Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 21, 2022
Open Peer Review Period: Oct 31, 2022 - Dec 26, 2022
Date Accepted: Mar 31, 2024
(closed for review but you can still tweet)
Data visualization preferences in remote measurement technology for individuals living with depression, epilepsy, and multiple sclerosis: a qualitative study
ABSTRACT
Background:
Remote Measurement Technology (RMT) involves the use of wearable devices and smartphone apps to measure health outcome in everyday life. RMT with feedback in the form of data visual representations can facilitate engagement and present opportunities for intervention. Studies to date focus broadly on multiple dimensions of service users’ design preferences and RMT user experiences (for example health variables of perceived importance and perceived quality of medical advice provided) as opposed to data visualisation preferences.
Objective:
To explore data visualisation preferences and priorities in remote measurement technology (RMT), with individuals living with depression, epilepsy, and multiple sclerosis (MS).
Methods:
Triangulated qualitative study comparing and thematically synthesising focus group discussions with user reviews of existing self-management applications (apps) and a systematic review of RMT data visualisation preferences. A total of 45 people participated in six focus groups across the three health conditions (depression, n = 17; epilepsy, n = 11; MS, n = 17).
Results:
Thematic analysis validated a major theme around design preferences and recommendations and identified a further four minor themes: (1) data reporting; (2) Impact of visualisation; (3) moderators of visualisation preferences; and (4) system-related factors and features.
Conclusions:
When used effectively, data visualizations are valuable, engaging components of RMT. Easy to use and intuitive data visualisation design was lauded by individuals with neurological and psychiatric conditions. Apps need to accommodate the unique requirements of service users, which can be achieved through personalisation that goes beyond customisation, using, for example, adaptive interfaces. Overall, this study offers aspiring RMT developers a comprehensive outline of the data visualisation preferences of individuals living with depressions, epilepsy, and MS. Clinical Trial: N/A
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