Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease
To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. We recruited 34 individuals with PD (17 with...
Uloženo v:
| Vydáno v: | Frontiers in neurology Ročník 14; s. 1096401 |
|---|---|
| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Switzerland
Frontiers Media S.A
28.02.2023
|
| Témata: | |
| ISSN: | 1664-2295, 1664-2295 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.
We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal
V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed
email every 2 weeks over the year after the study for self-reported falls.
Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).
These findings highlight the importance of considering precise digital measures, captured
sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD. |
|---|---|
| AbstractList | ObjectivesTo investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.MethodsWe recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a “best subsets selection strategy” was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls.ResultsTwenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50–1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84–1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).ConclusionsThese findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD. To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.ObjectivesTo investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone.We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls.MethodsWe recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal® V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed via email every 2 weeks over the year after the study for self-reported falls.Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).ResultsTwenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10).These findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD.ConclusionsThese findings highlight the importance of considering precise digital measures, captured via sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD. To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal V2 System) placed on the feet and lower back for a week of passive gait monitoring. Logistic regression employing a "best subsets selection strategy" was used to find combinations of measures that discriminated future fallers from non-fallers, and the Area Under Curve (AUC). Participants were followed email every 2 weeks over the year after the study for self-reported falls. Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). These findings highlight the importance of considering precise digital measures, captured sensors strategically placed on the feet and low back, to quantify several different aspects of gait (walking and turning) during daily life to improve the classification of future fallers in PD. |
| Author | Shah, Vrutangkumar V. Sowalsky, Kristen Mancini, Martina Nutt, John G. Carlson-Kuhta, Patricia Harker, Graham Jagodinsky, Adam Horak, Fay B. El-Gohary, Mahmoud McNames, James |
| AuthorAffiliation | 1 Department of Neurology, Oregon Health & Science University , Portland, OR , United States 2 APDM Wearable Technologies, A Clario Company , Portland, OR , United States 3 Department of Electrical and Computer Engineering, Portland State University , Portland, OR , United States |
| AuthorAffiliation_xml | – name: 1 Department of Neurology, Oregon Health & Science University , Portland, OR , United States – name: 2 APDM Wearable Technologies, A Clario Company , Portland, OR , United States – name: 3 Department of Electrical and Computer Engineering, Portland State University , Portland, OR , United States |
