# Determinants of the Uptake and Frequency of Use of a Web Portal Digital Health Intervention in Patients With Type 2 Diabetes and/or Coronary Heart Disease: Secondary Analysis of a Randomized Controlled Trial

## TL;DR

This study explores what influences people with diabetes or heart disease to start using and keep using a web-based health tool.

## Contribution

The study is the first to jointly examine determinants of uptake and frequency of use of a digital health intervention in a single sample.

## Key findings

- Higher education, openness, and intentions regarding physical activity and nutrition were linked to higher uptake of the web portal.
- Patient activation had a negative association with uptake, and no significant determinants were found for frequency of use.
- The study shows that factors driving initial use do not necessarily influence sustained engagement.

## Abstract

The targeted application and design of digital health interventions (DHIs) require an understanding of usage determinants. Usage includes uptake (initial use) and frequency (extent of use), but it is unclear whether both components are driven by the same determinants.

This study aimed to examine the determinants of uptake and frequency of use and assess whether they differ.

The investigated DHI was a web portal provided in an intervention for improving disease-related self-management. This study is a secondary analysis of intervention group data from a parallel-group randomized controlled trial. Eligibility criteria were being an adult and being diagnosed with type 2 diabetes and/or coronary heart disease. Sociodemographic, psychological, and health-related variables were examined as determinants. Determinants were analyzed using simple and multiple regression models. Uptake was analyzed using logistic regression, and frequency was analyzed using negative binomial regression with robust SEs. Frequency was analyzed for those who used the DHI at least once. Except for sociodemographic variables, all other variables were standardized to a range from 0 to 1. For simple regression, inflation of the α error due to multiple testing was controlled via the approach of Benjamini and Hochberg, and for multiple regression, it was controlled via the significance of the complete multiple regression model.

Of 462 intervention group members, 199 (43.1%) used the web portal at least once. After controlling for inflation of the α error, simple regression for uptake yielded significant effects for higher education (B=0.56, 95% CI 0.18-0.95; P=.004), openness (B=1.08, 95% CI 0.33-1.83; P=.005), intention regarding physical activity (B=2.28, 95% CI 1.30-3.26; P<.001), and intention regarding healthy nutrition (B=2.30, 95% CI 1.30-3.31; P<.001). The multiple regression model for uptake was highly significant (P<.001), with significant positive associations for intentions regarding physical activity (B=1.86, 95% CI 0.74-2.97; P=.001) and healthy nutrition (B=2.22, 95% CI 1.00-3.44; P<.001), as well as a significant negative association for patient activation (B=−3.20, 95% CI −4.95 to −1.46; P<.001). After controlling for inflation of the α error, simple regression for frequency yielded no statistically significant effect, and the multiple regression model for frequency was not significant (P=.07).

This study is innovative in jointly examining determinants of the uptake and frequency of use of the same DHI within a single context and sample. By demonstrating that factors driving uptake do not necessarily increase the frequency of use, it advances existing research. The study contributes to a more differentiated understanding of DHI use and shows that distinct strategies are required to promote adoption versus sustained engagement. Applying this approach to other DHIs and settings may support more targeted and equitable digital health implementation in real-world contexts, thereby optimizing digital health deployment strategies overall.