Characterizing and predicting person-specific, day-to-day, fluctuations in walking behavior

Abstract

Despite the positive health effect of physical activity, one third of the world’s population is estimated to be insufficiently active. Prior research has mainly investigated physical activity on an aggregate level over short periods of time, e.g., during 3 to 7 days at baseline and a few months later, post-intervention. To develop effective interventions, we need a better understanding of the temporal dynamics of physical activity. We proposed here an approach to studying walking behavior at “high-resolution” and by capturing the idiographic and day-to-day changes in walking behavior. We analyzed daily step count among 151 young adults with overweight or obesity who had worn an accelerometer for an average of 226 days (~25,000 observations). We then used a recursive partitioning algorithm to characterize patterns of change, here sudden behavioral gains and losses, over the course of the study. These behavioral gains or losses were defined as a 30% increase or reduction in steps relative to each participants’ median level of steps lasting at least 7 days. After the identification of gains and losses, fluctuation intensity in steps from each participant’s individual time series was computed with a dynamic complexity algorithm to identify potential early warning signals of sudden gains or losses. Results revealed that walking behavior change exhibits discontinuous changes that can be described as sudden gains and losses. On average, participants experienced six sudden gains or losses over the study. We also observed a significant and positive association between critical fluctuations in walking behavior, a form of early warning signals, and the subsequent occurrence of sudden behavioral losses in the next days. Altogether, this study suggests that walking behavior could be well understood under a dynamic paradigm. Results also provide support for the development of “just-in-time adaptive” behavioral interventions based on the detection of early warning signals for sudden behavioral losses.

Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project

“…A better evaluation or capture of an individual’s singular motion pattern or movement construction, from a musculoskeletal point of view, may allow practitioners to anticipate or track the evolution of some chronic diseases. In their recent work, Chevance et al proposed such an approach, showing that the anticipation and detection of the early signs of individuals’ movement changes, as an indicator of subsequent critical functional gain or loss, need to be considered as “early warning signals for sudden behavioral changes” [ 10 ]. Beyond PA and overall human movements themselves, early findings suggest that sudden gain or loss in complex systems could be predicted through early warning signals [ 11 , 12 ], such as slight changes or fluctuations in human motion and movement patterns.…”

“…On application to movement behaviors, the early anticipation of imminent body motion disruption or early detection of the first signs of fluctuations might represent a potentially reliable signal for delivering “just-in-time” interventions [ 13 ]. In that sense, Chevance et al observed that in adults with obesity, fluctuations in walking patterns were associated with the subsequent occurrence of behavioral losses in the following days, clearly demonstrating the need to develop new accessible methods to properly detect such early signals [ 10 ]. This need is clearly illustrated by high-quality clinical studies.…”

Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration

“…They do not speak to the effects of individual messages on subsequent physical activity. This gap is important because physical activity is a dynamic process that varies over time within each person and understanding those dynamics can improve predictions about behavior change [ 9 ]. Accelerometer data from the NHANES dataset revealed normative differences in physical activity as a function of the time of day and day of week [ 10 ].…”

“…To date, evaluations of physical activity changes following the COVID-19 pandemic have primarily focused on behavior aggregated over time across groups of people [ 5 , 19 ]. Less is known about how the dynamics of behavior changed over time despite evidence that physical activity involves substantial variance within the person over time [ 9 , 20 ]. Person-specific dynamical models can reveal if the pandemic impacted physical activity dynamics or behavioral changes in response to intervention messages.…”

Associations Between Social Cognitive Determinants and Movement-Related Behaviors in Studies Using Ecological Momentary Assessment Methods: Systematic Review

“…Therefore, methods that capture typical levels of movement-related behaviors and social cognitive determinants on a macrotimescale may overlook important information regarding decisions to engage in a bout of PA or SB across microtimescales (eg, hours and days). Previous research has documented that social cognitive determinants, PA, and SB are dynamic, varying within individuals across time and space [26][27][28]. Investigating associations between social cognitive determinants and movement-related behaviors in the content of daily life across microtimescales can elucidate the motivational determinants of movement-related behaviors and potentially enhance intervention efforts.…”