| Author_xml | – sequence: 1 givenname: Vrutangkumar V. surname: Shah fullname: Shah, Vrutangkumar V. – sequence: 2 givenname: Adam surname: Jagodinsky fullname: Jagodinsky, Adam – sequence: 3 givenname: James surname: McNames fullname: McNames, James – sequence: 4 givenname: Patricia surname: Carlson-Kuhta fullname: Carlson-Kuhta, Patricia – sequence: 5 givenname: John G. surname: Nutt fullname: Nutt, John G. – sequence: 6 givenname: Mahmoud surname: El-Gohary fullname: El-Gohary, Mahmoud – sequence: 7 givenname: Kristen surname: Sowalsky fullname: Sowalsky, Kristen – sequence: 8 givenname: Graham surname: Harker fullname: Harker, Graham – sequence: 9 givenname: Martina surname: Mancini fullname: Mancini, Martina – sequence: 10 givenname: Fay B. surname: Horak fullname: Horak, Fay B. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36937534$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk9vVCEUxYmpsbX2C7gw7HQzI_8e77EyptHapIkudE0uPN4MlYEReJpJ_PAynbFpXcgGcjn3d7jhPEcnMUWH0EtKlpwP6u0U3ZyXjDC-pERJQegTdEalFAvGVHfy4HyKLkq5JW1xpbjkz9Apl4r3HRdn6PcV-IohjrjOOfq4wnYNGWx12ZfqbcFTThs8gg87HPzksI82OygOg_HB1x2uCW-zG72teJobxeEJQihNiLcubYPDv3xd4y-Qv_tYUnxd8OjLHvECPW3S4i6O-zn69vHD18tPi5vPV9eX728WVkhVF4Z2knAgwo49gBCiV5JYoyYJHZUMOtsLDkZKw9kwKqugM5ZYZeQwGBgZP0fXB-6Y4FZvs99A3ukEXt8VUl5pyG3Y4LS0vFcgG6z5cEoGwZnpBFdOAnXENNa7A2s7m40brYs1Q3gEfXwT_Vqv0k9NCWlz8L4R3hwJOf2YXal644t1IUB0aS6a9cPQq2FgqklfPTS7d_n7f03ADgKbUynZTfcSSvQ-J_ouJ3qfE33MSWsa_mmyvkL1af9gH_7X-geIj8W_ |
| CitedBy_id | crossref_primary_10_1177_20552173251329825 crossref_primary_10_1038_s41531_025_00897_1 crossref_primary_10_3390_s25134071 crossref_primary_10_1002_agm2_70033 crossref_primary_10_1016_j_prdoa_2025_100393 crossref_primary_10_1038_s41746_024_01311_5 crossref_primary_10_1111_ene_70118 crossref_primary_10_1159_000538598 crossref_primary_10_1186_s12885_023_11546_2 crossref_primary_10_3389_fneur_2024_1443799 crossref_primary_10_1097_NPT_0000000000000523 crossref_primary_10_3389_fneur_2024_1387477 |
| Cites_doi | 10.1093/gerona/glw019 10.3389/fneur.2018.00018 10.1186/1471-2105-12-77 10.1007/978-0-387-84858-7 10.1002/mds.22340 10.1038/s41598-018-22492-6 10.3390/ijerph16122216 10.1186/s11556-019-0214-5 10.1016/j.clinph.2020.11.027 10.3389/fnagi.2018.00078 10.2196/resprot.3931 10.3390/s20236992 10.3390/s22165940 10.1016/S1474-4422(19)30397-7 10.4172/2155-9538.S1-007 10.1093/gerona/glu225 10.3390/s140100356 10.1038/s41598-018-24783-4 10.3233/NRE-151236 10.3390/s20205769 10.1111/j.1532-5415.2005.53221.x 10.3390/s21123974 10.1093/gerona/glx254 10.1093/ageing/afr055 10.1007/s00415-020-09696-5 10.1186/1743-0003-2-19 10.3389/fnagi.2022.808518 10.1155/2013/906274 10.1186/s12984-016-0154-5 10.1109/TBME.2020.3037820 10.1002/mds.25684 10.1016/j.patrec.2005.10.010 10.1016/j.ensci.2016.11.003 10.1186/s12984-020-00781-4 10.1016/j.gaitpost.2009.07.108 10.1002/mdc3.13601 10.1063/1.3147408 10.1016/j.parkreldis.2019.01.022 10.1371/journal.pone.0096675 10.1371/journal.pone.0139849 |
| ContentType | Journal Article |
| Copyright | Copyright © 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak. Copyright © 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak. 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak |
| Copyright_xml | – notice: Copyright © 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak. – notice: Copyright © 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak. 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak |
| DBID | AAYXX CITATION NPM 7X8 5PM DOA |
| DOI | 10.3389/fneur.2023.1096401 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 1664-2295 |
