|
1000
|
Abstract/Summary
|
-
<jats:title>Abstract</jats:title><jats:sec>
<jats:title>Background</jats:title>
<jats:p>The growing use of mobile health applications (apps) for managing diabetes and hypertension entails an increased need to understand their effectiveness among different population groups. It is unclear if efficacy and effectiveness trials currently provide evidence of differential effectiveness, and if they do, a summary of such evidence is missing. Our study identified to what extent sociocultural and socioeconomic inequalities were considered in effectiveness trials of mobile health apps in diabetic and hypertensive patients and if these inequalities moderated app effectiveness.</jats:p>
</jats:sec><jats:sec>
<jats:title>Methods</jats:title>
<jats:p>We built on our recent umbrella review that synthesized systematic reviews (SRs) of randomized controlled trials (RCTs) on the effectiveness of health apps. Using standard SR methodologies, we identified and assessed all primary RCTs from these SRs that focused on diabetes and/or hypertension and reported on health-related outcomes and inequality-related characteristics across intervention arms. We used the PROGRESS-Plus framework to define inequality-related characteristics that affect health opportunities and outcomes. We used harvest plots to summarize the subgroups (stratified analyses or interaction terms) on moderating effects of PROGRESS-Plus. We assessed study quality using the Risk of Bias 2 tool.</jats:p>
</jats:sec><jats:sec>
<jats:title>Results</jats:title>
<jats:p>We included 72 published articles of 65 unique RCTs. Gender, age, and education were the most frequently described PROGRESS-Plus characteristics at baseline in more than half of the studies. Ethnicity and occupation followed in 21 and 15 RCTs, respectively. Seven trials investigated the moderating effect of age, gender or ethnicity on app effectiveness through subgroup analyses. Results were equivocal and covered a heterogenous set of outcomes. Results showed some concerns for a high risk of bias, mostly because participants could not be blinded to their intervention allocation.</jats:p>
</jats:sec><jats:sec>
<jats:title>Conclusions</jats:title>
<jats:p>Besides frequently available gender, age, and education descriptives, other relevant sociocultural or socioeconomic characteristics were neither sufficiently reported nor analyzed. We encourage researchers to investigate how these characteristics moderate the effectiveness of health apps to better understand how effect heterogeneity for apps across different sociocultural or socioeconomic groups affects inequalities, to support more equitable management of non-communicable diseases in increasingly digitalized systems.</jats:p>
</jats:sec><jats:sec>
<jats:title>Registration</jats:title>
<jats:p><jats:ext-link xmlns:xlink='http://www.w3.org/1999/xlink' ext-link-type='uri' xlink:href='https://osf.io/89dhy/'>https://osf.io/89dhy/</jats:ext-link>.</jats:p>
</jats:sec>
|
|
1000
|
Hinweis
|
-
DeepGreen-ID: c2932e8b0e754bc185391630cf379fde ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
|
