has risen steadily as technological innovations have evolved over the past several decades. Websites were the first platform to be introduced, and eventually used for online schooling, soon after the advent of the World Wide Web. Access to information and updated content in a short period of time on a wide-screen device such as a computer made websites popular early in their development. With the technological revolution of smart phones, mobile applications have been developed on various operating systems and, through this progress, a new form of educational platform was initiated. The portable features of mobile applications represent a pioneer era of educational tools for medical professionals. Online communications have transformed into social media over the last decade and have since been adopted by much of the world. All three of these educational platforms have created a significant impact on medical education communities, specifically in radiology. We describe the relative strengths of each platform and illustrate how our experience over more than two decades guides our recommendations.

暂无摘要。

暂无摘要。