Texting while walking differently alters gait patterns in people with multiple sclerosis and healthy individuals

Corona, Federica; Pilloni, Giuseppina; Porta, Micaela; Coghe, Giancarlo; Cocco, Eleonora

doi:10.1016/j.msard.2017.11.021

Cited by 21 publications

(28 citation statements)

References 29 publications

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“…Initial reviews of studies investigating DT in pwMSs showed an overall reduced walking performance during DT in pwMSs indicating motor interference as the primary effect of CMI is motor interference (4,5). However, in subsequent years, other research has shown no significant differences in DTC on gait performance between persons with and without MS (6)(7)(8). For example, the systematic review of Learmonth et al (8) indicated there is a non-significant minimal difference in CMI between pwMSs and HCs, while the most recent systematic review from Postigo-Alonso et al (9) concluded there was a significant difference in CMI in pwMSs, although this investigated studies related specifically to motor task-and cognitive task-related factors.…”

Section: Introductionmentioning

confidence: 99%

Comparing 16 Different Dual–Tasking Paradigms in Individuals With Multiple Sclerosis and Healthy Controls: Working Memory Tasks Indicate Cognitive–Motor Interference

et al. 2020

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Background: Cognitive–motor interference (CMI) is measured by dual-tasking (DT), which involves motor and cognitive tasks. There is no consensus as to whether CMI is present in multiple sclerosis (MS). Objectives: We investigated the effects of 16 DT conditions by measuring motor complexity, cognitive domain, and task difficulty. Method: In total, 40 persons with MS (pwMSs) with Expanded Disease Status Scale (EDSS) 3.2 ± 1.7 and 31 age- and sex-matched healthy controls (HCs) completed 2 single walking, 8 single cognitive, and 2 complex walking tasks and 16 cognitive–motor DT. The main outcomes were mean values of gait velocity and the percentage change from single to DT (motor DT costs, mDTCs) and mean values of cognitive task accuracy and the percentage changes (cognitive DTC, cDTC). Results: Two-way analyses of variance showed the main effect of cognitive task yielded an F ratio of F (4, 268) = 72.35, p < 0.01, for mean gait velocity, and an F ratio of F (4, 304) = 17.12, p < 0.001, for mDTC, indicating that the mean velocity was significantly lower and the mDTC significantly higher for DS_B (mean = 1.27, SD = 0.03, and mean = 13.52, SD = 1.28, respectively). The main effect of cognitive task yielded an F ratio of F (4, 116) = 84.32, p < 0.001, with the lowest average accuracy for DS_B (mean = 43.95, SD = 3.33); no effect was found for cDTC. In pwMSs, the EDSS accounted for 28% ( F = 13.65, p = 0.001) of variance in a model predicting the highest mDTC. Conclusions: Overall, among different cognitive tasks added, the Digit Span backward was the most interfering cognitive task over gait velocity and accuracy. The effect was similar independently from the motor complexity and the group. PwMSs and HCs behaved in a similar manner at all motor complexity levels and during all cognitive task.

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Section: Introductionmentioning

confidence: 99%

Comparing 16 Different Dual–Tasking Paradigms in Individuals With Multiple Sclerosis and Healthy Controls: Working Memory Tasks Indicate Cognitive–Motor Interference

et al. 2020

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“…In particular, wearable inertial measurement units (IMUs), including accelerometers, gyroscopes, and magnetometers, represent cost-effective tools to perform objective assessments of walking in pwMS outside movement analysis labs [19,20], and even during free-living and community contexts [21,22]. IMUs have been widely used to analyse different locomotor tasks in pwMS, such as straight-line over ground [17,[23][24][25][26][27] and treadmill walking [28], standing up, walking, turning, and sitting down (e.g., the TUG) [15,29], walking with head turns and over/around obstacles [30,31], walking while texting [32], and stairway walking [33]. During these tests, several parameters have been extracted from IMUs, including spatio-temporal parameters [15,24,27,28,31,32,34], indexes of gait variability and stability [17,23,24,26,31,33], trunk sway metrics [15,23,30,34], and angular variables [15,25,27,34].…”