| ExternalDocumentID | oai_doaj_org_article_6c379a6b664443108432b5439e6a1e0b PMC10015637 36937534 10_3389_fneur_2023_1096401 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NICHD NIH HHS grantid: R01 HD100383 – fundername: ; |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ACGFO ACGFS ADBBV ADRAZ AENEX AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK E3Z EMOBN F5P GROUPED_DOAJ GX1 HYE KQ8 M48 M~E O5R O5S OK1 P2P PGMZT RNS RPM ACXDI IPNFZ NPM RIG 7X8 5PM |
| ID | FETCH-LOGICAL-c469t-b15603a04cd7aa4447960cb9f6a5162a5c743ab66b328d9c9a5bc0c9b688bad23 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 12 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000948888100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1664-2295 |
| IngestDate | Fri Oct 03 12:44:28 EDT 2025 Thu Aug 21 18:37:31 EDT 2025 Fri Sep 05 12:46:19 EDT 2025 Thu Apr 03 07:01:24 EDT 2025 Sat Nov 29 06:40:00 EST 2025 Tue Nov 18 22:38:17 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | inertial sensors daily life Parkinson's disease gait future falls turning |
| Language | English |
| License | Copyright © 2023 Shah, Jagodinsky, McNames, Carlson-Kuhta, Nutt, El-Gohary, Sowalsky, Harker, Mancini and Horak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c469t-b15603a04cd7aa4447960cb9f6a5162a5c743ab66b328d9c9a5bc0c9b688bad23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: Elisabetta Dell'Anna, Consultant, Milan, Italy; Andrea Cereatti, Polytechnic University of Turin, Italy This article was submitted to Movement Disorders, a section of the journal Frontiers in Neurology Edited by: Maurizio Ferrarin, Fondazione Don Carlo Gnocchi Onlus (IRCCS), Italy |
| OpenAccessLink | https://doaj.org/article/6c379a6b664443108432b5439e6a1e0b |
| PMID | 36937534 |
| PQID | 2788798829 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_6c379a6b664443108432b5439e6a1e0b pubmedcentral_primary_oai_pubmedcentral_nih_gov_10015637 proquest_miscellaneous_2788798829 pubmed_primary_36937534 crossref_primary_10_3389_fneur_2023_1096401 crossref_citationtrail_10_3389_fneur_2023_1096401 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-02-28 |
| PublicationDateYYYYMMDD | 2023-02-28 |
| PublicationDate_xml | – month: 02 year: 2023 text: 2023-02-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in neurology |
| PublicationTitleAlternate | Front Neurol |
| PublicationYear | 2023 |
| Publisher | Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media S.A |
| References | Gao (B13) 2018; 8 B23 Del Din (B11) 2019; 74 Arpan (B31) 2022; 22 Rispens (B38) 2015; 4 Hausdorff (B28) 2005; 6 Galperin (B37) 2019; 62 Shah (B40) 2020; 20 Del Din (B7) 2016; 13 Nieuwboer (B17) 2009; 30 El-Gohary (B32) 2014; 14 Haertner (B33) 2018; 10 Hausdorff (B29) 2009; 19 Nasreddine (B18) 2005; 53 Mancini (B20) 2013; 7 Leach (B35) 2018; 8 Mancini (B36) 2015; 37 Custodio (B22) 2016; 5 Shah (B9) 2021; 17 Mancini (B26) 2016; 71 Pelicioni (B2) 2019; 16 Shah (B16) 2022 Van Schooten (B30) 2015; 70 Corrà (B6) 2021; 21 Horak (B4) 2013; 28 Delval (B12) 2021; 132 Callisaya (B27) 2011; 40 Warmerdam (B10) 2020; 19 Rehman (B39) 2022; 14 Goetz (B3) 2008; 23 Weiss (B5) 2014; 9 Shah (B19) 2020; 267 Shah (B21) 2021; 68 Hoskovcová (B14) 2015; 10 Mancini (B34) 2018; 9 Rehman (B15) 2020; 20 Fawcett (B24) 2006; 27 Allen (B1) 2013; 2013 Hillel (B8) 2019; 16 Robin (B25) 2011; 12 |
