Purpose This study developed and tested an algorithm to classify accelerometer data as walking or non-walking using either GPS or travel diary data within a large sample of adults under free-living conditions. Methods Participants wore an accelerometer and a GPS unit, and concurrently completed a travel diary for 7 consecutive days. Physical activity (PA) bouts were identified using accelerometry count sequences. PA bouts were then classified as walking or non-walking based on a decision-tree algorithm consisting of 7 classification scenarios. Algorithm reliability was examined relative to two independent analysts’ classification of a 100-bout verification sample. The algorithm was then applied to the entire set of PA bouts. Results The 706 participants’ (mean age 51 years, 62% female, 80% non-Hispanic white, 70% college graduate or higher) yielded 4,702 person-days of data and had a total of 13,971 PA bouts. The algorithm showed a mean agreement of 95% with the independent analysts. It classified physical activity into 8,170 (58.5 %) walking bouts and 5,337 (38.2%) non-walking bouts; 464 (3.3%) bouts were not classified for lack of GPS and diary data. Nearly 70% of the walking bouts and 68% of the non-walking bouts were classified using only the objective accelerometer and GPS data. Travel diary data helped classify 30% of all bouts with no GPS data. The mean duration of PA bouts classified as walking was 15.2 min (SD=12.9). On average, participants had 1.7 walking bouts and 25.4 total walking minutes per day. Conclusions GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or non-walking behavior.

Our study demonstrates that nearly half of children with CID had abnormal US findings in 1 of 8 commonly affected joints. These findings did not correlate with subsequent clinical flares in up to 2 years of followup.

Objective. Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disease. An inexpensive and rapid imaging tool, infrared thermal imaging, was evaluated for its utility to detect active bone lesions in extremities of children with CNO.Methods. Children with suspected active CNO and healthy controls were enrolled. All subjects underwent infrared thermal imaging of the lower extremities. Patients in the CNO group also received a magnetic resonance imaging (MRI) examination. Hyperintensity within bone marrow on a fluid-sensitive T2-weighted MRI sequence was considered confirmatory for inflammation. Infrared thermal data were analyzed using custom software by dividing the leg below the knee into 3 equal segments longitudinally and adding the distal femur segment as an equal length above the knee. Median and 95th percentile temperatures were recorded for each leg segment. Temperature differences between inflamed and uninflamed segments in all subjects (both intersubject and intrasubject) were evaluated using a linear mixed-effects model.Results. Thirty children in the suspected/known CNO group and 31 healthy children were enrolled. In the healthy control group, males had significantly higher temperature in their lower extremities than females (P < 0.05). There was no difference in temperature detected between inflamed leg segments of patients with CNO versus uninflamed leg segments of the healthy control group. However, within the CNO group, significantly higher temperatures were detected for inflamed versus uninflamed distal tibia/fibula segments (P < 0.01).Conclusion. Children with active CNO lesions in the distal tibia/fibula exhibited higher regional temperatures on average than healthy extremities. Larger studies are warranted to further evaluate the clinical utility of infrared thermal imaging for CNO detection.