Evaluating the Efficacy of Web-Based Cognitive Behavioral Therapy for the Treatment of Patients With Bipolar II Disorder and Residual Depressive Symptoms: Protocol for a Randomized Controlled Trial
Open accessPublished version of a preprint
Abstract
Background: Bipolar disorder (BD) is a highly prevalent psychiatric condition that can significantly impact every aspect of a person’s life if left untreated. A subtype of BD, bipolar disorder type II (BD-II), is characterized by long depressive episodes and residual depression symptoms, with short-lived hypomanic episodes. Medication and psychotherapy, such as cognitive behavioral therapy (CBT), are the main treatment options for BD-II. CBT specific for BD-II involves the recognition of warning signs, potentially triggering stimuli, and the development of coping skills to increase euthymic periods and improve global functioning. However, access to in-person CBT may be limited by several barriers, including low availability, high costs, and geographical limitations. Thus, web-based adaptations of CBT (e-CBT) have become a promising solution to address these treatment barriers. Nevertheless, e-CBT for the treatment of BD-II remains understudied. Objective: The proposed study aims to establish the first e-CBT program specific for the treatment of BD-II with residual depressive symptoms. The primary objective of this study will be to determine the effect of e-CBT in managing BD symptomatology. The secondary objective will be to assess the effects of this e-CBT program on quality of life and resilience. The tertiary objective will involve gathering user feedback using a posttreatment survey to support the continuous improvement and optimization of the proposed program. Methods: Adult participants (N=170) with a confirmed diagnosis of BD-II experiencing residual depressive symptoms will be randomly assigned to either the e-CBT and treatment as usual (TAU; n=85) group or the TAU (n=85) control group. Participants in the control group will be able to participate in the web-based program after the first 13 weeks. The e-CBT program will consist of 13 weekly web-based modules designed following a validated CBT framework. Participants will complete module-related homework and receive asynchronous personalized feedback from a therapist. TAU will consist of standard treatment services conducted outside of this research study. Depression and manic symptoms, quality of life, and resiliency will be assessed using clinically validated symptomatology questionnaires at baseline, week 6, and week 13. Results: The study received ethics approval in March 2020, and participant recruitment is expected to begin in February 2023 through targeted advertisements and physician referrals. Data collection and analysis are expected to conclude by December 2024. Linear and binomial regression (continuous and categorical outcomes, respectively) will be conducted along with qualitative interpretive methods. Conclusions: The findings will be the first on the effectiveness of delivering e-CBT for patients with BD-II with residual depressive symptoms. This approach can provide an innovative method to address barriers to in-person psychotherapy by increasing accessibility and decreasing costs. Trial Registration: ClinicalTrials.gov NCT04664257; https://clinicaltrials.gov/ct2/show/NCT04664257 International Registered Report Identifier (IRRID): PRR1-10.2196/46157
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials
the CONSORT Group, Schulz, K. F., Altman, D. G., & Moher, D. (2010) · Trials · doi:10.1186/1745-6215-11-32 · Cited by 1,871 · see citing works
Scott, J., Paykel, E., Morriss, R., Bentall, R., Kinderman, P., Johnson, T., Abbott, R., & Hayhurst, H. (2006) · British Journal of Psychiatry · doi:10.1192/bjp.188.4.313 · Cited by 566 · see citing works
Musiat, P., Goldstone, P., & Tarrier, N. (2014) · BMC Psychiatry · doi:10.1186/1471-244x-14-109 · Cited by 273 · see citing works
Proudfoot, J., Clarke, J., Birch, M.-R., Whitton, A. E., Parker, G., Manicavasagar, V., Harrison, V., Christensen, H., & Hadzi-Pavlovic, D. (2013) · BMC Psychiatry · doi:10.1186/1471-244x-13-312 · Cited by 264 · see citing works
Bipolar depression: a major unsolved challenge
Baldessarini, R. J., Vázquez, G. H., & Tondo, L. (2020) · International Journal of Bipolar Disorders · doi:10.1186/s40345-019-0160-1 · Cited by 235 · see citing works
Self-citationThe efficacy of cognitive–behavioral therapy and the problem of drop‐out
Bados, A., Balaguer, G., & Saldaña, C. (2007) · Journal of Clinical Psychology · doi:10.1002/jclp.20368 · Cited by 196 · see citing works
The Effectiveness of Online Cognitive Behavioral Treatment in Routine Clinical Practice
Ruwaard, J., Lange, A., Schrieken, B., Dolan, C. V., & Emmelkamp, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040089 · Cited by 173 · see citing works
Telepsychiatry and e-Mental Health Services: Potential for improving Access to Mental Health Care
McGinty, K. L., Saeed, S. A., Simmons, S. C., & Yildirim, Y. (2006) · Psychiatric Quarterly · doi:10.1007/s11126-006-9019-6 · Cited by 81 · see citing works
Bonnín, C. D. M., González-Pinto, A., Solé, B., Reinares, M., González-Ortega, I., Alberich, S., Crespo, J. M., Salamero, M., Vieta, E., Martínez-Arán, A., et al. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.02.034 · Cited by 76 · see citing works
Online psychotherapy: trailblazing digital healthcare
McDonald, A., Eccles, J. A., Fallahkhair, S., & Critchley, H. D. (2020) · BJPsych Bulletin · doi:10.1192/bjb.2019.66 · Cited by 45 · see citing works
ALAVI, N., & HIRJI, A. (2020) · Journal of Psychiatric Practice · doi:10.1097/pra.0000000000000455 · Cited by 33 · see citing works
Self-citationOnline Cognitive Behavioral Therapy: An e-Mental Health Approach to Depression and Anxiety
Alavi, N., & Omrani, M. (2019) · doi:10.1007/978-3-319-99151-1 · Cited by 25 · see citing works
Self-citationAlavi, N., Stephenson, C., & Rivera, M. (2021) · JMIR Mental Health · doi:10.2196/27308 · Cited by 24 · see citing works
Self-citation
Flagged first, then by citations
- Authors
- Gutierrez, G., Stephenson, C., Eadie, J., Moghimi, E., Omrani, M., Groll, D., Soares, C. N., Milev, R., Vazquez, G., Yang, M., & Alavi, N.
- Affiliations
- Queen's University (CA)
- Corresponding author
- Alavi, Nazanin
- ORCID
- Venue
- JMIR Research Protocols
- Publisher
- JMIR Publications Inc.
- Published
- 2023-05-19
- Volume
- 12
- Pages
- e46157
- ISSN
- 1929-0748 (ISSN-L)
- PMC
- PMC10238953
- OpenAlex
- W4367019033
- Cited by
- 3
- References
- 13
- Citations by year
- 2025: 2 · 2024: 1
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Research Protocols (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Psychology › Clinical Psychology › Personality Disorders and Psychopathology
- Keywords
- bipolar II disorder, residual depressive symptoms, internet-based cognitive behavioral therapy, digital mental health interventions, quality of life, resilience, usual care, therapist feedback, user feedback, mood stabilizers, hypomanic symptoms, depressive symptoms, coping strategies
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review