Advancing the measurement of sedentary behaviour : classifying posture and physical (in-)activity

Sedentary behaviour, defined by a sitting body posture with minimal-intensity physical activity, is an emergent public health topic. The time spent sedentary is associated with the incidence of non-communicable chronic diseases such as type 2 diabetes and cardiovascular disease and significantly shortens life-expectancy in a dose-response relationship. Office workers are at particular risk of developing diseases related to sedentary behaviour due to their excessive sedentary work. Even though thigh-worn posture sensors are recommended to measure sedentary behaviour, the vast majority of the evidence was collected with waist-worn physical activity sensors, and we still lack a method to measure the posture and the physical activity component of sedentary behaviour simultaneously. This thesis aims to advance the measurement of sedentary behaviour in an office context by developing new device-based methods to measure both components simultaneously, and by validating and subsequently applying the most promising method to measure the actual amount of sedentary behaviour in the daily life of office workers. The method development showed that it is possible to measure both components of sedentary behaviour with only one sensor, preferably worn on the thigh or waist. While an accelerometer is sufficient for the thigh, an inertial-measurement-unit is preferable for the waist due to a significantly improved posture classification. The method validation subsequently confirmed that waist-worn physical activity sensors, the prevailing choice to measure sedentary behaviour, measure minimal-intensity physical activity. Furthermore, the study uncovered a serious postural dependency causing a systematic overestimation of minimal-intensity physical activity while sitting compared to standing. The subsequent method application considered the posture dependency and combined a thigh-worn posture sensor with a waist-worn physical activity sensor to POPAI, the Posture and Physical Activity Index. POPAI has a sensitivity of 92.5% and a specificity of 91.9% to measure sedentary behaviour and classified 45.0% of the office workers wake-time sedentary. The posture sensor alone overestimated sedentary time by 30.3%, and the physical activity sensor alone overestimated sedentary time by 22.5%. The difference can be explained by active sitting (2.0 hours per day) and inactive standing (1.8 hours per day), both of which are much more common than previously thought. This thesis confirms the recommendation to use a thigh-worn accelerometer to measure sedentary behaviour and adds the information that such a sensor is also able to measure physical (in-)activity in sitting. Thus, there is no need to approximate sedentary behaviour with sitting, nor is there a need to approximate it with inactivity. In fact, these approximations lead to inaccurate and imprecise results substantially overestimating sedentary behaviour. Due to the predominant use of physical activity sensors to measure sedentary behaviour, recommendations to limit sedentary behaviour should address a limitation of the time spent inactive rather than the time spent sitting. If it turns out that sitting matters, one could expect a much stronger relationship between sedentary behaviour measured with a combined method such as POPAI and detrimental health effects

Accuracy of KinectOne to quantify kinematics of the upper body

Motion analysis systems deliver quantitative information, e.g. on the progress of rehabilitation programs aimed at improving range of motion. Markerless systems are of interest for clinical application because they are low-cost and easy to use. The first generation of the Kinect™ sensor showed promising results in validity assessment compared to an established marker-based system. However, no literature is available on the validity of the new 'Kinect™ for Xbox one' (KinectOne) in tracking upper body motion. Consequently, this study was conducted to analyze the accuracy and reliability of the KinectOne in tracking upper body motion. Twenty subjects performed shoulder abduction in frontal and scapula plane, flexion, external rotation and horizontal flexion in two conditions (sitting and standing). Arm and trunk motion were analyzed using the KinectOne and compared to a marker-based system. Comparisons were made using Bland Altman statistics and Coefficient of Multiple Correlation. On average, differences between systems of 3.9±4.0° and 0.1±3.8° were found for arm and trunk motion, respectively. Correlation was higher for the arm than for the trunk motion. Based on the observed bias, the accuracy of the KinectOne was found to be adequate to measure arm motion in a clinical setting. Although trunk motion showed a very low absolute bias between the two systems, the KinectOne was not able to track small changes over time. Before the KinectOne can find clinical application, further research is required analyzing whether validity can be improved using a customized tracking algorithm or other sensor placement, and to analyze test-retest reliability

Active sitting with backrest support : is it feasible?

