Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications

Taraldsen, Kristin; Chastin, Sébastien; Riphagen, Ingrid I.; Vereijken, Beatrix; Helbostad, Jorunn L.

doi:10.1016/j.maturitas.2011.11.003

Cited by 166 publications

(127 citation statements)

References 44 publications

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“…However, few studies have utilized objective measurements using accelerometry to evaluate cardiovascular risk in older adults. The benefits of using accelerometry as an objective means to measure activity levels in older adults have been previously explored 8, 9. Most notably, these include triaxial measurement of movement in 3 orthogonal directions, so as to capture both linear and rotational movements, and measurement of both duration and intensity of activity.…”

Section: Introductionmentioning

confidence: 99%

Association of Accelerometry‐Measured Physical Activity and Cardiovascular Events in Mobility‐Limited Older Adults: The LIFE (Lifestyle Interventions and Independence for Elders) Study

Cochrane

Chen

Fitzgerald

et al. 2017

JAHA

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BackgroundData are sparse regarding the value of physical activity (PA) surveillance among older adults—particularly among those with mobility limitations. The objective of this study was to examine longitudinal associations between objectively measured daily PA and the incidence of cardiovascular events among older adults in the LIFE (Lifestyle Interventions and Independence for Elders) study.Methods and ResultsCardiovascular events were adjudicated based on medical records review, and cardiovascular risk factors were controlled for in the analysis. Home‐based activity data were collected by hip‐worn accelerometers at baseline and at 6, 12, and 24 months postrandomization to either a physical activity or health education intervention. LIFE study participants (n=1590; age 78.9±5.2 [SD] years; 67.2% women) at baseline had an 11% lower incidence of experiencing a subsequent cardiovascular event per 500 steps taken per day based on activity data (hazard ratio, 0.89; 95% confidence interval, 0.84–0.96; P=0.001). At baseline, every 30 minutes spent performing activities ≥500 counts per minute (hazard ratio, 0.75; confidence interval, 0.65–0.89 [P=0.001]) were also associated with a lower incidence of cardiovascular events. Throughout follow‐up (6, 12, and 24 months), both the number of steps per day (per 500 steps; hazard ratio, 0.90, confidence interval, 0.85–0.96 [P=0.001]) and duration of activity ≥500 counts per minute (per 30 minutes; hazard ratio, 0.76; confidence interval, 0.63–0.90 [P=0.002]) were significantly associated with lower cardiovascular event rates.ConclusionsObjective measurements of physical activity via accelerometry were associated with cardiovascular events among older adults with limited mobility (summary score >10 on the Short Physical Performance Battery) both using baseline and longitudinal data.Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01072500.

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Section: Introductionmentioning

confidence: 99%

Association of Accelerometry‐Measured Physical Activity and Cardiovascular Events in Mobility‐Limited Older Adults: The LIFE (Lifestyle Interventions and Independence for Elders) Study

Cochrane

Chen

Fitzgerald

et al. 2017

JAHA

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“…Today, most common way of measuring the physical activity is by using accelerometer technology i.e. motion sensors are mounted on the users' wrists, waist, and ankles [19], [20], [21]. However, using accelerometers, physical activity can be measured while persons are performing regular household work but composite activities (i.e.…”

Section: Introductionmentioning

confidence: 99%

Physical Activity Identification using Supervised Machine Learning and based on Pulse Rate

Ahmed¹,

Loutfi²

2013

IJACSA

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Abstract-Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly's physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a CaseBased Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate.

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“…In the case of self-report, it is acknowledged that tailored instruments are required, depending on participant features, for example age, as some activities may be more relevant in certain age groups, and cognitive development, which may affect recall accuracy. 18 However, researchers suggest that tailoring may also be required for objective monitoring, because physical characteristics, behaviours and activity patterns 19,20 may affect adherence to the measurement protocol, as well as interpretation of outputs. 21 Among objective measures, accelerometers and inclinometers are accurate for measuring PA levels and postures.…”

Section: Research Overview Contextmentioning

confidence: 99%

“…As a consequence, various algorithms for data processing and predictive equations for EE have been developed in recent years, each with their own strengths and limitations. 19,20,25 Among the accelerometers, different models of ActiGraph (AG;…”

Section: Research Overview Contextmentioning

confidence: 99%

“…26 However, valid methods for measuring sedentary behaviour and PA in specific age-groups, such as older adults, are still lacking. 20 Cut-points for interpreting AG accelerometer data have been developed using various equations and used in numerous large-scale studies. However, this approach has some limitations, as activities are defined in total counts in a predetermined time interval, which may only partially represent the activity under observation.…”

Section: Research Overview Contextmentioning

confidence: 99%

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Objective and subjective methods for measuring physical activity and sedentary behaviour in older adults

Aguilar-Farías¹

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The studies presented in this thesis highlight important issues relating to the measurement of physical activity (PA) and sedentary behaviour (SB) in older adults, especially relating to the transfer of these methods to free-living environments.Study One examined cut-points for ActiGraph GT3X+ (AG) for estimating time spent in SB in older adults in free-living environments. ActivPAL 3 ™ (AP) was used as the reference standard.Movement patterns were measured in 41 older adults over seven consecutive days with both AG and AP. The results suggested that different cut-points must be used in older adults. In the case of older adults when using 1-minute epochs, for example, 25 counts/minute must be considered when using the AG vertical axis only, or 200 counts/minute when using the three axes of the accelerometer. different activities were assessed. It was concluded that when selecting an equation to estimate total daily EE from accelerometer data, equations with more accurate estimates of sedentary behaviour might be preferred. However, when the main interest is to estimate total time spent in different physical activity levels, a method with higher agreement for categorising activities may be preferred. Study Two ConclusionThe work contained in this thesis will help researchers to 1) implement more accurate tools in the surveillance of PA and SB in larger studies, 2) better understand the strengths and limitations of current predictive methods when applying them in free-living environments, and 3) improve interpretation and translation of energy expenditure measures in older adults.

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Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: A systematic literature review of current knowledge and applications

Cited by 166 publications

References 44 publications

Association of Accelerometry‐Measured Physical Activity and Cardiovascular Events in Mobility‐Limited Older Adults: The LIFE (Lifestyle Interventions and Independence for Elders) Study

Association of Accelerometry‐Measured Physical Activity and Cardiovascular Events in Mobility‐Limited Older Adults: The LIFE (Lifestyle Interventions and Independence for Elders) Study

Physical Activity Identification using Supervised Machine Learning and based on Pulse Rate

Objective and subjective methods for measuring physical activity and sedentary behaviour in older adults

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