Research is integral to the Personal Health Informatics doctoral degree, as students and faculty work together to find impactful solutions to today’s health and wellness challenges. The transition from centralized, provider-centric treatment to personalized, patient-focused care requires researchers to cross the boundaries of public health, medicine, and social and computer sciences. which in turn prepares students for excellence in a specific research area of personal health informatics. It is this interdisciplinary experience that sets the program apart.
Research is integral to the Personal Health Informatics doctoral degree, as students and faculty work together to find impactful solutions to today’s health and wellness challenges. The transition from centralized, provider-centric treatment to personalized, patient-focused care requires researchers to cross the boundaries of public health, medicine, and social and computer sciences. which in turn prepares students for excellence in a specific research area of personal health informatics. It is this interdisciplinary experience that sets the program apart.
Research is integral to the Personal Health Informatics doctoral degree, as students and faculty work together to find impactful solutions to today’s health and wellness challenges. The transition from centralized, provider-centric treatment to personalized, patient-focused care requires researchers to cross the boundaries of public health, medicine, and social and computer sciences. which in turn prepares students for excellence in a specific research area of personal health informatics. It is this interdisciplinary experience that sets the program apart.
Research is integral to the Personal Health Informatics doctoral degree, as students and faculty work together to find impactful solutions to today’s health and wellness challenges. The transition from centralized, provider-centric treatment to personalized, patient-focused care requires researchers to cross the boundaries of public health, medicine, and social and computer sciences. which in turn prepares students for excellence in a specific research area of personal health informatics. It is this interdisciplinary experience that sets the program apart.
PHI Faculty: Matthew Goodwin
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
PHI Faculty: Stephen Intille
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
PHI Faculty: Timothy Bickmore
The purpose this project is to design a relational agent that takes patient family medical history in preparation for a clinical consultation.
Collaborator(s): Boston University School of Public Health
PHI Faculty: Stephen Intille
Longitudinal sensor data collected passively from wearable activity monitors and mobile phones will transform behavioral science by allowing researchers to use “big data,” but at the person-level, to understand how behavior and related environmental exposures impact health outcomes and personalize health intervention and research. We propose to develop and test a system that permits typical mobile application game players to help scientists improve this type of data, by adding additional annotations that enrich the data, making it more useful for behavioral science and more amenable to automatic processing. This will help researchers to better understand how individual-level behaviors relate to health outcomes in current research studies that collect personal-level sensor data such as NHANES and the Women’s Health Study, and future big data ventures such as the new Precision Medicine Initiative.
Collaborators: Dinesh John (Northeastern University), Seth Cooper (Northeastern University)
Supported by: NIH
PHI Faculty: Stephen Intille
We are working on a variety of projects studying how to use mobile sensor data, especially from accelerometers, to detect physical activity (type, duration, and intensity), sedentary behavior, and sleep in adults and children.
Collaborator(s): EveryFit, Inc., Stanford Medical School
Supported by: NIH/NCI and NIH/NHLBI
PHI Faculty: Cody Dunne, Stephen Intille
Type 1 diabetes (T1D), also called insulin dependent diabetes and juvenile diabetes, is an autoimmune disease which afflicts 1.25 million Americans. There are 40 thousand new diagnoses each year, and almost half of those are children and adolescents under age 20 years. The costs for our healthcare system are enormous, estimated at $14 billion annually. T1D is incurable, and people with T1D are estimated to lose over 10 years from their life expectancy. We are designing, building, and evaluating interactive visualization tools to help T1D patients and their caregivers make treatment decisions. In these visualizations we are showing data we collect using multiple devices and have data on the patient blood glucose levels, insulin administered, food eaten, exercise, and stress to name a few. Our tools are used by patients to understand the trends between scheduled events such as mealtimes, bedtimes, and overnight as well as irregular events like periods of exercise, stress, and illness. With this information, patients will be able to make more informed changes to their treatment protocols.
