Key Drivers and Facilitators of the Choice to Use mHealth Technology in People With Neurological Conditions: Observational Study
Status PubMed-not-MEDLINE Jazyk angličtina Země Kanada Médium electronic
Typ dokumentu časopisecké články
PubMed
35604761
PubMed Central
PMC9171601
DOI
10.2196/29509
PII: v6i5e29509
Knihovny.cz E-zdroje
- Klíčová slova
- digital health, discrete choice experiment, epilepsy, health data, health economics, mHealth, mobile technology, multiple sclerosis, neurological conditions, wearable biosensors, wearable technology,
- Publikační typ
- časopisecké články MeSH
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.
Epilepsy Action Leeds United Kingdom
Faculty of Science Charles University Prague Czech Republic
Health Economics Department London School of Hygiene and Tropical Medicine London United Kingdom
International Bureau for Epilepsy Dublin Ireland
Italian Multiple Sclerosis Society and Foundation Rome Italy
Merck Sharp and Dohme Information Technology Prague Czech Republic
Neurosciences Department King's College Hospital London United Kingdom
Psychology Department King's College London London United Kingdom
South London and Maudsley Biomedical Research Centre London United Kingdom
Zobrazit více v PubMed
Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, Wykes T, Richardson MP, RADAR-CNS Consortium Wearable technology in epilepsy: The views of patients, caregivers, and healthcare professionals. Epilepsy Behav. 2018 Aug;85:141–149. doi: 10.1016/j.yebeh.2018.05.044.S1525-5050(18)30356-1 PubMed DOI
Johansson D, Malmgren K, Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review. J Neurol. 2018 Aug;265(8):1740–1752. doi: 10.1007/s00415-018-8786-y. 10.1007/s00415-018-8786-y PubMed DOI PMC
Shek AC, Biondi A, Ballard D, Wykes T, Simblett SK. Technology-based interventions for mental health support after stroke: A systematic review of their acceptability and feasibility. Neuropsychol Rehabil. 2021 Apr;31(3):432–452. doi: 10.1080/09602011.2019.1701501. PubMed DOI
Simblett SK, Biondi A, Bruno E, Ballard D, Stoneman A, Lees S, Richardson MP, Wykes T, RADAR-CNS consortium Patients' experience of wearing multimodal sensor devices intended to detect epileptic seizures: A qualitative analysis. Epilepsy Behav. 2020 Jan;102:106717. doi: 10.1016/j.yebeh.2019.106717.S1525-5050(19)31129-1 PubMed DOI
Simblett SK, Bruno E, Siddi S, Matcham F, Giuliano L, López Jorge Hortas, Biondi A, Curtis H, Ferrão José, Polhemus A, Zappia M, Callen A, Gamble P, Wykes T, RADAR-CNS Consortium Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: A qualitative analysis. Epilepsy Behav. 2019 Aug;97:123–129. doi: 10.1016/j.yebeh.2019.05.035.S1525-5050(19)30123-4 PubMed DOI
Simblett SK, Evans J, Greer B, Curtis H, Matcham F, Radaelli M, Mulero P, Arévalo Maria Jesús, Polhemus A, Ferrao J, Gamble P, Comi G, Wykes T, RADAR-CNS consortium Engaging across dimensions of diversity: A cross-national perspective on mHealth tools for managing relapsing remitting and progressive multiple sclerosis. Mult Scler Relat Disord. 2019 Jul;32:123–132. doi: 10.1016/j.msard.2019.04.020.S2211-0348(19)30180-4 PubMed DOI
Simblett S, Greer B, Matcham F, Curtis H, Polhemus A, Ferrão José, Gamble P, Wykes T. Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings. J Med Internet Res. 2018 Jul 12;20(7):e10480. doi: 10.2196/10480. v20i7e10480 PubMed DOI PMC
Venkatesh V, Morris MG, Davis GB, Davis FD. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly. 2003;27(3):425. doi: 10.2307/30036540. DOI
Kim YH, Kim DJ, Wachter K. A study of mobile user engagement (MoEN): Engagement motivations, perceived value, satisfaction, and continued engagement intention. Decision Support Systems. 2013 Dec;56:361–370. doi: 10.1016/j.dss.2013.07.002. DOI
Khechine H, Lakhal S, Ndjambou P. A meta-analysis of the UTAUT model: Eleven years later. Can J Adm Sci. 2016 Jun 06;33(2):138–152. doi: 10.1002/cjas.1381. DOI
Arning K, Ziefle Martina. Different Perspectives on Technology Acceptance: The Role of Technology Type and Age. In: Holzinger A, Miesenberger K, editors. HCI and Usability for e-Inclusion. Berlin, Germany: Springer; 2009. pp. 20–41.
