Assessing Pain Severity in Sickle Cell Disease Using Animations and a Graphical Body Image
Bibliographic record
Abstract
Introduction: Adults living with sickle cell disease (SCD) most frequently seek medical care due to pain. To determine the most efficacious treatment plan for patients presenting with pain, providers must first accurately assess and diagnose the pain. Unfortunately, the current approaches for assessing pain are inadequate. Combined with medical provider biases, patients can often have their pain symptoms misinterpreted, ignored, or blatantly dismissed. To address this issue, we partnered with stakeholders, human-centered designers, and software engineers to design a novel pain assessment tool, called Painimation. Painimation allows patients to communicate their pain quality, intensity, and location using abstract animations and a paintable body image. Painimation has been validated in a general population with chronic pain, but there is limited data validating this approach in SCD. Preliminary data on the use of Painimation by adults with SCD (N = 67) found that those who described their pain using the “throbbing” animation had less severe pain symptoms than those endorsing the “shooting” animation. Objective: To replicate and extend prior findings by determining whether pain animations and body image data are associated with pain outcomes in a large cohort of adults with SCD. We hypothesized that the presence of shooting pain and greater body surface areas affected by pain would be associated with more severe pain outcomes and mental health symptoms. Methods: We performed a secondary analysis on baseline data from the “Cognitive Behavioral Therapy and Real-Time Pain Management Intervention for Sickle Cell via Mobile Application (CaRISMA)” Trial-a multisite randomized, controlled trial in adults with SCD and chronic pain. Eligible patients were randomized 1:1 to either digital cognitive behavioral therapy or digital education; in addition, both arms received at least 12 weeks of health coach support. At baseline, participants completed a battery of questionnaires and tracked their pain intensity (0-10) by the Visual Analog Scale (VAS) and mood daily via a mobile app. The Painimation app presents a front and back 2-dimensional body image that is paintable to indicate areas affected by pain. Users choose from abstract animations intended to represent different pain qualities; the intensity of the animations can be adjusted, and up to three can be selected. For the purpose of the analyses, participants were categorized into “Shooting Pain” vs “No Shooting Pain” and “Throbbing with Shooting and Stabbing” vs “Throbbing Alone,” based on our prior study. Participants were also split into groups based on whether the proportion of the body image painted was less than the median (<9.8% vs >=9.8%). Baseline characteristics and demographics were compared between groups, and multivariable regressions were used to estimate covariate-adjusted associations with the following outcomes at baseline: daily pain, Patient Reported Outcomes Measurement Information System (PROMIS) pain interference scale, Pain Catastrophizing Scale (PCS), Current Opioid Misuse Measure (COMM-9), and Adults Sickle Cell Quality of Life Measurement Information System (ASCQ-Me) pain severity and frequency. Results: The trial enrolled 359 adults, mean age 36.3 (SD = 10.5), 66% female, 93% Black race. The “Shooting” painimation and greater body image scores were associated with all outcomes in univariate analyses (all p<0.01; Table 1) except for the proportion of “happy” mood days and anxiety scores. After controlling for age, depression, anxiety, % body image, and site, the shooting animations were independently associated with greater daily pain intensity (beta = 0.64; p = 0.046). After controlling for age, depression, and site, greater body image score was associated with daily pain intensity (beta = 1.18; p < 0.001), pain interference (beta = 2.87; p < 0.001), ASCQ-Me pain frequency (beta = 3.91; p =< 0.005), and ASCQ-Me pain severity (beta = 4.50; p = 0.002). Conclusion: Both the “shooting” animation and body image measures were associated with more severe pain outcomes. This study demonstrates that animations and body image data can be used to assess pain severity in SCD, more objectively than the 0-10 numeric VAS scale. Future studies should explore whether pain location and specific animation selected are associated with pain etiology, and determine whether this approach can differentiate different types of pain in SCD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".