Determining Pain Catastrophizing From Daily Pain App Assessment Data: Role of Computer-Based Classification.


Pain Management Center, Departments of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: [Email]


This study compared persons with chronic pain who consistently reported that their pain was worsening with those who reported that their pain was improving or remaining the same per daily assessment data from a smartphone pain app. All participants completed baseline measures and were asked to record their progress every day by answering whether their overall condition had improved, remained the same, or gotten worse (perceived change) on a visual analogue scale. One hundred forty-four individuals with chronic pain who successfully entered daily assessments were included. Those persons who were classified as worse showed significantly higher pain intensity scores, greater activity interference, higher disability and mood disturbance scores, and higher scores on the Pain Catastrophizing Scale both at baseline and after 3 months (P < .001). Repeated measures analyses and multilevel modeling of perceived change data over different time intervals of 20 assessments over 40 days, 10 assessments over 20 days, and 5 assessments over 10 days were examined. These analyses demonstrated that group classification of better, same, and worse could be reliably determined, even with as few as 5 assessments. These results support the use of innovative mobile health technology to identify individuals who are prone to catastrophize about their pain. Perspective: This study demonstrated that daily assessment of overall perceived change with a smartphone pain app was positively correlated with the Pain Catastrophizing Scale and capturing short-term daily assessment trends data using computer-based classification methods might be a future way to help to identify individuals who tend to catastrophize about their pain.


Catastrophizing,chronic pain,machine learning,pain app,pain assessment,telemedicine,