Objective: To identify pelvic floor muscle therapy mobile health applications (apps) targeting women with urinary incontinence (UI), and evaluate them in a standardized fashion.

Methods: A systematic search of English language apps on the Canadian App Store (iOS) and Google Play (Android) Store was performed. Eligible apps were evaluated independently by 5 reviewers using the validated Mobile App Rating Scale (MARS) tool. Descriptive characteristics were summarized and MARS subscale and overall quality scores werereported.

Results: Of 139 mobile health apps identified, 20 unique apps were included for full review, of which there were 7 iOS only apps, 6 Android only apps, and 7 apps available in both stores. At the time of analysis, most apps had been updated within the last year (60%). Only 1 app had been trialed and verified by evidence in scientific literature. The majority of apps were free to download (80%). The median (interquartile range) MARS overall quality score was 3.7 (0.8) on a 0-5 scale, ranging from 2.7 to 4.1. The highest-rated subscale was "functionality" with a median score of 4.1 (0.6); the lowest-rated was "information" with a median score of 3.4 (0.6). The median MARS subjective quality score was 2.9 (1.0).

Conclusion: There are both free and paid apps available on-line that deliver pelvic floor muscle therapy programs. Evaluation using the MARS tool identified that many apps are not of high quality, and only 1 was evidence-based and has been trialed clinically. This knowledge is relevant to the choice of apps by both patients and caregivers.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.urology.2020.08.040DOI Listing

Publication Analysis

Top Keywords

apps
13
pelvic floor
12
floor muscle
12
urinary incontinence
8
muscle therapy
8
mobile health
8
mars tool
8
quality score
8
median score
8
mars
5

Similar Publications

Objective: To identify and synthesise existing literature about the use of mobile educational applications (apps) designed to enhance the learning experience of nurses and midwives.

Design: A narrative review using a systematic, structured and comprehensive search of the literature.

Data Sources: Medline Complete (EBSCO), CINAHL (EBSCO), ERIC (EBSCO) and Embase (OVID) electronic databases.

View Article and Find Full Text PDF

: Caregivers of children with chronic illnesses, including chronic pain, experience high levels of distress, which impacts their own mental and physical health as well as child outcomes. Virtual care solutions offer opportunities to provide accessible support, yet most overlook caregivers' needs. We conducted a scoping review to create an interactive Evidence and Gap Map (EGM) of virtual care solutions across a stepped care continuum (i.

View Article and Find Full Text PDF

Background/objectives: As fitness apps increasingly incorporate social interaction features, users may find themselves overwhelmed by an excess of received support, struggling to effectively manage it. Highlighting a novel recipient-centric perspective, we aim to investigate the impact of social support overload on users' life burnout and discontinuance within fitness apps.

Methods: Utilizing Social Support Theory and Basic Psychological Needs Theory, we develop a model to examine how emotional, network, and informational support overload affect life burnout and discontinuance through the frustration of basic psychological needs: autonomy, competence, and relatedness.

View Article and Find Full Text PDF

Accuracy of Artificial Intelligence Based Chatbots in Analyzing Orthopedic Pathologies: An Experimental Multi-Observer Analysis.

Diagnostics (Basel)

January 2025

Center for Musculoskeletal Surgery, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.

The rapid development of artificial intelligence (AI) is impacting the medical sector by offering new possibilities for faster and more accurate diagnoses. Symptom checker apps show potential for supporting patient decision-making in this regard. Whether the AI-based decision-making of symptom checker apps shows better performance in diagnostic accuracy and urgency assessment compared to physicians remains unclear.

View Article and Find Full Text PDF

Development and validation of the eHealth Literacy and Use Scale (eHLUS) to measure medical app literacy.

Public Health

January 2025

Technical University of Munich, TUM School of Medicine and Health, Department Health and Sport Sciences, Social Determinants of Health, Munich, Germany.

Objectives: This study aimed to develop and validate the eHealth Literacy and Use Scale (eHLUS), a German assessment tool designed to measure health literacy in the context of using medical apps. This scale enhances traditional eHealth literacy tools by focusing on the unique requirements of medical app use, such as integration into everyday life, and self-efficacy.

Study Design: This study employed a mixed-method design.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!