Ergonomics science recommends office chairs that promote active sitting to reduce sitting related complaints. Since current office chairs do not fulfil this recommendation, a new chair was developed by inverting an existing dynamic chair principle. This study compares active sitting on the inverted chair during a simulated computer based office task to two existing dynamic office chairs (n=8). Upper body stability was analysed using Friedman ANOVA (p=.01). Additionally, participants completed a questionnaire to rate their comfort and activity after half a working day. The inverted chair allowed the participants to perform a substantial range of lateral spine flexion (11.5°) with the most stable upper body posture (≤11mm, ≤2°, p≤0.01). The results of this study suggest that the inverted chair supports active sitting with backrest support during computer based office work. However, according to comfort and activity ratings, results should be verified in a future field study with 24 participants.ZHAW Zurich University of Applied SciencesAccepte

Where to place which sensor to measure sedentary behaviour? A method development and comparison among various sensor placements and signal types

Background: Sedentary Behaviour (SB) is associated with several chronic diseases and especially office workers are at increased risk. SB is defined by a sitting or reclined body posture with an energy expenditure ≤1.5 METs. However, current objective methods to measure SB are not consistent with its definition. There is no consensus on which sensor placement and type to be used. Aim: To compare the accuracy of newly developed artificial intelligence models for 15 sensor placements in combination with four signal types (accelerometer only/plus gyroscope and/or magnetometer) to detect posture and physical in-/activity while desk-based activities. Method: Signal features for the model development were extracted from sensor raw data of 30 office workers performing 10 desk-based tasks, each lasting 5 minutes. Direct observation (posture) and indirect calorimetry (in-/activity) served as reference criteria. The best classification model for each sensor was identified and compared among the sensor placements, both using Friedman and post-hoc Wilcoxon tests (p≤0.05). Results: Posture was most accurately measured with a lower body sensor, while in-/activity was most accurately measured with an upper body or waist sensor. The inclusion of additional signal types improved the posture classification for some placements, while the acceleration signal already contained the relevant signal information for the in-/activity classification. Overall, the thigh accelerometer most accurately classified desk-based SB. Conclusion: This study favours, in line with previous work, the measurement of SB with a thigh worn accelerometer, and adds the information that this sensor is also accurate in measuring physical in-/activity while sitting and standing.Swiss National Science FoundationAccepte

Physiological motion axis for the seat of a dynamic office chair

Objective: The aim of this study was to determine and verify the optimal location of the motion axis (MA) for the seat of a dynamic office chair. Background: A dynamic seat that supports pelvic motion may improve physical well-being and decrease the risk of sitting-associated disorders. However, office work requires an undisturbed view on the work task, which means a stable position of the upper trunk and head. Current dynamic office chairs do not fulfill this need. Consequently, a dynamic seat was adapted to the physiological kinematics of the human spine. Method: Three-dimensional motion tracking in free sitting helped determine the physiological MA of the spine in the frontal plane. Three dynamic seats with physiological, lower, and higher MA were compared in stable upper body posture (thorax inclination) and seat support of pelvic motion (dynamic fitting accuracy). Spinal kinematics during sitting and walking were compared. Results: The physiological MA was at the level of the 11th thoracic vertebra, causing minimal thorax inclination and high dynamic fitting accuracy. Spinal motion in active sitting and walking was similar. Conclusion: The physiological MA of the seat allows considerable lateral flexion of the spine similar to walking with a stable upper body posture and a high seat support of pelvic motion. Application: The physiological MA enables lateral flexion of the spine, similar to walking, without affecting stable upper body posture, thus allowing active sitting while focusing on work

Detecting prolonged sitting bouts with the ActiGraph GT3X

The ActiGraph has a high ability to measure physical activity; however, it lacks an accurate posture classification to measure sedentary behavior. The aim of the present study was to develop an ActiGraph (waist-worn, 30 Hz) posture classification to detect prolonged sitting bouts, and to compare the classification to proprietary ActiGraph data. The activPAL, a highly valid posture classification device, served as reference criterion. Both sensors were worn by 38 office workers over a median duration of 9 days. An automated feature selection extracted the relevant signal information for a minute-based posture classification. The machine learning algorithm with optimal feature number to predict the time in prolonged sitting bouts (>= 5 and >= 10 minutes) was searched and compared to the activPAL using Bland-Altman statistics. The comparison included optimized and frequently used cut-points (100 and 150 counts per minute (cpm), with and without low-frequency-extension (LFE) filtering). The new algorithm predicted the time in prolonged sitting bouts most accurate (bias <= 7 minutes/d). Of all proprietary ActiGraph methods, only 150 cpm without LFE predicted the time in prolonged sitting bouts non-significantly different from the activPAL (bias <= 18 minutes/d). However, the frequently used 100 cpm with LFE accurately predicted total sitting time (bias <= 7 minutes/d). To study the health effects of ActiGraph measured prolonged sitting, we recommend using the new algorithm. In case a cut-point is used, we recommend 150 cpm without LFE to measure prolonged sitting and 100 cpm with LFE to measure total sitting time. However, both cpm cut-points are not recommended for a detailed bout analysis.NoneAccepte

