Measuring Activity Performance of Older Adults Using the activPAL: A Rapid Review

Chan, Charice S.; Slaughter, Sarah; Jones, Christopher; Ickert, Carla; Wagg, Adrian

doi:10.3390/healthcare5040094

Cited by 23 publications

(23 citation statements)

References 44 publications

(204 reference statements)

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“…Baseline levels of time spent walking and step count in both CG and GR residents were low, but were similar to those of older adults in an Australian care home [65]. Accelerometers and pedometers generally become less accurate as walking speed decreases [66,67]. The activPAL tends to underreport step count at gait speeds less than 0.47 m/s [68].…”

Section: Main Findings Of This Studymentioning

confidence: 87%

A Pilot Randomised Clinical Trial of a Novel Approach to Reduce Sedentary Behaviour in Care Home Residents: Feasibility and Preliminary Effects of the GET READY Study

Giné-Garriga

Dall

Sandlund

et al. 2020

IJERPH

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Care-home residents are among the most sedentary and least active of the population. We aimed to assess the feasibility, acceptability, safety, and preliminary effects of an intervention to reduce sedentary behaviour (SB) co-created with care home residents, staff, family members, and policymakers within a pilot two-armed pragmatic cluster randomized clinical trial (RCT). Four care homes from two European countries participated, and were randomly assigned to control (usual care, CG) or the Get Ready intervention (GR), delivered by a staff champion one-to-one with the care home resident and a family member. A total of thirty-one residents participated (51.6% female, 82.9 (13.6) years old). GR involves six face to face sessions over a 12-week period with goal-oriented prompts for movement throughout. The feasibility and acceptability of the intervention were assessed and adverse events (AEs) were collected. The preliminary effects of the GR on SB, quality of life, fear of falling, and physical function were assessed. Means and standard deviations are presented, with the mean change from baseline to post-intervention calculated along with 95% confidence intervals. The CG smoked more, sat more, and had more functional movement difficulties than the GR at baseline. The GR intervention was feasible and acceptable to residents and staff. No AEs occurred during the intervention. GR participants showed a decrease in daily hours spent sitting/lying (Cohen’s d = 0.36) and an increase in daily hours stepping, and improvements in health-related quality of life, fear of falling, and habitual gait speed compared to usual care, but these effects need confirmation in a definitive RCT. The co-created GR was shown to be feasible and acceptable, with no AEs.

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Section: Main Findings Of This Studymentioning

confidence: 87%

A Pilot Randomised Clinical Trial of a Novel Approach to Reduce Sedentary Behaviour in Care Home Residents: Feasibility and Preliminary Effects of the GET READY Study

Giné-Garriga

Dall

Sandlund

et al. 2020

IJERPH

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“…Several reviews have emphasized that accelerometers are suitable to assess physical activity, however, mainly distinguish between walking and sedentary behavior to date. Currently, there is still a lack of standardized methods for quantifying accelerometer-assessed activity [ 10 , 12 , 24 ].…”

Section: Discussionmentioning

confidence: 99%

“…Accelerometers have been increasingly used to assess physical activity outside of hospitals and in gait laboratories mainly by distinguishing between walking and sedentary behavior, e.g., in stroke patients and older adults [ 10 , 11 , 12 , 13 ]. Current sensors can store data for long periods of time (weeks), are user-friendly and detect valid and reliable body accelerations in walking populations.…”

Section: Introductionmentioning

confidence: 99%

“…Current sensors can store data for long periods of time (weeks), are user-friendly and detect valid and reliable body accelerations in walking populations. They are affordable and robust enough to be used in many different environments and are thereby most suitable for detecting mobilization characteristics in neurological patients during and after discharge from hospital care [ 10 , 12 , 13 , 14 , 15 ]. Multi-sensor approaches have been utilized to characterize the link between trunk accelerations and walking, and this approach have detected, e.g., the lack of pelvic accelerations and impaired head position or upper extremity function.…”

Section: Introductionmentioning

confidence: 99%

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Accelerometric Trunk Sensors to Detect Changes of Body Positions in Immobile Patients

