The Ins and Outs of an Online Bipolar Education Program: A Study of Program Attrition
Open accessAbstract
BACKGROUND: The science of eHealth interventions is rapidly evolving. However, despite positive outcomes, evaluations of eHealth applications have thus far failed to explain the high attrition rates that are associated with some eHealth programs. Patient adherence remains an issue, and the science of attrition is still in its infancy. To our knowledge, there has been no in-depth qualitative study aimed at identifying the reasons for nonadherence to-and attrition from- online interventions. OBJECTIVE: This paper explores the predictors of attrition and participant-reported reasons for nonadherence to an online psycho-education program for people newly diagnosed with a bipolar disorder. METHODS: As part of an ongoing randomized controlled trial (RCT) evaluating an online psycho-education program for people newly diagnosed with a bipolar disorder, we undertook an in-depth qualitative study to identify participants' reasons for nonadherence to, and attrition from, the online intervention as well as a quantitative study investigating predictors of attrition. Within the RCT, 370 participants were randomly allocated to 1 of 2 active interventions or an attention control condition. Descriptive analyses and chi-square tests were used to explore the completion rates of 358 participants, and standard regression analysis was used to identify predictors of attrition. The data from interviews with a subsample of 39 participants who did not complete the online program were analyzed using "thematic analysis" to identify patterns in reported reasons for attrition. RESULTS: Overall, 26.5% of the sample did not complete their assigned intervention. Standard multiple regression analysis revealed that young age (P= .004), male gender (P= .001), and clinical recruitment setting (P= .001) were significant predictors of attrition (F(7,330)= 8.08, P< .001). Thematic analysis of interview data from the noncompleter subsample revealed that difficulties associated with the acute phases of bipolar disorder, not wanting to think about one's illness, and program factors such as the information being too general and not personally tailored were the major reasons for nonadherence. CONCLUSIONS: The dropout rate was equivalent to other Internet interventions and to face-to-face therapy. Findings from our qualitative study provide participant-reported reasons for discontinuing the online intervention, which, in conjunction with the quantitative investigations about predictors, add to understanding about Internet interventions. However, further research is needed to determine whether there are systematic differences between those who complete and those who do not complete eHealth interventions. Ultimately, this may lead to the identification of population subgroups that most benefit from eHealth interventions and to informing the development of strategies to improve adherence. TRIAL REGISTRATION: ACTRN12608000411347; http://www.anzctr.org.au/ACTRN12608000411347.aspx (Archived by WebCite at http://www.webcitation.org/5uX4uYwVN).
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Previous studies have found that younger participants and those with higher depressive symptoms are more likely to drop out of a mental health intervention.
“Previous studies have also found that younger participants are more likely to drop out of a mental health intervention, as are participants with higher depressive symptoms (Christensen, Griffiths, & Farrer, 2009; Nicholas et al., 2010).”
From A randomized controlled trial of strong minds: A school-based mental health program combining acceptance and commitment therapy and positive psychology (2016) · cites this paper as Nicholas et al., 2010
Researchers have identified factors that correlate with adherence, including patient characteristics, context of delivery, aspects of delivery, and characteristics of the intervention.
“Researchers have also identified factors that correlate with adherence, including patient characteristics [17] (eg, gender), the context of delivery [18] (eg, classroom), aspects of the delivery [19] (eg, therapist support), and characteristics of the intervention itself [20] (eg, tailored content).”
From Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data (2014) · cites this paper as [17]
Many researchers have found that women, older people, and well-educated people are all more likely to demonstrate adherence to Web-based interventions than males, younger people, and less-educated people.
“Although many researchers have found that women, older people, and well-educated people are all more likely to demonstrate adherence to Web-based interventions than males, younger people, and less-educated people [65,66,68,69]”
From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [66]
Characteristics of the intervention that may improve adherence include a strong theoretical foundation, perceived personal relevance, perceived effectiveness, tailoring, persuasive technologies, credibility, social networking, and regular "push factors" including human support and/or periodic prompts.
“Characteristics of the intervention that may improve adherence to the intervention include a strong theoretical foundation [41], perceived personal relevance to the user [65,66], perceived effectiveness [67,68], tailoring [69,70], persuasive technologies [71], credibility [72,73], social networking [74,75], and regular “push factors” including human support [76-78] and/or periodic prompts (eg, by”
From Web-Based Interventions for Behavior Change and Self-Management: Potential, Pitfalls, and Progress (2012) · cites this paper as [66]
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- Authors
- Nicholas, J., Proudfoot, J., Parker, G., Gillis, I., Burckhardt, R., Manicavasagar, V., & Smith, M.
- Affiliations
- Black Dog Institute (AU)
- Western Sydney University (AU)
- Corresponding author
- Proudfoot, Judith
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2010-12-19
- Volume
- 12
- Issue
- 5
- Pages
- e57
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3057316
- OpenAlex
- W1974545280
- Cited by
- 97
- References
- 29
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 3 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 2 · 2019: 8 · 2018: 8 · 2017: 13 · 2016: 11 · 2015: 6 · 2014: 7 · 2013: 6 · 2012: 8
- FWCI
- 8.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Australian Rotary Health
- Medical Research Council
- National Health and Medical Research Council (510135)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- attrition, dropout, online psychoeducation, bipolar disorder, digital health interventions, participant adherence, predictors of attrition, reasons for discontinuation, personalized interventions, young age, male gender, online intervention, mental health education programs, completion rates
- MeSH terms
- Adult, Bipolar Disorder, Female, Humans, Male, Middle Aged, Patient Dropouts, Patient Education as Topic, Regression Analysis, Self-Help Groups, Social Support, Therapy, Computer-Assisted, User-Computer Interface, Information Storage and Retrieval, Treatment Outcome, Internet, Young Adult
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