2026-09-22T17:58:41Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/110642 2023-07-11 col_2142_5131 col_2142_14781 com_2142_5130 com_2142_687 com_2142_397

DSpace SAF Submission Ingestion Package generated from Vireo submission #16231 on 2021-09-16 at 17:02:33 Made available in DSpace on 2021-09-17T02:34:20Z (GMT). No. of bitstreams: 2 STALLARD-THESIS-2021.pdf: 5838303 bytes, checksum: 2efd1bd0b354f70eee4035ca3bc5101a (MD5) LICENSE.txt: 4211 bytes, checksum: b47dfb2cefa0129996ad6caf59803373 (MD5) Previous issue date: 2021-04-12 Embargo set by: Seth Robbins for item 118485 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only application/pdf http://hdl.handle.net/2142/110642 en Copyright 2021 Holly Rose Stallard Chapman-Novakofski, Karen M Stallard, Holly Rose 2021-09-17T02:34:20Z 2021-09-17T02:34:20Z 2023-09-17T02:34:57Z 2021-04-12 2021-05 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. Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01 The student, Holly Stallard, accepted the attached license on 2021-04-02 at 11:39. The student, Holly Stallard, submitted this Thesis for approval on 2021-04-02 at 11:58. This Thesis was approved for publication on 2021-04-12 at 15:52. type 2 diabetes health apps Health app use predictors in adults with diabetes: a qualitative and quantitative analysis using the unified theory of acceptance and use of technology 2 text Thesis Food Science & Human Nutrition Food Science & Human Nutrition University of Illinois at Urbana-Champaign Thesis M.S.