Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study
Notice bibliographique
Résumé
BACKGROUND: Approximately 1 in 5 children and adolescents live with chronic pain, with musculoskeletal (MSK) pain being one of the most prevalent subtypes. Unfortunately, some studies show that less than half of youth (<18 years) experience improvements with existing evidence-based treatments. Self-report measures-the current gold standard for monitoring the pain experience-are limited in their use as a single point-of-care assessment and their vulnerability to recall bias. The ubiquitous adoption of wearable technology presents a promising solution for improved monitoring of the pain experience via real-time tracking through a multisystemic lens. OBJECTIVE: The purpose of this study is (1) to develop a digital biosignature of the pain experience in youth with chronic MSK pain and (2) to assess the feasibility, acceptability, and appropriateness of the captured data for future applications. METHODS: All aspects of this study were designed in partnership with people with lived experience and patient advocacy partners. This is a longitudinal observational cohort study, with all study activities taking place remotely over a 12-week period. Up to 500 youth (between 14 and 24 years old) with chronic MSK pain will be enrolled through a multipronged recruitment strategy to ensure a representative sample. The participants will wear an Apple Watch throughout the study to continuously monitor physiological, sleep, and physical activity metrics. In addition, participants will complete brief "daily check-in" surveys that include gold-standard measures of the pain experience (eg, pain interference, mood, fatigue) and the option to report a "pain flare" (ie, a temporary but noticeable worsening of the usual pain symptoms). Participants will also complete a modified online Trier Social Stress Test and a 30-second sit-to-stand task to capture individual responses to standardized challenges. Traditional machine learning and deep representation learning methods will be used to develop a digital biosignature of the pain experience. The accuracy of the biosignature will be assessed through measures of model performance as compared to gold-standard self-reports. RESULTS: This study was funded in September 2024, with data collection beginning in March 2025. As of December 15, 2025, 190 participants are enrolled, with data collection and analysis ongoing. CONCLUSIONS: This is the first study to leverage wearable health technology for real-time monitoring of the pain experience in youth with chronic MSK pain. The resulting digital end points are expected to heighten the rigor of clinical trials and provide opportunities for individually tailored interventions. The second phase of this study will investigate the implementation of "wellness alerts" triggered by abnormal smartwatch readings. Alerts would empower users toward preemptive self-management strategies, thereby enhancing self-efficacy in those living with chronic MSK pain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/84781.
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 | 0,039 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,006 |
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 ».