| References_xml | – volume: 71 start-page: 1102 year: 2016 ident: B26 article-title: Continuous monitoring of turning mobility and its association to falls and cognitive function: a pilot study publication-title: J Gerontol A. doi: 10.1093/gerona/glw019 – volume: 9 start-page: 1 year: 2018 ident: B34 article-title: Turn around freezing: Community-living turning behavior in people with Parkinson's disease publication-title: Front Neurol. doi: 10.3389/fneur.2018.00018 – volume: 12 start-page: 1 year: 2011 ident: B25 article-title: pROC: an open-source package for R and S+ to analyze and compare ROC curves publication-title: BMC Bioinform. doi: 10.1186/1471-2105-12-77 – ident: B23 doi: 10.1007/978-0-387-84858-7 – volume: 23 start-page: 2129 year: 2008 ident: B3 article-title: Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results publication-title: Movement Disor. doi: 10.1002/mds.22340 – volume: 8 start-page: 1 year: 2018 ident: B35 article-title: Natural turn measures predict recurrent falls in community-dwelling older adults: A longitudinal cohort study publication-title: Sci Rep. doi: 10.1038/s41598-018-22492-6 – volume: 16 start-page: 2216 year: 2019 ident: B2 article-title: Falls in parkinson's disease subtypes: Risk factors, locations and circumstances publication-title: Int J Environ Res Public Health. doi: 10.3390/ijerph16122216 – volume: 16 start-page: 1 year: 2019 ident: B8 article-title: Is every-day walking in older adults more analogous to dual-task walking or to usual walking? Elucidating the gaps between gait performance in the lab and during 24 / 7 monitoring publication-title: Eur Rev Aging Phys Activity. doi: 10.1186/s11556-019-0214-5 – volume: 132 start-page: 536 year: 2021 ident: B12 article-title: Do kinematic gait parameters help to discriminate between fallers and non-fallers with Parkinson's disease? publication-title: Clin Neurophysiol. doi: 10.1016/j.clinph.2020.11.027 – volume: 10 start-page: 1 year: 2018 ident: B33 article-title: Effect of fear of falling on turning performance in Parkinson' s disease in the lab and at home study cohort demographics and clinical publication-title: Front Aging Neurosci. doi: 10.3389/fnagi.2018.00078 – volume: 4 start-page: e3931 year: 2015 ident: B38 article-title: Do extreme values of daily-life gait characteristics provide more information about fall risk than median values? publication-title: JMIR Res Protoc. doi: 10.2196/resprot.3931 – volume: 20 start-page: 1 year: 2020 ident: B15 article-title: Gait analysis with wearables can accurately classify fallers from non-fallers: A step toward better management of neurological disorders publication-title: Sensors. doi: 10.3390/s20236992 – volume: 22 start-page: 5940 year: 2022 ident: B31 article-title: Fall prediction based on instrumented measures of gait and turning in daily life in people with multiple sclerosis publication-title: Sensors doi: 10.3390/s22165940 – volume: 19 start-page: 462 year: 2020 ident: B10 article-title: Long-term unsupervised mobility assessment in movement disorders publication-title: Lancet Neurol. doi: 10.1016/S1474-4422(19)30397-7 – volume: 7 start-page: 1 year: 2013 ident: B20 article-title: Mobility Lab to Assess Balance and Gait with Synchronized Body-worn Sensors publication-title: J Bioeng Biomed Sci. doi: 10.4172/2155-9538.S1-007 – volume: 70 start-page: 608 year: 2015 ident: B30 article-title: Ambulatory fall-risk assessment: amount and quality of daily-life gait predict falls in older adults publication-title: J Gerontol A. doi: 10.1093/gerona/glu225 – volume: 14 start-page: 356 year: 2014 ident: B32 article-title: Continuous monitoring of turning in patients with movement disability publication-title: Sensors (Switzerland). doi: 10.3390/s140100356 – volume: 8 start-page: 7129 year: 2018 ident: B13 article-title: Model-based and model-free machine learning techniques for diagnostic prediction and classification of clinical outcomes in Parkinson's disease publication-title: Scientific Reports. doi: 10.1038/s41598-018-24783-4 – volume: 37 start-page: 3 year: 2015 ident: B36 article-title: Continuous monitoring