This research focuses on investigating the multifaceted characteristics (demographic, mental, social, physical, etc.) of caregivers and identifying the significant factors that predict caregiver burden using advanced machine learning techniques. A mobile app (\"Caregiver Well-being,\" designed and developed by IDEA lab) and Garmin wearable devices were provided to participating caregivers to collect both qualitative (survey) and quantitative (vital) data, aiding health providers in understanding, addressing, and enhancing caregivers' well-being. The study employs a mixed methods approach, combining quantitative data tools such as surveys and machine learning algorithms with design science research to comprehend factors related to caregiver burden and devise mechanisms for awareness building, ultimately striving to enhance Caregiver Quality of Life (QOL). The research focuses on cancer caregivers due to the unique challenges posed by caring for cancer patients. Caregivers of cancer patients face considerable physical, emotional, and quality-of-life effects, making understanding their challenges crucial. The study aims to answer research questions related to demographics, burden factors, machine learning's role in identifying significant factors, and the design of an application to measure and enhance caregivers' QOL in the USA. The study adopts a design science theoretical framework based on the caregiver health model, expectation confirmation model, and mPERMA reference theory to instantiate a technologicalsolution (a mobile app) to measure caregivers' well-being. The research utilizes a comprehensive secondary dataset of cancer caregivers and employs machine learning to predict caregiver burden, contributing to the body of knowledge and offering potential solutions for known problems in caregiving. Nevertheless, the research encounters obstacles such as hurdles in recruiting participants, the possibility of bias in selecting participants for the study, and technological problems associated with gathering data via mobile apps and wearable devices. Notwithstanding these obstacles, the study is anticipated to provide useful insights into the burden and well-being of cancer carers, hence facilitating future research in this crucial domain.
","abstract_html":"<p>This research focuses on investigating the multifaceted characteristics (demographic, mental, social, physical, etc.) of caregivers and identifying the significant factors that predict caregiver burden using advanced machine learning techniques. A mobile app ("Caregiver Well-being," designed and developed by IDEA lab) and Garmin wearable devices were provided to participating caregivers to collect both qualitative (survey) and quantitative (vital) data, aiding health providers in understanding, addressing, and enhancing caregivers' well-being. The study employs a mixed methods approach, combining quantitative data tools such as surveys and machine learning algorithms with design science research to comprehend factors related to caregiver burden and devise mechanisms for awareness building, ultimately striving to enhance Caregiver Quality of Life (QOL). The research focuses on cancer caregivers due to the unique challenges posed by caring for cancer patients. Caregivers of cancer patients face considerable physical, emotional, and quality-of-life effects, making understanding their challenges crucial. The study aims to answer research questions related to demographics, burden factors, machine learning's role in identifying significant factors, and the design of an application to measure and enhance caregivers' QOL in the USA. The study adopts a design science theoretical framework based on the caregiver health model, expectation confirmation model, and mPERMA reference theory to instantiate a technologicalsolution (a mobile app) to measure caregivers' well-being. The research utilizes a comprehensive secondary dataset of cancer caregivers and employs machine learning to predict caregiver burden, contributing to the body of knowledge and offering potential solutions for known problems in caregiving. Nevertheless, the research encounters obstacles such as hurdles in recruiting participants, the possibility of bias in selecting participants for the study, and technological problems associated with gathering data via mobile apps and wearable devices. Notwithstanding these obstacles, the study is anticipated to provide useful insights into the burden and well-being of cancer carers, hence facilitating future research in this crucial domain.</p>","abstract_has_math":false,"creators":["Moniruzzaman, Md."],"institution":null,"degree_name":"Information Systems and Technology, PhD","degree_level":"Restricted to Claremont Colleges Dissertation","degree_discipline":"Center for Information Systems and Technology","degree_department":null,"school":null,"contributors":["Saeideh Heshmati","Paula Healani Palmer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-01-01T08:00:00Z","date_published":"2023-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:36Z","subjects":["Cancer","caregiver","feature selection","Machine learning model","support","wellbeing","Medicine and Health Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/755","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Saeideh Heshmati","Paula Healani Palmer"]},{"key":"dc:creator","label":"Author","values":["Moniruzzaman, Md."