Collaborator: Boston Children’s Hospital
PHI Faculty: Timothy Bickmore
The purpose of this project is to develop a conversational agent system that counsels terminally ill patients in order to alleviate their suffering and improve their quality of life. Although many interventions have now been developed to address palliative care for specific chronic diseases, little has been done to address the overall quality of life for older adults with serious illness, spanning not only the functional aspects of symptom and medication management, but the affective aspects of suffering. In this project, we are developing a relational agent to counsel patients at home about medication adherence, stress management, advanced care planning, and spiritual support, and to provide referrals to palliative care services when needed.
Collaborators: Boston Medical Center
Supported by: NIH National Institute for Nursing Research
PHI Faculty: Timothy Bickmore
Most persons with spinal cord injury (SCI) require training and support for self-care management to help prevent the development of serious secondary conditions after hospital discharge. However, adherence to self-care management behaviors is poor once training with a therapist has ended. We have designed a virtual coach system, in which a relational agent engages individuals with SCI in simulated face-to-face conversations at home, to provide support and motivate adherence to self-care guidelines.
Collaborators: Boston University School of Public Health
Supported by: Nielsen Foundation
PHI Faculty: Matthew Goodwin
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
PHI Faculty: Stephen Intille
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
PHI Faculty: Timothy Bickmore
The purpose this project is to design a relational agent that takes patient family medical history in preparation for a clinical consultation.
Collaborator(s): Boston University School of Public Health
PHI Faculty: Stephen Intille
Longitudinal sensor data collected passively from wearable activity monitors and mobile phones will transform behavioral science by allowing researchers to use “big data,” but at the person-level, to understand how behavior and related environmental exposures impact health outcomes and personalize health intervention and research. We propose to develop and test a system that permits typical mobile application game players to help scientists improve this type of data, by adding additional annotations that enrich the data, making it more useful for behavioral science and more amenable to automatic processing. This will help researchers to better understand how individual-level behaviors relate to health outcomes in current research studies that collect personal-level sensor data such as NHANES and the Women’s Health Study, and future big data ventures such as the new Precision Medicine Initiative.
Collaborators: Dinesh John (Northeastern University), Seth Cooper (Northeastern University)
Supported by: NIH
PHI Faculty: Stephen Intille
We are working on a variety of projects studying how to use mobile sensor data, especially from accelerometers, to detect physical activity (type, duration, and intensity), sedentary behavior, and sleep in adults and children.
Collaborator(s): EveryFit, Inc., Stanford Medical School
Supported by: NIH/NCI and NIH/NHLBI
PHI Faculty: Cody Dunne, Stephen Intille
Type 1 diabetes (T1D), also called insulin dependent diabetes and juvenile diabetes, is an autoimmune disease which afflicts 1.25 million Americans. There are 40 thousand new diagnoses each year, and almost half of those are children and adolescents under age 20 years. The costs for our healthcare system are enormous, estimated at $14 billion annually. T1D is incurable, and people with T1D are estimated to lose over 10 years from their life expectancy. We are designing, building, and evaluating interactive visualization tools to help T1D patients and their caregivers make treatment decisions. In these visualizations we are showing data we collect using multiple devices and have data on the patient blood glucose levels, insulin administered, food eaten, exercise, and stress to name a few. Our tools are used by patients to understand the trends between scheduled events such as mealtimes, bedtimes, and overnight as well as irregular events like periods of exercise, stress, and illness. With this information, patients will be able to make more informed changes to their treatment protocols.
Collaborator: Boston Children’s Hospital
PHI Faculty: Timothy Bickmore
The purpose of this project is to develop a conversational agent system that counsels terminally ill patients in order to alleviate their suffering and improve their quality of life. Although many interventions have now been developed to address palliative care for specific chronic diseases, little has been done to address the overall quality of life for older adults with serious illness, spanning not only the functional aspects of symptom and medication management, but the affective aspects of suffering. In this project, we are developing a relational agent to counsel patients at home about medication adherence, stress management, advanced care planning, and spiritual support, and to provide referrals to palliative care services when needed.
