Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The use of recommender systems in mobile health apps for weight control has grown, but user app uptake and engagement remain limited. The objective of our scoping review was to explore the influence of recommender systems on mHealth app user engagement, identify the theoretical frameworks that have been applied on digital health interventions designed for weight management, and examine the key aspects that support tailoring user engagement through recommender systems. Based on existing literature, we identified 13 articles on recommender systems for weight management. Themes emerged, including theoretical underpinnings, authors' domain knowledge, user motivation, and design. Most studies used constructs from the social cognitive theory. We found inconsistencies between authors' domain knowledge and the intervention's content, with few professionals from the health and psychology fields. Only 46% of articles measured user engagement, whereas gamification and tailored messages were common app features. Despite some positive weight change results, more attention is needed toward implementing behavior theory and other strategies to promote app user engagement. Future studies should more accurately measure user motivation and identify the best features and behavioral constructs to increase app user interaction. Larger studies with a more diverse population are needed to generalize findings and evaluate weight loss maintenance and physical activity habits among users of recommender system.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/obr.13863 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!