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2026-03-08T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Center for Information Systems and Technology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Restricted to Claremont Colleges Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Information Systems and Technology, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Cancer","caregiver","feature selection","Machine learning model","support","wellbeing","Medicine and Health Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/755"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research focuses on investigating the multifaceted characteristics (demographic, mental, social, physical, etc.) of caregivers and identifying the significant factors that predict caregiver burden using advanced machine learning techniques. A mobile app (\"Caregiver Well-being,\" designed and developed by IDEA lab) and Garmin wearable devices were provided to participating caregivers to collect both qualitative (survey) and quantitative (vital) data, aiding health providers in understanding, addressing, and enhancing caregivers' well-being. The study employs a mixed methods approach, combining quantitative data tools such as surveys and machine learning algorithms with design science research to comprehend factors related to caregiver burden and devise mechanisms for awareness building, ultimately striving to enhance Caregiver Quality of Life (QOL). The research focuses on cancer caregivers due to the unique challenges posed by caring for cancer patients. Caregivers of cancer patients face considerable physical, emotional, and quality-of-life effects, making understanding their challenges crucial. The study aims to answer research questions related to demographics, burden factors, machine learning's role in identifying significant factors, and the design of an application to measure and enhance caregivers' QOL in the USA. The study adopts a design science theoretical framework based on the caregiver health model, expectation confirmation model, and mPERMA reference theory to instantiate a technologicalsolution (a mobile app) to measure caregivers' well-being. The research utilizes a comprehensive secondary dataset of cancer caregivers and employs machine learning to predict caregiver burden, contributing to the body of knowledge and offering potential solutions for known problems in caregiving. Nevertheless, the research encounters obstacles such as hurdles in recruiting participants, the possibility of bias in selecting participants for the study, and technological problems associated with gathering data via mobile apps and wearable devices. Notwithstanding these obstacles, the study is anticipated to provide useful insights into the burden and well-being of cancer carers, hence facilitating future research in this crucial domain.
"]},{"key":"dc:title","label":"Title","values":["Understanding Cancer Caregiver Burnout using Machine Learning Models and Providing Support for Wellbeing with Wearable-device based Mobile App"]}]}],"canonical_facts":{"dc:contributor":["Saeideh Heshmati","Paula Healani Palmer"],"dc:creator":["Moniruzzaman, Md."],"dc:date.available":["2026-03-08T08:00:00Z"],"dc:description.abstract":["This research focuses on investigating the multifaceted characteristics (demographic, mental, social, physical, etc.) of caregivers and identifying the significant factors that predict caregiver burden using advanced machine learning techniques. A mobile app (\"Caregiver Well-being,\" designed and developed by IDEA lab) and Garmin wearable devices were provided to participating caregivers to collect both qualitative (survey) and quantitative (vital) data, aiding health providers in understanding, addressing, and enhancing caregivers' well-being. The study employs a mixed methods approach, combining quantitative data tools such as surveys and machine learning algorithms with design science research to comprehend factors related to caregiver burden and devise mechanisms for awareness building, ultimately striving to enhance Caregiver Quality of Life (QOL). The research focuses on cancer caregivers due to the unique challenges posed by caring for cancer patients. Caregivers of cancer patients face considerable physical, emotional, and quality-of-life effects, making understanding their challenges crucial. The study aims to answer research questions related to demographics, burden factors, machine learning's role in identifying significant factors, and the design of an application to measure and enhance caregivers' QOL in the USA. The study adopts a design science theoretical framework based on the caregiver health model, expectation confirmation model, and mPERMA reference theory to instantiate a technologicalsolution (a mobile app) to measure caregivers' well-being. The research utilizes a comprehensive secondary dataset of cancer caregivers and employs machine learning to predict caregiver burden, contributing to the body of knowledge and offering potential solutions for known problems in caregiving. Nevertheless, the research encounters obstacles such as hurdles in recruiting participants, the possibility of bias in selecting participants for the study, and technological problems associated with gathering data via mobile apps and wearable devices. Notwithstanding these obstacles, the study is anticipated to provide useful insights into the burden and well-being of cancer carers, hence facilitating future research in this crucial domain.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/755"],"dc:subject":["Cancer","caregiver","feature selection","Machine learning model","support","wellbeing","Medicine and Health Sciences"],"dc:title":["Understanding Cancer Caregiver Burnout using Machine Learning Models and Providing Support for Wellbeing with Wearable-device based Mobile App"],"thesis:degree_discipline":["Center for Information Systems and Technology"],"thesis:degree_level":["Restricted to Claremont Colleges Dissertation"],"thesis:degree_name":["Information Systems and Technology, PhD"]},"updated_at":"2026-07-24T01:40:36Z"}