AppsFromResearch
ORCATECH Mobile App for Research icon

ORCATECH Mobile App for Research

Evidence Tier:DOCUMENTED

Published in academic literature

For:Researchers & AcademicsGeneral Public & EnthusiastsPatients & Caregivers

App Summary

The ORCATECH Mobile App for Research is part of a larger platform that allows researchers to continuously monitor the health and behavior of older adults, using phone data and in-home sensors to assess domains like social engagement, mobility, and sleep. An evaluation (N=301) of this privacy-protecting platform across diverse cohorts of older adults demonstrated its ability to capture long-term, real-world data on key wellness indicators. The authors conclude this system provides a sensitive method for health surveillance and clinical trial monitoring, offering a minimally obtrusive alternative to traditional in-person assessments.

App Screenshots

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Detailed Description

Functionality & Mechanism

Developed by the Oregon Center for Aging & Technology (ORCATECH), this application facilitates passive, longitudinal data collection on participant health and behavior. The system operates unobtrusively on a mobile device, monitoring call and text message metadata, including frequency and communication patterns, without recording content. Its architecture is engineered for privacy, anonymizing all data points through a unique identification number. The application does not require active user input, functioning as a continuous background data collection instrument for research.

Evidence & Research Context

  • The application is a component of the Collaborative Aging Research Using Technology (CART) research platform, a suite of technologies for unobtrusive, in-home monitoring of older adults.
  • The CART platform was deployed in a study involving 301 older adults from diverse cohorts to collect continuous, long-term data across multiple wellness domains.
  • Data from the broader platform inform algorithms that identify patterns in mobility, sleep, social engagement, and cognitive function for health surveillance and early disease detection.
  • Associated research details the scalable, technology-agnostic platform design, emphasizing data privacy, longevity, and sharability for the scientific community.

Intended Use & Scope

The ORCATECH Mobile App is designed exclusively for researchers as a data collection instrument within approved study protocols. Its primary utility is the passive, unobtrusive monitoring of behavioral metadata. The application does not provide clinical feedback, alerts, or real-time data analysis. It is intended for observational research and is not a diagnostic or interventional tool.

Studies & Publications

2 publications

Peer-reviewed research associated with this app.

Development/Design Paper

The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community

Beattie et al. (2020)

Describes the research-driven development of this app
Introduction: Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities. Methods: CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants' homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing. Results: The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (n = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160–169 min), physical mobility (e.g., mean daily transitions between rooms = 96–155), sleep (e.g., mean nightly sleep duration = 6.3–7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15–45%) were collected across cohorts. Conclusion: The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.
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Development/Design Paper

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

Kaye et al. (2018) · Journal of Visualized Experiments

Describes the research-driven development of this app
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.
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ORCATECH Mobile App for Research

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