Studies on mHealth Apps Often Neglect Relevant Details
New research indicates that studies on app-based mHealth interventions often leave out important data, limiting their practicality.
A new study found that although mobile health (mHealth) applications are said to promote physical activity, research surrounding them often fails to account for critical factors, indicating low generalizability levels.
Research indicates that a lack of physical activity can lead to poor health outcomes, often resulting in higher costs to the healthcare system. Although mHealth app development and use are on the rise, and these tools are expected to promote physical activity, their practicality in real-world settings remains a gray area.
Thus, researchers aimed to gain insight into how pragmatic research on mHealth app interventions is, that is, how they translate into real-world settings. Gathering various interventive efforts for the study, eligibility criteria included using apps, occurrence within health promotion or preventative care locations, including device-based physical activity outcomes, and a randomized study approach.
The final study sample included assessments of 22 interventions, including a total participant population of 3,555. Among these interventions, the population size ranged from 27 to 833, and the length of the studies ranged between two weeks and six months. Regarding the methods of measurement that studies used, 77 percent used activity monitors or fitness trackers, and the remaining 23 percent used app-based accelerometry measures.
When evaluating these cases, researchers used two frameworks: the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) method and the Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) method.
Although the rate of data reporting for RE-AIM was only 18 percent, the rates of reach, effectiveness, adoption, implementation, and maintenance were 44 percent, 52 percent, 3 percent, 10 percent, and 12.4 percent, respectively.
Meanwhile, according to the PRECIS-2 scale, 63 percent of study designs were equally explanatory and pragmatic. Across all interventions, the overall score was 2.93 out of 5.
Based on the results, researchers found that app-based mHealth physical activity studies omit important study characteristics, limiting generalizability and real-world use. Further, they noted that real-world applicability should be more of a central focus.
Similarly, previous research has also indicated that mobile app use in healthcare can contain limitations.
Research from July found that an artificial intelligence (AI)-based app did not lead to noticeable improvements in musculoskeletal health among patients with neck and low back pain.
Given that this type of condition requires self-management, researchers aimed to apply a smartphone app to this process to determine its effects. They did this alongside the implementation of usual care and usual care with web-based tools to compare impact.
After noticing that the adjusted mean difference in Musculoskeletal Health Questionnaire scores between the groups was marginal, researchers could not determine a high level of efficacy associated with the app.