Collaborators: Boston Medical Center
Supported by: NIH National Institute for Nursing Research
PHI Faculty: Timothy Bickmore
Most persons with spinal cord injury (SCI) require training and support for self-care management to help prevent the development of serious secondary conditions after hospital discharge. However, adherence to self-care management behaviors is poor once training with a therapist has ended. We have designed a virtual coach system, in which a relational agent engages individuals with SCI in simulated face-to-face conversations at home, to provide support and motivate adherence to self-care guidelines.
Collaborators: Boston University School of Public Health
Supported by: Nielsen Foundation
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
The purpose this project is to design a relational agent that takes patient family medical history in preparation for a clinical consultation.
Collaborator(s): Boston University School of Public Health
Longitudinal sensor data collected passively from wearable activity monitors and mobile phones will transform behavioral science by allowing researchers to use “big data,” but at the person-level, to understand how behavior and related environmental exposures impact health outcomes and personalize health intervention and research. We propose to develop and test a system that permits typical mobile application game players to help scientists improve this type of data, by adding additional annotations that enrich the data, making it more useful for behavioral science and more amenable to automatic processing. This will help researchers to better understand how individual-level behaviors relate to health outcomes in current research studies that collect personal-level sensor data such as NHANES and the Women’s Health Study, and future big data ventures such as the new Precision Medicine Initiative.
Collaborators: Dinesh John (Northeastern University), Seth Cooper (Northeastern University)
Supported by: NIH
Type 1 diabetes (T1D), also called insulin dependent diabetes and juvenile diabetes, is an autoimmune disease which afflicts 1.25 million Americans. There are 40 thousand new diagnoses each year, and almost half of those are children and adolescents under age 20 years. The costs for our health care system are enormous, estimated at $14 billion annually. T1D is incurable, and people with T1D are estimated to lose over 10 years from their life expectancy. We are designing, building, and evaluating interactive visualization tools to help T1D patients and their caregivers make treatment decisions. In these visualizations we are showing data we collect using multiple devices and have data on the patient blood glucose levels, insulin administered, food eaten, exercise, and stress to name a few. Our tools are used by patients to understand the trends between scheduled events such as mealtimes, bedtimes, and overnight as well as irregular events like periods of exercise, stress, and illness. With this information, patients will be able to make more informed changes to their treatment protocols.
Collaborator: Boston Children’s Hospital
PHI Faculty: Matthew Goodwin
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
PHI Faculty: Stephen Intille
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
PHI Faculty: Timothy Bickmore
The purpose this project is to design a relational agent that takes patient family medical history in preparation for a clinical consultation.
Collaborator(s): Boston University School of Public Health
PHI Faculty: Stephen Intille
Longitudinal sensor data collected passively from wearable activity monitors and mobile phones will transform behavioral science by allowing researchers to use “big data,” but at the person-level, to understand how behavior and related environmental exposures impact health outcomes and personalize health intervention and research. We propose to develop and test a system that permits typical mobile application game players to help scientists improve this type of data, by adding additional annotations that enrich the data, making it more useful for behavioral science and more amenable to automatic processing. This will help researchers to better understand how individual-level behaviors relate to health outcomes in current research studies that collect personal-level sensor data such as NHANES and the Women’s Health Study, and future big data ventures such as the new Precision Medicine Initiative.
Collaborators: Dinesh John (Northeastern University), Seth Cooper (Northeastern University)
Supported by: NIH
PHI Faculty: Stephen Intille
We are working on a variety of projects studying how to use mobile sensor data, especially from accelerometers, to detect physical activity (type, duration, and intensity), sedentary behavior, and sleep in adults and children.
