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Published on: March 28, 2018
'We are very individual': anticipated effects on stroke survivors of using their person-generated health data
Gerardo Luis Dimaguila1,2, Frances Batchelor3,4, Mark Merolli2
1School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia dgl@student.unimelb.edu.au.
Background:
Person-generated health data (PGHD) are produced by people when they use health information technologies. People who use PGHD may experience changes in their health and care process, such as engagement with their own healthcare, and their sense of social support and connectedness. Research into evaluating those reported effects has not kept up; thus, a method for measuring PGHD outcomes was previously designed and applied to the exemplar case of Kinect-based stroke rehabilitation systems. A key step of the method ensures that the patient's voice is included. Allowing stroke survivors to participate in the development and evaluation of health services and treatment can inform healthcare providers on decisions about stroke care, and thereby improve health outcomes.
Objective:
This paper presents the perspectives of stroke survivors and clinicians on the anticipated effects of stroke survivors' use of PGHD from a poststroke simulated rehabilitation technology.
Methods:
This study gathered the perspectives of stroke survivors and clinicians through three focus groups and three interviews, recruited for convenience. Participants were also asked questions intended to encourage them to comment on the initial items of the patient-reported outcome measure-PGHD. Deductive thematic analysis was performed.
Results:
This paper has further demonstrated that outcomes of using PGHD can be measured. For instance, stroke survivors described that using PGHD could result in positive, negative and nil effects on their health behaviours. Survivors and clinicians had varying perspectives in three of the six themes presented, and emphasise the importance of allowing stroke survivors to participate in the evaluation of digital health services.

