Tying mobile health tools to the users’ needs – Motivational drivers

Abstract

<p>Objective: The primary aim of this thesis is to contribute novel insights into the distinctive attributes of ICT systems, with a particular emphasis on features preferred by users in the realm of mobile health (mHealth) applications and devices. The study aimed at identifying motivational factors that enhance and sustain the usage and adaption of mHealth applications, wearables, and trackers among both healthy individuals and those affected by chronic diseases (sickle cell and diabetes). <p>Methods: In total, 584 participants completed the survey and answered the specific questions important for this thesis. A descriptive analysis of the demographics as well as regular use of tracking technologies and of the most motivating features of wearable sensors was performed. Further, the approach of binary logistic regression was applied to investigate the association between the importance of specific features and age, gender and health status. <p>Results: The descriptive analysis revealed that relevant personalized feedback and the ease of use of mobile health apps, wearables and trackers represent the most motivating features for a prolonged use. The logistic regression analysis revealed a statistically significant and positive association between having a chronic disease, age, gender, and the importance of notifications of mobile phones and managing a condition. The point estimates for several features like sensor accuracy and range of values as well as ergonomic and design and personalized/tailored features indicated a positive association between people with chronic diseases, age and gender. But these results were inconclusive. <p>Conclusion: This study provided valuable insight into the motivational drivers and adoption patterns of mobile Health applications and wearable devices among young and elderly individuals with and without chronic diseases. However, external validity and generalizability of the results was not given due to study limitation and low statistical power. Further research is therefore needed.