Mood disorders medications: predictors of nonadherence – review of the current literature
Abstract
Studies have shown that there are several factors that predict nonadherence among patients with mood disorders. The aim of the present review is to identify the predictors of nonadherence among these patients. A careful review of the literature was conducted investigating several potential predictors of nonadherence among patients with mood disorders. A total of 217 relevant articles from peer-reviewed journals were considered, and articles that met our inclusion criteria (n = 54) were selected for this review. The authors identified several predictors of nonadherence among patients with mood disorders including younger age (below 40 years old), comorbidity with substance use and personality disorders, patients' beliefs, poor insight, illness severity, treatment-related side effects, specific features of the disease and a poor therapeutic alliance. Substance use disorder and illness severity are significant predictors of nonadherence especially in patients with bipolar disorder; whereas, treatment side effects are of primary importance for depressive disorder. The authors could not carry out a meta-analysis given that the studies considered in this review assessed patients at different time points and included different measurements of nonadherence. Moreover, articles cited in this review may reflect the authors' choice, and the authors did not investigate the adherence to a specific class of drugs commonly used in the management of mood disorders. Given the high social, clinical and economic impact of nonadherence among patients who are affected by mood disorders, it is critical to recognize patients at high risk of nonadherence in order to inform future strategies to examine and improve adherence to treatment. Further research is needed to clarify this issue.
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The use of digital therapeutics may help provide consistent treatment benefits, reduce treatment-related AEs, and align treatment expectations and goals of patients and HCPs.
“The use of digital therapeutics may help provide consistent treatment benefits, reduce treatment-related AEs, and align the treatment expectations and goals of patients and HCPs”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [96]
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BackgroundDigital health technologies and major depressive disorder
McIntyre, R. S., Greenleaf, W., Bulaj, G., Taylor, S. T., Mitsi, G., Saliu, D., Czysz, A., Silvesti, G., Garcia, M., & Jain, R. (2023) · CNS Spectrums · doi:10.1017/s1092852923002225 · Cited by 38 · see citing works
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- Authors
- Pompili, M., Venturini, P., Palermo, M., Stefani, H., Seretti, M. E., Lamis, D. A., Serafini, G., Amore, M., & Girardi, P.
- Affiliations
- Azienda Ospedaliera Sant'Andrea (IT)
- Emory University (US)
- University of Genoa (IT)
- Venue
- Expert Review of Neurotherapeutics
- Publisher
- Informa UK Limited
- Published
- 2013-07
- Volume
- 13
- Issue
- 7
- Pages
- 809-825
- ISSN
- 1473-7175 (ISSN-L)
- 1744-8360
- OpenAlex
- W1998678846
- Cited by
- 120
- References
- 87
- Citations by year
- 2026: 3 · 2025: 29 · 2024: 9 · 2023: 11 · 2022: 12 · 2021: 6 · 2020: 9 · 2019: 4 · 2018: 6 · 2017: 14 · 2016: 7 · 2015: 3 · 2014: 6 · 2013: 1
- FWCI
- 0.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Review of Neurotherapeutics (publishedVersion)
- PubMed (publishedVersion)
- CINECA IRIS Institutial Research Information System (University of Genoa) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Keywords
- medication non-adherence, mood disorders, bipolar disorder, depression, predictors of non-adherence, substance use disorders, personality disorders, treatment side effects, disease severity, therapeutic relationship, insight, patient beliefs, comorbidities
- MeSH terms
- Adult, Humans, Mood Disorders, Medication Adherence
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 ›
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