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Record W4395668388 · doi:10.1002/cl2.1398

Mobile apps to reduce depressive symptoms and alcohol use in youth: A systematic review and meta‐analysis

2024· review· en· W4395668388 on OpenAlex
Olivia Magwood, Ammar Saad, Dominique Ranger, Kate Volpini, Franklin Rukikamirera, Rinila Haridas, Shahab Sayfi, Jeremie Alexander, Yvonne Tan, Kevin Pottie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCampbell Systematic Reviews · 2024
Typereview
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsDalhousie UniversityWestern UniversityMcMaster UniversityQueen's UniversityUniversity of British ColumbiaBruyèreUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMental healthAnxietyDepression (economics)Mobile appsPsychologyPsychiatrymHealthDepressive symptomsSystematic reviewClinical psychologyMedicineMEDLINEWorld Wide WebPsychological interventionComputer science

Abstract

fetched live from OpenAlex

Abstract Background Among youth, symptoms of depression, anxiety, and alcohol use are associated with considerable illness and disability. Youth face many personal and health system barriers in accessing mental health care. Mobile applications (apps) offer youth potentially accessible, scalable, and anonymous therapy and other support. Recent systematic reviews on apps to reduce mental health symptoms among youth have reported uncertain effectiveness, but analyses based on the type of app‐delivered therapy are limited. Objectives We conducted this systematic review with youth co‐researchers to ensure that this review addressed the questions that were most important to them. The objective of this review is to synthesize the best available evidence on the effectiveness of mobile apps for the reduction of depressive symptoms (depression, generalized anxiety, psychological distress) and alcohol use among youth. Search Methods We conducted electronic searches of the following bibliographic databases for studies published between January 1, 2008, and July 1, 2022: MEDLINE (via Ovid), Embase (via Ovid), PsycINFO (via Ovid), CINAHL (via EBSCOHost), and CENTRAL (via the Cochrane Library). The search used a combination of indexed terms, free text words, and MeSH headings. We manually screened the references of relevant systematic reviews and included randomized controlled trials (RCTs) for additional eligible studies, and contacted authors for full reports of identified trial registries or protocols. Selection Criteria We included RCTs conducted among youth aged 15–24 years from any setting. We did not exclude populations on the basis of gender, socioeconomic status, geographic location or other personal characteristics. We included studies which assessed the effectiveness of app‐delivered mental health support or therapy interventions that targeted the management of depressive disorders and/or alcohol use disorders. We excluded apps that targeted general wellness, apps which focused on prevention of psychological disorders and apps that targeted bipolar disorder, psychosis, post‐traumatic stress disorder, attention‐deficit hyperactivity disorder, substance use disorders (aside from alcohol), and sleep disorders. Eligible comparisons included usual care, no intervention, wait‐list control, alternative or controlled mobile applications. We included studies which reported outcomes on depressive symptoms, anxiety symptoms, alcohol use and psychological distress over any follow‐up period. Data Collection and Analysis We standardized the PICO definitions (population, intervention, comparison, and outcome) of each included study and grouped studies by the type of therapy or support offered by the app. Whenever app design and clinical homogeneity allowed, we meta‐analyzed outcomes using a random‐effects model. Outcome data measured using categorical scales were synthesized using odds ratios. Outcome data measured using continuous scales were synthesized as the standardized mean difference. We assessed the methodological quality of each included study using the Cochrane Risk of Bias 2.0 tool and we assessed certainty of the evidence using the GRADE approach. Main Results From 5280 unique citations, we included 36 RCTs published in 37 reports and conducted in 15 different countries (7984 participants). Among the 36 included trials, we assessed two with an overall low risk of bias, 8 trials with some concern regarding risk of bias, and 26 trials with a high risk of bias. Interventions varied in the type of therapy or supports offered. The most common intervention designs employed mindfulness training, cognitive behavioral therapy (CBT), or a combination of the two (mindfulness + CBT). However, other interventions also included self‐monitoring, medication reminders, cognitive bias modification or positive stimulation, dialectical behavioral therapy, gamified health promotion, or social skill building. Mindfulness apps led to short term improvements in depressive symptoms when compared to a withheld control (SMD = −0.36; 95% CI [−0.63, −0.10]; p = 0.007, n = 3 RCTs, GRADE: very low certainty) and when compared to an active control (SMD = −0.27; 95% CI [−0.53, −0.01]; p = 0.04, n = 2 RCTs, GRADE: very low). Apps delivering this type of support also significantly improved symptoms of anxiety when compared to a withheld control (SMD = −0.35; 95% CI [−0.60, −0.09]; p = 0.008, n = 3 RCTs, GRADE: very low) but not when compared to an active control (SMD = −0.24; 95% CI [−0.50, 0.02]; p = 0.07, n = 2 RCTs, GRADE: very low). Mindfulness apps showed improvements in psychological stress that approached statistical significance among participants receiving the mindfulness mobile apps compared to those in the withheld control (SMD = −0.27; 95% CI [−0.56, 0.03]; p = .07, n = 4 RCTs, GRADE: very low). CBT apps also led to short‐term improvements in depressive symptoms when compared to a withheld control (SMD = −0.40; 95% CI [−0.80, 0.01]; p = 0.05, n = 2 RCTs, GRADE: very low) and when compared to an active control (SMD = −0.59; 95% CI [−0.98, −0.19]; p = 0.003, n = 2 RCTs, GRADE: very low). CBT‐based apps also improved symptoms of anxiety compared to a withheld control (SMD = −0.51; 95% CI [−0.94, −0.09]; p = 0.02, n = 3 RCTs, GRADE: very low) but not when compared to an active control (SMD = −0.26; 95% CI [−1.11, 0.59]; p = 0.55, n = 3 RCTs, GRADE: very low). Apps which combined mindfulness and CBT did not significantly improve symptoms of depression (SMD = −0.20; 95% CI [−0.42, 0.02]; p = 0.07, n = 2 RCTs, GRADE: very low) or anxiety (SMD = −0.21; 95% CI [−0.49, 0.07]; p = 0.14, n = 2 RCTs, GRADE: very low). However, these apps did improve psychological distress (SMD = −0.43; 95% CI [−0.74, −0.12]; p = 0.006, n = 2 RCTs, GRADE: very low). The results of trials on apps to reduce alcohol use were inconsistent. We did not identify any harms associated with the use of apps to manage mental health concerns. All effectiveness results had a very low certainty of evidence rating using the GRADE approach, meaning that apps which deliver therapy or other mental health support may reduce symptoms of depression, anxiety and psychological distress but the evidence is very uncertain. Authors' Conclusions We reviewed evidence from 36 trials conducted among youth. According to our meta‐analyses, the evidence is very uncertain about the effect of apps on depression, anxiety, psychological distress, and alcohol use. Very few effects were interpreted to be of clinical importance. Most of the RCTs were small studies focusing on efficacy for youth at risk for depressive symptoms. Larger trials are needed to evaluate effectiveness and allow for further analysis of subgroup differences. Longer trials are also needed to better estimate the clinical importance of these apps over the long term.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.424
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0300.005
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.241
GPT teacher head0.472
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it