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Enregistrement W4280544774 · doi:10.1111/1346-8138.16435

A randomized controlled trial on a novel behavioral treatment for individuals with skin picking and other body‐focused repetitive behaviors

2022· article· en· W4280544774 sur OpenAlexaff
Steffen Moritz, Danielle Penney, Sarah Weidinger, Tana Gabbert, Stella Schmotz

Notice bibliographique

RevueThe Journal of Dermatology · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueObsessive-Compulsive Spectrum Disorders
Établissements canadiensDouglas Mental Health University Institute
Organismes subventionnairesnon disponible
Mots-clésMedicineRandomized controlled trialClinical trialPsychiatrySurgeryInternal medicine

Résumé

récupéré en direct d'OpenAlex

Body-focused repetitive behaviors (BFRBs), such as skin picking/dermatillomania, trichotillomania, lip-cheek biting, and nail biting, are a frequent but underdiagnosed and undertreated disorder at the crossroads of dermatology and psychiatry.1 The disorder is characterized by the compulsive manipulation of the skin, nails and/or hair, which the patient is unable to resist, frequently resulting in severe impairment. While no medication to date has been approved to treat BFRBs, behavioral treatments, especially habit reversal training (HRT), decoupling, and decoupling in sensu, have demonstrated some efficacy (the techniques are described here2). This is the first study examining whether amalgamating these three techniques into a single self-help manual reduces BFRB symptoms. This study was set up as a controlled online trial (preregistration: DRKS00024525, local ethics approval LPEK-0254) wherein 279 individuals with BFRBs (female 66%, age 32.9 years, SD 11.53; lifetime prevalence, multiple disorders could be endorsed: skin picking 68.1%, trichotillomania 28%, nail biting 52%, lip-cheek biting 52.7%), recruited via social media, were randomly assigned (blind/unbiased 1:1 allocation) to either a wait list control group (n = 140) or an experimental group (n = 139) receiving a self-help manual entitled Free from BFRB (free access at www.uke.de/free-from-bfrb). The manual teaches HRT, decoupling, and decoupling in sensu following brief psychoeducation on the symptoms and consequences of BFRBs (see Appendix S1). Participants, aged between 18 and 75 years with no history of schizophrenia or acute suicidality, were assessed online 6 weeks apart. The primary outcome was the Generic BFRB Scale 8 (GBS-8).2 The WHOQOL-BREF global quality-of-life item (QoL)3 and the PHQ-94 represented secondary outcomes (see description in the Appendix S1). Participants in the intervention condition had to practice the self-help techniques independently. A total of 535 participants were excluded blind to results, mainly due to premature cancellation of the pre-assessment. Groups did not differ on any baseline characteristics (demographic variables, psychopathology, lifetime prevalence of different BFRBs). The expectation–maximization algorithm was used for the intention-to-treat (ITT) analyses. At post-assessment, the experimental group significantly improved on the GBS-8 with respect to all types of analyses (group assignments were made according self-report data) at a medium-to-large effect size (Figure 1; ITT: p < 0.001, d = 0.666; complete cases (CC, nTreatment = 73): p < 0.001, d = 0.613; per-protocol (PP) sample who had at least read the Free from BFRB manual (PP read, nTreatment = 70): p < 0.001, d = 0.665; PP sample who practiced the techniques in the manual (PP practiced, nTreatment = 62): p < 0.001, d = 0.775). Likewise, QoL improved significantly for all analyses at a small-to-medium effect size (ITT: p < 0.001, d = 0.492; CC: p = 0.008, d = 0.415; PP read: p < 0.006, d = 0.438; PP practiced: p < 0.009, d = 0.432). Depression scores improved at trend level for the CC and PP read analyses and yielded significance for the ITT and PP practiced analyses at a mainly small effect size (ITT: p = 0.042, d = 0.246; CC: p = 0.071, d = 0.283; PP read: p = 0.059, d = 0.299; PP practiced: p = 0.030, d = 0.357). We also assessed the subjective effectiveness of the full manual at post-assessment via self-report (based on the Client Satisfaction Questionnaire, CSQ), which was good ("I would use the manual again", slight to complete endorsement 98.4%, mostly or completely 77.4%; "I would recommend to friend with similar problems": 88.4%, 79.1%; "My BFRBs decreased through the use of the program": 77.4%, 22.6%). Furthermore, of those who completed the trial, 23.3% in the experimental group showed improvement greater than 35% on the GBS-8 (wait list control: 8.3%, p = 0.007; 25% improvement: 43.8 vs. 13.5%, p < 0.001). The test–retest reliability of the GBS-8 was r = 0.68, signifying satisfactory internal validity. Study limitations include a lack of verified diagnoses and missing follow-up data. The noncompletion rate was 39%. This study demonstrates the efficacy of a manual combining three behavioral self-help treatments to reduce the symptoms of BFRBs. Results are encouraging given those with BFRBs are often reluctant to seek professional help. Future work should assess moderators (i.e. subgroups that benefit from treatment), target comorbid emotional problems (e.g. depression), and consider treatment of the sensory irritations giving rise to BFRBs, all of which may augment efficacy. Appendix S1 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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)0,003
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 enseignants0,022
Score d'incertitude au seuil0,075

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0040,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,002
Communication savante0,0010,002
Science ouverte0,0020,001
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0220,002

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 Opus0,023
Tête enseignante GPT0,330
Écart entre enseignants0,306 · 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

Citations9
Publié2022
Routes d'admission1
Résumé présentoui

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