of turning in Parkinson's disease: Rehabilitation potential publication-title: NeuroRehabilitation. doi: 10.3233/NRE-151236 – volume: 20 start-page: 5769 year: 2020 ident: B40 article-title: Effect of bout length on gait measures in people with and without Parkinson's disease during daily life publication-title: Sensors. doi: 10.3390/s20205769 – volume: 53 start-page: 695 year: 2005 ident: B18 article-title: The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment publication-title: J Am Geriatr Soc. doi: 10.1111/j.1532-5415.2005.53221.x – volume: 21 start-page: 3974 year: 2021 ident: B6 article-title: Comparison of laboratory and daily-life gait speed assessment during on and off states in parkinson's disease publication-title: Sensors. doi: 10.3390/s21123974 – volume: 74 start-page: 500 year: 2019 ident: B11 article-title: Analysis of free-living gait in older adults with and without Parkinson's disease and with and without a history of falls: identifying generic and disease-specific characteristics publication-title: J Gerontol Series A Biol Sci Med Sci. doi: 10.1093/gerona/glx254 – volume: 40 start-page: 481 year: 2011 ident: B27 article-title: Gait, gait variability and the risk of multiple incident falls in older people: A population-based study publication-title: Age Ageing. doi: 10.1093/ageing/afr055 – volume: 267 start-page: 1188 year: 2020 ident: B19 article-title: Quantity and quality of gait and turning in people with multiple sclerosis, Parkinson's disease and matched controls during daily living publication-title: J Neurol. doi: 10.1007/s00415-020-09696-5 – volume: 6 start-page: 1 year: 2005 ident: B28 article-title: Gait variability: methods, modeling and meaning publication-title: J Neuroeng Rehabil. doi: 10.1186/1743-0003-2-19 – volume: 14 start-page: 808518 year: 2022 ident: B39 article-title: Investigating the impact of environment and data aggregation by walking bout duration on parkinson's disease classification using machine learning publication-title: Front Aging Neurosci. doi: 10.3389/fnagi.2022.808518 – volume: 2013 start-page: 906274 year: 2013 ident: B1 article-title: Recurrent falls in Parkinson's disease: a systematic review publication-title: Parkinsons Dis. doi: 10.1155/2013/906274 – volume: 13 start-page: 1 year: 2016 ident: B7 article-title: Free-living gait characteristics in ageing and Parkinson's disease: Impact of environment and ambulatory bout length publication-title: J NeuroEng Rehabilit. doi: 10.1186/s12984-016-0154-5 – volume: 68 start-page: 2615 year: 2021 ident: B21 article-title: Inertial sensor algorithms to characterize turning in neurological patients with turn hesitations publication-title: IEEE Trans Biomed Eng. doi: 10.1109/TBME.2020.3037820 – volume: 28 start-page: 1544 year: 2013 ident: B4 article-title: Objective biomarkers of balance and gait for Parkinson's disease using body-worn sensors publication-title: Movement Disor. doi: 10.1002/mds.25684 – volume: 27 start-page: 861 year: 2006 ident: B24 article-title: An introduction to ROC analysis publication-title: Pattern Recognit Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 5 start-page: 20 year: 2016 ident: B22 article-title: Predictive model for falling in Parkinson disease patients publication-title: Eneurologicalsci. doi: 10.1016/j.ensci.2016.11.003 – volume: 17 start-page: 1 year: 2021 ident: B9 article-title: Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson's disease, and matched controls publication-title: J NeuroEng Rehabilit. doi: 10.1186/s12984-020-00781-4 – volume: 30 start-page: 459 year: 2009 ident: B17 article-title: Reliability of the new freezing of gait questionnaire : Agreement between patients