Evidence map›Paper›PMID 37878374›Full record

ArticleJMIR research protocols2023

Improving Mild to Moderate Depression With an App-Based Self-Guided Intervention: Protocol for a Randomized Controlled Trial.

Ina Beintner, André Kerber, Clara Dominke, Ulrich Voderholzer

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 30% of its field
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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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

4 authors at 2 institutions in 1 country.

Ina BeintnerMindDoc Health GmbH, Munich, Germany.ORCID https://orcid.org/0000-0003-1779-9878
André KerberDivision of Clinical-Psychological Intervention, Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany.ORCID https://orcid.org/0000-0002-8588-7784
Clara DominkeMindDoc Health GmbH, Munich, Germany.ORCID https://orcid.org/0000-0002-9134-7848
Ulrich VoderholzerSchoen Clinic Roseneck, Prien am Chiemsee, Germany.ORCID https://orcid.org/0000-0003-0261-3145
Freie Universität Berlin · DEUniversity of Freiburg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression is one of the most prevalent mental disorders and frequently co-occurs with other mental disorders. Despite the high direct and indirect costs to both individuals and society, more than 80% of those diagnosed with depression remain with their primary care physician and do not receive specialized treatment. Self-guided digital interventions have been shown to improve depression and, due to their scalability, have a large potential public health impact. Current digital interventions often focus on specific disorders, while recent research suggests that transdiagnostic approaches are more suitable.

objectiveThis paper presents the protocol for a study that aims to assess the efficacy of a self-guided transdiagnostic app-based self-management intervention in patients with mild or moderate depression with and without comorbid mental disorders. Specifically, we are investigating the impact of the intervention on symptoms of depression, quality of life, anxiety symptoms, and mental health-related patient empowerment and self-management skills.

methodsThe intervention under investigation, MindDoc with Prescription, is a self-guided digital intervention aimed at supporting individuals with mild to moderate mental disorders from the internalizing spectrum, including depression. The app can be used as a low-threshold psychosocial intervention. Up to 570 adult patients will be randomized to either receive the intervention in addition to care as usual or only care as usual. We are including adults with a permanent residency in Germany and mild or moderate depression according to International Classification of Diseases, 10th Revision, criteria (F32.0, F32.1, F33.0, and F33.1). Clinical interviews will be conducted to confirm the diagnosis. Data will be collected at baseline as well as 8 weeks and 6 months after randomization. The primary outcome will be depression symptom severity after 8 weeks. Secondary outcomes will be quality of life, anxiety symptom severity, and patient empowerment and self-management behaviors. Data will be analyzed using multiple imputations, using the intention-to-treat principle, while sensitivity analyses will be based on additional imputation strategies and a per-protocol analysis.

resultsRecruitment for the trial started on February 7, 2023, and the first participant was randomized on February 14, 2023. As of September 5, 2023, 275 participants have been included in the trial and 176 have provided the primary outcome. The rate of missing values in the primary outcome is approximately 20%.

conclusionsData from this efficacy trial will be used to establish whether access to the intervention is associated with an improvement in depression symptoms in individuals diagnosed with mild or moderate depression. The study will contribute to expanding the evidence base on transdiagnostic digital interventions.

trial registrationGerman Registry of Clinical Trials DRKS00030852; https://drks.de/search/de/trial/DRKS00030852. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46651.

Indexed as

depressiondigital appdigital interventione-mental healthinterventionmental healthmHealthmobile appmobile healthmobile phonepublic healthrandomized controlled trialself-managementtransdiagnosticunguidedunguided digital intervention

Identifiers

PMID37878374
PMCPMC10632923
OpenAlexW4386621063

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.