Walking Objectively Measured

Bumjoon Kang, Anne Vernez-Moudon, Dr. es Sc,. Philip M. Hurvitz, PhD., Lucas Reichley, and Brian E. Saelens. Medicine and Science in Sports and Exercise. 2013 Feb 22. [Epub ahead of print] PMID: 23439414


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.

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.

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.

GPS and travel diary information can be helpful in classifying most accelerometer-derived PA bouts into walking or non-walking behavior.

Research supported by R01 HL091881 (BE Saelens PI)

[A: Walk1-GPS example] The PA bout was non-dwell with GPS median speed of 3.2 kmh-1 and therefore was classified as a walking bout, even though no walking trip was reported in the diary when the bout occurred. [B: Walk2-Diary example] The PA bout had no GPS data, but had time overlap with a diary-based walking trip, so was classified as a walking bout. [C: Walk3-Diary example] The PA bout had no GPS data, no walking trip was recorded in the diary near the bout, but since it had time overlap with a diary-based bus trip it was classi­fied as a walking bout assumed to be associated with the bus trip. [D: Walk4-Diary example] The PA bout had no GPS data and no overlap with a diary-based trip, but had a diary-based walking trip 4.5 min after it and was thus classified as a walking bout assumed to be associated with the walking trip but with diary time errors. [E: NonWalk2-GPS example] The PA bout was a dwell bout (the GPS point cluster circle radius = 12.8ft) and thus was not a walking bout. [F: NonWalk3-Diary example] The PA bout had no GPS data, but occurred within a diary-based place and had no other diary-based trips close in time.

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