Key Drivers and Facilitators of the Choice to Use mHealth Technology in People With Neurological Conditions: Observational Study

Sara Simblett*, Mark Pennington, Matthew Quaife, Evangelia Theochari, Patrick Burke, Giampaolo Brichetto, Julie Devonshire, Simon Lees, Ann Little, Angie Pullen, Amanda Stoneman, Sarah Thorpe, Janice Weyer, Ashley Polhemus, Jan Novak, Erin Dawe-Lane, Daniel Morris, Magano Mutepua, Clarissa Odoi, Emma WilsonTil Wykes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background: There is increasing interest in the potential uses of mobile health (mHealth) technologies, such as wearable biosensors, as supplements for the care of people with neurological conditions. However, adherence is low, especially over long periods. If people are to benefit from these resources, we need a better long-term understanding of what influences patient engagement. Previous research suggests that engagement is moderated by several barriers and facilitators, but their relative importance is unknown. Objective: To determine preferences and the relative importance of user-generated factors influencing engagement with mHealth technologies for 2 common neurological conditions with a relapsing-remitting course: multiple sclerosis (MS) and epilepsy. Methods: In a discrete choice experiment, people with a diagnosis of MS (n=141) or epilepsy (n=175) were asked to select their preferred technology from a series of 8 vignettes with 4 characteristics: privacy, clinical support, established benefit, and device accuracy; each of these characteristics was greater or lower in each vignette. These characteristics had previously been emphasized by people with MS and or epilepsy as influencing engagement with technology. Mixed multinomial logistic regression models were used to establish which characteristics were most likely to affect engagement. Subgroup analyses explored the effects of demographic factors (such as age, gender, and education), acceptance of and familiarity with mobile technology, neurological diagnosis (MS or epilepsy), and symptoms that could influence motivation (such as depression). Results: Analysis of the responses to the discrete choice experiment validated previous qualitative findings that a higher level of privacy, greater clinical support, increased perceived benefit, and better device accuracy are important to people with a neurological condition. Accuracy was perceived as the most important factor, followed by privacy. Clinical support was the least valued of the attributes. People were prepared to trade a modest amount of accuracy to achieve an improvement in privacy, but less likely to make this compromise for other factors. The type of neurological condition (epilepsy or MS) did not influence these preferences, nor did the age, gender, or mental health status of the participants. Those who were less accepting of technology were the most concerned about privacy and those with a lower level of education were prepared to trade accuracy for more clinical support. Conclusions: For people with neurological conditions such as epilepsy and MS, accuracy (ie, the ability to detect symptoms) is of the greatest interest. However, there are individual differences, and people who are less accepting of technology may need far greater reassurance about data privacy. People with lower levels of education value greater clinician involvement. These patient preferences should be considered when designing mHealth technologies.

Original languageEnglish
Article numbere29509
JournalJMIR Formative Research
Volume6
Issue number5
DOIs
Publication statusPublished - 1 May 2022

Keywords

  • digital health
  • discrete choice experiment
  • epilepsy
  • health data
  • health economics
  • mHealth
  • mobile technology
  • multiple sclerosis
  • neurological conditions
  • wearable biosensors
  • wearable technology

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