Patterns of Skin Picking in Skin Picking Disorder: Ecological Momentary Assessment Study
Notice bibliographique
Résumé
Background Skin picking disorder (SPD) is an understudied mental illness that is classified as a body-focused repetitive behavior disorder. Literature suggests that pathological skin picking is strongly integrated into the daily lives of affected individuals and may involve a high degree of variability in terms of episode characteristics, frequency, and intensity. However, existing data on the phenomenology of SPD are limited and typically involve retrospective assessments, which may fail to accurately capture the behavior’s variability. Objective This study aimed to investigate skin picking in the daily lives of individuals with SPD by using ecological momentary assessment (EMA). The first aim focused on the description of skin picking patterns (eg, characteristics, intensity, and distribution of episodes and urges), and the second aim explored differences in characteristics and patterns between automatic and focused skin picking. Methods Participants were recruited online and underwent a web-based screening, a diagnostic telephone interview, and a comprehensive online self-report questionnaire before participating in an EMA protocol. The latter included 10 consecutive days with 7 pseudorandom, time-contingent assessments per day between 8 AM and 10 PM. The EMA questionnaire assessed the current skin picking urge, the occurrence of the behavior, and a detailed assessment of the episodes’ characteristics (eg, length, intensity, and consciousness) if applicable. Results The final sample consisted of 57 participants, who completed at least 70% of the scheduled assessments (n=54, 94.7% female: mean age 29.3, SD 6.77 years). They completed 3758 EMAs and reported 1467 skin picking episodes. Skin picking occurred frequently (mean 2.57, SD 1.12 episodes per day and person) in relatively short episodes (10-30 min; 10 min: nepisodes=642, 43.8%; 20 min: nepisodes=312, 21.3%; 30 min: nepisodes=217, 14.8%), and it was distributed quite evenly throughout the day and across different days of the week. Focused and automatic episodes were relatively balanced across all reported episodes (focused: nepisodes=806, 54.9%) and over the course of the day. The analyses showed statistically significant differences between self-reported triggers for the different styles. Visual or tactile cues and the desire to pick the skin were more important for the focused style (visual or tactile cues: mean focused style [Mf]=4.01, SD 0.69 vs mean automatic style [Ma]=3.47, SD 0.99; P<.001; SMD=0.64; desire to pick: Mf=2.61, SD 1.06 vs Ma=1.94, SD 1.03; P<.001; SMD=0.82), while boredom and concentration problems were more prominent in automatic skin picking (boredom: Mf=1.69, SD 0.89 vs Ma=1.84, SD 0.89; P=.03; SMD=–0.31; concentration problems: Mf=2.06, SD 0.87 vs Ma=2.31, SD 1.06; P=.006; SMD=–0.41). Conclusions These results contribute to an enhanced understanding of the phenomenology of SPD using a more rigorous assessment methodology. Our findings underscore that picking can impact affected persons multiple times throughout their daily lives. Trial Registration German Clinical Trials Register DRKS00025168; https://tinyurl.com/mr35pdwh
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».