{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108434"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108434","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The role of mobile technology for fall risk assessment for individuals with multiple sclerosis","abstract":"Multiple Sclerosis (MS) is a chronic, progressive neurogenerative disease that affects one million people in the United States (Wallin et al., 2019). Common MS symptoms include impaired coordination, poor walking and balance, and fatigue, and these symptoms put people with MS (pwMS) at a higher risk for falls (Cameron & Nilsagard, 2018). Falls are highly prevalent among pwMS and can result in detrimental consequences including bone fractures and even death (Matsuda et al., 2011). To prevent falls and fall related injuries, it is important to first assess for multiple risk factors and then intervene through targeted treatments (Palumbo et al., 2015). Fall risk can be assessed through self-report measures, clinical performance tests, or with technology such as force plates and motion capture systems (Kanekar & Aruin, 2013). However, clinicians have time constraints, technology is expensive, and trained personnel is needed. Moreover, due to the COVID-19 pandemic, access to in-person clinical visits is limited. As a result, pwMS may not receive fall risk screening and remain vulnerable to fall related injuries. Mobile technology offers a solution to increase access to fall risk screening using an affordable, ubiquitous, and portable tool (Guise et al., 2014; Marrie et al., 2019). Therefore, the overarching goal of this study was to develop a usable fall risk health application (app) for pwMS to self-assess their fall risk in the home setting. Four studies were performed: 1) smartphone accelerometry was tested to measure postural control in pwMS; 2) a fall risk algorithm was developed for a mobile health app; 3) a fall risk app, Steady-MS, was developed and its usability was tested; and 4) the feasibility of home-based procedures for using Steady-MS was determined. Results suggest that smartphone accelerometry can assess postural control in pwMS. This information was used to develop an algorithm to measure overall fall risk in pwMS and was then incorporated into Steady-MS. Steady-MS was found to be usable among MS users and feasible to use in the home setting. The results from this project demonstrate that pwMS can independently assess their fall risk with Steady-MS in their homes. For the first time, pwMS are equipped to self-assess their fall risk and can monitor and manage their risk. Home-based assessments also opens the potential to offer individualized and targeted treatments to prevent falls. Ultimately, Steady-MS increases access to home-based assessments to reduce falls and improve functional independence for those with MS.","abstract_html":"Multiple Sclerosis (MS) is a chronic, progressive neurogenerative disease that affects one million people in the United States (Wallin et al., 2019). Common MS symptoms include impaired coordination, poor walking and balance, and fatigue, and these symptoms put people with MS (pwMS) at a higher risk for falls (Cameron & Nilsagard, 2018). Falls are highly prevalent among pwMS and can result in detrimental consequences including bone fractures and even death (Matsuda et al., 2011). To prevent falls and fall related injuries, it is important to first assess for multiple risk factors and then intervene through targeted treatments (Palumbo et al., 2015). Fall risk can be assessed through self-report measures, clinical performance tests, or with technology such as force plates and motion capture systems (Kanekar & Aruin, 2013). However, clinicians have time constraints, technology is expensive, and trained personnel is needed. Moreover, due to the COVID-19 pandemic, access to in-person clinical visits is limited. As a result, pwMS may not receive fall risk screening and remain vulnerable to fall related injuries. Mobile technology offers a solution to increase access to fall risk screening using an affordable, ubiquitous, and portable tool (Guise et al., 2014; Marrie et al., 2019). Therefore, the overarching goal of this study was to develop a usable fall risk health application (app) for pwMS to self-assess their fall risk in the home setting. Four studies were performed: 1) smartphone accelerometry was tested to measure postural control in pwMS; 2) a fall risk algorithm was developed for a mobile health app; 3) a fall risk app, Steady-MS, was developed and its usability was tested; and 4) the feasibility of home-based procedures for using Steady-MS was