Collaborator(s): EveryFit, Inc., Stanford Medical School
Supported by: NIH/NCI and NIH/NHLBI
PHI Faculty: Cody Dunne, Stephen Intille
Type 1 diabetes (T1D), also called insulin dependent diabetes and juvenile diabetes, is an autoimmune disease which afflicts 1.25 million Americans. There are 40 thousand new diagnoses each year, and almost half of those are children and adolescents under age 20 years. The costs for our healthcare system are enormous, estimated at $14 billion annually. T1D is incurable, and people with T1D are estimated to lose over 10 years from their life expectancy. We are designing, building, and evaluating interactive visualization tools to help T1D patients and their caregivers make treatment decisions. In these visualizations we are showing data we collect using multiple devices and have data on the patient blood glucose levels, insulin administered, food eaten, exercise, and stress to name a few. Our tools are used by patients to understand the trends between scheduled events such as mealtimes, bedtimes, and overnight as well as irregular events like periods of exercise, stress, and illness. With this information, patients will be able to make more informed changes to their treatment protocols.
Collaborator: Boston Children’s Hospital
PHI Faculty: Timothy Bickmore
The purpose of this project is to develop a conversational agent system that counsels terminally ill patients in order to alleviate their suffering and improve their quality of life. Although many interventions have now been developed to address palliative care for specific chronic diseases, little has been done to address the overall quality of life for older adults with serious illness, spanning not only the functional aspects of symptom and medication management, but the affective aspects of suffering. In this project, we are developing a relational agent to counsel patients at home about medication adherence, stress management, advanced care planning, and spiritual support, and to provide referrals to palliative care services when needed.
Collaborators: Boston Medical Center
Supported by: NIH National Institute for Nursing Research
PHI Faculty: Timothy Bickmore
Most persons with spinal cord injury (SCI) require training and support for self-care management to help prevent the development of serious secondary conditions after hospital discharge. However, adherence to self-care management behaviors is poor once training with a therapist has ended. We have designed a virtual coach system, in which a relational agent engages individuals with SCI in simulated face-to-face conversations at home, to provide support and motivate adherence to self-care guidelines.
Collaborators: Boston University School of Public Health
Supported by: Nielsen Foundation
PHI Faculty: Matthew Goodwin
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
PHI Faculty: Stephen Intille
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
PHI Faculty: Timothy Bickmore
The purpose this project is to design a relational agent that takes patient family medical history in preparation for a clinical consultation.
Collaborator(s): Boston University School of Public Health
PHI Faculty: Stephen Intille
Longitudinal sensor data collected passively from wearable activity monitors and mobile phones will transform behavioral science by allowing researchers to use “big data,” but at the person-level, to understand how behavior and related environmental exposures impact health outcomes and personalize health intervention and research. We propose to develop and test a system that permits typical mobile application game players to help scientists improve this type of data, by adding additional annotations that enrich the data, making it more useful for behavioral science and more amenable to automatic processing. This will help researchers to better understand how individual-level behaviors relate to health outcomes in current research studies that collect personal-level sensor data such as NHANES and the Women’s Health Study, and future big data ventures such as the new Precision Medicine Initiative.
Collaborators: Dinesh John (Northeastern University), Seth Cooper (Northeastern University)
Supported by: NIH
PHI Faculty: Stephen Intille
We are working on a variety of projects studying how to use mobile sensor data, especially from accelerometers, to detect physical activity (type, duration, and intensity), sedentary behavior, and sleep in adults and children.
Collaborator(s): EveryFit, Inc., Stanford Medical School
Supported by: NIH/NCI and NIH/NHLBI
PHI Faculty: Cody Dunne, Stephen Intille
Type 1 diabetes (T1D), also called insulin dependent diabetes and juvenile diabetes, is an autoimmune disease which afflicts 1.25 million Americans. There are 40 thousand new diagnoses each year, and almost half of those are children and adolescents under age 20 years. The costs for our healthcare system are enormous, estimated at $14 billion annually. T1D is incurable, and people with T1D are estimated to lose over 10 years from their life expectancy. We are designing, building, and evaluating interactive visualization tools to help T1D patients and their caregivers make treatment decisions. In these visualizations we are showing data we collect using multiple devices and have data on the patient blood glucose levels, insulin administered, food eaten, exercise, and stress to name a few. Our tools are used by patients to understand the trends between scheduled events such as mealtimes, bedtimes, and overnight as well as irregular events like periods of exercise, stress, and illness. With this information, patients will be able to make more informed changes to their treatment protocols.
