OBJECTIVE: This scoping review aims to identify (1) strategies used to implement web-based apps for health screening, (2) frameworks used for implementing web-based apps for health screening, (3) outcome measures of implementation strategies, and (4) effective implementation strategies.
METHODS: This scoping review was conducted based on Arksey and O'Malley's framework. After identifying the review question, two researchers independently screened and selected relevant literature from PubMed, Embase, Cochrane, Cumulative Index of Nursing and Allied Health Literature, PsycINFO, International Standard Randomised Controlled Trial Number Registry, OpenGrey, ClinicalTrials.gov, World Health Organization International Clinical Trials Registry Platform, and Web of Science. This was followed by charting the data using a standardized form. Finally, we collated, summarized, and reported the results quantitatively and qualitatively based on the review objectives.
RESULTS: A total of 16,476 studies were retrieved, of which 5669 were duplicates. From a total of 10,807 studies, 10,784 studies were excluded based on their titles and abstracts. There were 23 full-text articles reviewed, and 4 articles were included in the final analysis. Many studies were excluded because they focused on the effectiveness and not on the implementation of web-based apps. Facilitation was the most cited implementation strategy used, followed by reminders, clinical champions, and educational meetings and materials. Only 2 studies used implementation frameworks to guide the evaluation of their studies. Common outcome measures for implementation strategies were feasibility, fidelity, and penetration. Implementation strategies reported to be effective were quality improvement meetings, facilitation, educational meetings, and clinical champions.
CONCLUSIONS: There is a dearth of literature on the implementation of web-based apps for health screening. Implementation strategies were developed without any reported use of implementation theories or frameworks in most studies. More research on the development and evaluation of web-based screening app implementations is needed.
METHODS: A review and comparison of mHealth apps in pediatric care found in Google's Play Store (Android system) and Apple's App Store (iOS system) were performed. For the structured review of the available literature, Google Scholar, PubMed, IEEE Xplore Digital Library, and Science Direct online databases were used for the literature search. The assessment criteria used for comparison included requirement for Internet connection, size of application, information on disease, diagnostic tools, medical calculator, information on disease treatments, dosage recommendations, and drug interaction checker.
RESULTS: Fifty mHealth apps for general pediatric care and 8 mHealth apps for specific pediatric diseases were discussed in the literature. Of the 90 mHealth apps we reviewed, 27 that fulfilled the study criteria were selected for quality assessment. Medscape, Skyscape, and iGuideline scored the highest (score=7), while PediaBP scored the lowest (score=3).
CONCLUSIONS: Medscape, Skyscape, and iGuideline are the most comprehensive mHealth apps for HCPs as quick references for pediatric care. More studies about mHealth apps in pediatric care are warranted to ensure the quality and reliability of mHealth apps.
METHODS: A review and comparison of mHealth apps for caring of older people available in Google's Play Store (Android system) and Apple's App Store (iOS system) were performed. Systematic review of previous relevant literature were conducted. The assessment criteria used for comparison were requirement for Internet connection, information of disease, size of app, diagnostics and assessment tools, medical calculator, dosage recommendations and indications, clinical updates, drugs interaction checker, and information on disease management.
RESULTS: Twenty-five mHealth apps were assessed. Medscape and Skyscape Medical Library are the most comprehensive mHealth apps for general drug information, medical references, clinical score, and medical calculator. Alzheimer's Disease Pocketcard and Confusion: Delirium & Dementia: A Bedside Guide apps are recommended for clinical assessment, diagnosis, drug information, and management of geriatric patients with Alzheimer disease, delirium, and dementia.
CONCLUSIONS: More studies about mHealth apps for caring of older people are warranted to ensure the quality and reliability of the mHealth apps.
METHODS: Using datasets collected from Asian regions of Bangladesh, China, Indonesia, Iran, Malaysia, Pakistan, Taiwan, Thailand, and Vietnam, data from 10,397 participants (mean age = 22.40 years; 44.8% men) were used for analyses. All participants completed the SABAS using an online survey or paper-and-pencil mode.
RESULTS: Findings from confirmatory factor analysis, Rasch analysis, and network analysis all indicate a one-factor structure for the SABAS. Moreover, the one-factor structure of the SABAS was measurement invariant across age (21 years or less vs. above 21 years) and gender (men vs. women) in metric, scalar, and strict invariance. The one-factor structure was invariant across regions in metric but not scalar or strict invariance.
CONCLUSION: The present study findings showed that the SABAS possesses a one-factor structure across nine Asian regions; however, noninvariant findings in scalar and strict levels indicate that people in the nine Asian regions may interpret the importance of each SABAS item differently. Age group and gender group comparisons are comparable because of the invariance evidence for the SABAS found in the present study. However, cautions should be made when comparing SABAS scores across Asian regions.
OBJECTIVE: This study aimed to evaluate the utility and usability of ScreenMen.
METHODS: This study used both qualitative and quantitative methods. Healthy men working in a banking institution were recruited to participate in this study. They were purposively sampled according to job position, age, education level, and screening status. Men were asked to use ScreenMen independently while the screen activities were being recorded. Once completed, retrospective think aloud with playback was conducted with men to obtain their feedback. They were asked to answer the System Usability Scale (SUS). Intention to undergo screening pre- and postintervention was also measured. Qualitative data were analyzed using a framework approach followed by thematic analysis. For quantitative data, the mean SUS score was calculated and change in intention to screening was analyzed using McNemar test.
RESULTS: In total, 24 men participated in this study. On the basis of the qualitative data, men found ScreenMen useful as they could learn more about their health risks and screening. They found ScreenMen convenient to use, which might trigger men to undergo screening. In terms of usability, men thought that ScreenMen was user-friendly and easy to understand. The key revision done on utility was the addition of a reminder function, whereas for usability, the revisions done were in terms of attracting and gaining users' trust, improving learnability, and making ScreenMen usable to all types of users. To attract men to use it, ScreenMen was introduced to users in terms of improving health instead of going for screening. Another important revision made was emphasizing the screening tests the users do not need, instead of just informing them about the screening tests they need. A Quick Assessment Mode was also added for users with limited attention span. The quantitative data showed that 8 out of 23 men (35%) planned to attend screening earlier than intended after using the ScreenMen. Furthermore, 4 out of 12 (33%) men who were in the precontemplation stage changed to either contemplation or preparation stage after using ScreenMen with P=.13. In terms of usability, the mean SUS score of 76.4 (SD 7.72) indicated that ScreenMen had good usability.
CONCLUSIONS: This study showed that ScreenMen was acceptable to men in terms of its utility and usability. The preliminary data suggested that ScreenMen might increase men's intention to undergo screening. This paper also presented key lessons learned from the beta testing, which is useful for public health experts and researchers when developing a user-centered mobile Web app.