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An evaluation of diabetes targeted apps for Android smartphone in relation to behaviour change techniques
Author(s) -
Hoppe C. D.,
Cade J. E.,
Carter M.
Publication year - 2017
Publication title -
journal of human nutrition and dietetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.951
H-Index - 70
eISSN - 1365-277X
pISSN - 0952-3871
DOI - 10.1111/jhn.12424
Subject(s) - medicine , diabetes mellitus , mobile apps , android (operating system) , gestational diabetes , smartphone application , smartphone app , type 2 diabetes , confidence interval , diabetes management , world wide web , multimedia , computer science , pregnancy , endocrinology , gestation , biology , genetics , operating system
Background Mobile applications (apps) could support diabetes management through dietary, weight and blood glucose self‐monitoring, as well as by promoting behaviour change. The present study aimed to evaluate diabetes apps for content, functions and behaviour change techniques ( BCT s). Methods Diabetes self‐management apps for Android smartphones were searched for on the Google Play Store. Ten apps each from the following search terms were included; ‘diabetes’, ‘diabetes type 1’, ‘diabetes type 2’, ‘gestational diabetes’. Apps were evaluated by being scored according to their number of functions and BCT s, price, and user rating. Results The mean ( SD ) number of functions was 8.9 (5.9) out of a possible maximum of 27. Furthermore, the mean ( SD ) number of BCT s was 4.4 (2.6) out of a possible maximum of 26. Apps with optimum BCT had significantly more functions [13.8; 95% confidence interval ( CI ) = 11.9–15.9] than apps that did not (4.7; 95% CI = 3.2–6.2; P < 0.01) and significantly more BCT s (5.8; 95% CI = 4.8–7.0) than apps without (3.1; 95% CI = 2.2–4.1; P < 0.01). Additionally, apps with optimum BCT also cost more than other apps. In the adjusted models, highly rated apps had an average of 4.8 (95% CI = 0.9–8.7; P = 0.02) more functions than lower rated apps. Conclusions ‘Diabetes apps’ include few functions or BCT s compared to the maximum score possible. Apps with optimum BCT s could indicate higher quality. App developers should consider including both specific functions and BCT s in ‘diabetes apps’ to make them more helpful. More research is needed to understand the components of an effective app for people with diabetes.