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Record W3139572451 · doi:10.2196/27039

A Mobile Phone–Based Intervention to Reduce Mental Health Problems in Health Care Workers During the COVID-19 Pandemic (PsyCovidApp): Randomized Controlled Trial

2021· article· en· W3139572451 on OpenAlexvenueno aff
María Antonia Fiol-deRoque, María Jesús Serrano-Ripoll, Rafael Jiménez, Rocío Zamanillo‐Campos, Aina M. Yáñez, Miquel Bennasar‐Veny, Alfonso Leiva, Elena Gervilla, Esther García Buades, Mauro Garcı́a-Toro, Pablo Alonso‐Coello, Isabel Ruíz-Pérez, Carolina Sitges, Javier García‐Campayo, Joan Llobera, Ignacio Ricci‐Cabello

Bibliographic record

VenueJMIR mhealth and uhealth · 2021
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersInstituto de Salud Carlos IIIGovern de les Illes Balears
KeywordsmHealthMental healthMedicinePsychological interventionRandomized controlled trialContext (archaeology)Health careAnxietyeHealthBurnoutTelemedicinePopulationPsychiatryClinical psychologyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: The global health emergency generated by the COVID-19 pandemic is posing an unprecedented challenge to health care workers, who are facing heavy workloads under psychologically difficult situations. Mental mobile Health (mHealth) interventions are now being widely deployed due to their attractive implementation features, despite the lack of evidence about their efficacy in this specific population and context. OBJECTIVE: The aim of this trial is to evaluate the effectiveness of a psychoeducational, mindfulness-based mHealth intervention to reduce mental health problems in health care workers during the COVID-19 pandemic. METHODS: We conducted a blinded, parallel-group, controlled trial in Spain. Health care workers providing face-to-face health care to patients with COVID-19 were randomly assigned (1:1) to receive the PsyCovidApp intervention (an app targeting emotional skills, healthy lifestyle behavior, burnout, and social support) or a control app (general recommendations about mental health care) for 2 weeks. The participants were blinded to their group allocation. Data were collected telephonically at baseline and after 2 weeks by trained health psychologists. The primary outcome was a composite of depression, anxiety, and stress (overall score on the Depression Anxiety Stress Scale-21 [DASS-21]). Secondary outcomes were insomnia (Insomnia Severity Index), burnout (Maslach Burnout Inventory Human Services Survey), posttraumatic stress (Davidson Trauma Scale), self-efficacy (General Self-Efficacy Scale), and DASS-21 individual scale scores. Differences between groups were analyzed using general linear modeling according to an intention-to-treat protocol. Additionally, we measured the usability of the PsyCovidApp (System Usability Scale). The outcome data collectors and trial statisticians were unaware of the treatment allocation. RESULTS: Between May 14 and July 25, 2020, 482 health care workers were recruited and randomly assigned to PsyCovidApp (n=248) or the control app (n=234). At 2 weeks, complete outcome data were available for 436/482 participants (90.5%). No significant differences were observed between the groups at 2 weeks in the primary outcome (standardized mean difference -0.04; 95% CI -0.11 to 0.04; P=.15) or in the other outcomes. In our prespecified subgroup analyses, we observed significant improvements among health care workers consuming psychotropic medications (n=79) in the primary outcome (-0.29; 95% CI -0.48 to -0.09; P=.004), and in posttraumatic stress, insomnia, anxiety, and stress. Similarly, among health care workers receiving psychotherapy (n=43), we observed improvements in the primary outcome (-0.25; 95% CI -0.49 to -0.02; P=.02), and in insomnia, anxiety, and stress. The mean usability score of PsyCovidApp was high (87.21/100, SD 12.65). After the trial, 208/221 participants in the intervention group (94.1%) asked to regain access to PsyCovidApp, indicating high acceptability. CONCLUSIONS: In health care workers assisting patients with COVID-19 in Spain, PsyCovidApp, compared with a control app, reduced mental health problems at 2 weeks only among health care workers receiving psychotherapy or psychotropic medications. TRIAL REGISTRATION: ClinicalTrials.gov NCT04393818; https://clinicaltrials.gov/ct2/show/NCT04393818.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.059
GPT teacher head0.454
Teacher spread0.395 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations192
Published2021
Admission routes1
Has abstractyes

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