Effectiveness of eHealth-based psychological interventions for depression treatment in patients with type 1 or type 2 diabetes mellitus: a systematic review
Open accessAbstract
BACKGROUND: Comorbidity between diabetes mellitus and depression is highly prevalent. The risk of depression in a person with diabetes is approximately twice that of a person without this disease. Depression has a major impact on patient well-being and control of diabetes. However, despite the availability of effective and specific therapeutic interventions for the treatment of depression in people with diabetes, 50% of patients do not receive psychological treatment due to insufficient and difficult accessibility to psychological therapies in health systems. The use of information and communication technologies (ICTs) has therefore been proposed as a useful tool for the delivery of psychological interventions, but it continues to be a field in which scientific evidence is recent and controversial. This systematic review aims to update the available information on the efficacy of psychological interventions delivered through ICTs to improve depressive symptomatology in patients with diabetes. METHODS: A systematic review of the literature was performed following the PRISMA guidelines and using MEDLINE, Embase, PubMed, Web of Science, PsycINFO, Scopus, and Cochrane Library databases to search for randomized clinical trials of eHealth treatments for patients with diabetes and comorbid depression from 1995 through 2020. In addition, studies related to follow-up appointments were identified. Inclusion criteria were as follows: (a) randomized clinical trials (RCTs); (b) patients with type 1 and type 2 diabetes; (c) adult population over 18 years of age; (d) presence of depressive symptomatology assessed with standardized instruments; (e) treatments for depression based on established psychotherapeutic techniques and principles; (f) delivered through eHealth technologies. We did not limit severity of depressive symptomatology, delivery setting or comparison group (treatment as usual or other treatment). Two coauthors independently reviewed the publications identified for inclusion and extracted data from the included studies. A third reviewer was involved to discuss discrepancies found. The PEDro scale was used to assess the quality of the RCTs. No meta-analysis of the results was performed. The protocol used for this review is available in PROSPERO (Reg; CRD42020180405). RESULTS: The initial search identified 427 relevant scientific publications. After removing duplicates and ineligible citations, a total of 201 articles were analyzed in full text. Ten articles met the criteria of this review and were included, obtaining very good scientific quality after evaluation with the PEDro scale. The main results show that the eHealth psychological intervention for depression in patients with diabetes showed beneficial effects both at the end of treatment and in the short (3 months) and long term (6 and 12 months) for the improvement of depressive symptomatology. The methodology used (type of diabetes, eHealth technology used, recruitment context, implementation and follow-up) was very heterogeneous. However, all studies were based on cognitive-behavioral tools and used standardized assessment instruments to evaluate depressive symptomatology or diagnosis of MDD. Glycemic control was assessed by glycosylated hemoglobin, but no benefits were found in improving glycemic control. Only four studies included psychoeducational content on diabetes and depression, but none used tools to improve or enhance adherence to medical prescriptions or diabetes self-care. CONCLUSIONS: ICT-based psychological interventions for the treatment of depression in people with diabetes appear to be effective in reducing depressive symptomatology but do not appear to provide significant results with regard to glycemic control. Nonetheless, the scientific evidence reported to date is still very limited and the methodology very diverse. In addition, no studies have implemented these systems in routine clinical practice, and no studies are available on the economic analysis of these interventions. Future research should focus on studying and including new tools to ensure improvements in diabetes outcomes and not only on psychological well-being in order to advance knowledge about these treatments. Economic evaluations should also be undertaken to analyze whether these treatment programs implemented using eHealth technologies are cost-effective.
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Psychological interventions targeting depressive symptoms have shown efficacy when delivered via digital technologies.
“Psychological interventions targeting depressive symptoms have shown efficacy when delivered via digital technologies (397).”
From 5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes:<i>Standards of Care in Diabetes—2023</i> (2023) · cites this paper as [397]
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- Authors
- Varela-Moreno, E., Carreira Soler, M., Guzmán-Parra, J., Jódar-Sánchez, F., Mayoral-Cleries, F., & Anarte-Ortíz, M. T.
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- Frontiers in Psychology
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- Frontiers Media SA
- Published
- 2022-01-31
- Volume
- 12
- Pages
- 746217-746217
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC8842796
- OpenAlex
- W4210528916
- Cited by
- 29
- References
- 52
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- 2025: 8 · 2024: 8 · 2023: 8 · 2022: 5
- FWCI
- 1.29
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- RISalud Institutional Health Repository of Andalucía (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repositorio Institucional de la Universidad de Málaga (University of Málaga) (submittedVersion, other-oa)
- Funders
- Universidad de Málaga
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- e-health, psychological intervention, depression, diabetes mellitus, type 1 diabetes, type 2 diabetes, information and communication technologies, psychotherapy delivery, medication adherence, treatment discontinuation, follow-up visits, digital mental health, access to mental health care, quality of life, glycemic control, diabetes self-management
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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