# Acceptance of Men Living With HIV Toward Treatment-Supportive Mobile Apps Using the Unified Theory of Acceptance and Use of Technology: Cross-Sectional Study

## TL;DR

This study explores how men living with HIV accept treatment-supportive mobile apps, identifying key factors like age, health literacy, and app usability.

## Contribution

The study applies the UTAUT model to assess acceptance of eHealth apps among men with HIV, revealing specific predictors of app adoption.

## Key findings

- 45.3% of participants reported high acceptance of treatment-supportive mobile apps.
- Age, health literacy, eHealth literacy, and UTAUT factors like effort expectancy and social influence significantly predict acceptance.
- The UTAUT model explained 72% of the variance in app acceptance among participants.

## Abstract

Despite a 40-year prevalence of HIV, the AIDS epidemic prevails. Effective AIDS treatment requires specialist care and high adherence often hindered by structural issues in care access. Innovative eHealth solutions like treatment-supportive mobile apps can help address these issues. Successful implementation depends on user acceptance. Acceptance can be operationalized as behavioral intention and measured through the Unified Theory of Acceptance and Use of Technology (UTAUT).

This study examines the acceptance and its influencing factors of treatment-supportive mobile apps among men living with HIV.

A cross-sectional study was conducted among 172 men living with HIV between September 2021 and April 2024. In addition to the collection of sociodemographic, medical, and eHealth-related data, acceptance and its influencing factors were assessed by applying the UTAUT model. A multiple hierarchical regression analysis was conducted.

High acceptance of treatment-supportive mobile apps in men living with HIV was reported by 45.3% (n=78) of the participants. Significant predictors of acceptance were age (β=−0.27; P<.001); health literacy regarding disease (β=0.11; P<.001); eHealth literacy (β=0.10; P=.001); internet anxiety (β=−0.18; P=.04); and the UTAUT predictors: effort expectancy (β=0.38; P<.001), performance expectancy (β=0.24; P<.001), and social influence (β=0.40; P<.001). The UTAUT model explained 72% of the variance in acceptance.

Since the acceptance of eHealth services is a reliable indicator of the actual usage behavior, the results of this study are a promising basis for the successful implementation of eHealth offerings in the group of men living with HIV.