Evidence map›Paper›PMID 35727613›Full record

ReviewJMIR diabetes2022

Smartphone Apps for Diabetes Medication Adherence: Systematic Review.

Sheikh Mohammed Shariful Islam, Vinaytosh Mishra, Muhammad Umer Siddiqui, Jeban Chandir Moses, Sasan Adibi, Lemai Nguyen, Nilmini Wickramasinghe

Open access · goldAbstract readReview
In one paragraph

Review in JMIR diabetes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
10.0field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 3 countries.

Sheikh Mohammed Shariful IslamInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-7926-9368
Vinaytosh MishraCollege of Healthcare Management and Economics, Gulf Medical University, Ajman, United Arab Emirates.ORCID https://orcid.org/0000-0002-6360-910X
Muhammad Umer SiddiquiDepartment of Internal Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0001-5375-8702
Jeban Chandir MosesSchool of Information Technology, Deakin University, Burwood, Australia.ORCID https://orcid.org/0000-0002-4628-8850
Sasan AdibiSchool of Information Technology, Deakin University, Burwood, Australia.ORCID https://orcid.org/0000-0002-3868-6269
Lemai NguyenSchool of Information Technology, Deakin University, Burwood, Australia.ORCID https://orcid.org/0000-0003-3695-7245
Nilmini WickramasingheIverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia.ORCID https://orcid.org/0000-0002-1314-8843
Deakin University · AUGulf Medical University · AESwinburne University of Technology · AUThomas Jefferson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes is one of the leading noncommunicable chronic diseases globally. In people with diabetes, blood glucose levels need to be monitored regularly and managed adequately through healthy lifestyles and medications. However, various factors contribute to poor medication adherence. Smartphone apps can improve medication adherence in people with diabetes, but it is not clear which app features are most beneficial.

objectiveThis study aims to systematically review and evaluate high-quality apps for diabetes medication adherence, which are freely available to the public in Android and Apple app stores and present the technical features of the apps.

methodsWe systematically searched Apple App Store and Google Play for apps that assist in diabetes medication adherence, using predefined selection criteria. We assessed apps using the Mobile App Rating Scale (MARS) and calculated the mean app-specific score (MASS) by taking the average of app-specific scores on 6 dimensions, namely, awareness, knowledge, attitudes, intention to change, help-seeking, and behavior change rated on a 5-point scale (1=strongly disagree and 5=strongly agree). We used the mean of the app's performance on these 6 dimensions to calculate the MASS. Apps that achieved a total MASS mean quality score greater than 4 out of 5 were considered to be of high quality in our study. We formulated a task-technology fit matrix to evaluate the apps for diabetes medication adherence.

resultsWe identified 8 high-quality apps (MASS score≥4) and presented the findings under 3 main categories: characteristics of the included apps, app features, and diabetes medication adherence. Our framework to evaluate smartphone apps in promoting diabetes medication adherence considered physiological factors influencing diabetes and app features. On evaluation, we observed that 25% of the apps promoted high adherence and another 25% of the apps promoted moderate adherence. Finally, we found that 50% of the apps provided low adherence to diabetes medication.

conclusionsOur findings show that almost half of the high-quality apps publicly available for free did not achieve high to moderate medication adherence. Our framework could have positive implications for the future design and development of apps for patients with diabetes. Additionally, apps need to be evaluated using a standardized framework, and only those promoting higher medication adherence should be prescribed for better health outcomes.

Indexed as

applicationsappsdiabetesdigital healthmedication adherencemHealthmobile healthsmartphonestask-technology fit

Identifiers

PMID35727613
PMCPMC9257622
OpenAlexW4283274605

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.