# Trust and anxiety as primary drivers of digital health acceptance in multiple sclerosis: toward an extended disease-specific technology acceptance model

## TL;DR

This study explores how trust and anxiety influence the acceptance of digital health tools among people with multiple sclerosis, suggesting a new model for understanding technology adoption in chronic diseases.

## Contribution

The paper introduces an extended disease-specific technology acceptance model for multiple sclerosis, emphasizing trust and anxiety as key factors.

## Key findings

- Trust in technology and technological anxiety are the strongest predictors of behavioral intention to use digital health tools in MS patients.
- Symptom severity moderates acceptance pathways, reducing the impact of perceived ease of use and increasing the impact of anxiety.

## Abstract

Digital health applications and AI-supported wearables may benefit people with Multiple Sclerosis (MS), yet fluctuating cognitive and physical symptoms could shape adoption in ways not fully captured by traditional acceptance models.

To identify determinants of digital health acceptance in MS, focusing on emotional factors and disease-related moderators, and to compare these patterns with individuals living with other chronic conditions.

An online survey (Winter 2024/2025) assessed Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) constructs in MS patients (n = 64) and a comparison group (n = 14). Measures included Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI), Social Influence (SI), Trust in Technology (TT), Technological Anxiety (TA), and self-reported wearable/app use.

Groups did not differ significantly in PU, PEOU, BI, or SI (all p > .05), though between-group comparisons should be interpreted cautiously given the small comparison group size (n = 14). However, MS participants reported substantially lower regular wearable use [χ2(2) = 7.83, p = .020]. TT (β = .52, p < .001) and TA (β = –.38, p < .001) were the strongest predictors of BI, whereas PU and PEOU contributed minimally. Symptom severity moderated acceptance pathways, weakening PEOU effects and amplifying TA effects.

Findings reveal an intention–behavior gap in MS and show that emotional and capability-related factors outweigh cognitive predictors. We outline foundational elements of an Extended Disease-Specific Technology Acceptance Model for MS integrating trust, anxiety, and symptom burden. Digital health tools for MS should prioritize trust-building and anxiety-reducing design features.