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Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study
BACKGROUND: The use of digital technologies is increasing in health care. However, studies evaluating digital health technologies can be characterized by selective nonparticipation of older people, although older people represent one of the main user groups of health care. OBJECTIVE: We examined whe...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305561/ https://www.ncbi.nlm.nih.gov/pubmed/32501275 http://dx.doi.org/10.2196/17884 |
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author | Poli, Arianna Kelfve, Susanne Klompstra, Leonie Strömberg, Anna Jaarsma, Tiny Motel-Klingebiel, Andreas |
author_facet | Poli, Arianna Kelfve, Susanne Klompstra, Leonie Strömberg, Anna Jaarsma, Tiny Motel-Klingebiel, Andreas |
author_sort | Poli, Arianna |
collection | PubMed |
description | BACKGROUND: The use of digital technologies is increasing in health care. However, studies evaluating digital health technologies can be characterized by selective nonparticipation of older people, although older people represent one of the main user groups of health care. OBJECTIVE: We examined whether and how participation in an exergame intervention study was associated with age, gender, and heart failure (HF) symptom severity. METHODS: A subset of data from the HF-Wii study was used. The data came from patients with HF in institutional settings in Germany, Italy, the Netherlands, and Sweden. Selective nonparticipation was examined as resulting from two processes: (non)recruitment and self-selection. Baseline information on age, gender, and New York Heart Association Functional Classification of 1632 patients with HF were the predictor variables. These patients were screened for HF-Wii study participation. Reasons for nonparticipation were evaluated. RESULTS: Of the 1632 screened patients, 71% did not participate. The nonrecruitment rate was 21%, and based on the eligible sample, the refusal rate was 61%. Higher age was associated with lower probability of participation; it increased both the probabilities of not being recruited and declining to participate. More severe symptoms increased the likelihood of nonrecruitment. Gender had no effect. The most common reasons for nonrecruitment and self-selection were related to physical limitations and lack of time, respectively. CONCLUSIONS: Results indicate that selective nonparticipation takes place in digital health research and that it is associated with age and symptom severity. Gender effects cannot be proven. Such systematic selection can lead to biased research results that inappropriately inform research, policy, and practice. TRIAL REGISTRATION: ClinicalTrial.gov NCT01785121, https://clinicaltrials.gov/ct2/show/NCT01785121 |
format | Online Article Text |
id | pubmed-7305561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-73055612020-06-24 Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study Poli, Arianna Kelfve, Susanne Klompstra, Leonie Strömberg, Anna Jaarsma, Tiny Motel-Klingebiel, Andreas J Med Internet Res Original Paper BACKGROUND: The use of digital technologies is increasing in health care. However, studies evaluating digital health technologies can be characterized by selective nonparticipation of older people, although older people represent one of the main user groups of health care. OBJECTIVE: We examined whether and how participation in an exergame intervention study was associated with age, gender, and heart failure (HF) symptom severity. METHODS: A subset of data from the HF-Wii study was used. The data came from patients with HF in institutional settings in Germany, Italy, the Netherlands, and Sweden. Selective nonparticipation was examined as resulting from two processes: (non)recruitment and self-selection. Baseline information on age, gender, and New York Heart Association Functional Classification of 1632 patients with HF were the predictor variables. These patients were screened for HF-Wii study participation. Reasons for nonparticipation were evaluated. RESULTS: Of the 1632 screened patients, 71% did not participate. The nonrecruitment rate was 21%, and based on the eligible sample, the refusal rate was 61%. Higher age was associated with lower probability of participation; it increased both the probabilities of not being recruited and declining to participate. More severe symptoms increased the likelihood of nonrecruitment. Gender had no effect. The most common reasons for nonrecruitment and self-selection were related to physical limitations and lack of time, respectively. CONCLUSIONS: Results indicate that selective nonparticipation takes place in digital health research and that it is associated with age and symptom severity. Gender effects cannot be proven. Such systematic selection can lead to biased research results that inappropriately inform research, policy, and practice. TRIAL REGISTRATION: ClinicalTrial.gov NCT01785121, https://clinicaltrials.gov/ct2/show/NCT01785121 JMIR Publications 2020-06-05 /pmc/articles/PMC7305561/ /pubmed/32501275 http://dx.doi.org/10.2196/17884 Text en ©Arianna Poli, Susanne Kelfve, Leonie Klompstra, Anna Strömberg, Tiny Jaarsma, Andreas Motel-Klingebiel. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 05.06.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Poli, Arianna Kelfve, Susanne Klompstra, Leonie Strömberg, Anna Jaarsma, Tiny Motel-Klingebiel, Andreas Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study |
title | Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study |
title_full | Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study |
title_fullStr | Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study |
title_full_unstemmed | Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study |
title_short | Prediction of (Non)Participation of Older People in Digital Health Research: Exergame Intervention Study |
title_sort | prediction of (non)participation of older people in digital health research: exergame intervention study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305561/ https://www.ncbi.nlm.nih.gov/pubmed/32501275 http://dx.doi.org/10.2196/17884 |
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