Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis.
Journal
Journal of affective disorders
ISSN: 1573-2517
Titre abrégé: J Affect Disord
Pays: Netherlands
ID NLM: 7906073
Informations de publication
Date de publication:
15 02 2020
15 02 2020
Historique:
received:
25
09
2019
revised:
13
11
2019
accepted:
30
11
2019
pubmed:
24
1
2020
medline:
7
2
2021
entrez:
24
1
2020
Statut:
ppublish
Résumé
Low engagement and attrition from app interventions is an increasingly recognized challenge for interpreting and translating the findings from digital health research. Focusing on randomized controlled trials (RCTs) of smartphone apps for depressive symptoms, we aimed to establish overall dropout rates, and how this differed between different types of apps. A systematic review of RCTs of apps targeting depressive symptoms in adults was conducted in May 2019. Random-effects meta-analysis were applied to calculate the pooled dropout rates in intervention and control conditions. Trim-and-fill analyses were used to adjust estimates after accounting for publication bias. The systematic search retrieved 2,326 results. 18 independent studies were eligible for inclusion, using data from 3,336 participants randomized to either smartphone interventions for depression (n = 1,786) or control conditions (n = 1,550). The pooled dropout rate was 26.2%. This increased to 47.8% when adjusting for publication bias. Study retention rates did not differ between depression vs. placebo apps, clinically-diagnosed vs. self-reported depression, paid vs. unpaid assessments, CBT vs. non-CBT apps, or mindfulness vs. non-mindfulness app studies. Dropout rates were higher in studies with large samples, but lower in studies offering human feedback and in-app mood monitoring. High dropout rates present a threat to the validity of RCTs of mental health apps. Strategies to improve retention may include providing human feedback, and enabling in-app mood monitoring. However, it critical to consider bias when interpreting results of apps for depressive symptoms, especially given the strong indication of publication bias, and the higher attrition in larger studies.
Sections du résumé
BACKGROUND
Low engagement and attrition from app interventions is an increasingly recognized challenge for interpreting and translating the findings from digital health research. Focusing on randomized controlled trials (RCTs) of smartphone apps for depressive symptoms, we aimed to establish overall dropout rates, and how this differed between different types of apps.
METHODS
A systematic review of RCTs of apps targeting depressive symptoms in adults was conducted in May 2019. Random-effects meta-analysis were applied to calculate the pooled dropout rates in intervention and control conditions. Trim-and-fill analyses were used to adjust estimates after accounting for publication bias.
RESULTS
The systematic search retrieved 2,326 results. 18 independent studies were eligible for inclusion, using data from 3,336 participants randomized to either smartphone interventions for depression (n = 1,786) or control conditions (n = 1,550). The pooled dropout rate was 26.2%. This increased to 47.8% when adjusting for publication bias. Study retention rates did not differ between depression vs. placebo apps, clinically-diagnosed vs. self-reported depression, paid vs. unpaid assessments, CBT vs. non-CBT apps, or mindfulness vs. non-mindfulness app studies. Dropout rates were higher in studies with large samples, but lower in studies offering human feedback and in-app mood monitoring.
DISCUSSION
High dropout rates present a threat to the validity of RCTs of mental health apps. Strategies to improve retention may include providing human feedback, and enabling in-app mood monitoring. However, it critical to consider bias when interpreting results of apps for depressive symptoms, especially given the strong indication of publication bias, and the higher attrition in larger studies.
Identifiants
pubmed: 31969272
pii: S0165-0327(19)32606-0
doi: 10.1016/j.jad.2019.11.167
pii:
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
413-419Subventions
Organisme : NIMH NIH HHS
ID : K23 MH116130
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.