Digital Monitoring and Chronic Pain Trajectories in Patients Receiving Tertiary Pain Care
Abstract
Importance Chronic pain management relies on baseline prognostic models, although digital monitoring may better capture long-term patient trajectories. Objective To describe 6-month chronic pain trajectories using weekly assessments and to evaluate the value of routinely collected baseline sociodemographic and clinical variables in estimating trajectories. Design, Setting, and Participants This multicenter mobile health cohort study was conducted in French tertiary pain clinics between September 2019 and 2025. Adults with chronic noncancer pain initiating follow-up in a pain clinic and completing at least 6 months of follow-up were eligible. Exposure Participation in the eDOL digital health program with weekly assessments alongside routine care. Main Outcomes and Measures The primary outcomes were weekly bodily comfort, sleep, and mood measured on 11-point numeric rating scale and aggregated into monthly medians. Trajectory stability, time spent in predefined clinical states, latent trajectory classes, and performance of baseline variables in estimating outcomes were assessed using linear mixed-effects models, time-in-state analyses, gaussian mixture models , and machine learning. Informative attrition was addressed using inverse probability of censoring weighting and joint longitudinal-survival modeling. Results Among 1477 participants (76.8% [95% CI, 74.6%-78.9%] aged 35-64 years; 82.1% [95% CI, 77.9%-86.3%] women; median pain duration >5 years), most were receiving multimodal treatment for multimorbid chronic pain. Average trajectories remained stable, with no clinically meaningful change in patient-reported outcomes. Three trajectory classes were identified: stable (1266 participants; 85.7% [95% CI, 83.9%-87.5%]), improving (61 participants; 4.1% [95% CI, 3.2%-5.2%]), and worsening (150 participants; 10.2% [95% CI, 8.6%-11.7%]). Baseline characteristics had limited value in estimating pain trajectories. Conclusions and Relevance In tertiary pain clinics patients, trajectories of bodily comfort, sleep, and mood were predominantly stable over 6 months. Baseline characteristics poorly estimated long-term trajectories, supporting continuous digital monitoring and individualized change detection rather than baseline-only prognostic stratification.
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BibTeX
@article{kerckhove2026digital,
title = {{Digital Monitoring and Chronic Pain Trajectories in Patients Receiving Tertiary Pain Care}},
author = {Nicolas Kerckhove and Armel Soubeiga and Jules Phalip and Célian Bertin and Bastien Doreau and Xavier Moisset and Noémie Delage and Nicolas Authier and Violaine Antoine and Bruno Pereira},
journal = {JAMA Network Open},
year = {2026},
month = aug,
doi = {10.1001/jamanetworkopen.2026.27217},
url = {https://hal.science/hal-05762641v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Digital Monitoring and Chronic Pain Trajectories in Patients Receiving Tertiary Pain Care
AU - Nicolas Kerckhove
AU - Armel Soubeiga
AU - Jules Phalip
AU - Célian Bertin
AU - Bastien Doreau
AU - Xavier Moisset
AU - Noémie Delage
AU - Nicolas Authier
AU - Violaine Antoine
AU - Bruno Pereira
PY - 2026
DA - 2026/08/04
JO - JAMA Network Open
DO - 10.1001/jamanetworkopen.2026.27217
UR - https://hal.science/hal-05762641v1
AB - Importance Chronic pain management relies on baseline prognostic models, although digital monitoring may better capture long-term patient trajectories. Objective To describe 6-month chronic pain trajectories using weekly assessments and to evaluate the value of routinely collected baseline sociodemographic and clinical variables in estimating trajectories. Design, Setting, and Participants This multicenter mobile health cohort study was conducted in French tertiary pain clinics between September 2019 and 2025. Adults with chronic noncancer pain initiating follow-up in a pain clinic and completing at least 6 months of follow-up were eligible. Exposure Participation in the eDOL digital health program with weekly assessments alongside routine care. Main Outcomes and Measures The primary outcomes were weekly bodily comfort, sleep, and mood measured on 11-point numeric rating scale and aggregated into monthly medians. Trajectory stability, time spent in predefined clinical states, latent trajectory classes, and performance of baseline variables in estimating outcomes were assessed using linear mixed-effects models, time-in-state analyses, gaussian mixture models , and machine learning. Informative attrition was addressed using inverse probability of censoring weighting and joint longitudinal-survival modeling. Results Among 1477 participants (76.8% [95% CI, 74.6%-78.9%] aged 35-64 years; 82.1% [95% CI, 77.9%-86.3%] women; median pain duration >5 years), most were receiving multimodal treatment for multimorbid chronic pain. Average trajectories remained stable, with no clinically meaningful change in patient-reported outcomes. Three trajectory classes were identified: stable (1266 participants; 85.7% [95% CI, 83.9%-87.5%]), improving (61 participants; 4.1% [95% CI, 3.2%-5.2%]), and worsening (150 participants; 10.2% [95% CI, 8.6%-11.7%]). Baseline characteristics had limited value in estimating pain trajectories. Conclusions and Relevance In tertiary pain clinics patients, trajectories of bodily comfort, sleep, and mood were predominantly stable over 6 months. Baseline characteristics poorly estimated long-term trajectories, supporting continuous digital monitoring and individualized change detection rather than baseline-only prognostic stratification.
SN - 2574-3805
ER -