Background & Aims
The fundamental goal of pain rehabilitation processes is to optimize an individual’s self-rated quality of life. Technological advancements in pain management provide the potential for tailored solutions to enhance patients’ well-being (1. 2), addressing the multifaceted nature of persistent pain. Digital self-guided pain management approaches offer an efficient, secure, and easily accessible treatment pathway. This multicenter study aims to evaluate the effectiveness of Paindrainer, an innovative Augmented Intelligence (AI)-powered digital tool, in managing chronic back and neck pain through a focus on truly individualized, patient-centric care. The study aim was to investigate how neural network powered, 360° patient-centric, digital self-management tool that promotes behavior change affects the daily functions in adults with chronic back and neck pain, as well as analyse the health economic effects of such tool in pain treatment.
Methods
Study Procedure: Participants maintained standard treatments while logging daily activities on Paindrainer during the 12 weeks study period. After an 8-day learning period, Paindrainers neural network presents individualized daily plans of activities, predicting resulting pain levels for each user.
Inclusion/Exclusion Criteria: Participants, aged >18, with >3 months of low back or neck pain (Numeric Rating Scale [NRS] score ?4), were included. Exclusions comprised planed surgeries, severe psychiatric illness, substance use disorder, active serious illness, malignancy-related pain, and ongoing litigation.
Assessment Questionnaires:
Primary outcomes focused on pain interference, measured by PROMIS 6a. Secondary outcomes included PROMIS (3) physical function, anxiety, depression, pain intensity, pain catastrophizing, and Chronic Pain Acceptance Questionnaire as well as analysis of logged daily data.
Results
Pain interference, significantly decreased from baseline at both 6 and 12 weeks (p<0.0001 and p=0.020, respectively), with an overall reduction in 73.8% of the subjects. Secondary outcomes improved physical function (72.5%), reduced anxiety (81.3%) and depression (100%), and significant decreased pain intensity were observed (p<0.05). The digital tool also enhanced work capacity by over 1 hour /day in more than 50% of subjects. Health economics calculations estimated the total societal gain for the use of Paindrainer at around $8,700 per patient per year, comprising a monetized health gain ($4,550 per patient per year), production gain ($3,370 per patient per year), and healthcare cost savings ($797 per patient per year) (4) This study shows that chronic pain is associated with a significant burden including a very low quality-of-life, high healthcare costs and production loss, which implies that that there is an unmet need. The study also finds that there may be a substantial soc
Conclusions
Self-managing chronic pain with a neural network-powered digital coach, grounded in behavioral health principles, significantly improved pain interference, physical function, depression, anxiety, and pain catastrophizing over the 12-week study. The result clearly supports the significant benefit of accessible, individualized and cost-effective digital therapeutics.
References
1.Goldberg DS, McGee SJ. Pain as a global public health priority. BMC Public Health 2011;11.
2. Gaskin DJ, Richard P. The Economic Costs of Pain in the United States. J Pain 2012;13(8):715–24.
3. Gershon RC, Rothrock N, Hanrahan R, Bass M, Cella D. The use of PROMIS and assessment center to deliver patient-reported outcome measures in clinical research. J Appl Meas 2010;11(3):304–14.
4. IHE, https://ihe.se
Presenting Author
Maria Rosén Klement
Poster Authors
Maria Klement
PhD
Department of Immunotechnology
Lead Author
Antje M Barreveld
MD
Department of Anesthesiology, Tufts University School of Medicine, Newton-Wellesley Hospital
Lead Author
Carl Borrebaeck
Professor
carl.borrebaeck@immun.lth.se
Lead Author
Neel Mehta
MD
Department of Anesthesiology, Division of Pain Management, Weill Cornell Medicine
Lead Author
Topics
- Evidence, Clinical Trials, Systematic Review, Guidelines, and Implementation Science