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Enregistrement W2258970805 · doi:10.2196/mhealth.4231

MoodHacker Mobile Web App With Email for Adults to Self-Manage Mild-to-Moderate Depression: Randomized Controlled Trial

2016· article· en· W2258970805 sur OpenAlexvenueno aff
Amelia J Birney, Rebecca Gunn, Jeremy K Russell, Dennis V. Ary

Notice bibliographique

RevueJMIR mhealth and uhealth · 2016
Typearticle
Langueen
DomainePsychology
ThématiqueDigital Mental Health Interventions
Établissements canadiensnon disponible
Organismes subventionnairesNational Institute of Mental HealthNational Institutes of Health
Mots-clésRandomized controlled trialmHealthDepression (economics)Psychological interventionMedicineOutreachAbsenteeismIntervention (counseling)TelemedicinePsychiatryGerontologyPsychologyHealth care

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Worldwide, depression is rated as the fourth leading cause of disease burden and is projected to be the second leading cause of disability by 2020. Annual depression-related costs in the United States are estimated at US $210.5 billion, with employers bearing over 50% of these costs in productivity loss, absenteeism, and disability. Because most adults with depression never receive treatment, there is a need to develop effective interventions that can be more widely disseminated through new channels, such as employee assistance programs (EAPs), and directly to individuals who will not seek face-to-face care. OBJECTIVE: This study evaluated a self-guided intervention, using the MoodHacker mobile Web app to activate the use of cognitive behavioral therapy (CBT) skills in working adults with mild-to-moderate depression. It was hypothesized that MoodHacker users would experience reduced depression symptoms and negative cognitions, and increased behavioral activation, knowledge of depression, and functioning in the workplace. METHODS: A parallel two-group randomized controlled trial was conducted with 300 employed adults exhibiting mild-to-moderate depression. Participants were recruited from August 2012 through April 2013 in partnership with an EAP and with outreach through a variety of additional non-EAP organizations. Participants were blocked on race/ethnicity and then randomly assigned within each block to receive, without clinical support, either the MoodHacker intervention (n=150) or alternative care consisting of links to vetted websites on depression (n=150). Participants in both groups completed online self-assessment surveys at baseline, 6 weeks after baseline, and 10 weeks after baseline. Surveys assessed (1) depression symptoms, (2) behavioral activation, (3) negative thoughts, (4) worksite outcomes, (5) depression knowledge, and (6) user satisfaction and usability. After randomization, all interactions with subjects were automated with the exception of safety-related follow-up calls to subjects reporting current suicidal ideation and/or severe depression symptoms. RESULTS: At 6-week follow-up, significant effects were found on depression, behavioral activation, negative thoughts, knowledge, work productivity, work absence, and workplace distress. MoodHacker yielded significant effects on depression symptoms, work productivity, work absence, and workplace distress for those who reported access to an EAP, but no significant effects on these outcome measures for those without EAP access. Participants in the treatment arm used the MoodHacker app an average of 16.0 times (SD 13.3), totaling an average of 1.3 hours (SD 1.3) of use between pretest and 6-week follow-up. Significant effects on work absence in those with EAP access persisted at 10-week follow-up. CONCLUSIONS: This randomized effectiveness trial found that the MoodHacker app produced significant effects on depression symptoms (partial eta(2) = .021) among employed adults at 6-week follow-up when compared to subjects with access to relevant depression Internet sites. The app had stronger effects for individuals with access to an EAP (partial eta(2) = .093). For all users, the MoodHacker program also yielded greater improvement on work absence, as well as the mediating factors of behavioral activation, negative thoughts, and knowledge of depression self-care. Significant effects were maintained at 10-week follow-up for work absence. General attenuation of effects at 10-week follow-up underscores the importance of extending program contacts to maintain user engagement. This study suggests that light-touch, CBT-based mobile interventions like MoodHacker may be appropriate for implementation within EAPs and similar environments. In addition, it seems likely that supporting MoodHacker users with guidance from counselors may improve effectiveness for those who seek in-person support. TRIAL REGISTRATION: ClinicalTrials.gov NCT02335554; https://clinicaltrials.gov/ct2/show/NCT02335554 (Archived by WebCite at http://www.webcitation.org/6dGXKWjWE).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,050

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,003
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,0150,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,393
Écart entre enseignants0,366 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations178
Publié2016
Routes d'admission1
Résumé présentoui

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