Rauen

Schaffrath

Pradhan

et al. 2018

Sensors

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Mobilization, verticalization and position change are mandatory for severely affected neurological patients in early neurorehabilitation in order to improve neurological status and prevent complications. However, with the exception of hospitals and rehabilitation facilities, this activity is not usually monitored and so far the automated monitoring of position changes in immobile patients has not been investigated. Therefore, we investigated whether accelerometers on the upper trunk could reliably detect body position changes in immobile patients. Thirty immobile patients in early neurorehabilitation (Barthel Index ≤ 30) were enrolled. Two tri-axial accelerometers were placed on the upper trunk and on the thigh. Information on the position and position changes of the subject were derived from accelerometer data and compared to standard written documentation in the hospital over 24 h. Frequency and duration of different body positions (supine, sidelying, sitting) were measured. Data are presented as mean ± SEM. Groups were compared using one-way ANOVA or Kruskal-Wallis-test. Differences were considered significant if p < 0.05. Trunk sensors detected 100% and thigh sensors 66% of position changes (p = 0.0004) compared to standard care documentation. Furthermore, trunk recording also detected additional spontaneous body position changes that were not documented in standard care (81.8 ± 4.4% of all position changes were documented in standard care documentation) (p < 0.0001). We found that accelerometric trunk sensors are suitable for recording position changes and mobilization of severely affected patients. Our findings suggest that using accelerometers for care documentation is useful for monitoring position changes and mobilization frequencies in and outside of hospital for severely affected neurological patients. Accelerometric sensors may be valuable in monitoring continuation of care plans after intensive neurorehabilitation.

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“…The activPAL micro (PAL Technologies Ltd. 2019) is a small and slim activity monitor that includes a 3-axis accelerometer (see Table 1). It is used quite often in clinical, older aged residents and hospital trials (Chan et al 2017). The activPAL software allows setting the start time for recording, while the device is plugged in the docking station.…”

Section: Wearablesmentioning

confidence: 99%

Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

et al. 2020

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Ageing is associated with a decline in physical activity and a decrease in the ability to perform activities of daily living, affecting physical and mental health. Elderly people or patients could be supported by a human activity recognition (HAR) system that monitors their activity patterns and intervenes in case of change in behavior or a critical event has occurred. A HAR system could enable these people to have a more independent life. In our approach, we apply machine learning methods from the field of human activity recognition (HAR) to detect human activities. These algorithmic methods need a large database with structured datasets that contain human activities. Compared to existing data recording procedures for creating HAR datasets, we present a novel approach, since our target group comprises of elderly and diseased people, who do not possess the same physical condition as young and healthy persons. Since our targeted HAR system aims at supporting elderly and diseased people, we focus on daily activities, especially those to which clinical relevance in attributed, like hygiene activities, nutritional activities or lying positions. Therefore, we propose a methodology for capturing data with elderly and diseased people within a hospital under realistic conditions using wearable and ambient sensors. We describe how this approach is first tested with healthy people in a laboratory environment and then transferred to elderly people and patients in a hospital environment. We also describe the implementation of an activity recognition chain (ARC) that is commonly used to analyse human activity data by means of machine learning methods and aims to detect activity patterns. Finally, the results obtained so far are presented and discussed as well as remaining problems that should be addressed in future research.

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Measuring Activity Performance of Older Adults Using the activPAL: A Rapid Review

Cited by 23 publications

References 44 publications

A Pilot Randomised Clinical Trial of a Novel Approach to Reduce Sedentary Behaviour in Care Home Residents: Feasibility and Preliminary Effects of the GET READY Study

A Pilot Randomised Clinical Trial of a Novel Approach to Reduce Sedentary Behaviour in Care Home Residents: Feasibility and Preliminary Effects of the GET READY Study

Accelerometric Trunk Sensors to Detect Changes of Body Positions in Immobile Patients

Advanced Sensing and Human Activity Recognition in Early Intervention and Rehabilitation of Elderly People

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