Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System
Stride-to-stride fluctuations during walking reflect age-related changes in gait adaptability and are estimated with nonlinear measures that confine data collection to controlled settings. Smartphones, with their embedded accelerometers, may provide accessible gait analysis throughout the day. This...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7175 |
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| author | Vincenzo E. Di Bacco William H. Gage |
| author_facet | Vincenzo E. Di Bacco William H. Gage |
| author_sort | Vincenzo E. Di Bacco |
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| description | Stride-to-stride fluctuations during walking reflect age-related changes in gait adaptability and are estimated with nonlinear measures that confine data collection to controlled settings. Smartphones, with their embedded accelerometers, may provide accessible gait analysis throughout the day. This study investigated age-related differences in linear and nonlinear gait measures estimated from a smartphone accelerometer (SPAcc) in an unconstrained, free-living environment. Thirteen young adults (YA) and 11 older adults (OA) walked within a shopping mall with a SPAcc placed in their front right pants pocket. The inter-stride interval, calculated as the time difference between ipsilateral heel contacts, was used for dependent measures calculations. One-way repeated-measures analysis of variance revealed significant (<i>p</i> < 0.05) age-related differences (mean: YA, OA) for stride-time standard deviation (0.04 s, 0.05 s) and coefficient of variation (3.47%, 4.16%), sample entropy (SaEn) scale 1 (1.70, 1.86) and scale 3 (2.12, 1.80), and statistical persistence decay (31 strides, 23 strides). The fractal scaling index was not different between groups (0.93, 0.95), but exceeded those typically found in controlled settings, suggesting an upregulation in adaptive behaviour likely to accommodate the increased challenge of free-living walking. These findings support the SPAcc as a viable telehealth instrument for remote monitoring of gait dynamics, with implications for unsupervised fall-risk assessment. |
| format | Article |
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| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e459dc0061f148f8aa4d5247e4a404962025-08-20T01:54:04ZengMDPI AGSensors1424-82202024-11-012422717510.3390/s24227175Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer SystemVincenzo E. Di Bacco0William H. Gage1School of Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, CanadaSchool of Kinesiology and Health Science, York University, Toronto, ON M3J 1P3, CanadaStride-to-stride fluctuations during walking reflect age-related changes in gait adaptability and are estimated with nonlinear measures that confine data collection to controlled settings. Smartphones, with their embedded accelerometers, may provide accessible gait analysis throughout the day. This study investigated age-related differences in linear and nonlinear gait measures estimated from a smartphone accelerometer (SPAcc) in an unconstrained, free-living environment. Thirteen young adults (YA) and 11 older adults (OA) walked within a shopping mall with a SPAcc placed in their front right pants pocket. The inter-stride interval, calculated as the time difference between ipsilateral heel contacts, was used for dependent measures calculations. One-way repeated-measures analysis of variance revealed significant (<i>p</i> < 0.05) age-related differences (mean: YA, OA) for stride-time standard deviation (0.04 s, 0.05 s) and coefficient of variation (3.47%, 4.16%), sample entropy (SaEn) scale 1 (1.70, 1.86) and scale 3 (2.12, 1.80), and statistical persistence decay (31 strides, 23 strides). The fractal scaling index was not different between groups (0.93, 0.95), but exceeded those typically found in controlled settings, suggesting an upregulation in adaptive behaviour likely to accommodate the increased challenge of free-living walking. These findings support the SPAcc as a viable telehealth instrument for remote monitoring of gait dynamics, with implications for unsupervised fall-risk assessment.https://www.mdpi.com/1424-8220/24/22/7175nonlinear dynamicsstatistical persistenceentropyvariabilitywearablesfree-living walking |
| spellingShingle | Vincenzo E. Di Bacco William H. Gage Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System Sensors nonlinear dynamics statistical persistence entropy variability wearables free-living walking |
| title | Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System |
| title_full | Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System |
| title_fullStr | Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System |
| title_full_unstemmed | Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System |
| title_short | Monitoring Age-Related Changes in Gait Complexity in the Wild with a Smartphone Accelerometer System |
| title_sort | monitoring age related changes in gait complexity in the wild with a smartphone accelerometer system |
| topic | nonlinear dynamics statistical persistence entropy variability wearables free-living walking |
| url | https://www.mdpi.com/1424-8220/24/22/7175 |
| work_keys_str_mv | AT vincenzoedibacco monitoringagerelatedchangesingaitcomplexityinthewildwithasmartphoneaccelerometersystem AT williamhgage monitoringagerelatedchangesingaitcomplexityinthewildwithasmartphoneaccelerometersystem |