Figure 1
Data Processing with Artifact Correction Algorithm and Savgol Filter.
A schematic of the activity over a week for a participant is shown, where each participant was selected as a chronic pain participant (n = 688) or non-chronic pain participant (n = 3552). The data was then processed using an artifact correction algorithm, smoothed using a Savgal-Golay filter and standardized with a min-max scalar, which resulted in an overall reduction in noise.
Figure 2
Convolutional Long Short-Term Memory (LSTM) Model Diagram.
A diagram of the data processing for the convolutional LSTM model is provided in Figure 2. The data (model input in figure) was shaped into a 7 day by 24 hour (y-axis) by 60 minute (x-axis) for each participant. These seven daily activity logs were then fed into the Conv LSTM layers and into dense layers to make a final model prediction.
Table 1
Sociodemographic information from the NHANES 2003–2004 cohort with Physical Activity and Chronic Pain data.
| NO CHRONIC PAIN | CHRONIC PAIN | |
|---|---|---|
| Race and Ethnicity | ||
| Mexican American | 776 | 97 |
| Other Hispanic | 114 | 9 |
| Non-Hispanic White | 1817 | 436 |
| Non-Hispanic Black | 697 | 116 |
| Other Race-Including Multi-Racial | 148 | 30 |
| Gender | ||
| Male | 1768 | 278 |
| Female | 1784 | 410 |
| Age | ||
| <20 | 0 | 0 |
| 20–30 | 701 | 63 |
| 30–40 | 577 | 113 |
| 40–50 | 573 | 126 |
| 50–60 | 411 | 124 |
| 60–70 | 534 | 135 |
| 70–84 | 591 | 101 |
| >85 | 0 | 0 |
Figure 3
AUC Curve of False Positive vs False Negative Rate. AUC, Area Under the Curve.
The AUC curve represents the interaction between sensitivity and 1-specificity to show the overall predictability of the model (AUC val = 0.60, AUC test = 0.57).