Photo by Steven HWG on Unsplash
Our group is collaborating with the other researchers at the UW, Seattle VA Hospital, Cleveland Clinic, and the Mayo Clinic on a new study — Technology for Early Diagnosis of Dementia (TEDD) — to use sensor technologies to improve early diagnosis of Alzheimers Disease and Dementia with Lewy Bodies. Early discimination of individuals on the trajectory to these two different diseases may lead more appropriate treatments to improve outcomes.
Photo by Luke Chesser on Unsplash
This work leverages our group’s previous work with wearable motion and physiologic sensors. In the current study we will be collaborating with experts in sleep monitoring and cognitive assessments.
The Principal Investigator, Dr. Debby Tsuang will lead this study funded by the NIH National Institutes of Aging.
The study will recruit participants from sites in different locations in the US, who have either Alzhiemer’s Disease or Dementia with Lewy Bodies, who will use a variety of sensors which will provide potential valuable data useful in disciminating the mobility, sleep, and other quality of life indicators of older adults who have these diseases. In a later phase of the study, we will use this information to examine a larger cohort of adults with mild cognitive impairment, and track the assocations between their cognitive decline over time and various sensor data.
Our group is finishing a new diet and nutrition-tracking app called Bitesome. Similar to the many diet-tracking apps that already exist for smartphones, the new app allows people to track the foods they each. But, somewhat differently, Bitesome tracks numerous factors that help public health and nutrition scientists better understand the context the underlies diet.
Bitesome utilizes sensors on the smartphones to better understand dietary context. This includes, GPS, motion, and camera data. Not only can researchers see when and where meals occur, but the nutritional content of each food item, query the neighborhood food environment, observe the physical activity that occurred before and after meals, etc.
Moreover, to better support science, researchers can access real-time Bitesome data from participants enrolled in research studies using a secure web portal. The website will support researchers by providing data reports for subjects in the study.
Bitesome is currently undergoing testing. The app will be available for iOS iPhones and Android smartphones in early 2018, starting with deployment in the ENACTS hypertension intervention study in Seattle — a new study funded by the National Institutes of Health, which is focused on minority health disparities.
The new Bitesome app builds upon previous smartphone app development in our research group, including past and ongoing studies that have used our CalFit smartphone app.
National Vital Statistics System, CDC, and the U.S. Census Bureau
Hypertension is a leading risk factor for cardiovascular disease, affecting 1 in 3 US adults or about 78 million people. It is very common as well as understudied in American Indians, Alaska Natives, and Native Hawaiians and Pacific Islanders. A new center called Native-Controlling Hypertension and Risk Through Technology (Native-CHART) will bring together academic and community partners, who will conduct research and outreach to improve hypertension in these populations.
Native-CHART is one of two new centers funded by the NIH National Intitutes of Minority Health and Health Disparities (NIMHD) that together will share $20 million over the next 5 years. Native-CHART is co-led by Dedra Buchwald at Washington State University and Spero Manson at the University of Colorado. The center will have broad reach across Native populations, including Satellite Centers in Alaska and the Pacific Northwest, Rocky Mountain, Plains, Southwest, Northern, and Southeast regions.
New technologies will play a key role in Native-CHART, both as tools for research, as well as tools for engaging with groups in the center. For instance, mobile technologies will play a role in collecting objective data on a variety of risk factors including diet, food environments, and physical activity for hypertension. Dr. Edmund Seto’s group will help develop mobile apps that track aspects of cardiovascular risk for the center.
The European PHENOTYPE study is investigating the relationship between the natural environment and human health and wellbeing. The study led by Mark Nieuwenhuijsen at ISGlobal (previously CREAL) aims to explore people’s exposures to greenspace through the use of a variety of assessment methods.
One approach that was used in a subset of approximately 400 PHENOTYPE study subjects in four European countries was an Android smartphone app. The app, CalFit was developed by my research group explicitly for exposure assessment studies. The app tracks time-location patterns using GPS and physical activity using accelerometry. Different versions of the CalFit app were developed to fit the needs of different environmental health studies, including studies in the US and in China. For the PHENOTYPE study an Ecological Momentary Assessment (EMA) module was developed that asked study subjects to answer occasional questions about their affective state (e.g., stress, happiness, etc.) in different environments. The EMA also prompts subjects to collect videos of their surroundings.
The app was designed for Android version 2.3.6, and was only deployed on Samsung Galaxy Y study phones. Since development of the CalFit app was carried out a few years ago, no testing on current generation Android phones has been done, and it is unclear whether the app still operates as designed with newer versions of Android or newer phones. The app is available here, without support and with minimal documentation:
CalFit Program v20130716 with Dutch, English, Lithuanian, and Spanish PHENOTYPE surveys
While CalFit is no longer being supported, my group is developing a new app from scratch to leverage the current capabilities of smart devices. It will be released under a new name, and with a new focus not only on environmental exposure assessment, but on behavioral health. Look for this new app to be used in the NIH “Native-Controlling Hypertension and Risk Through Technology (NATIVE-CHART)” study led by Dedra Buchwald at Washington State University.
