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Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models

BACKGROUND: Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioecono...

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Autores principales: Ortiz, Abigail, Park, Yunkyung, Gonzalez-Torres, Christina, Alda, Martin, Blumberger, Daniel M., Burnett, Rachael, Husain, M. Ishrat, Sanches, Marcos, Mulsant, Benoit H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192477/
https://www.ncbi.nlm.nih.gov/pubmed/37195477
http://dx.doi.org/10.1186/s40345-023-00297-5
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author Ortiz, Abigail
Park, Yunkyung
Gonzalez-Torres, Christina
Alda, Martin
Blumberger, Daniel M.
Burnett, Rachael
Husain, M. Ishrat
Sanches, Marcos
Mulsant, Benoit H.
author_facet Ortiz, Abigail
Park, Yunkyung
Gonzalez-Torres, Christina
Alda, Martin
Blumberger, Daniel M.
Burnett, Rachael
Husain, M. Ishrat
Sanches, Marcos
Mulsant, Benoit H.
author_sort Ortiz, Abigail
collection PubMed
description BACKGROUND: Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence. METHODS: Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes. RESULTS: Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed “perfect” adherence; 37.1% showed “good” adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence. CONCLUSIONS: Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-023-00297-5.
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spelling pubmed-101924772023-05-19 Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models Ortiz, Abigail Park, Yunkyung Gonzalez-Torres, Christina Alda, Martin Blumberger, Daniel M. Burnett, Rachael Husain, M. Ishrat Sanches, Marcos Mulsant, Benoit H. Int J Bipolar Disord Research BACKGROUND: Several studies have reported on the feasibility of electronic (e-)monitoring using computers or smartphones in patients with mental disorders, including bipolar disorder (BD). While studies on e-monitoring have examined the role of demographic factors, such as age, gender, or socioeconomic status and use of health apps, to our knowledge, no study has examined clinical characteristics that might impact adherence with e-monitoring in patients with BD. We analyzed adherence to e-monitoring in patients with BD who participated in an ongoing e-monitoring study and evaluated whether demographic and clinical factors would predict adherence. METHODS: Eighty-seven participants with BD in different phases of the illness were included. Patterns of adherence for wearable use, daily and weekly self-rating scales over 15 months were analyzed to identify adherence trajectories using growth mixture models (GMM). Multinomial logistic regression models were fitted to compute the effects of predictors on GMM classes. RESULTS: Overall adherence rates were 79.5% for the wearable; 78.5% for weekly self-ratings; and 74.6% for daily self-ratings. GMM identified three latent class subgroups: participants with (i) perfect; (ii) good; and (iii) poor adherence. On average, 34.4% of participants showed “perfect” adherence; 37.1% showed “good” adherence; and 28.2% showed poor adherence to all three measures. Women, participants with a history of suicide attempt, and those with a history of inpatient admission were more likely to belong to the group with perfect adherence. CONCLUSIONS: Participants with higher illness burden (e.g., history of admission to hospital, history of suicide attempts) have higher adherence rates to e-monitoring. They might see e-monitoring as a tool for better documenting symptom change and better managing their illness, thus motivating their engagement. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40345-023-00297-5. Springer Berlin Heidelberg 2023-05-17 /pmc/articles/PMC10192477/ /pubmed/37195477 http://dx.doi.org/10.1186/s40345-023-00297-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Ortiz, Abigail
Park, Yunkyung
Gonzalez-Torres, Christina
Alda, Martin
Blumberger, Daniel M.
Burnett, Rachael
Husain, M. Ishrat
Sanches, Marcos
Mulsant, Benoit H.
Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models
title Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models
title_full Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models
title_fullStr Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models
title_full_unstemmed Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models
title_short Predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using Growth Mixture Models
title_sort predictors of adherence to electronic self-monitoring in patients with bipolar disorder: a contactless study using growth mixture models
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10192477/
https://www.ncbi.nlm.nih.gov/pubmed/37195477
http://dx.doi.org/10.1186/s40345-023-00297-5
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