Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions

O’Brien, Myles W.; Daley, W. Seth; Schwartz, Beverly D.; Shivgulam, Madeline E.; Wu, Yanlin; Kimmerly, Derek S.; Frayne, Ryan J.

doi:10.3390/s23020587

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…This may be explained by relatively short time intervals of lying down within our free-living data without much rotation and often supine position. Thus, researchers wanting to differentiate between sitting and lying in positionally constrained individuals or aiming to detect short lying periods may still need to choose a multi-sensor setup to ensure sufficient accuracy [ 9 , 30 , 33 ].…”

Section: Discussionmentioning

confidence: 99%

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types

Lendt,

Braun,

Biallas

et al. 2024

Int J Behav Nutr Phys Act

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Background The more accurate we can assess human physical behaviour in free-living conditions the better we can understand its relationship with health and wellbeing. Thigh-worn accelerometry can be used to identify basic activity types as well as different postures with high accuracy. User-friendly software without the need for specialized programming may support the adoption of this method. This study aims to evaluate the classification accuracy of two novel no-code classification methods, namely SENS motion and ActiPASS. Methods A sample of 38 healthy adults (30.8 ± 9.6 years; 53% female) wore the SENS motion accelerometer (12.5 Hz; ±4 g) on their thigh during various physical activities. Participants completed standardized activities with varying intensities in the laboratory. Activities included walking, running, cycling, sitting, standing, and lying down. Subsequently, participants performed unrestricted free-living activities outside of the laboratory while being video-recorded with a chest-mounted camera. Videos were annotated using a predefined labelling scheme and annotations served as a reference for the free-living condition. Classification output from the SENS motion software and ActiPASS software was compared to reference labels. Results A total of 63.6 h of activity data were analysed. We observed a high level of agreement between the two classification algorithms and their respective references in both conditions. In the free-living condition, Cohen’s kappa coefficients were 0.86 for SENS and 0.92 for ActiPASS. The mean balanced accuracy ranged from 0.81 (cycling) to 0.99 (running) for SENS and from 0.92 (walking) to 0.99 (sedentary) for ActiPASS across all activity types. Conclusions The study shows that two available no-code classification methods can be used to accurately identify basic physical activity types and postures. Our results highlight the accuracy of both methods based on relatively low sampling frequency data. The classification methods showed differences in performance, with lower sensitivity observed in free-living cycling (SENS) and slow treadmill walking (ActiPASS). Both methods use different sets of activity classes with varying definitions, which may explain the observed differences. Our results support the use of the SENS motion system and both no-code classification methods.

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Section: Discussionmentioning

confidence: 99%

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types

Lendt,

Braun,

Biallas

et al. 2024

Int J Behav Nutr Phys Act

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“…Inclinometer-based sensors measure the inclination of body part(s) to estimate postural information such as standing, sitting, and lying [12]. Typically, the inclinometerbased sensors are thigh-worn, where vertical thigh orientation typically indicates a standing posture and horizontal thigh orientation indicates a sitting posture [13]. However, this approach has some challenges to distinguish lying from sitting [14].…”

Section: Introductionmentioning

confidence: 99%

Measurement of Sedentary Behavior—The Outcomes of the Angle for Posture Estimation (APE) Method

Vähä-Ypyä,

Husu,

Sievänen

et al. 2024

Sensors

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Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth’s gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = −0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term “sedentary behavior” for studies wherein the classification of SB is based on both intensity and posture.

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Validity of ActivPAL CREA software detection of sitting and lying during free-living conditions

et al. 2023

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Objective. Approaches to differentiate sitting and lying are available within the default activPAL software from a single thigh-worn monitor. Dual-monitor methods use multiple monitors positioned on the thigh and torso to characterize sitting versus lying. We evaluated the validity between these two methods to measure waking sitting and lying time in free-living conditions. We also examined if the degree-threshold distinguishing sitting/lying for the dual-monitor (<30 and <45) impacted results. Approach. Thirty-five young adults (24±3 years, 16 females) wore an activPAL 24-hrs per day on their thigh and torso during free-living conditions (average: 6.8±1.0 days, 239 total). Data were processed using the default activPAL software (thigh-only) or a custom MATLAB program (dual-monitor). Main Results. The single-monitor recorded less lying time (59±99 mins/day) and more sitting time (514±203 mins/day) than the dual-monitor method regardless of 30 (lying: 85±94 mins/day; sitting: 488±166 mins/day) or 45 lying threshold (lying: 170±142, sitting: 403±164 mins/day; all, p<0.001). The single monitor lying time was weakly correlated to the dual-monitor (30: =0.25, 45: =0.21; both, p<0.001), whereas sitting was moderate-strong (30: =0.76, 45: =0.58; both, p<0.001). However, the mean absolute error was 81 mins/day (30) and 132 min/day (45) for both lying and sitting.Significance. The method of differentiating sitting/lying from a single thigh-worn activPAL records more sitting time and less lying time compared to a dual-monitor configuration (regardless of degree-threshold) that considered the position of the torso. A further refinement of algorithms or implementation of multiple-monitor methods may be needed for researchers to derive detailed sedentary positions. 

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Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions

Cited by 6 publications

References 32 publications

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types

Thigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types

Measurement of Sedentary Behavior—The Outcomes of the Angle for Posture Estimation (APE) Method

Validity of ActivPAL CREA software detection of sitting and lying during free-living conditions

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