Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis
Falls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social conseque...
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| description | Falls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social consequences, negatively impacting quality of life. Identifying individuals at high risk for falls is crucial, particularly for those aged ≥60 years and living in residential care settings; current professional guidelines favor personalized, multifactorial fall risk assessment approaches for effective fall prevention.
This study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences.
This retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses.
The quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 (ρ=0.960, P |
| doi_str_mv | 10.2196/55681 |
| format | Article |
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This study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences.
This retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses.
The quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 (ρ=0.960, P<.001). Subgroups revealed significant strong correlations between FRS at T1 and falls at T2, particularly for older adults with slower gait speeds (ρ=0.954, P<.001) and those using walking aids (ρ=0.955, P<.001). Threshold values revealed that an FRS of 45%, 32%, and 24% corresponded to the expectation of a fall within 6, 12, and 24 months, respectively. Distribution-based minimum clinically important difference values were established, providing ranges for small, medium, and large effect sizes for FRS changes.
The FRS exhibits good prognostic validity for predicting future falls, particularly in specific subgroups. The findings support a stratified fall risk assessment approach and emphasize the significance of early and personalized intervention. This study contributes to the knowledge base on fall risk, despite limitations such as demographic focus and potential assessment interval variability.</description><identifier>EISSN: 2561-7605</identifier><identifier>DOI: 10.2196/55681</identifier><identifier>PMID: 39631046</identifier><language>eng</language><publisher>Canada: JMIR Publications</publisher><subject>Accidental Falls - prevention & control ; Aged ; Aged, 80 and over ; Algorithms ; Balance ; Computer vision ; Data Analysis ; Falls ; Fatalities ; Female ; Gait ; Geriatric Assessment - methods ; Germany - epidemiology ; Health risks ; Humans ; Injuries ; Injury prevention ; Male ; Mobile Applications ; Mobility ; Older people ; Prevention ; Prognosis ; Public health ; Questionnaires ; Reproducibility of Results ; Retrospective Studies ; Risk assessment ; Risk Assessment - methods ; Risk factors ; Smartphones ; Telemedicine</subject><ispartof>JMIR aging, 2024-12, Vol.7, p.e55681</ispartof><rights>Sónia A Alves, Steffen Temme, Seyedamirhosein Motamedi, Marie Kura, Sebastian Weber, Johannes Zeichen, Wolfgang Pommer, André Baumgart. Originally published in JMIR Aging (https://aging.jmir.org).</rights><rights>2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0003-0411-9933 ; 0000-0001-6057-1874 ; 0009-0005-3324-0611 ; 0000-0002-5283-5566 ; 0009-0004-8081-0835 ; 0009-0002-7002-8474 ; 0000-0002-6897-5387 ; 0000-0001-6053-3720</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,865,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39631046$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Alves, Sónia A</creatorcontrib><creatorcontrib>Temme, Steffen</creatorcontrib><creatorcontrib>Motamedi, Seyedamirhosein</creatorcontrib><creatorcontrib>Kura, Marie</creatorcontrib><creatorcontrib>Weber, Sebastian</creatorcontrib><creatorcontrib>Zeichen, Johannes</creatorcontrib><creatorcontrib>Pommer, Wolfgang</creatorcontrib><creatorcontrib>Baumgart, André</creatorcontrib><title>Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis</title><title>JMIR aging</title><addtitle>JMIR Aging</addtitle><description>Falls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social consequences, negatively impacting quality of life. Identifying individuals at high risk for falls is crucial, particularly for those aged ≥60 years and living in residential care settings; current professional guidelines favor personalized, multifactorial fall risk assessment approaches for effective fall prevention.
This study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences.
This retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses.
The quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 (ρ=0.960, P<.001). Subgroups revealed significant strong correlations between FRS at T1 and falls at T2, particularly for older adults with slower gait speeds (ρ=0.954, P<.001) and those using walking aids (ρ=0.955, P<.001). Threshold values revealed that an FRS of 45%, 32%, and 24% corresponded to the expectation of a fall within 6, 12, and 24 months, respectively. Distribution-based minimum clinically important difference values were established, providing ranges for small, medium, and large effect sizes for FRS changes.
