Assessing the acceptability and adoption of digital health solutions for medication and lifestyle management in chronic illness among adults in Ibadan: a cross-sectional study

Abstract Introduction Digital health technologies such as mobile apps and wearable devices provide the opportunities of enhancing medication adherence and facilitating lifestyle modification in chronic disease care. However, their impact in Nigeria remains limited due to low adoption rates. Understanding acceptability, the degree to which potential users consider an intervention appropriate based on what they think or feel about the intervention,1 is crucial to guide implementation and enhance adoption. Aim This study aimed to assess the acceptability of digital health solutions for medication and lifestyle management among adults with chronic illness. Method A cross-sectional study was conducted among 200 adults (≥18 years) with chronic illness attending outpatient clinics of healthcare facilities in Ibadan between May and September 2025. Participants were recruited consecutively, and data was collected using a validated interviewer semi-structured questionnaire. The acceptability and willingness to use digital health were assessed using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework which assess users’ perceptions across five constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC) and Behavioural Intention (BI).2 BI was used as the outcome variable reflecting willingness to adopt digital health solutions. Data was analysed using SPSS version 23. Descriptive statistics were used to summarise participant characteristics and construct scores. Multiple linear regression was conducted to identify predictors of behavioural intention to use digital health solutions. The statistical significance was set at p 0.05. Result Hypertension (66%) and diabetes (12%) were the predominant conditions. The overall acceptability was moderate-to-high (mean 3.88 ± 0.84/5). UTAUT construct means were: PE = 3.89 ± 0.87, EE = 3.93 ± 0.82, SI = 4.26 ± 0.83, and FC = 4.11 ± 0.84. Reliability analysis showed acceptable internal consistency for EE, FC, and BI (α = 0.70–0.79), while PE was lower (α = 0.50). Regression analysis (R2 = 0.181) identified EE as the only significant predictor of BI (β = 0.285, p = 0.002), while PE (β = 0.108, p = 0.180), SI (β = 0.076, p = 0.355), and FC (β = 0.048, p = 0.608) were not significant. No significant differences in acceptability were observed by age group, gender, or education level. Key motivators included doctor’s recommendation (53.5%), ease-of-use (32.5%), and perceived health benefits (11.5%), while main barriers were poor power supply (45%), privacy concerns (30%), reliability and accuracy concerns (29%), and affordability (29%). Conclusion Adults with chronic illness in Ibadan demonstrated moderate-to-high acceptability of digital health solutions, with effort expectancy emerging as the key predictor of willingness to adopt. Acceptability was broadly consistent across demographic groups. Recommendations from healthcare providers, easier setup and use, and perceived benefits with health managements are key drivers for acceptability while affordability, poor internet access, and privacy concerns were common barriers. These findings highlight the need for user-friendly designs and supportive infrastructure to strengthen adoption. Collaborations with healthcare providers and addressing structural barriers may improve uptake, relieve pressure on overstretched health facilities, and support better chronic illness management. The limitation of this study is its predominantly quantitative design which may not fully capture the depth of participants’ perceptions.