Evidence map›Paper›PMID 35357325›Full record

ArticleJMIR research protocols2022

Effectiveness of a Mindfulness-Based Mobile Application for the Treatment of Depression in Ambulatory Care: Protocol for a Randomized Controlled Trial.

Jan Sarlon, Jessica P K Doll, Aline Schmassmann, Serge Brand, Naomi Ferreira, Markus Muehlhauser, Stefanie Urech-Meyer, Nina Schweinfurth, Undine Emmi Lang, Annette Beatrix Bruehl

Registry-linked trialAbstract read
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In one paragraph

Article in JMIR research protocols, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05060393 (Efficacy of Mindfulness-based Mobile Application Use in Patients With Depressive Disorder), which is not on this 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.

NCT05060393 naunknown statusnot on this map

Efficacy of Mindfulness-based Mobile Application Use in Patients With Depressive Disorder

TypeinterventionalSponsorUniversity of BaselRan2021 to 2023Enrolled140ConditionsDepressionArmsMindfulness-based cognitive therapy
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Trial
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

10 authors.

Jan SarlonUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-0818-1830
Jessica P K DollUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-5692-294X
Aline SchmassmannUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-0632-4471
Serge BrandUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-2175-2765
Naomi FerreiraUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-0746-9659
Markus MuehlhauserUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-4295-0691
Stefanie Urech-MeyerUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-1565-5539
Nina SchweinfurthUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0001-7518-050X
Undine Emmi LangUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-3585-6533
Annette Beatrix BruehlUniversity Psychiatric Clinics, University of Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-4704-4986

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPatients with major depressive disorder (MDD) often experience relapses despite regular treatment with pharmacotherapy and psychotherapy. Further, long waiting lists and more demand than treatment capacity characterize ambulatory settings. Mindfulness-based interventions proved to be effective in relapse prevention in MDD. Next, mindfulness-based interventions in the form of free mobile applications can be an effective augmentation of the treatment as usual and can fill a gap in ambulatory care.

objectiveGiven this background, the aim of this randomized controlled study is to assess the effectiveness of additional MBI via a mobile app on the symptom severity and stress levels, compared to treatment as usual.

methodsA total of 140 individuals with MDD will be randomly allocated to the intervention or control condition. The intervention consists of the daily use of the mindfulness mobile application Headspace for thirty days (up to 10 minutes a day). The control condition will be treatment as usual. At baseline and four weeks later, the following key outcome dimensions will be assessed: self-rated (Beck Depression Inventory) and experts' rated symptoms of MDD (Hamilton Depression Rating Scale); secondary outcome variables will be blood pressure, heart rate, and respiratory rate and changes in tobacco and alcohol consumption and medication as a proxy of perceived stress.

resultsThis study was funded in February 2021 and approved by the institutional review board on April 15, 2021, and it started in May 2021. As of December 2021, we enrolled 30 participants. The findings are expected to be published in spring 2023.

conclusionsWe hypothesize that compared to the control conditions, individuals with MDD of the mobile app-condition will have both lower self- and experts' rated symptoms of MDD and more favorable stress-related levels. While the risk for medical events is low, the immediate benefit for participants could be a decrease in symptom severity and reduction of the stress level.

trial registrationClinical Trials.gov NCT05060393; https://clinicaltrials.gov/ct2/show/NCT05060393. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/33423.

Indexed as

depressionehealthmhealthmindfulnessstress level

Identifiers

PMID35357325
PMCPMC9015747

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Registered trials

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.