Exploring patterns of daily physical and sedentary behaviour in community-dwelling older adults

Lord, Sue; Chastin, Sébastien; McInnes, Lynn; Little, Linda; Briggs, Pam; Rochester, Lynn

doi:10.1093/ageing/afq166

Cited by 120 publications

(169 citation statements)

References 23 publications

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“…An 'ambulation bout' was defined as any 15-second data epoch with at least two steps 4,12,13 . Volume of ambulation activity was determined using measures of total number of steps per day and total time in minutes per day spent sitting/lying, standing, walking and in upright positions.…”

Section: Measures Of Ambulation Activitymentioning

confidence: 99%

“…'Ambulation activity bout' metrics provide detailed information regarding variations in activity across the day 12,13 and are used to investigate characteristics such as the frequency and intensity of ambulation activity after stroke [11][12][13] . To date, only one study has investigated changes in ambulation activity bouts over time, and this was limited to the first six weeks following stroke 4 .…”

Section: Introductionmentioning

confidence: 99%

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Recovery of ambulation activity across the first six months post-stroke

2016

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word count: 245 Word count: 2754 Highlights Stroke survivors increase walking activity over the first 6 months post-discharge.  Stroke survivors increase daily sitting time over the first 6-monts post-discahrge.  Stroke survivors spend least time in long duration and high intensity activity.  Interventions to target activity should be considered 3-months post-discharge. AbstractStroke survivors commonly adopt sedentary activity behaviours by the chronic phase of recovery. However, the change in activity behaviours from the subacute to chronic phase of stroke is variable. This study explored the recovery of ambulation activity (volume and bouts) at one, three and six months after hospital discharge post-stroke. A total of 42 stroke survivors were recruited at hospital discharge and followed up one, three and six months later. At follow-up, ambulation activity was measured over four days using the ActivPAL TM accelerometer. Measures included volume of activity and frequency and intensity of ambulation activity bouts per day. Linear mixed effects modelling was used to determine changes over time. There was wide variation in activity. Total step counts across all time points were below required levels for health benefits (mean 4592 SD 3411). Most activity was spread across short bouts. While most number of bouts was of low intensity, most time was spent in moderate intensity ambulation across all time points. Daily step count and time spent walking and sitting/lying increased from one month to three and six months. The number of and time spent in short and medium duration bouts increased from one to six months. Time in long duration bouts increased at three months only. Time spent in moderate intensity ambulation increased over time. No change was observed for any other measures. In future, it would be valuable to identify strategies to increase engagement in activity behaviours to improve health outcomes after stroke.

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Section: Measures Of Ambulation Activitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recovery of ambulation activity across the first six months post-stroke

2016

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“…It is encouraged that the older adults are engaged in physical activity to optimize their health outcomes as activity level has been shown to be associated with survival rate, independence, and quality of life [13]. Physical activity often diminishes as age increases, especially in older women [14].…”

Section: Related Workmentioning

confidence: 99%

Prediction of mobility entropy in an Ambient Intelligent environment

Chernbumroong

Lotfi

Langensiepen

2014

2014 IEEE Symposium on Intelligent Agents (IA)

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Abstract-Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understanding human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term.In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues.

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“…The activPAL accelerometer has also been used to determine habitual behaviour, whilst also determining the interplay between sedentary behaviour and periods of physical activity [35].…”

Section: Capturing Human Digital Memory Datamentioning

confidence: 99%