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Predictors of program interest in a digital health pilot study for heart health
Digital health programs can play a key role in supporting lifestyle changes to prevent and reduce cardiovascular disease (CVD) risk. A key concern for new programs is understanding who is interested in participating. Thus, the primary objective of this study was to utilize electronic health records...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389705/ https://www.ncbi.nlm.nih.gov/pubmed/37523348 http://dx.doi.org/10.1371/journal.pdig.0000303 |
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author | Lockwood, Kimberly G. Pitter, Viveka Kulkarni, Priya R. Graham, Sarah A. Auster-Gussman, Lisa A. Branch, OraLee H. |
author_facet | Lockwood, Kimberly G. Pitter, Viveka Kulkarni, Priya R. Graham, Sarah A. Auster-Gussman, Lisa A. Branch, OraLee H. |
author_sort | Lockwood, Kimberly G. |
collection | PubMed |
description | Digital health programs can play a key role in supporting lifestyle changes to prevent and reduce cardiovascular disease (CVD) risk. A key concern for new programs is understanding who is interested in participating. Thus, the primary objective of this study was to utilize electronic health records (EHR) to predict interest in a digital health app called Lark Heart Health. Because prior studies indicate that males are less likely to utilize prevention-focused digital health programs, secondary analyses assessed sex differences in recruitment and enrollment. Data were drawn from an ongoing pilot study of the Heart Health program, which provides digital health behavior coaching and surveys for CVD prevention. EHR data were used to predict whether potential program participants who received a study recruitment email showed interest in the program by “clicking through” on the email to learn more. Primary objective analyses used backward elimination regression and eXtreme Gradient Boost modeling. Recruitment emails were sent to 8,649 patients with available EHR data; 1,092 showed interest (i.e., clicked through) and 345 chose to participate in the study. EHR variables that predicted higher odds of showing interest were higher body mass index (BMI), fewer elevated lab values, lower HbA1c, non-smoking status, and identifying as White. Secondary objective analyses showed that, males and females showed similar program interest and were equally represented throughout recruitment and enrollment. In summary, BMI, elevated lab values, HbA1c, smoking status, and race emerged as key predictors of program interest; conversely, sex, age, CVD history, history of chronic health issues, and medication use did not predict program interest. We also found no sex differences in the recruitment and enrollment process for this program. These insights can aid in refining digital health tools to best serve those interested, as well as highlight groups who may benefit from behavioral intervention tools promoted by additional recruitment efforts tailored to their interest. |
format | Online Article Text |
id | pubmed-10389705 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103897052023-08-01 Predictors of program interest in a digital health pilot study for heart health Lockwood, Kimberly G. Pitter, Viveka Kulkarni, Priya R. Graham, Sarah A. Auster-Gussman, Lisa A. Branch, OraLee H. PLOS Digit Health Research Article Digital health programs can play a key role in supporting lifestyle changes to prevent and reduce cardiovascular disease (CVD) risk. A key concern for new programs is understanding who is interested in participating. Thus, the primary objective of this study was to utilize electronic health records (EHR) to predict interest in a digital health app called Lark Heart Health. Because prior studies indicate that males are less likely to utilize prevention-focused digital health programs, secondary analyses assessed sex differences in recruitment and enrollment. Data were drawn from an ongoing pilot study of the Heart Health program, which provides digital health behavior coaching and surveys for CVD prevention. EHR data were used to predict whether potential program participants who received a study recruitment email showed interest in the program by “clicking through” on the email to learn more. Primary objective analyses used backward elimination regression and eXtreme Gradient Boost modeling. Recruitment emails were sent to 8,649 patients with available EHR data; 1,092 showed interest (i.e., clicked through) and 345 chose to participate in the study. EHR variables that predicted higher odds of showing interest were higher body mass index (BMI), fewer elevated lab values, lower HbA1c, non-smoking status, and identifying as White. Secondary objective analyses showed that, males and females showed similar program interest and were equally represented throughout recruitment and enrollment. In summary, BMI, elevated lab values, HbA1c, smoking status, and race emerged as key predictors of program interest; conversely, sex, age, CVD history, history of chronic health issues, and medication use did not predict program interest. We also found no sex differences in the recruitment and enrollment process for this program. These insights can aid in refining digital health tools to best serve those interested, as well as highlight groups who may benefit from behavioral intervention tools promoted by additional recruitment efforts tailored to their interest. Public Library of Science 2023-07-31 /pmc/articles/PMC10389705/ /pubmed/37523348 http://dx.doi.org/10.1371/journal.pdig.0000303 Text en © 2023 Lockwood et al 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 author and source are credited. |
spellingShingle | Research Article Lockwood, Kimberly G. Pitter, Viveka Kulkarni, Priya R. Graham, Sarah A. Auster-Gussman, Lisa A. Branch, OraLee H. Predictors of program interest in a digital health pilot study for heart health |
title | Predictors of program interest in a digital health pilot study for heart health |
title_full | Predictors of program interest in a digital health pilot study for heart health |
title_fullStr | Predictors of program interest in a digital health pilot study for heart health |
title_full_unstemmed | Predictors of program interest in a digital health pilot study for heart health |
title_short | Predictors of program interest in a digital health pilot study for heart health |
title_sort | predictors of program interest in a digital health pilot study for heart health |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10389705/ https://www.ncbi.nlm.nih.gov/pubmed/37523348 http://dx.doi.org/10.1371/journal.pdig.0000303 |
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