Telemetric activity monitoring as an indicator of long-term changes in health and well-being of older people

Paavilainen, Paula; Korhonen, Ilkka; Partinen, Markku

doi:10.4017/gt.2005.04.02.003.00

Cited by 18 publications

(12 citation statements)

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“…CRS has also showed sensitivity to detect health changes in long-term monitoring [24]. In our data values 0.3 on average were associated with better functioning half, and 0.57 with worse functioning.…”

Section: Discussionmentioning

confidence: 63%

“…A c i r c a d i a n r h y t h m s t r e n g t h ( C R S ) v a r i a b l e w a s calculated by dividing average night-time activity (11 pm-5 am) by the average activity of the previous day (8 am-8 pm) for each 24-hour period [23,24]. Averages of these values from during the selected actigraphy periods have been used in the analysis.…”

Section: Actigraphic Data Analysis Proceduresmentioning

confidence: 99%

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Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents

Merilahti

Viramo

Korhonen

2016

IEEE J. Biomed. Health Inform.

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Sleep problems and disrupted circadian rhythms are common among older adults and may be associated with several health issues and physical functioning status. Wearable continuous monitoring of physical activity enables unobtrusive monitoring of circadian activity and sleep patterns. The objective of this retrospective study was to analyze whether physical functioning status (Activities of Daily Living assessment of Resident Assessment Instrument) is associated with diurnal activity rhythm and sleep patterns measured with wearable activity sensor in nursing home residents during their normal daily life. Continuous activity data were collected by the wearable sensor from 16 nursing home residents (average age of 90.7 years, seven demented subjects, one female) in their daily life over several months (12-18 months). The subjects' physical activity and sleep were quantified by several parameters from the activity data. In the cross-sectional analysis, physical functioning status was associated with the strength (RHO = 0.78, ) and the stability (RHO = 0.72, ) of the activity rhythm when the level of dementia was not controlled. In the longitudinal analysis (12-18 months), at an individual level the activity rhythm indices and activity level had the strongest correlations with changes in physical functioning but the associations were to some extent individual. In these long-term case recordings, decrease in the physical functioning was most strongly associated with decreasing levels of activity, stability, and strength of the activity rhythm, and with increasing fragmentation of rhythm and daytime passivity. Daily wearable monitoring of physical activity may hence reveal information about functioning state and health of older adults. However, since the changes in activity patterns implying changes in physical functioning status may not be consistent between the individuals, a multivariate approach is recommended for monitoring of these changes by continuous physical activity measurement.

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Section: Discussionmentioning

confidence: 63%

Section: Actigraphic Data Analysis Proceduresmentioning

confidence: 99%

Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents

Merilahti

Viramo

Korhonen

2016

IEEE J. Biomed. Health Inform.

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“…Paavilainen et al [2] conducted a survey among 16 residents of a long term care facility for elders. Using a proprietary activity detection system, they established an association between changes in actual health status and circadian activity rhythm.…”

Section: Related Workmentioning

confidence: 99%

“…Sarela et al [3] provide further details on their implementation and study. In our work we initially experimented with the same proprietary sensor used in [2,3], however due to its closed architecture we decided to switch to another sensor.…”

Section: Related Workmentioning

confidence: 99%

Fast matching of sensor data with manual observations

Biswas

Jayachandran

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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In systems and trials concerning wearable sensors and devices used for medical data collection, the validation of sensor data with respect to manual observations is very important. However, this is often problematic because of feigned behavior, errors in manual recording (misclassification), gaps in recording (missing readings), missed observations and timing mismatch between manual observations and sensor data due to a difference in time granularity. Using sleep activity pattern monitoring as an example we present a fast algorithm for matching sensor data with manual observations. Major components include a) signal analysis to classify states of sleep activity pattern, b) matching of states with Sleep Diary (SD) and c) automated detection of anomalies and reconciliation of mismatches between the SD and the sensor data.

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“…Paavilainen et al [2] conducted a survey among 16 residents of a long term care facility for elders. Using the Vivago activity detection system, consisting of wrist-worn sensors and proprietary RF base stations, they establish an association between changes in actual health status and circadian activity rhythm.…”

Section: Related Workmentioning

confidence: 99%

An Extensible System for Sleep Activity Pattern Monitoring

Biswas

Jayachandran

et al. 2007

2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information

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For home based health-care and wellness management the considerations of extensibility and personalization are very important. In this paper we present an architectural framework for health-care and wellness management, and illustrate its use with a sleep activity pattern monitoring application. The key benefits of our architectural framework are that new sensors and sensing modalities as well as other hardware devices may be readily incorporated in an incremental manner. Software modules, such as signal processing algorithms and self-help oriented User Interfaces may be easily added, and the responses can be personalized and customized to suit the needs of patient, caregiver or doctor.

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Telemetric activity monitoring as an indicator of long-term changes in health and well-being of older people

Cited by 18 publications

References 0 publications

Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents

Wearable Monitoring of Physical Functioning and Disability Changes, Circadian Rhythms and Sleep Patterns in Nursing Home Residents

Fast matching of sensor data with manual observations

An Extensible System for Sleep Activity Pattern Monitoring

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