The FRS exhibits good prognostic validity for predicting future falls, particularly in specific subgroups. The findings support a stratified fall risk assessment approach and emphasize the significance of early and personalized intervention. This study contributes to the knowledge base on fall risk, despite limitations such as demographic focus and potential assessment interval variability.</description><subject>Accidental Falls - prevention & control</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Balance</subject><subject>Computer vision</subject><subject>Data Analysis</subject><subject>Falls</subject><subject>Fatalities</subject><subject>Female</subject><subject>Gait</subject><subject>Geriatric Assessment - methods</subject><subject>Germany - epidemiology</subject><subject>Health risks</subject><subject>Humans</subject><subject>Injuries</subject><subject>Injury prevention</subject><subject>Male</subject><subject>Mobile Applications</subject><subject>Mobility</subject><subject>Older people</subject><subject>Prevention</subject><subject>Prognosis</subject><subject>Public health</subject><subject>Questionnaires</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Risk assessment</subject><subject>Risk Assessment - methods</subject><subject>Risk factors</subject><subject>Smartphones</subject><subject>Telemedicine</subject><issn>2561-7605</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNo1kN1KAzEUhIMgKtpXkIDXq_nZZLPe1Wq1ULCo6GU53ZzWaLpZk7TQJ_I1XaxeHebwzTAMIQPOLgWv9ZVS2vADciKU5kWlmTomg5Q-GGNCcCZrdkSOZa0lZ6U-Id93W_AbyK5d0fyOdBbDqg0pu4ZCa-nIu9Y14OkreGdd3tGw_OXG4D19cumTPjchIr3F6LZo6TiGde-kw0lxA6l_rB8QfH6nw66jyxD3xlnELbbZhfaaPmGOIXXY5D6gV-CLtxC9pbeQgQ5b8Lvk0hk5XIJPOPi7p-RlfPcyeiimj_eT0XBadEbwQpXGlI3Rtq4QVM1kIyQKrUtbSVTaVqXRDS6grgUqYzlWhoGs1HIhDJiGyVNysY_tYvjaYMrzj7CJfYc0l7wUwpiqkj11_kdtFmu08y66NcTd_H9W-QPTAXZi</recordid><startdate>20241204</startdate><enddate>20241204</enddate><creator>Alves, Sónia A</creator><creator>Temme, Steffen</creator><creator>Motamedi, Seyedamirhosein</creator><creator>Kura, Marie</creator><creator>Weber, Sebastian</creator><creator>Zeichen, Johannes</creator><creator>Pommer, Wolfgang</creator><creator>Baumgart, André</creator><general>JMIR Publications</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-0411-9933</orcidid><orcidid>https://orcid.org/0000-0001-6057-1874</orcidid><orcidid>https://orcid.org/0009-0005-3324-0611</orcidid><orcidid>https://orcid.org/0000-0002-5283-5566</orcidid><orcidid>https://orcid.org/0009-0004-8081-0835</orcidid><orcidid>https://orcid.org/0009-0002-7002-8474</orcidid><orcidid>https://orcid.org/0000-0002-6897-5387</orcidid><orcidid>https://orcid.org/0000-0001-6053-3720</orcidid></search><sort><creationdate>20241204</creationdate><title>Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis</title><author>Alves, Sónia A ; Temme, Steffen ; Motamedi, Seyedamirhosein ; Kura, Marie ; Weber, Sebastian ; Zeichen, Johannes ; Pommer, Wolfgang ; Baumgart, André</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p821-54884c86d97ea5903c23e2664d73e56d7486ceba992e58d1e780a375fb28a8c03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accidental Falls - prevention & control</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Balance</topic><topic>Computer vision</topic><topic>Data Analysis</topic><topic>Falls</topic><topic>Fatalities</topic><topic>Female</topic><topic>Gait</topic><topic>Geriatric Assessment - methods</topic><topic>Germany - epidemiology</topic><topic>Health risks</topic><topic>Humans</topic><topic>Injuries</topic><topic>Injury prevention</topic><topic>Male</topic><topic>Mobile Applications</topic><topic>Mobility</topic><topic>Older people</topic><topic>Prevention</topic><topic>Prognosis</topic><topic>Public health</topic><topic>Questionnaires</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Risk assessment</topic><topic>Risk Assessment - methods</topic><topic>Risk factors</topic><topic>Smartphones</topic><topic>Telemedicine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Alves, Sónia A</creatorcontrib><creatorcontrib>Temme, Steffen</creatorcontrib><creatorcontrib>Motamedi, Seyedamirhosein</creatorcontrib><creatorcontrib>Kura, Marie</creatorcontrib><creatorcontrib>Weber, Sebastian</creatorcontrib><creatorcontrib>Zeichen, Johannes</creatorcontrib><creatorcontrib>Pommer, Wolfgang</creatorcontrib><creatorcontrib>Baumgart, André</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Nursing and Allied Health Journals</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Nursing & Allied Health Premium</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>JMIR aging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Alves, Sónia A</au><au>Temme, Steffen</au><au>Motamedi, Seyedamirhosein</au><au>Kura, Marie</au><au>Weber, Sebastian</au><au>Zeichen, Johannes</au><au>Pommer, Wolfgang</au><au>Baumgart, André</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis</atitle><jtitle>JMIR aging</jtitle><addtitle>JMIR Aging</addtitle><date>2024-12-04</date><risdate>2024</risdate><volume>7</volume><spage>e55681</spage><pages>e55681-</pages><eissn>2561-7605</eissn><abstract>Falls pose a significant public health concern, with increasing occurrence due to the aging population, and they are associated with high mortality rates and risks such as multimorbidity and frailty. Falls not only lead to physical injuries but also have detrimental psychological and social consequences, negatively impacting quality of life. Identifying individuals at high risk for falls is crucial, particularly for those aged ≥60 years and living in residential care settings; current professional guidelines favor personalized, multifactorial fall risk assessment approaches for effective fall prevention.
This study aimed to explore the prognostic validity of the Fall Risk Score (FRS), a multifactorial-based metric to assess fall risk (using longitudinal real-world data), and establish the clinical relevance of the FRS by identifying threshold values and the minimum clinically important differences.
This retrospective cohort study involved 617 older adults (857 observations: 615 of women, 242 of men; mean age 83.3, SD 8.7 years; mean gait speed 0.49, SD 0.19 m/s; 622 using walking aids) residing in German residential care facilities and used the LINDERA mobile health app for fall risk assessment. The study focused on the association between FRS at the initial assessment (T1) and the normalized number of falls at follow-up (T2). A quadratic regression model and Spearman correlation analysis were utilized to analyze the data, supported by descriptive statistics and subgroup analyses.
The quadratic model exhibited the lowest root mean square error (0.015), and Spearman correlation analysis revealed that a higher FRS at T1 was linked to an increased number of falls at T2 (ρ=0.960, P<.001). Subgroups revealed significant strong correlations between FRS at T1 and falls at T2, particularly for older adults with slower gait speeds (ρ=0.954, P<.001) and those using walking aids (ρ=0.955, P<.001). Threshold values revealed that an FRS of 45%, 32%, and 24% corresponded to the expectation of a fall within 6, 12, and 24 months, respectively. Distribution-based minimum clinically important difference values were established, providing ranges for small, medium, and large effect sizes for FRS changes.
The FRS exhibits good prognostic validity for predicting future falls, particularly in specific subgroups. The findings support a stratified fall risk assessment approach and emphasize the significance of early and personalized intervention. This study contributes to the knowledge base on fall risk, despite limitations such as demographic focus and potential assessment interval variability.</abstract><cop>Canada</cop><pub>JMIR Publications</pub><pmid>39631046</pmid><doi>10.2196/55681</doi><orcidid>https://orcid.org/0000-0003-0411-9933</orcidid><orcidid>https://orcid.org/0000-0001-6057-1874</orcidid><orcidid>https://orcid.org/0009-0005-3324-0611</orcidid><orcidid>https://orcid.org/0000-0002-5283-5566</orcidid><orcidid>https://orcid.org/0009-0004-8081-0835</orcidid><orcidid>https://orcid.org/0009-0002-7002-8474</orcidid><orcidid>https://orcid.org/0000-0002-6897-5387</orcidid><orcidid>https://orcid.org/0000-0001-6053-3720</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accidental Falls - prevention & control Aged Aged, 80 and over Algorithms Balance Computer vision Data Analysis Falls Fatalities Female Gait Geriatric Assessment - methods Germany - epidemiology Health risks Humans Injuries Injury prevention Male Mobile Applications Mobility Older people Prevention Prognosis Public health Questionnaires Reproducibility of Results Retrospective Studies Risk assessment Risk Assessment - methods Risk factors Smartphones Telemedicine |
| title | Evaluating the Prognostic and Clinical Validity of the Fall Risk Score Derived From an AI-Based mHealth App for Fall Prevention: Retrospective Real-World Data Analysis |
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