Investigating the Effects of Applying Different Actigraphy Processing Approaches to Examine the Sleep Data of Patients With Neuropathic Pain
Bibliographic record
Abstract
Individuals suffering from neuropathic pain commonly report issues associated with sleep. To measure sleep in this population, researchers have used actigraphy. Historically, actigraphy data have been analyzed in the form of counts; however, due to the proprietary nature, many opt to quantify data in its raw form. Various processing techniques exist to accomplish this; however, it remains unclear how they compare to one another. This study sought to compare sleep measures derived using the GGIR R package versus the GENEActiv (GA) R Markdown tool in a neuropathic pain population. It was hypothesized that the processing techniques would yield significantly different sleep outcomes. One hundred and twelve individuals (mean age = 52.72 ± 13.01 years; 60 M) with neuropathic pain in their back and/or lower limbs were included. While simultaneously undergoing spinal cord stimulation, actigraphy devices were worn on the wrist for a minimum of 7 days (GA; 50 Hz). Upon completing the protocol, sleep outcome measures were calculated using (a) the GGIR R package and (b) the GA R Markdown tool. To compare these algorithms, paired-samples t tests and Bland–Altman plots were used to compare the total sleep time, sleep efficiency, wake after sleep onset, sleep onset time, and rise times. According to the paired-samples t test, the GA R Markdown yielded lower total sleep time and sleep efficiency and a greater wake after sleep onset, compared with the GGIR package. Furthermore, later sleep onset times and earlier rise times were reported by the GGIR package compared with the GA R Markdown.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".