Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Sep 25, 2025
Date Accepted: Jun 23, 2026
Functionality Review of Mobile Apps for the Tracking and Self-Management of Fatigue: Systematic Search and Evaluation
ABSTRACT
Background:
Fatigue and chronic fatigue syndrome (CFS) have a considerable impact on quality of life, thus motivating people to develop skills for better management of their fatigue. While the number of commercial apps in this domain has increased, there has been limited exploration of their functionalities.
Objective:
This paper aims to address this research gap through a functionality review of 17 top-rated iOS and Android apps for fatigue with the aim to articulate design implications for technologies focused on supporting the management of fatigue.
Methods:
We conducted a systematic analysis by searching the app marketplaces which resulted in the initial identification of 427 Apple apps and 1218 Google apps. From these, 17 apps were shortlisted for review after applying a screening process. The functionalities of these apps were then coded through a week-long usage of the apps for an expert evaluation.
Results:
Findings reveal the prevalence of functionalities for tracking fatigue, related symptoms, for visualizing tracked content, for assessing the user’s condition, and for providing interventions for the management of fatigue. Functionalities providing interventions for self-management of fatigue are surprisingly limited, with the most relevant ones being pacing based on energy estimation.
Conclusions:
The top-ranked apps for fatigue in the major marketplaces support three main functionalities under the scope of tracking fatigue along with related data, and visualizing such data, with limited provision of self-management interventions. Drawing from these findings, we articulate implications for the sensitive design of technologies to support the management of fatigue, including supporting of hybrid tracking, combined visualizations to support sense-making of fatigue data with related factors, and supporting energy estimates and pacing interventions.
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