A new shoe sole technology that transfers the ground composition to the sole of the foot : a user experience evaluation

Introduction: Neither comfortable, shock-absorbing shoes nor minimal shoes do stimulate the mechanoreceptors of the sole of the foot. This lack of stimulation leads to worse proprioception, poor posture and risk of injuries [1] A new sole technology is introduced, which transfers the ground composition to the sole of the foot and may provide enough stability through an integrated footbed (Figure1). Methods: The stimuli transmitting shoe sole technology is performed mechanically. The shoe sole consists of hard plastic balls, which are pushed towards the sole of the foot due to uneven surfaces (Figure2). This technologies’ user experience was evaluated. The tests consisted of a two-week user study that evaluated three shoe sole in daily life as well as a one-hour monitored parcourse evaluating the shoe sole on specific grounds. All participants were healthy with shoe size EU38-43. The user study included 20 participants (Ø 64 years). Additionally, 10 persons (Ø 41 years) participated in the parcourse. Questionnaires covered intensity of sensory transmission, general walking comfort and complaints and the effect of the ground composition on comfort. Answering options were on a Likert scale as well as open questions. Results & Discussion Intensity: Most of the participants rated the stimulus transmission as very or rather strong. Nobody rated it as very weak. In the parcourse, the strongest sensation was on coarse stones and pavement transitions, followed by the forest floor (Figure3). Comfort & complaints: The majority perceived the shoe sole as very or rather comfortable. Participants perceived the stimuli strongest in the forefoot, where also most of the complaints occurred. The complaints were reported as tired feet, pain, pressure and burning feet, and occurred roughly every third day. Conclusions: All participants perceived the stimuli transmission of the shoe sole. However, the product polarizes. While some considered the stimuli as comfortable, others found them too strong. The forefoot was the part with the strongest stimuli sensation, but also with the most complaints

Concurrent and discriminant validity of ActiGraph waist and wrist cut-points to measure sedentary behaviour, activity level, and posture in office work

Background: Sedentary Behaviour (SB) gets an increasing attention from ergonomics and public health due to its associated detrimental health effects. A large number of studies record SB with ActiGraph counts-per-minute cut-points, but we still lack valid information about what the cut-points tell us about office work. This study therefore analysed the concurrent and discriminant validity of commonly used cut-points to measure SB, activity level, and posture. Methods: Thirty office workers completed four office tasks at three workplaces (conventional chair, activity-promoting chair, and standing desk) while wearing two ActiGraphs (waist and wrist). Indirect calorimetry and prescribed posture served as reference criteria. Generalized Estimation Equations analysed workplace and task effects on the activity level and counts-per-minute, and kappa statistics and ROC curves analysed the cut-point validity. Results: The activity-promoting chair (p < 0.001, ES ≥ 0.66) but not the standing desk (p = 1.0) increased the activity level, and both these workplaces increased the waist (p ≤ 0.003, ES ≥ 0.63) but not the wrist counts-per-minute (p = 0.74) compared to the conventional chair. The concurrent and discriminant validity was higher for activity level (kappa: 0.52–0.56 and 0.38–0.45, respectively) than for SB and posture (kappa ≤0.35 and ≤ 0.19, respectively). Furthermore, the discriminant validity for activity level was higher for task effects (kappa: 0.42–0.48) than for workplace effects (0.13–0.24). Conclusions: ActiGraph counts-per-minute for waist and wrist placement were – independently of the chosen cut-point – a measure for activity level and not for SB or posture, and the cut-points performed better to detect task effects than workplace effects. Waist cut-points were most valid to measure the activity level in conventional seated office work, but they showed severe limitations for sit-stand desks. None of the placements was valid to detect the increased activity on the activity-promoting chair. Caution should therefore be paid when analysing the effect of workplace interventions on activity level with ActiGraph waist and wrist cut-points