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

International Journal of Bipolar Disorders

Research

Rachael Burnett¹  Marcos Sanches¹  Yunkyung Park¹  Martin Alda²  Christina Gonzalez-Torres³  Benoit H. Mulsant³  Abigail Ortiz³  M. Ishrat Husain³  Daniel M. Blumberger³ 

[1] Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada;Department of Psychiatry, Dalhousie University, Halifax, NS, Canada;National Institute of Mental Health, Klecany, Czech Republic;Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada;Campbell Family Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada;

关键词: Bipolar disorder;    Adherence;    Electronic monitoring;   

DOI  :  10.1186/s40345-023-00297-5

 received in 2023-02-21, accepted in 2023-04-14,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundSeveral 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.MethodsEighty-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.ResultsOverall 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.ConclusionsParticipants 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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