If you have interest in using the new app, please contact me: Edmund Seto firstname.lastname@example.org
Just heard, NIH NHLBI will fund a new study with collaborators from USC (Genevieve Dunton, Mary Ann Pentz, and Chih-Ping Chou), UC Berkeley (Michael Jerrett), and Northeastern (Stephen Intille), and UW (Edmund Seto) to develop new statistical modeling approaches to analyze large data from Ecological Momentary Assessment (EMA) studies.
This new study builds upon the work we’ve done with the CalFit system, using smartphone-based EMAs to study the associations between mood, physical activity, and a person’s environment.
Working with researchers from University of North Carolina Chapel Hill and the Shanghai CDC, we just finished fieldwork for a study of beverage consumption using the CalFit smartphone system.
We used the CalFit smartphone application to conduct surveys of beverages consumed by everyday people living in and around the city in Shanghai. The study will compare beverage consumption recorded by phone versus methods used in the Chinese National Health and Nutrition Survey. Additionally, the CalFit system will provide information on the spatial and temporal patterns of beverages consumed across urban and rural neighborhoods.
CalFit EMA allows researchers to conduct Ecological Momentary Assessments (EMAs) using smartphones.
EMAs are short surveys — a few questions — that occur during random times during the day. They measure in-the-moment actions, emotions, and location. The use of smartphones for EMA takes advantage of the fact that many people are already used to and comfortable with carrying their phone, and responding during the day to phone calls, text messages, emails, calendar events, etc. Mobile EMAs capture what traditional paper questionnaires and direct observation studies cannot because researchers cannot following subjects for long periods of time (and even if they did so they would likely alter the subjects’ behaviors).
CalFit EMA is part of the CalFit system of apps. CalFit EMA together with CalFit D (a time-location and physical activity tracker) provides rich contextual sensing of an individuals health.
CalFit EMA is extremely flexible. It has these features:
- Runs on inexpensive Android 2.3.3+ phones (e.g., ~$130 Samsung Galaxy Y phone).
- Completely customizable questionnaires.
- Can be used with or without a SIM-activated Android phone.
- Using a SIM-activited phone with a mobile data plan allows for real-time data uploading to an Internet server. If used without a SIM, data are simply stored on the phone’s memory card, and researchers can download the data later.
- The phone logs times and locations when EMAs are completed allowing for studies of both temporal and spatial contextual factors.
CalFit EMA was developed by Dr. Seto through a grant from the NIH NIEHS R01ES020409.
If interested in using CalFit EMA, contact Dr. Seto.
According to the National Institutes of Health, nearly 26 million people in the U.S. have diabetes (8.3% of the population). Over 200,000 persons less than 20 years old have either type 1 or 2 diabetes.
Working with the Center of Excellence in Diabetes & Endocrinology (CEDE) in Sacramento (Dr. Prakasam), researchers from UC Berkeley (Dr. Seto) and Aalborg University in Denmark (Dr. Hejlesen and Visiting PhD Student Jensen) are studying the efficacy of continuous glucose monitors (CGMs) among insulin pump users. In this randomized clinical trial, one group of diabetics will receive regular notifications when their CGMs detect low levels of interstitial fluid glucose versus a comparison group that will not. Study subjects will be closely monitored using the CalFit smartphone system, which will record their physical activity levels and their dietary intake of carbohydrates.
This study is supported by the Danish Agency for Science, Technology and Innovation (DASTI).
Recently, PhD student, David Holstius and I organized a sensor build, inviting friends from within the EHS program and collaborators from EECS program to make prototypes of a particulate matter air pollution sensor.
The result of this was the creation of 15 monitors that have been shipped to China, where they are being used by Hilary Ong, a UCSF Pediatrics student who I am mentoring to conduct a study of pediatric obesity and its relationship to air pollution exposure. The study is being conducting in Kunming, where my other PhD student Jenna is doing her dissertation work on food environments.
We’re calling these prototypes PANDAs, befitting their first use in China. PANDA is an acronym for something I don’t remember. David has also created a companion software package called BAMBOO that processes data from the PANDAs. BAMBOO is probably also an acronym for something that I can’t remember.
Stay tuned for the results from our PANDA study.
Fun times in San Diego at Wireless Health 2012. We’ve really seen the field of wireless devices and sensors within the health field explode over the last few years in all directions: in the consumer market, research, clinical care, and public health.
To kick off, I showed a picture from 5 years ago of a researcher in my lab wearing something like 7 sensors all over — absolutely crazy, but cool in that we were able to collect all sorts of data on movement, location, and environmental exposure. It motivated the questions of my talk:
- Do all these sensors need to be “wearable”?
- Do all sensors need to be “automated”?
The talk was a good opportunity to demonstrate some of the recent work that Jenna is doing in China, along with EECS PhD student, Victor Shia using CalFit smartphones to comprehensively study obesity risk: continuous inobtrusive physical activity assessment using the smartphone’s accelerometry, diet assessment using phone videos, exposure to environmental stressors like air pollution and noise (e.g., our noise modeling work), locational context like overlaying GPS data on food environment (e.g., foodscoremap.com).
I finished by showing some results we’ve collected in China based on people’s self-report data using a Ecological Momentary Assessment (EMA)-like smartphone app on what makes for “Happy Meals”.