Tech Revolution: Monitoring and the Power of Real-Time Data
Fifteen years ago, Diane Mahoney, then at Hebrew SeniorLife’s Hebrew Rehabilitation Center, Roslindale, Massachusetts, conducted one of the earliest studies of home monitoring systems for aging. Mahoney and colleagues installed a suite of simple sensors in the homes of frail elders. Infrared motion detectors recorded when someone walked by. Contact sensors revealed when someone opened a medication drawer or refrigerator door. The system sent the data to the younger relatives of the participants, and triggered an alert if something out of the ordinary happened. In one case, an alert went out when an elderly man had been motionless for an unusually long time. His relative phoned to check on him, and when there was no answer, called the building manager. The manager entered the apartment to find the man unresponsive. The manager summoned aid. The man, who had fallen into a diabetic coma, survived.
This vignette illustrates the potential of home monitoring technology to improve care. Mahoney, now at MGH Institute of Health Professions, Boston, notes that in the early days, many in the field questioned whether older adults would use the technology. Her research has allayed that concern. “If the technology is designed in a way that is perceived to be valuable to elders and caregivers, they will adopt it and be generally satisfied with the outcomes,” Mahoney said (see also Mahoney et al., 2008, and Mahoney, 2010).
Today, such monitoring systems are commercially available from companies, and in use in many retirement and assisted-living communities. A leader in this field is Jeffrey Kaye, who directs the Oregon Center for Aging and Technology (ORCATECH) in Portland. Kaye is interested in the potential of home monitoring systems to provide real-time, objective data on how a person’s functional and cognitive status changes over time. He believes this would produce more reliable, sensitive information than do standard clinical assessments. Current clinical assessments are done infrequently, cannot take into account whether a cognitively impaired person is having a good day or a bad day, and require the person to recall past activities. The upshot is that these assessments inaccurately reflect the person’s day-to-day experience, Kaye said.
To track functional and cognitive changes in elderly people, Kaye and colleagues use a system that includes not only motion, contact, and medication sensors, as well as load sensors that detect someone lying on a bed, but also monitors social activity. This includes telephone sensors that record the amount of time spent on the phone and whether the call was likely to be personal. Computer algorithms look for words used more frequently in business calls, such as “eligibility” or “representative,” versus words used mostly in casual conversations, such as “adorable,” “biscuits,” or “casinos.” People also tend to speak more slowly during business calls, Kaye said. Software programs count time spent on the computer in video chatting or e-mail. Motion sensors track time spent out of the house, and voice sensors record time spent talking aloud, which typically reflects face-to-face conversations with other people. The sensors require no attention from the participants. Pre-empting Big Brother concerns, they do not record calls or conversations, only the minutes spent talking with others. This system was a finalist in the Alzheimer’s Challenge 2012 (the iChange team). Kaye and colleagues have been testing it in more than 250 homes for over three years.
The scientists found that most of the measures of functional and cognitive status drop off over time in people with mild cognitive impairment (MCI), but not in cognitively healthy elderly. Intriguingly, increased day-to-day variability emerged as one of the earliest signs of decline, perhaps reflecting the fact that people in the early stages of AD tend to have “bad days” and “good days,” Kaye said. They cannot stick to their daily routines anymore. In particular, the researchers have found that higher daily variations in walking speed predict cognitive decline (see Dodge et al., 2012). This system detects change in early MCI with greater sensitivity than current clinical methods, Kaye claimed, hence, is suitable to provide better outcome measures for clinical trials. Another advantage for researchers is that a complete home monitoring system costs fairly little—about $1,000. It provides data for years, Kaye pointed out. In comparison, an amyloid PET scan can run from $3,000 to $6,000. “There has been tremendous interest from the pharmaceutical industry in this methodology,” Kaye said.
