Dataset Information

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.

SUBMITTER: Chevance G

PROVIDER: S-EPMC8121346 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:To get insights into the temporal pattern of commensal Escherichia coli populations, we sampled the feces of four healthy cows from the same herd in the Hwange District of Zimbabwe daily over 25 days. The cows had not received antibiotic treatment during the previous 3 months. We performed viable E. coli counts and characterized the 326 isolates originating from the 98 stool samples at a clonal level, screened them for stx and eae genes, and tested them for their antibiotic susceptibilities. We observed that E. coli counts and dominant clones were different among cows, and very few clones were shared. No clone was shared by three or four cows. Clone richness and evenness were not different between cows. Within each host, the variability in the E. coli count was evidenced between days, and no clone was found to be dominant during the entire sampling period, suggesting the existence of clonal interference. Dominant clones tended to persist longer than subdominant ones and were mainly from phylogenetic groups A and B1. Five E. coli clones were found to contain both the stx1 and stx2 genes, representing 6.3% of the studied isolates. All cows harbored at least one Shiga toxin-producing E. coli (STEC) strain. Resistance to tetracycline, penicillins, trimethoprim, and sulfonamides was rare and observed in three clones that were shed at low levels in two cows. This study highlights the fact that the commensal E. coli population, including the STEC population, is host specific, is highly dynamic over a short time frame, and rarely carries antibiotic resistance determinants in the absence of antibiotic treatment.IMPORTANCE The literature about the dynamics of commensal Escherichia coli populations is very scarce. Over 25 days, we followed the total E. coli counts daily and characterized the sampled clones in the feces of four cows from the same herd living in the Hwange District of Zimbabwe. This study deals with the day-to-day dynamics of both quantitative and qualitative aspects of E. coli commensal populations, with a focus on both Shiga toxin-producing E. coli and antibiotic-resistant E. coli strains. We show that the structure of these commensal populations was highly specific to the host, even though the cows ate and roamed together, and was highly dynamic between days. Such data are of importance to understand the ecological forces that drive the dynamics of the emergence of E. coli clones of particular interest within the gastrointestinal tract and their transmission between hosts.

Project description:The temporal dynamics of stride-to-stride fluctuations in steady-state walking reveal important information about locomotor control and can be quantified using so-called fractal analyses, notably the detrended fluctuation analysis (DFA). Gait dynamics are often collected during treadmill walking using 3-D motion capture to identify gait events from kinematic data. The sampling frequency of motion capture systems may impact the precision of event detection and consequently impact the quantification of stride-to-stride variability. This study aimed i) to determine if collecting multiple walking trials with different sampling frequency affects DFA values of spatiotemporal parameters during treadmill walking, and ii) to determine the reliability of DFA values across downsampled conditions. Seventeen healthy young adults walked on a treadmill while their gait dynamics was captured using different sampling frequency (60, 120 and 240 Hz) in each condition. We also compared data from the highest sampling frequency to downsampled versions of itself. We applied DFA to the following time series: step length, time and speed, and stride length, time and speed. Reliability between experimental conditions and between downsampled conditions were measured with 1) intraclass correlation estimates and their 95% confident intervals, calculated based on a single-measurement, absolute-agreement, two-way mixed-effects model (ICC 3,1), and 2) Bland-Altman bias and limits of agreement. Both analyses revealed a poor reliability of DFA results between conditions using different sampling frequencies, but a relatively good reliability between original and downsampled spatiotemporal variables. Collectively, our results suggest that using sampling frequencies of 120 Hz or 240 Hz provide similar results, but that using 60 Hz may alter DFA values. We recommend that gait kinematics should be collected at around 120 Hz, which provides a compromise between event detection accuracy and processing time.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets