Prediction of sustained harmonic walking in the free-living environment using raw accelerometry data

Objective. Using raw, sub-second level, accelerometry data, we propose and validate a method for identifying and characterizing walking in the free-living environment. We focus on the sustained harmonic walking (SHW), which we define as walking for at least 10 seconds with low variability of step frequency. Approach. We utilize the harmonic nature of SHW and quantify local periodicity of the tri-axial raw accelerometry data. We also estimate fundamental frequency of observed signals and link it to the instantaneous walking (step-to-step) frequency (IWF). Next, we report total time spent in SHW, number and durations of SHW bouts, time of the day when SHW occurred and IWF for 49 healthy, elderly individuals. Main results. Sensitivity of the proposed classification method was found to be 97%, while specificity ranged between 87% and 97% and prediction accuracy between 94% and 97%. We report total time in SHW between 140 and 10 minutes-per-day distributed between 340 and 50 bouts. We estimate the average IWF to be 1.7 steps-per-second. Significance. We propose a simple approach for detection of SHW and estimation of IWF, based on Fourier decomposition. The resulting approach is fast and allows processing of a week-long raw accelerometry data (approx. 150 million measurements) in relatively short time (~half an hour) on a common laptop computer (2.8 GHz Intel Core i7, 16 GB DDR3 RAM)

Stride variability measures derived from wrist- and hip-worn accelerometers

Many epidemiological and clinical studies use accelerometry to objectively measure physical activity using the activity counts, vector magnitude, or number of steps. These measures use just a fraction of the information in the raw accelerometry data as they are typically summarized at the minute level. To address this problem we define and estimate two gait measures of temporal stride-to-stride variability based on raw accelerometry data: Amplitude Deviation (AD) and Phase Deviation (PD). We explore the sensitivity of our approach to on-body placement of the accelerometer by comparing hip, left and right wrist placements. We illustrate the approach by estimating AD and PD in 46 elderly participants in the Developmental Epidemiologic Cohort Study (DECOS) who worn accelerometers during a 400 meter walk test. We also show that AD and PD have a statistically significant association with the gait speed and sit-to-stand test performanc

Use of Functional Linear Models to Detect Associations between Characteristics of Walking and Continuous Responses Using Accelerometry Data

Various methods exist to measure physical activity. Subjective methods, such as diaries and surveys, are relatively inexpensive ways of measuring one’s physical activity; however, they are prone to measurement error and bias due to self-reporting. Wearable accelerometers offer a non-invasive and objective measure of one’s physical activity and are now widely used in observational studies. Accelerometers record high frequency data and each produce an unlabeled time series at the sub-second level. An important activity to identify from the data collected is walking, since it is often the only form of activity for certain populations. Currently, most methods use an activity summary which ignores the nuances of walking data. We propose methodology to model specific continuous responses with a functional linear model utilizing spectra obtained from the local fast Fourier transform (FFT) of walking as a predictor. Utilizing prior knowledge of the mechanics of walking, we incorporate this as additional information for the structure of our transformed walking spectra. The methods were applied to the in-the-laboratory data obtained from the Developmental Epidemiologic Cohort Study (DECOS)

Differentiating Between Walking and Stair Climbing Using Raw Accelerometry Data

Wearable accelerometers provide an objective measure of human physical activity. They record high frequency unlabeled three-dimensional time series data. We extract meaningful features from the raw accelerometry data and based on them develop and evaluate a classification method for the detection of walking and its sub-classes, i.e. level walking, descending stairs and ascending stairs. Our methodology is tested on a sample of 32 middle-aged subjects for whom we extracted features based on the Fourier and wavelet transforms. We build subject-specific and group-level classification models utilizing a tree-based methodology. We evaluate the effects of sensor location and tuning parameters on the classification accuracy of the tree models. In the group-level classification setting, we propose a robust feature inter-subject normalization and evaluate its performance compared to unnormalized data. The overall classification accuracy for the three activities at the subject-specific level was on average 87.6%, with the ankle-worn accelerometers showing the best performance with an average accuracy 90.5%. At the group-level, the average overall classification accuracy for the three activities using the normalized features was 80.2% compared to 72.3% for the unnormalized features. In summary, a framework is provided for better use and feature extraction from raw accelerometry data to differentiate among different walking modalities as well as considerations for study design

Bedtime habits in adults with and without type 2 diabetes

This study aimed to identify determinants of objectively-estimated bedtime habits and to determine if these bedtime habits differed between adults with and without type 2 diabetes. Adults with accelerometry data from the National Health and Nutrition Examination Survey 2003-2004 and 2005-2006 cohorts were classified as having no diabetes or type 2 diabetes and matched for age, gender, and BMI across the two groups. Multivariate linear regression models assessed bedtime habits (time-in-bed, early versus late bedtime periods, regularity), chronotype (mid-points), and type 2 diabetes status. While the results indicated no differences in bedtime habits between adults with and without type 2 diabetes, an interesting finding was the support for an association between objectively-estimated earlier bedtime midpoints and greater physical activity

2019 update of the WSES guidelines for management of Clostridioides (Clostridium) difficile infection in surgical patients

In the last three decades, Clostridium difficile infection (CDI) has increased in incidence and severity in many countries worldwide. The increase in CDI incidence has been particularly apparent among surgical patients. Therefore, prevention of CDI and optimization of management in the surgical patient are paramount. An international multidisciplinary panel of experts from the World Society of Emergency Surgery (WSES) updated its guidelines for management of CDI in surgical patients according to the most recent available literature. The update includes recent changes introduced in the management of this infection.Peer reviewe

Adaptive empirical pattern transformation (ADEPT) with application to walking stride segmentation

Quantifying gait parameters and ambulatory monitoring of changes in these parameters have become increasingly important in epidemiological and clinical studies. Using high-density accelerometry measurements, we propose adaptive empirical pattern transformation (ADEPT), a fast, scalable, and accurate method for segmentation of individual walking strides. ADEPT computes the covariance between a scaled and translated pattern function and the data, an idea similar to the continuous wavelet transform. The difference is that ADEPT uses a data-based pattern function, allows multiple pattern functions, can use other distances instead of the covariance, and the pattern function is not required to satisfy the wavelet admissibility condition. Compared to many existing approaches, ADEPT is designed to work with data collected at various body locations and is invariant to the direction of accelerometer axes relative to body orientation. The method is applied to and validated on accelerometry data collected during a equation M1-m outdoor walk of equation M2 study participants wearing accelerometers on the wrist, hip, and both ankles. Additionally, all scripts and data needed to reproduce presented results are included in supplementary material available at Biostatistics online

Validation of Gait Characteristics Extracted From Raw Accelerometry During Walking Against Measures of Physical Function, Mobility, Fatigability, and Fitness

Background Data collected by wearable accelerometry devices can be used to identify periods of sustained harmonic walking. This report aims to establish whether the features of walking identified in the laboratory and free-living environments are associated with each other as well as measures of physical function, mobility, fatigability, and fitness. Methods Fifty-one older adults (mean age 78.31) enrolled in the Developmental Epidemiologic Cohort Study were included in the analyses. The study included an “in-the-lab” component as well as 7 days of monitoring “in-the-wild” (free living). Participants were equipped with hip-worn Actigraph GT3X+ activity monitors, which collect raw accelerometry data. We applied a walking identification algorithm and defined features of walking, including participant-specific walking acceleration and cadence. The association between these walking features and physical function, mobility, fatigability, and fitness was quantified using linear regression analysis. Results Acceleration and cadence estimated from “in-the-lab” and “in-the-wild” data were significantly associated with each other (p < .05). However, walking acceleration “in-the-lab” was on average 96% higher than “in-the-wild,” whereas cadence “in-the-lab” was on average 20% higher than “in-the-wild.” Acceleration and cadence were associated with measures of physical function, mobility, fatigability, and fitness (p < .05) in both “in-the-lab” and “in-the-wild” settings. In addition, “in-the-wild” daily walking time was associated with fitness (p < .05). Conclusions The quantitative difference in proposed walking features indicates that participants may overperform when observed “in-the-lab.” Also, proposed features of walking were significantly associated with measures of physical function, mobility, fatigability, and fitness, which provides evidence of convergent validity

Habitual physical activity patterns in a nationally representative sample of U.S. adults

Physical inactivity is a leading determinant of noncommunicable diseases. Yet, many adults remain physically inactive. Physical activity guidelines do not account for the multidimensionality of physical activity, such as the type or variety of physical activity behaviors. This study identified patterns of physical activity across multiple dimensions (e.g., frequency, duration, and variety) using a nationally representative sample of adults. Sociodemographic characteristics, health behaviors, and clinical characteristics associated with each physical activity pattern were defined. Multivariate finite mixture modeling was used to identify patterns of physical activity among 2003–2004 and 2005–2006 adult National Health and Nutrition Examination Survey participants. Chi-square tests were used to identify sociodemographic differences within each physical activity cluster and test associations between the physical activity clusters with health behaviors and clinical characteristics. Five clusters of physical activity patterns were identified: (a) low frequency, short duration (n = 730, 13%); (b) low frequency, long duration (n = 392, 7%); (c) daily frequency, short duration (n = 3,011, 55%); (d) daily frequency, long duration (n = 373, 7%); and (e) high frequency, average duration (n = 964, 18%). Walking was the most common form of activity; highly active adults engaged in more varied types of activity. High-activity clusters were comprised of a greater proportion of younger, White, nonsmoking adult men reporting moderate alcohol use without mobility problems or chronic health conditions. Active females engaged in frequent short bouts of activity. Data-driven approaches are useful for identifying clusters of physical activity that encompass multiple dimensions of activity. These activity clusters vary across sociodemographic and clinical subgroups