Evidence map›Paper›PMID 40928841›Full record

SynthesisJournal of medical Internet research2025

Comparison of Cost-Effectiveness Between Digital Health Interventions and Pharmacotherapy for Depression: Systematic Review.

Jiae Im, Byeong-Chan Oh, Ha-Jun Song, Jeong-Min Choi, Dong-Ho Yeo, Eui-Kyung Lee

Abstract readSystematic ReviewComparative Study
In one paragraph

Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jiae ImSchool of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0009-0006-4029-7541
Byeong-Chan OhSchool of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0000-0001-5169-4477
Ha-Jun SongSchool of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0009-0004-8224-2018
Jeong-Min ChoiSchool of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0009-0005-1360-7076
Dong-Ho YeoSchool of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0009-0003-5469-6242
Eui-Kyung LeeSchool of Pharmacy, Sungkyunkwan University, Gyeonggi-do, Republic of Korea.ORCID https://orcid.org/0000-0003-0601-7754

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOwing to the unique characteristics of digital health interventions (DHIs), a tailored approach to economic evaluation is needed-one that is distinct from that used for pharmacotherapy. However, the absence of clear guidelines in this area is a substantial gap in the evaluation framework.

objectiveThis study aims to systematically review and compare the economic evaluation literature on DHIs and pharmacotherapy for the treatment of depression.

methodsWe searched for articles published between January 2013 and October 2023 in Ovid MEDLINE, Embase, Cochrane Library, and PsycINFO databases. Studies were eligible if they evaluated DHIs or pharmacotherapies for depression and reported economic outcomes. We extracted data on the study characteristics, input parameters, and economic evaluation modeling components. Chi-square tests were used to analyze the frequency of various components across intervention types. A qualitative comparison was performed to assess the costs, effects, and modeling aspects of each intervention. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist was used to evaluate the quality of the selected studies.

resultsA total of 42 articles were included, of which 23 (23/42, 55%) focused on DHIs and 19 (19/42, 45%) on pharmacotherapy. Cost-utility analysis was used more frequently in pharmacotherapy (16/19, 84%) than in DHIs (12/23, 52%), with a significant difference between the 2 intervention types (P=.01). Similarly, the types of comparators differed significantly, with DHIs more often being compared to usual care (12/23, 52%) or waitlist controls (5/23, 22%) and pharmacotherapy studies mainly involving active controls (17/19, 89%; P<.001). In addition, pharmacotherapy was more likely to be used in model-based studies (13/19, 68%), whereas DHIs predominantly relied on trial-based studies (17/23, 74%; P=.006). Although not statistically significant (P=.28), a notable trend was observed: the payer perspective was most commonly applied in pharmacotherapy studies (10/19, 53%), compared with approximately 30% (7/23) in DHIs. Furthermore, studies with a time horizon exceeding 12 months were more common for pharmacotherapy (5/19, 26%) than for the DHIs (3/23, 13%). Assessment using the CHEERS checklist indicated that pharmacotherapy studies generally had higher reporting quality compared with the quality of DHI studies in areas such as study parameters, comparators, time horizon, and discount rate.

conclusionsCompared with pharmacotherapy, DHIs involved a higher proportion of trial-based studies reporting short-term outcomes and studies with ambiguously defined cost items. This underscores the need for improved measurement and modeling to accurately capture the costs and effectiveness of DHIs.

trial registrationPROSPERO CRD42023471565; https://www.crd.york.ac.kr/PROSPERO/wiew/CRD42023471565.

Indexed as

Antidepressive AgentsCost-Benefit AnalysisDepressionTelemedicineDigital HealthHumansAntidepressive Agentsdepressiondigital health interventioneconomic evaluationpharmacotherapysystematic review

Identifiers

PMID40928841
PMCPMC12461167

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.