determined. Results suggest that smartphone accelerometry can assess postural control in pwMS. This information was used to develop an algorithm to measure overall fall risk in pwMS and was then incorporated into Steady-MS. Steady-MS was found to be usable among MS users and feasible to use in the home setting. The results from this project demonstrate that pwMS can independently assess their fall risk with Steady-MS in their homes. For the first time, pwMS are equipped to self-assess their fall risk and can monitor and manage their risk. Home-based assessments also opens the potential to offer individualized and targeted treatments to prevent falls. Ultimately, Steady-MS increases access to home-based assessments to reduce falls and improve functional independence for those with MS.","abstract_has_math":false,"creators":["Hsieh, Katherine L"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Kinesiology","degree_department":null,"school":null,"contributors":["Sosnoff, Jacob J","Fanning, Jason T","Rice, Laura A","Rogers, Wendy A"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-10-07T20:59:26Z","date_published":"2020-10-07T20:59:26Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Falls","Multiple Sclerosis","Smartphone Technology"],"languages":["en"],"rights":["Copyright 2020 Katherine Hsieh"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108434","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sosnoff, Jacob J","Fanning, Jason T","Rice, Laura A","Rogers, Wendy A"]},{"key":"dc:creator","label":"Author","values":["Hsieh, Katherine L"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-10-07T20:59:26Z","2020-07-01","2020-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Kinesiology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Falls","Multiple Sclerosis","Smartphone Technology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Katherine Hsieh"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108434"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Multiple Sclerosis (MS) is a chronic, progressive neurogenerative disease that affects one million people in the United States (Wallin et al., 2019). Common MS symptoms include impaired coordination, poor walking and balance, and fatigue, and these symptoms put people with MS (pwMS) at a higher risk for falls (Cameron & Nilsagard, 2018). Falls are highly prevalent among pwMS and can result in detrimental consequences including bone fractures and even death (Matsuda et al., 2011). To prevent falls and fall related injuries, it is important to first assess for multiple risk factors and then intervene through targeted treatments (Palumbo et al., 2015). Fall risk can be assessed through self-report measures, clinical performance tests, or with technology such as force plates and motion capture systems (Kanekar & Aruin, 2013). However, clinicians have time constraints, technology is expensive, and trained personnel is needed. Moreover, due to the COVID-19 pandemic, access to in-person clinical visits is limited. As a result, pwMS may not receive fall risk screening and remain vulnerable to fall related injuries. Mobile technology offers a solution to increase access to fall risk screening using an affordable, ubiquitous, and portable tool (Guise et al., 2014; Marrie et al., 2019). Therefore, the overarching goal of this study was to develop a usable fall risk health application (app) for pwMS to self-assess their fall risk in the home setting. Four studies were performed: 1) smartphone accelerometry was tested to measure postural control in pwMS; 2) a fall risk algorithm was developed for a mobile health app; 3) a fall risk app, Steady-MS, was developed and its usability was tested; and 4) the feasibility of home-based procedures for using Steady-MS was determined. Results suggest that smartphone accelerometry can assess postural control in pwMS. This information was used to develop an algorithm to measure overall fall risk in pwMS and was then incorporated into Steady-MS. Steady-MS was found to be usable among MS users and feasible to use in the home setting. The results from this project demonstrate that pwMS can independently assess their fall risk with Steady-MS in their homes. For the first time, pwMS are equipped to self-assess their fall risk and can monitor and manage their risk. Home-based assessments also opens the potential to offer individualized and targeted treatments to prevent falls. Ultimately, Steady-MS increases access to home-based assessments to reduce falls and improve functional independence for those with MS.