Venkatesh V, Morris MG. Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly. 2000 Mar;24(1):115. doi: 10.2307/3250981. DOI
Scott JE, Walczak S. Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance. Inf Manag. 2009 May;46(4):221–232. doi: 10.1016/j.im.2008.10.003. DOI
de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012 Feb;21(2):145–72. doi: 10.1002/hec.1697. PubMed DOI
Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ. 2000 Jun 03;320(7248):1530–3. doi: 10.1136/bmj.320.7248.1530. PubMed DOI PMC
Atkinson-Clark E, Charokopou Mata, Van Osselaer Nancy, Hiligsmann Mickaël. A discrete-choice experiment to elicit preferences of patients with epilepsy for self-management programs. Epilepsy Behav. 2018 Feb;79:58–67. doi: 10.1016/j.yebeh.2017.11.015. S1525-5050(17)30764-3 PubMed DOI
Bauer B, Brockmeier Bernd, Devonshire Virginia, Charbonne Arthur, Wach Daniela, Hendin Barry. An international discrete choice experiment assessing patients' preferences for disease-modifying therapy attributes in multiple sclerosis. Neurodegener Dis Manag. 2020 Dec;10(6):369–382. doi: 10.2217/nmt-2020-0034. PubMed DOI
Martinez E, Garcia JM, Muñoz D, Comellas M, Gozalbo I, Lizan L, Polanco C. Patient preferences for treatment of multiple sclerosis with disease-modifying therapies: a discrete choice experiment. PPA. 2016 Sep;Volume 10:1945–1956. doi: 10.2147/ppa.s114619. PubMed DOI PMC
Jonker MF, Donkers B, Goossens LM, Hoefman RJ, Jabbarian LJ, de Bekker-Grob EW, Versteegh MM, Harty G, Wong SL. Summarizing Patient Preferences for the Competitive Landscape of Multiple Sclerosis Treatment Options. Med Decis Making. 2020 Feb;40(2):198–211. doi: 10.1177/0272989X19897944. PubMed DOI
Wijnen B, de Kinderen R J A, Colon A J, Dirksen C D, Essers B A B, Hiligsmann M, Leijten F S S, Ossenblok P P W, Evers S M A A. Eliciting patients' preferences for epilepsy diagnostics: a discrete choice experiment. Epilepsy Behav. 2014 Feb;31:102–9. doi: 10.1016/j.yebeh.2013.11.029.S1525-5050(13)00630-6 PubMed DOI
Reed Johnson F, Van Houtven George, Ozdemir Semra, Hass S, White J, Francis G, Miller DW, Phillips JT. Multiple sclerosis patients' benefit-risk preferences: serious adverse event risks versus treatment efficacy. J Neurol. 2009 Apr;256(4):554–62. doi: 10.1007/s00415-009-0084-2. PubMed DOI
Rosato R, Testa Silvia, Oggero Alessandra, Molinengo Giorgia, Bertolotto Antonio. Quality of life and patient preferences: identification of subgroups of multiple sclerosis patients. Qual Life Res. 2015 Sep;24(9):2173–82. doi: 10.1007/s11136-015-0952-4. PubMed DOI
Ettinger AB, Carter JA, Rajagopalan K. Patient versus neurologist preferences: A discrete choice experiment for antiepileptic drug therapies. Epilepsy Behav. 2018 Mar;80:247–253. doi: 10.1016/j.yebeh.2018.01.025.S1525-5050(18)30014-3 PubMed DOI