Collaborator: Boston Children’s Hospital
PHI Faculty: Timothy Bickmore
The purpose of this project is to develop a conversational agent system that counsels terminally ill patients in order to alleviate their suffering and improve their quality of life. Although many interventions have now been developed to address palliative care for specific chronic diseases, little has been done to address the overall quality of life for older adults with serious illness, spanning not only the functional aspects of symptom and medication management, but the affective aspects of suffering. In this project, we are developing a relational agent to counsel patients at home about medication adherence, stress management, advanced care planning, and spiritual support, and to provide referrals to palliative care services when needed.
Collaborators: Boston Medical Center
Supported by: NIH National Institute for Nursing Research
PHI Faculty: Timothy Bickmore
Most persons with spinal cord injury (SCI) require training and support for self-care management to help prevent the development of serious secondary conditions after hospital discharge. However, adherence to self-care management behaviors is poor once training with a therapist has ended. We have designed a virtual coach system, in which a relational agent engages individuals with SCI in simulated face-to-face conversations at home, to provide support and motivate adherence to self-care guidelines.
Collaborators: Boston University School of Public Health
Supported by: Nielsen Foundation
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with ASD. Stereotypical motor movements are complex and thought to serve a multiplicity of functions. While no one theory has obtained overwhelming support, there is evidence for biological, operant, and homeostatic interpretations.
Collaborator(s): MIT Media Lab, The Groden Center, Inc.
Supported by: Nancy Lurie Marks Family Foundation
This project is exploring how mobile technology can be used to support active commuting via bicycling, where individuals who would normally be commuting by car may instead commute via bicycle. They may do this because of a novel technological system that would create a car-free riding experience, dramatically enhancing comfort and safety.
The purpose this project is to design a relational agent that takes patient family medical history in preparation for a clinical consultation.
Collaborator(s): Boston University School of Public Health
Longitudinal sensor data collected passively from wearable activity monitors and mobile phones will transform behavioral science by allowing researchers to use “big data,” but at the person-level, to understand how behavior and related environmental exposures impact health outcomes and personalize health intervention and research. We propose to develop and test a system that permits typical mobile application game players to help scientists improve this type of data, by adding additional annotations that enrich the data, making it more useful for behavioral science and more amenable to automatic processing. This will help researchers to better understand how individual-level behaviors relate to health outcomes in current research studies that collect personal-level sensor data such as NHANES and the Women’s Health Study, and future big data ventures such as the new Precision Medicine Initiative.
Collaborators: Dinesh John (Northeastern University), Seth Cooper (Northeastern University)
Supported by: NIH
Type 1 diabetes (T1D), also called insulin dependent diabetes and juvenile diabetes, is an autoimmune disease which afflicts 1.25 million Americans. There are 40 thousand new diagnoses each year, and almost half of those are children and adolescents under age 20 years. The costs for our health care system are enormous, estimated at $14 billion annually. T1D is incurable, and people with T1D are estimated to lose over 10 years from their life expectancy. We are designing, building, and evaluating interactive visualization tools to help T1D patients and their caregivers make treatment decisions. In these visualizations we are showing data we collect using multiple devices and have data on the patient blood glucose levels, insulin administered, food eaten, exercise, and stress to name a few. Our tools are used by patients to understand the trends between scheduled events such as mealtimes, bedtimes, and overnight as well as irregular events like periods of exercise, stress, and illness. With this information, patients will be able to make more informed changes to their treatment protocols.
Collaborator: Boston Children’s Hospital