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","The student, Katherine Hsieh, accepted the attached license on 2020-06-26 at 16:01.","The student, Katherine Hsieh, submitted this Dissertation for approval on 2020-06-26 at 16:18.","This Dissertation was approved for publication on 2020-07-01 at 17:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15472 on 2020-10-02 at 15:10:39","Made available in DSpace on 2020-10-07T20:59:26Z (GMT). No. of bitstreams: 2 HSIEH-DISSERTATION-2020.pdf: 2586830 bytes, checksum: 1ce84fc5151a9c143a0d8566893909cf (MD5) LICENSE.txt: 4212 bytes, checksum: 90e3bde78dce596e2134918d60b6252f (MD5) Previous issue date: 2020-07-01"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The role of mobile technology for fall risk assessment for individuals with multiple sclerosis"]}]}],"canonical_facts":{"dc:contributor":["Sosnoff, Jacob J","Fanning, Jason T","Rice, Laura A","Rogers, Wendy A"],"dc:creator":["Hsieh, Katherine L"],"dc:date":["2020-10-07T20:59:26Z","2020-07-01","2020-08"],"dc:description":["Multiple Sclerosis (MS) is a chronic, progressive neurogenerative disease that affects one million people in the United States (Wallin et al., 2019). Common MS symptoms include impaired coordination, poor walking and balance, and fatigue, and these symptoms put people with MS (pwMS) at a higher risk for falls (Cameron & Nilsagard, 2018). Falls are highly prevalent among pwMS and can result in detrimental consequences including bone fractures and even death (Matsuda et al., 2011). To prevent falls and fall related injuries, it is important to first assess for multiple risk factors and then intervene through targeted treatments (Palumbo et al., 2015). Fall risk can be assessed through self-report measures, clinical performance tests, or with technology such as force plates and motion capture systems (Kanekar & Aruin, 2013). However, clinicians have time constraints, technology is expensive, and trained personnel is needed. Moreover, due to the COVID-19 pandemic, access to in-person clinical visits is limited. As a result, pwMS may not receive fall risk screening and remain vulnerable to fall related injuries. Mobile technology offers a solution to increase access to fall risk screening using an affordable, ubiquitous, and portable tool (Guise et al., 2014; Marrie et al., 2019). Therefore, the overarching goal of this study was to develop a usable fall risk health application (app) for pwMS to self-assess their fall risk in the home setting. Four studies were performed: 1) smartphone accelerometry was tested to measure postural control in pwMS; 2) a fall risk algorithm was developed for a mobile health app; 3) a fall risk app, Steady-MS, was developed and its usability was tested; and 4) the feasibility of home-based procedures for using Steady-MS was determined. Results suggest that smartphone accelerometry can assess postural control in pwMS. This information was used to develop an algorithm to measure overall fall risk in pwMS and was then incorporated into Steady-MS. Steady-MS was found to be usable among MS users and feasible to use in the home setting. The results from this project demonstrate that pwMS can independently assess their fall risk with Steady-MS in their homes. For the first time, pwMS are equipped to self-assess their fall risk and can monitor and manage their risk. Home-based assessments also opens the potential to offer individualized and targeted treatments to prevent falls. Ultimately, Steady-MS increases access to home-based assessments to reduce falls and improve functional independence for those with MS.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2020-10-02 without embargo terms","The student, Katherine Hsieh, accepted the attached license on 2020-06-26 at 16:01.","The student, Katherine Hsieh, submitted this Dissertation for approval on 2020-06-26 at 16:18.","This Dissertation was approved for publication on 2020-07-01 at 17:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #15472 on 2020-10-02 at 15:10:39","Made available in DSpace on 2020-10-07T20:59:26Z (GMT). No. of bitstreams: 2 HSIEH-DISSERTATION-2020.pdf: 2586830 bytes, checksum: 1ce84fc5151a9c143a0d8566893909cf (MD5) LICENSE.txt: 4212 bytes, checksum: 90e3bde78dce596e2134918d60b6252f (MD5) Previous issue date: 2020-07-01"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108434"],"dc:language":["en"],"dc:rights":["Copyright 2020 Katherine Hsieh"],"dc:subject":["Falls","Multiple Sclerosis","Smartphone Technology"],"dc:title":["The role of mobile technology for fall risk assessment for individuals with multiple sclerosis"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Kinesiology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:48Z"}