RehabMove 2018: ECOLOGICAL MOMENTARY ASSESSMENT OF EXERCISE AND NEUROPATHIC PAIN EXPERIENCED BY MEN WITH SCI: PARTICIPANT PERCEPTIONS
Bibliographic record
Abstract
Abstract A couple of laboratory-based studies have examined the effects of exercise on neuropathic pain (NP) in adults with spinal cord injury (SCI). However, pain levels observed within a laboratory may not be representative of what is experienced in the real-world. Ecological momentary assessment (EMA) can address this limitation by assessing pain in real time, and within an individual’s natural environment. Despite these advantages, EMA protocols have had limited useand development in SCI research. This study evaluated participants’ perceptions of: a) their daily NP fluctuations, and b) the utility of EMA to assess their NP patterns. Six physically active men with chronic SCI participated in a 6-day Smartphone based EMA protocol assessing their NP from pre- to post-exercise and four other times throughout the day. A semi-structured exit interview followed this protocol. Qualitative and quantitative data were analyzed to provide comprehensive insight on participants’ perceptions of their NP and their perceived value of using EMA to map their NP patterns. All 6 participants reported that exercise reduced their NP sensations. For some participants, the momentary measurement aspect of EMA increased their awareness of their NP in a negative manner. Conversely, increased awareness was beneficial for others as it made them critically think about the specific NP sensations being felt. Participants stated that the EMA protocol was not onerous and did not impede on daily life, which was supported by compliance rates to the EMA prompts (80.56%-113.89%; M=94.91). Using EMA to gain an understanding of the temporal aspects of NP, and how NP changes following exercise, were perceived to be useful. Tracking NP patterns may have value for those who experience high levels of NP, or when engaging in new activities. Overall, EMA shows promise as a viable methodology to evaluate temporal aspects of NP in the SCI population.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".