Diabetes & Endocrinology
RCT
● RCT
Web Portal Use in Type 2 Diabetes: Uptake Driven by Intentions, Not Frequency
Journal of medical Internet research
Published March 27, 2026
Scholl Maximilian, Lendt Claas, Appelbaum Sebastian, Biallas Bianca, Brenk-Franz Katja, Chermette Ch…
PubMed ↗
DOI ↗
This secondary analysis of a randomized controlled trial assessed determinants of uptake and frequency of use of a web portal digital health intervention (DHI) aimed at improving self-management in adults with type 2 diabetes and/or coronary heart disease. The study analyzed data from 462 participants in the intervention group, focusing on sociodemographic, psychological, and health-related variables. Uptake was evaluated using logistic regression, while frequency was assessed with negative binomial regression. Results indicated that 43.1% of participants used the portal at least once. Significant determinants of uptake included 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), and intentions regarding physical activity (B=2.28, 95% CI 1.30-3.26; P<.001) and healthy nutrition (B=2.30, 95% CI 1.30-3.31; P<.001). The multiple regression model confirmed significant positive associations for physical activity (B=1.86, 95% CI 0.74-2.97; P=.001) and nutrition intentions (B=2.22, 95% CI 1.00-3.44; P<.001), with a negative association for patient activation (B=-3.20, 95% CI -4.95 to -1.46; P<.001). No significant determinants were found for frequency of use. These findings suggest that distinct strategies are needed to enhance initial adoption versus sustained engagement in DHIs, highlighting the importance of tailored approaches in digital health implementation.
AI Accuracy Review: 9/10
· Auto-published
Have you ever wondered why some people seem to thrive with health apps while others don’t? For patients with type 2 diabetes, using digital health tools can be a game-changer, but not everyone gets on board. In a recent study, only about 43% of participants used a web portal aimed at improving their health management. It turns out that factors like higher education levels and a strong desire to eat healthy significantly influenced whether patients tried the app and how often they used it afterward. Interestingly, just wanting to improve their health wasn’t enough for some; those who felt more activated about their health were less likely to engage with the portal. This highlights a crucial point: what gets someone to start using a health app doesn’t guarantee they’ll keep using it. For patients, this means that tailored support and encouragement are essential for both starting and maintaining the use of these tools. While this study sheds light on usage patterns, it also points to the need for future research to develop strategies that keep patients engaged long-term. Digital health tools have the potential to make a real difference, but they need to be designed with patient needs in mind.
What this means for you: Understanding what drives patients to use health apps can lead to better support and improved health outcomes.
View Original Abstract ↓
BACKGROUND: 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.
OBJECTIVE: This study aimed to examine the determinants of uptake and frequency of use and assess whether they differ.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.