Data visualization as an intervention for pediatric chronic pain: a pilot feasibility study protocol for a randomized controlled crossover trial
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a common and costly condition in youth, associated with negative implications that reach far beyond the pain experience itself (e.g., interference with recreational, social, and academic activities, mental health sequelae). As a self-appraised condition, pain experience is influenced by patient's biases and meaning-making in relation to their symptoms and triggers. We propose that interacting with self-reported data will impact the experience of pain by altering understanding and expectations of symptom experience and how pain interacts with other factors (e.g., sleep, emotions, social interactions). In this study, we aim to establish the feasibility and acceptability of using a data visualization platform to track and monitor symptoms and their relationship with other factors, versus simply daily reporting of symptoms using a smartphone-based Ecological Momentary Assessment (EMA). METHODS: This protocol is for a randomized, single-center, open-label crossover trial. We aim to recruit 50 typically developing youth aged 12-18 years with chronic pain to take part in two phases of data collection. The trial will utilize an A-B counterbalanced design in which participants will be randomly assigned to receive either Part A (EMA alone for 7 days) or Part B (EMA plus visualization platform for 7 days) first and then receive the opposite phase after a 7-day break (washout period). Key outcomes will be participant reports of acceptability and feasibility, EMA completion rates, barriers, and perceptions of the benefits or risks of participation. Secondary exploratory analyses will examine the relationship between EMA-reported symptoms over time and in relation to baseline measures, as well as pilot data on any improvements in symptoms related to engaging with the data visualization platform. DISCUSSION: This protocol describes the feasibility and pilot testing of a novel approach to promoting self-management and facilitating symptom appraisal using visualized data. We aim to determine whether there is a sufficient rationale, both from the perspective of feasibility and patient satisfaction/acceptability, to conduct a larger randomized controlled trial of this intervention. This intervention has the potential to support clinical care for youth with chronic pain and other conditions where self-appraisal and understanding of symptom patterns are a critical component of functional recovery. TRIAL REGISTRATION: Open Science Framework doi: https://doi.org/10.17605/OSF.IO/HQX7C . Registered on October 25, 2021, osf.io/hqx7c.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".