Holmes EA, Plumpton C, Baker GA, Jacoby A, Ring A, Williamson P, Marson A, Hughes DA. Patient-Focused Drug Development Methods for Benefit-Risk Assessments: A Case Study Using a Discrete Choice Experiment for Antiepileptic Drugs. Clin Pharmacol Ther. 2019 Mar;105(3):672–683. doi: 10.1002/cpt.1231. PubMed DOI PMC
Lloyd A, McIntosh E, Price M. The importance of drug adverse effects compared with seizure control for people with epilepsy: a discrete choice experiment. Pharmacoeconomics. 2005;23(11):1167–81. doi: 10.2165/00019053-200523110-00008.23118 PubMed DOI
Powell G, Holmes Emily A F, Plumpton Catrin O, Ring Adele, Baker Gus A, Jacoby Ann, Pirmohamed Munir, Marson Anthony G, Hughes Dyfrig A. Pharmacogenetic testing prior to carbamazepine treatment of epilepsy: patients' and physicians' preferences for testing and service delivery. Br J Clin Pharmacol. 2015 Nov;80(5):1149–59. doi: 10.1111/bcp.12715. doi: 10.1111/bcp.12715. PubMed DOI PMC
Wicks P, Brandes David, Park Jinhee, Liakhovitski Dimitri, Koudinova Tatiana, Sasane Rahul. Preferred features of oral treatments and predictors of non-adherence: two web-based choice experiments in multiple sclerosis patients. Interact J Med Res. 2015 Mar 05;4(1):e6. doi: 10.2196/ijmr.3776. v4i1e6 PubMed DOI PMC
Quaife M, Terris-Prestholt F, Di Tanna GL, Vickerman P. How well do discrete choice experiments predict health choices? A systematic review and meta-analysis of external validity. Eur J Health Econ. 2018 Nov;19(8):1053–1066. doi: 10.1007/s10198-018-0954-6.10.1007/s10198-018-0954-6 PubMed DOI
Ryan M, Skåtun Diane. Modelling non-demanders in choice experiments. Health Econ. 2004 Apr;13(4):397–402. doi: 10.1002/hec.821. PubMed DOI
Calero Valdez A, Ziefle M. The users’ perspective on the privacy-utility trade-offs in health recommender systems. Int J Hum Comput. 2019 Jan;121:108–121. doi: 10.1016/j.ijhcs.2018.04.003. DOI
Simblett S, Greer Ben, Matcham Faith, Curtis Hannah, Polhemus Ashley, Ferrão José, Gamble Peter, Wykes Til. Barriers to and Facilitators of Engagement With Remote Measurement Technology for Managing Health: Systematic Review and Content Analysis of Findings. J Med Internet Res. 2018 Jul 12;20(7):e10480. doi: 10.2196/10480. v20i7e10480 PubMed DOI PMC
Venkatesh V, Thong JYL, Xu X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 2012;36(1):157. doi: 10.2307/41410412. DOI
Reed Johnson F, Lancsar E, Marshall D, Kilambi V, Mühlbacher Axel, Regier DA, Bresnahan BW, Kanninen B, Bridges JF. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health. 2013;16(1):3–13. doi: 10.1016/j.jval.2012.08.2223. S1098-3015(12)04162-9 PubMed DOI
Bech M, Gyrd-Hansen D. Effects coding in discrete choice experiments. Health Econ. 2005 Oct;14(10):1079–83. doi: 10.1002/hec.984. PubMed DOI
Europe's partnership for health. Innovative Medicines Initiative. [2022-05-01]. https://www.imi.europa.eu .