Many other groups are devising improvements on home monitoring. For example, Ho Ting Cheng and Weihua Zhuang at the University of Waterloo, Ontario, Canada, have developed a Bluetooth-enabled home monitoring system that performed well in an early feasibility study (see Cheng and Zhuang, 2010). Stephen Bonasera at the University of Nebraska Medical Center, Omaha, in collaboration with Lance Perez at the University of Nebraska-Lincoln, works on motion sensors that use radio-frequency identification (RFID). Participants stick a small RFID tag on their clothing, and every time the circuit passes through a sensor, the sensor logs it. An advantage to this system is that it could be used to independently track more than one person in the same home, because each person would wear a unique tag, Bonasera said. He is currently validating the approach.
Bonasera believes real-time monitoring could transform clinical trials. To bring down costs, researchers need a cheap, passive, continuous monitoring system that stays with a person at all times, he said. The ideal device for this already exists, he believes. It is the smartphone. The accelerometer—tiny silicon springs that move in accordance with gravity (see video)—that allows the device to switch from portrait to landscape mode also enables it to collect data on a person’s movements simply by being carried in a pocket. In addition, the phone’s GPS tracks the user’s movement within the community. The extent of this movement defines a person’s “lifespace.” Lifespace tends to shrink as AD approaches, and larger lifespace has been shown to predict better health outcomes, Bonasera notes. The smartphone can be programmed to send monitoring data to a central computer at regular intervals. This methodology could be easily scaled up to follow thousands of people in a trial, Bonasera suggested.
With collaborators Katrin Schenk at Randolph College, Lynchburg, Virginia, and Evan Goulding at Northwestern University, Evanston, Illinois, Bonasera has tested the validity of the accelerometer and GPS data on 100 participants. The technology provides a more accurate measure of people’s activities than keeping a journal does. It can measure factors such as a person’s walking speed and the number of footsteps they take, Bonasera said. He now wants to prove that this methodology can work in a clinical trial. He plans to enroll 20 participants with mild or moderate AD who are about to start taking cholinesterase inhibitors. Bonasera will record a month of data before they start medication, and compare the measures to a month of data after they start. Cholinesterase inhibitors modestly improve cognition and daily coping in people at that stage of disease. Bonasera hopes that, by using the continuous activity data, he can show a statistically significant improvement in daily functioning in a much smaller group of participants than researchers would normally need for a clinical trial. “If we can prove this concept, pharmaceutical companies and organizations might be interested in using this technology. If we can make clinical trials cheaper and enroll fewer patients, we can do more trials and test more agents,” Bonasera said.
Other groups are also taking advantage of the ubiquity of cell phones. Ginger.io, a startup company with offices in San Francisco, California, and Cambridge, Massachusetts, has developed a mobile phone app that tracks people’s health through a combination of passive recording (location, movement, phone use) and active self-reports. The information goes to the participants’ healthcare providers to help manage and improve their health. The company is customizing the app for chronic diseases such as diabetes and chronic pain. Sai Moturu leads a project to tailor the software to Alzheimer’s disease, for example, by adding cognitive tests. This project won the Alzheimer’s Challenge 2012 (see ARF related news story).
Moturu noted that designing the AD app presents special challenges because users will be cognitively impaired. For example, AD patients may be unable to fill out self-reports, leading to a greater reliance on passively collected data in this population. People with AD may forget to carry their cell phones, in which case it will not collect any information. Another issue is patient privacy, said Sabih Mir at Ginger.io. For the company’s other apps, patients choose to participate, retain control of their own data, and share it only with their healthcare provider. This reassures patients that their health information will not be used against them by insurers or employers, for example. AD patients may be unable to manage their own data, and need help from caregivers or healthcare professionals. Because of these obstacles, Mir could not put a timeframe on when the AD app will come on the market.
Mahoney raised the privacy concern as well. In her initial trials, caregivers resisted cameras in the home. However, after using the system for a time, “people seemed to develop a trust in us and in this technology,” Mahoney said. Users then recommended adding cameras to help determine whether an alert was real or a false alarm. “The key to it all is that the caregivers controlled whether the cameras were on or off. That seemed to allay privacy concerns.” For their part, seniors became willing to trade privacy for being able to age in place, Mahoney said. “Elders tend to embrace technology that enables them to stay in the environment they prefer.”
