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Record W3131359766 · doi:10.1186/s12888-021-03504-8

The effects of self-management interventions on depressive symptoms in adults with chronic physical disease(s) experiencing depressive symptomatology: a systematic review and meta-analysis

2021· review· en· W3131359766 on OpenAlexafffund
Lydia Ould Brahim, Sylvie Lambert, Nancy Feeley, Chelsea Coumoundouros, Jamie Schaffler, Jane McCusker, Erica E. M. Moodie, John William Kayser, Kendall Kolne, Éric Belzile, Christine Genest

Bibliographic record

VenueBMC Psychiatry · 2021
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalMcGill University
FundersFonds de Recherche du Québec - SantéCanada Excellence Research Chairs, Government of CanadaRéseau de recherche portant sur les interventions en sciences infirmières du QuébecMcGill University
KeywordsPsychological interventionPsychosocialMedicinePsycINFOMeta-analysisCINAHLPopulationDepression (economics)MEDLINERandomized controlled trialAnxietyQuality of life (healthcare)PsychiatryPhysical therapyClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic diseases are the leading cause of death worldwide. It is estimated that 20% of adults with chronic physical diseases experience concomitant depression, increasing their risk of morbidity and mortality. Low intensity psychosocial interventions, such as self-management, are part of recommended treatment; however, no systematic review has evaluated the effects of depression self-management interventions for this population. The primary objective was to examine the effect of self-management interventions on reducing depressive symptomatology in adults with chronic disease(s) and co-occurring depressive symptoms. Secondary objectives were to evaluate the effect of these interventions on improving other psychosocial and physiological outcomes (e.g., anxiety, glycemic control) and to assess potential differential effect based on key participant and intervention characteristics (e.g., chronic disease, provider). METHODS: Studies comparing depression self-management interventions to a control group were identified through a) systematic searches of databases to June 2018 [MEDLINE (1946 -), EMBASE (1996 -), PsycINFO (1967 -), CINAHL (1984 -)] and b) secondary 'snowball' search strategies. The methodological quality of included studies was critically reviewed. Screening of all titles, abstracts, and full texts for eligibility was assessed independently by two authors. Data were extracted by one author and verified by a second. RESULTS: Fifteen studies were retained: 12 for meta-analysis and three for descriptive review. In total, these trials included 2064 participants and most commonly evaluated interventions for people with cancer (n = 7) or diabetes (n = 4). From baseline to < 6-months (T1), the pooled mean effect size was - 0.47 [95% CI -0.73, - 0.21] as compared to control groups for the primary outcome of depression and - 0.53 [95% CI -0.91, - 0.15] at ≥ 6-months (T2). Results were also significant for anxiety (T1) and glycemic control (T2). Self-management skills of decision-making and taking action were significant moderators of depression at T1. CONCLUSION: Self-management interventions show promise in improving depression and anxiety in those with concomitant chronic physical disease. The findings may contribute to the development of future Self-management interventions and delivering evidence-based care to this population. Further high-quality RCTs are needed to identify sources of heterogeneity and investigate key intervention components.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.030
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0210.030
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.331
Teacher spread0.314 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2021
Admission routes2
Has abstractyes

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