Method Article

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

DOI:

10.3791/56942

July 27th, 2018

In This Article

Summary

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Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

Abstract

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An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

Introduction

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Prevailing clinical research is fraught with limitations in the reliability and validity of data captured because of inherent shortcomings of assessment methodology. Interviews are constrained by the times when the clinician and patient can coordinate schedules. Time allotted for examinations is limited by what the volunteer can reasonably be asked to do in a single session. These brief, widely spaced sessions - even if augmented by occasional telephone calls or Internet queries - severely limit the potential to detect meaningful change in function or wellbeing over time. Current test sessions are largely composed of requests for information that can be difficult to r....

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Protocol

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All participants provided written informed consent. Life Laboratory participants are asked to live their lives as they normally would to allow longitudinal observational study of their life activities and patterns for the rest of their lives. They may withdraw at any time if they wish. The study protocol was approved by the Oregon Health & Science University (OHSU) Institutional Review Board (Life Laboratory OHSU IRB #2765).

1. Preparation

  1. Prior to deployment, add all equipment to the Console inventory system online. Assign a name to each device or sensor along with its serial number and MAC address, allowing it to be st....

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Results

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The ORCATECH suite of technology makes it possible to collect a uniquely rich data set about the life patterns of people as they go about their usual activities. The sensor system allows unobtrusive and continuous monitoring of the volunteers in their own homes. The system has been used in dozens of studies involving hundreds of volunteers in research examining key domains of health and function such as walking speed and mobility, medication-taking behavior, mood, time in or out of home, .......

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Discussion

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We have described a basic system or platform enabling home- and community-based remote sensing and reporting of salient health and wellbeing measures on a continuous basis. The system is intended to be used primarily in research at this time.

Where possible, the system uses open source tools and sensors or devices taking advantage of available APIs and software development kits (SDK). The system is designed to be technology "agnostic" such that a wide variety of sensors or devices can .......

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Disclosures

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The authors have nothing to disclose.

Acknowledgements

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The research described here was supported by grants from the National Institutes of Health, National Institute on Aging (U2CAG054397, P30 AG024978, P30 AG008017, R01 AG042191, R01 AG024059), Intel, the Foundation for the National Institutes of Health and the Robert Wood Johnson Foundation.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Raspberry Pi 3 Model BRaspberry Pi FoundationRaspberry Pi 3 Model B
Motion SensorNYCE Sensors IncNCZ-3041-HA
Door/Window SensorNYCE Sensors IncNCZ-3011-HA
Curtain Motion SensorNYCE Sensors IncNCZ-3045-HA
iSortTimerCapiSort
Home Stealth USB Phone RecorderFihoFi3001B
Automatic ProAutomaticAUT-350C
Body Cardio ScaleNokiaWBS04
Activite/Steel Activity MonitorNokiaHWA01 STEEL
Alta 2FitbitFB406
Charge 2FitbitFB407
Flex 2FitbitFB403
Zigbee USB StickSilicon LabsETRX3USB
WorkTimeNestersoftWorkTime Corporate

References

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  1. Peetoom, K. K., Lexis, M. A., Joore, M., Dirksen, C. D., De Witte, L. P. Literature review on monitoring technologies and their outcomes in independently living elderly people. Disabil Rehabil Assist Technol. 10 (4), 271-294 (2015).
  2. Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A., Rios Rincon, A.

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Tags

Remote Activity MonitoringHealth Data CollectionPassive Infrared SensorsDoor Contact SensorsWearable ActigraphsMedication Adherence TrackingWalking Speed VariabilityCognitive Function AssessmentHome Based Sensor DeploymentEcological Momentary Assessment

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