with Parkinson' s disease and their carers publication-title: Gait Posture. doi: 10.1016/j.gaitpost.2009.07.108 – year: 2022 ident: B16 article-title: Effect of Levodopa and Environmental Setting on Gait and Turning Digital Markers Related to Falls in People with Parkinson's Disease publication-title: Movement Disorders Clin Pract. doi: 10.1002/mdc3.13601 – volume: 19 start-page: 1 year: 2009 ident: B29 article-title: Gait dynamics in Parkinson's disease: Common and distinct behavior among stride length, gait variability, and fractal-like scaling publication-title: Chaos. doi: 10.1063/1.3147408 – volume: 62 start-page: 85 year: 2019 ident: B37 article-title: Associations between daily-living physical activity and laboratory-based assessments of motor severity in patients with falls and Parkinson's disease publication-title: Parkinsonism Relat Disor. doi: 10.1016/j.parkreldis.2019.01.022 – volume: 9 start-page: e96675 year: 2014 ident: B5 article-title: Objective assessment of fall risk in Parkinson's disease using a body-fixed sensor worn for 3 days publication-title: PLoS ONE. doi: 10.1371/journal.pone.0096675 – volume: 10 start-page: e0139849 year: 2015 ident: B14 article-title: Predicting falls in Parkinson disease: What is the value of instrumented testing in off medication state? publication-title: PLoS ONE. doi: 10.1371/journal.pone.0139849 |
| SSID | ssj0000399363 |
| Score | 2.379216 |
| Snippet | To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD)... ObjectivesTo investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 1096401 |
| SubjectTerms | daily life future falls gait inertial sensors Neurology Parkinson's disease turning |
| Title | Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/36937534 https://www.proquest.com/docview/2788798829 https://pubmed.ncbi.nlm.nih.gov/PMC10015637 https://doaj.org/article/6c379a6b664443108432b5439e6a1e0b |
| Volume | 14 |
| WOSCitedRecordID | wos000948888100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1664-2295 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000399363 issn: 1664-2295 databaseCode: DOA dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1664-2295 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000399363 issn: 1664-2295 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Ni9UwEA-6iHiR9bu7ukQQPEjdtknzcVxlVw_u4kHh3UKSplgofctrV1hY_Nt3Jul7vCeiFy89tGkbZiaZ3ySZ3xDyRjivvOVFrgBO59zzkOta25yhMyqY9dzHqiVf5MWFWiz0161SX3gmLNEDJ8EdC8-ktsIJcNzg7ArFWeVqcKNB2DIUDmdfQD1bwVScg9HvCpayZCAK08ct8kO-x2LhyKAk-FwFZu2JImH_n1Dm74clt7zP2T55OMNGepK6-4jcCcNjcv983hh_Qm4-2W6idmgouBBc6qB-l4mZYh4JbWzXX9O-awPtBsSLY6CJqPuaTkt6ucIPTjQRjdDW9v0IDWk6Zk5xzZZimnTMGHs70nl35yn5fnb67ePnfC6skHuIhqfcYfo0swX3jbQW5CohjvFOt8LWpahs7QFXWBC5Y5VqtNe2dr7w2gmlnG0q9ozsDcshvCBUc8EbQCWlDDV3SqqmLUTrkAbPSV26jJRrIRs_s45j8YveQPSBijFRMQYVY2bFZOTd5p3LxLnx19YfUHeblsiXHW-AFZnZisy_rCgjr9eaNzC-cNPEDmF5NZoKj1tqiEN0Rp4nS9j8igkAdzXjGVE7NrLTl90nQ_cjcniXMYedyYP_0ftD8gAlkjLtX5K9aXUVXpF7_ufUjasjclcu1FEcH3A9_3V6C1n4FPg |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Gait+and+turning+characteristics+from+daily+life+increase+ability+to+predict+future+falls+in+people+with+Parkinson%27s+disease&rft.jtitle=Frontiers+in+neurology&rft.au=Shah%2C+Vrutangkumar+V.&rft.au=Jagodinsky%2C+Adam&rft.au=McNames%2C+James&rft.au=Carlson-Kuhta%2C+Patricia&rft.date=2023-02-28&rft.issn=1664-2295&rft.eissn=1664-2295&rft.volume=14&rft_id=info:doi/10.3389%2Ffneur.2023.1096401&rft.externalDBID=n%2Fa&rft.externalDocID=10_3389_fneur_2023_1096401 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-2295&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-2295&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-2295&client=summon |