Health app use predictors in adults with diabetes: a qualitative and quantitative analysis using the unified theory of acceptance and use of technology 2

Stallard, Holly Rose

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https://hdl.handle.net/2142/110642 Copy

Description

Title

Health app use predictors in adults with diabetes: a qualitative and quantitative analysis using the unified theory of acceptance and use of technology 2

Author(s)

Stallard, Holly Rose

Issue Date

2021-04-12

Director of Research (if dissertation) or Advisor (if thesis)

Chapman-Novakofski, Karen M

Department of Study

Food Science & Human Nutrition

Discipline

Food Science & Human Nutrition

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• type 2 diabetes
• health apps

Abstract

Background Type 2 diabetes is a chronic disease requiring careful management and monitoring. There is an association between lower socioeconomic status and increased risk of developing type 2 diabetes and experiencing complications. There is evidence that technology, specifically health apps, can be effective in assisting in diabetes education and self-management; however, not much is known about predictors of health app use in this population. This study aimed to explore predictors of app use in people with type 2 diabetes in relation to the Unified Theory of Acceptance and Use of Technology 2. Methods Predictors of health app use in people with type 2 diabetes were analyzed using a mixed-methods approach. The sample was from the observational, longitudinal Real People with Diabetes Study population. Survey data were collected and evaluated for predictors of health app use. Statistical analysis was run using SPSS 25 and 26. Interviews focused on the use of technology and tracking to manage diabetes were also conducted with 35 participants. The interviews were audio-recorded, transcribed verbatim, and thematically analyzed. Results A total of 48 participants were included in the quantitative analysis. Binary logistic regression showed that effort expectancy was the strongest predictor of health app use (β = 1.23; p =.056). Interviews were conducted with 19 app users and 16 non-app users. Important themes such as tracking, accountability, convenience, and social support were mapped to the constructs of performance expectancy, social influence, and effort expectancy. Conclusions Effort expectancy, performance expectancy, and social influence were the most important predictors of health app use in this sample of participants with type 2 diabetes. Future studies should test the utility of these findings in interventions for technology use in diabetes self-care management. While this sample was predominantly white and female and thus not generalizable to all people with type 2 diabetes, these results may have key implications for patients with lower socioeconomic status.

Graduation Semester

2021-05

Type of Resource

Thesis

Permalink

http://hdl.handle.net/2142/110642

Copyright and License Information

Copyright 2021 Holly Rose Stallard

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