Speeds that minimize energetic cost during steady-state walking have been observed during lab-based investigations of walking biomechanics and energetics. However, in real-world scenarios, humans walk in a variety of contexts that can elicit different walking strategies, and may not always prioritize minimizing energetic cost. To investigate whether individuals tend to select energetically optimal speeds in real-world situations and how contextual factors influence gait, we conducted a study combining data from lab and real-world experiments. Walking kinematics and context were measured during daily life over a week (N=17) using wearable sensors and a mobile phone. To determine context, we utilized self-reported activity logs, GPS data and follow-up exit interviews. Additionally, we estimated energetic cost using respirometry over a range of gait speeds in the lab. Gross and net cost of transport were calculated for each participant, and were used to identify energetically optimal walking speed ranges for each participant. The proportion of real-world steady-state stride speeds within these ranges (gross and net) were identified for all data and for each context. We found that energetically optimal speeds predicted by gross cost of transport were more predictive of walking speeds used during daily life than speeds that would minimize net cost of transport. On average, 82.2% of all steady-state stride speeds were energetically optimal for gross cost of transport for all contexts and participants, while only 45.6% were energetically optimal for net cost of transport. These results suggest that while energetic cost is a factor considered by humans when selecting gait speed in daily life, it is not the sole determining factor. Context contributes to the observed variability in movement parameters both within and between individuals.

Author contributions

Conceptualization: L.B., K.B., S.M.C., K.A.S.; Methodology: L.B., S.M.C., K.A.S.; Software: L.B.; Validation: L.B.; Formal analysis: L.B.; Investigation: L.B.; Resources: L.B., K.B., K.A.S.; Data curation: L.B.; Writing - original draft: L.B.; Writing - review & editing: L.B., K.B., S.M.C., K.A.S.; Visualization: L.B., K.B., S.M.C., K.A.S.; Supervision: L.B., K.B., S.M.C., K.A.S.; Project administration: L.B., K.B., S.M.C., K.A.S.; Funding acquisition: L.B., K.B., S.M.C., K.A.S.

Funding

This research was supported by the Precision Health Initiative at the University of Michigan and the Patricia C. Schroeder Family Fund Award.

Data availability

The dataset used and analyzed during the current study is available from the corresponding author upon reasonable request.

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