For seniors living at home, doctors would like to have a quick, simple way to keep tabs on their cognitive status. The Technology Research for Independent Living (TRIL) Centre, based in Dublin, Ireland, has developed a technological approach to do this. In a project called “Dear Diary,” overseen by Richard Reilly at Trinity College Dublin, participants receive written material in the mail, then phone in to a central computer system. Participants read aloud passages of text and answer questions from their written packet. Voice processing software then breaks down not what they say, but how they say it, TRIL academic director Brian Caulfield told Alzforum. For example, people with cognitive impairment pause more during speech, speak more flatly, and have more difficulty describing a picture than do cognitively normal people. “The remote cognitive assessment methodology performs extremely well when you compare the results against a standard Mini-Mental State Exam as performed by a healthcare professional,” Caulfield said. “It is a low-cost, easily deployable system for tracking a person’s baseline cognitive function. That means we will be able to identify deviations from that baseline more easily than in the past, and therefore will be able to trigger earlier interventions.”
Automated cognitive assessment by telephone. © TRIL Centre
Are projects such as these only the beginning of how technology will transform assessment and monitoring of cognitively impaired people? Mahoney sees potential but voices caveats as well. As technology advances to monitor more variables, she foresees an information glut that could trigger too many false alarms. “Caregivers don’t want more data; they want meaningful data. They don’t like false alerts,” she said. As monitoring systems become commonplace and offered by telecommunications companies or home security providers, installers need to be properly trained on how to deal with cognitively impaired people. Having a stranger come into the home to install technology can be traumatic for someone with dementia, Mahoney pointed out.
At the same time, some fears about new technology have proved unfounded. In the early days, institutional review boards were concerned that installing monitoring equipment in the homes of seniors would isolate them socially, Mahoney said. Relatives would call less often because they could see via computer reports that their senior was doing all right, the thinking went. Instead, the opposite happened. Mahoney cited the example of a man who called his elderly father for 15 minutes every day during his lunch hour. The son used the time to ask his father if he had eaten, bathed, and taken his medicine. The father said, “Who wants their son calling up every day nagging?” Once the monitors were in place, the son could see that his father had no problems, and began to use his phone calls to discuss interesting topics, like the grandson’s baseball games. “The calls became more enjoyable,” Mahoney said. In addition, the older adults in her studies liked having the technology in their homes. “I’ve seen this over and over again. People love to try something contemporary. They talk about it in senior housing. Instead of isolating them, it can give them new ways of engagement.” For more on how technology can transform elder care, see Part 3.—Madolyn Bowman Rogers.
This is Part 2 of a four-part series. See also Part 1, Part 3, Part 4. Read a PDF of the entire series.
- Patient-Reported Outcomes Win Alzheimer's Challenge 2012
- Tech Revolution: Help or Hal? Smart Homes to Ease Elder Care
- Will Technology Revolutionize Dementia Diagnosis and Care?
- Tech Revolution: Behavioral and Cognitive Interventions
- Mahoney DM, Mutschler PH, Tarlow B, Liss E. Real world implementation lessons and outcomes from the Worker Interactive Networking (WIN) project: workplace-based online caregiver support and remote monitoring of elders at home. Telemed J E Health. 2008 Apr;14(3):224-34. PubMed.
- Mahoney DF. An Evidence-Based Adoption of Technology Model for Remote Monitoring of Elders' Daily Activities. Ageing Int. 2010 Sep 23;36(1):66-81. PubMed.
- Dodge HH, Mattek NC, Austin D, Hayes TL, Kaye JA. In-home walking speeds and variability trajectories associated with mild cognitive impairment. Neurology. 2012 Jun 12;78(24):1946-52. PubMed.
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