Section: Introductionmentioning

confidence: 99%

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Angelini

Carpinella

Cattaneo

et al. 2019

Sensors

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Inertial measurement units (IMUs) allow accurate quantification of gait impairment of people with multiple sclerosis (pwMS). Nonetheless, it is not clear how IMU-based metrics might be influenced by pragmatic aspects associated with clinical translation of this approach, such as data collection settings and gait protocols. In this study, we hypothesised that these aspects do not significantly alter those characteristics of gait that are more related to quality and energetic efficiency and are quantifiable via acceleration related metrics, such as intensity, smoothness, stability, symmetry, and regularity. To test this hypothesis, we compared 33 IMU-based metrics extracted from data, retrospectively collected by two independent centres on two matched cohorts of pwMS. As a worst-case scenario, a walking test was performed in the two centres at a different speed along corridors of different lengths, using different IMU systems, which were also positioned differently. The results showed that the majority of the temporal metrics (9 out of 12) exhibited significant between-centre differences. Conversely, the between-centre differences in the gait quality metrics were small and comparable to those associated with a test-retest analysis under equivalent conditions. Therefore, the gait quality metrics are promising candidates for reliable multi-centric studies aiming at assessing rehabilitation interventions within a routine clinical context.

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“…Further works have demonstrated the ability of mean and symmetry spatio-temporal parameters as well as autocorrelation measures in both laboratory and freeliving gait to delineate between PD subjects and healthy controls (31,69,72,118). Accelerometer-based measures of gait smoothness (HR) have also been shown to delineate between MS and healthy subjects prior to any measurable changes in mean spatio-temporal parameters (119), while dual-task gait in these patients has shown to result in a significant difference in mean spatio-temporal parameters compared to controls (63). In HD, significant differences have been demonstrated for autocorrelation measures, mean spatio-temporal parameters and both linear and nonlinear variability measures against healthy subjects (47,110).…”

Section: Delineating Pathological and Healthy Subjectsmentioning

confidence: 99%

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review

Mobbs¹,

Perring²,

Raj³

et al. 2022

mHealth

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Background: Wearable sensors, particularly accelerometers alone or combined with gyroscopes and magnetometers in an inertial measurement unit (IMU), are a logical alternative for gait analysis. While issues with intrusive and complex sensor placement limit practicality of multi-point IMU systems, single-point IMUs could potentially maximize patient compliance and allow inconspicuous monitoring in daily-living.Therefore, this review aimed to examine the validity of single-point IMUs for gait metrics analysis and identify studies employing them for clinical applications. Methods:The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were followed utilizing the following databases: PubMed; MEDLINE; EMBASE and Cochrane. Four databases were systematically searched to obtain relevant journal articles focusing on the measurement of gait metrics using single-point IMU sensors.Results: A total of 90 articles were selected for inclusion. Critical analysis of studies was conducted, and data collected included: sensor type(s); sensor placement; study aim(s); study conclusion(s); gait metrics and methods; and clinical application. Validation research primarily focuses on lower trunk sensors in healthy cohorts. Clinical applications focus on diagnosis and severity assessment, rehabilitation and intervention efficacy and delineating pathological subjects from healthy controls.Discussion: This review has demonstrated the validity of single-point IMUs for gait metrics analysis and their ability to assist in clinical scenarios. Further validation for continuous monitoring in daily living scenarios and performance in pathological cohorts is required before commercial and clinical uptake can be expected.

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Texting while walking differently alters gait patterns in people with multiple sclerosis and healthy individuals

Cited by 21 publications

References 29 publications

Comparing 16 Different Dual–Tasking Paradigms in Individuals With Multiple Sclerosis and Healthy Controls: Working Memory Tasks Indicate Cognitive–Motor Interference

Comparing 16 Different Dual–Tasking Paradigms in Individuals With Multiple Sclerosis and Healthy Controls: Working Memory Tasks Indicate Cognitive–Motor Interference

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

Gait metrics analysis utilizing single-point inertial measurement units: a systematic review

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