The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.
The effectiveness and cost-effectiveness of e-health interventions for depression and anxiety in primary care: A systematic review and meta-analysis
Open accessAbstract
Purpose Psychological interventions are labor-intensive and expensive, but e-health interventions may support them in primary care. In this study, we systematically reviewed the effectiveness and cost-effectiveness of e-health interventions for depressive and anxiety symptoms and disorders in primary care.Methods We searched MEDLINE, Cochrane library, Embase, and PsychINFO until January 2018, for randomized controlled trials of e-health interventions for depression or anxiety in primary care. Two reviewers independently screened the identified publications, extracted data, and assessed risk of bias using the Cochrane Collaboration's tool.Results Out of 3617 publications, we included 14 that compared 33 treatments in 4183 participants. Overall, the methodological quality was poor to fair. The pooled effect size of e-health interventions was small (standardized mean difference = -0.19, 95%CI -0.31 to -0.06) for depression compared to control groups in the short-term, but this was maintained in the long-term (standardized mean difference = -0.22, 95%CI -0.35 to -0.09). Further analysis showed that e-health for depression had a small effect compared to care as usual and a moderate effect compared to waiting lists. One trial on anxiety showed no significant results. Four trials reported on cost-effectiveness.Limitations The trials studied different types of e-health interventions and had several risks of bias. Moreover, only one study was included for anxiety.Conclusions E-health interventions for depression have a small effect in primary care, with a moderate effect compared to waiting lists. The approach also appeared to be cost-effective for depression. However, we found no evidence for its effectiveness for anxiety.
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“However, offering these interventions in an online format may have unique benefits of increased accessibility and scalability, thereby lowering the threshold for participation [136] and potential cost-effectiveness [137].”
From New evidence in the booming field of online mindfulness: An updated meta-analysis of randomized controlled trials (2021) · cites this paper as [137]
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“Nevertheless, despite internet interventions’ attested efficacy [21] in treating various mental health conditions and its potential cost-effectiveness [22,23], internet interventions targeting cancer survivors are scant [24].”
From Development and Evaluation of the Usefulness, Usability, and Feasibility of iNNOV Breast Cancer: Mixed Methods Study (2022) · cites this paper as [22]
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“This contributes to extensive heterogeneity between trials. 595”
From Time for united action on depression: a Lancet–World Psychiatric Association Commission (2022) · cites this paper as [595]
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Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. E., & Cohen, J. (1989) · Technometrics · doi:10.2307/1270020 · Cited by 99,603
Statistical power analysis for the behavioral sciences
Cohen, J. (1990) · Computers Environment and Urban Systems · doi:10.1016/0198-9715(90)90050-4 · Cited by 65,417
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
The Cochrane Collaboration's tool for assessing risk of bias in randomised trials
Higgins, J. P. T., Altman, D. G., Gotzsche, P. C., Juni, P., Moher, D., Oxman, A. D., Savovic, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., et al. (2011) · BMJ · doi:10.1136/bmj.d5928 · Cited by 33,817
Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648
Diagnostic and Statistical Manual of Mental Disorders, 5th ed.
Carter, M. J. (2014) · Therapeutic Recreation Journal · Cited by 15,154
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Koretz, D., Merikangas, K. R., Rush, A. J., Walters, E. E., & Wang, P. S. (2003) · JAMA · doi:10.1001/jama.289.23.3095 · Cited by 7,988
Whiteford, H. A., Degenhardt, L., Rehm, J., Baxter, A. J., Ferrari, A. J., Erskine, H. E., Charlson, F. J., Norman, R. E., Flaxman, A. D., Johns, N., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)61611-6 · Cited by 6,006
Anxiety disorders in primary care: prevalence, impairment, comorbidity, and detection
Kroenke, K., Spitzer, R. L., Williams, J. B. W., Monahan, P. O., & Löwe, B. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-146-5-200703060-00004 · Cited by 4,741
Antidepressant Drug Effects and Depression Severity: A Patient-Level Meta-analysis
Fournier, J. C., DeRubeis, R. J., Hollon, S. D., Dimidjian, S., Amsterdam, J. D., Shelton, R. C., & Fawcett, J. (2010) · JAMA · doi:10.1001/jama.2009.1943 · Cited by 2,009
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Vomhof, M., van Eickels, D., Tönnies, J., Wildenauer, A., Feißt, M., Wensing, M., Szecsenyi, J., Hartmann, M., Friederich, H., Brunoni, C., et al. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.122280
Metadata
- Authors
- Massoudi, B., Holvast, F., Bockting, C. L. H., Burger, H., & Blanker, M. H.
- Affiliations
- University Medical Center Groningen (NL)
- University of Groningen (NL)
- Corresponding author
- Massoudi, Btissame
- Venue
- Journal of Affective Disorders
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 245
- Pages
- 728-743
- ISSN
- 0165-0327 (ISSN-L)
- OpenAlex
- W2900267347
- Cited by
- 101
- References
- 73
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 19 · 2023: 13 · 2022: 19 · 2021: 21 · 2020: 10 · 2019: 4
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Affective Disorders (publishedVersion)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Health Sciences › Medicine › Psychiatry and Mental health › Mental Health Treatment and Access
- Keywords
- digital health interventions, depression, anxiety, primary care, cost-effectiveness, digital mental health, waiting list comparison, usual care, psychological intervention
- MeSH terms
- Anxiety, Cost-Benefit Analysis, Depression, Humans, Primary Health Care, Waiting Lists, Randomized Controlled Trials as Topic, Telemedicine
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