Smartphone App-Based Assessment of Gait During Normal and Dual-Task Walking: Demonstration of Validity and Reliability

The Reliability and Validity of the OneStep Smartphone Application for Gait Analysis among Patients Undergoing Rehabilitation for Unilateral Lower Limb Disability

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Gait variability, fractal dynamics, and statistical regularity of treadmill and overground walking recorded with a smartphone

BACKGROUND

The nonlinear variability present during steady-state gait may provide a signature of health and showcase one's walking adaptability. Although treadmills can capture vast amounts of walking data required for estimating variability within a small space, gait patterns may be misrepresented compared to an overground setting. Smartphones may provide a low-cost and user-friendly estimate of gait patterns among a variety of walking settings. However, no study has investigated differences in gait patterns derived from a smartphone between treadmill walking (TW) and overground walking (OW).

RESEARCH QUESTION

This study implemented a smartphone accelerometer to compare differences in temporal gait variability and gait dynamics between TW and OW.

METHODS

Sixteen healthy adults (8F; 24.7 ± 3.8 years) visited the laboratory on three separate days and completed three 8-minute OW and three TW trials, at their preferred speed, during each visit. The inter-stride interval was calculated as the time difference between right heel contact events located within the vertical accelerometery signals recorded from a smartphone while placed in participants front right pant pocket during walking trials. The inter-stride interval series was used to calculate stride time standard deviation (SD) and coefficient of variation (COV), statistical persistence (fractal scaling index), and statistical regularity (sample entropy). Two-way analysis of variance compared walking condition and laboratory visits for each measure.

RESULTS

Compared to TW, OW displayed significantly (p < 0.01) greater stride time SD (0.014 s, 0.020 s), COV (1.26 %, 1.82 %), fractal scaling index (0.70, 0.79) and sample entropy (1.43, 1.63). No differences were found between visits for all measures.

SIGNIFICANCE

Smartphone-based assessment of gait provides the ability to distinguish between OW and TW conditions, similar to previously established methodologies. Furthermore, smartphones may be a low-cost and user-friendly tool to estimate gait patterns outside the laboratory to improve ecological validity, with implications for free-living monitoring of gait among various populations.

Validity and Reliability of a Smartphone Application for Home Measurement of Four-Meter Gait Speed in Older Adults

The four-meter gait speed (4MGS) is a recommended physical performance test in older adults but is challenging to implement clinically. We developed a smartphone application (App) with a four-meter ribbon for remote 4MGS testing at home. This study aimed to assess the validity and reliability of this smartphone App-based assessment of the home 4MGS. We assessed the validity of the smartphone App by comparing it against a gold standard video assessment of the 4MGS conducted by study staff visiting community-dwelling older adults and against the stopwatch-based measurement. Moreover, we assessed the test-retest reliability in two supervised sessions and three additional sessions performed by the participants independently, without staff supervision. The 4MGS measured by the smartphone App was highly correlated with video-based 4MGS (r = 0.94), with minimal differences (mean = 0.07 m/s, ± 1.96 SD = 0.12) across a range of gait speeds. The test-retest reliability for the smartphone App 4MGS was high (ICC values: 0.75 to 0.93). The home 4MGS in older adults can be measured accurately and reliably using a smartphone in the pants pocket and a four-meter strip of ribbon. Leveraging existing technology carried by a significant portion of the older adult population could overcome barriers in busy clinical settings for this well-established objective mobility test.

Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research

Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Validation of gait analysis using smartphones: Reliability and validity

Objective

This study aims to validate the reliability and validity of gait analysis using smartphones in a controlled environment.

Methods

Thirty healthy adults attached smartphones to the waist and thigh, while an inertial measurement unit was fixed at the shank as a reference device; each participant was asked to walk six gait cycles at self-selected low, normal, and high speeds. Thirty-five cerebral small vessel disease patients were recruited to attach the smartphone to the thigh, performing single-task (ST), cognitive dual-task (DT1), and physical dual-task walking (DT2) to obtain gait parameters.

Results

The results from the healthy group indicate that, regardless of whether attached to the thigh or waist, the smartphones calculated gait parameters with good reliability (ICC2,1 > 0.75) across three different walking speeds. There were no significant differences in the gait parameters between the smartphone attached to the thigh and the IMU across all three walking speeds (P > 0.05). However, significant differences were observed between the smartphone at the waist and the IMU during the stance phase, swing phase, stance time, and stride length at high speeds (P < 0.05). At the same time, measurements of other gait parameters were similar (P > 0.05). Patients demonstrated significant differences in the cadence, stride time, stance phase, swing phase, stance time, stride length, and walking speed between ST and DT1 (P < 0.05). Significant differences were observed in the stance phase, swing phase, stride length, and walking speed between ST and DT2 (P < 0.05).

Conclusions

This study demonstrates the feasibility of using built-in smartphone sensors for gait analysis in a controlled environment.

The validity of smartphone-based spatiotemporal gait measurements during walking with and without head turns: Comparison with the GAITRite® system

Smartphone accelerometry has potential to provide clinicians with specialized gait analysis not available in most clinical settings. The Gait&Balance Application (G&B App) uses smartphone accelerometry to assess spatiotemporal gait parameters under two conditions: walking looking straight ahead and walking with horizontal head turns. This study investigated the validity of G&B App gait parameters compared with the GAITRite® pressure-sensitive walkway. Healthy young and older adults (age range 21-85 years) attended a single session where a smartphone was secured over the lumbosacral junction. Data were collected concurrently with the app and GAITRite® systems as participants completed the two walking conditions. Spatiotemporal gait parameters for 54 participants were determined from both systems and agreement evaluated with partial Pearson's correlation coefficients and limits of agreement. The results demonstrated moderate to excellent validity for G&B App measures of step time (rp 0.97, 95 % CI [0.96, 0.98]), walking speed (rp 0.83 [0.78, 0.87]), and step length (rp 0.74, [0.66, 0.80]) when walking looking straight ahead, and results were comparable with head turns. The validity of walking speed and step length measures was influenced by sex and height. G&B App measures of step length variability, step time variability, step length asymmetry, and step time asymmetry had poor validity. The G&B App has potential to provide valid measures of unilateral and bilateral step time, unilateral and bilateral step length, and walking speed, under two walking conditions in healthy young and older adults. Further research should validate this tool in clinical conditions and optimise the algorithm for demographic characteristics.

Watch your step: A pilot study of smartphone use effect on young females' gait performance while walking up and down stairs and escalators

The increasingly ubiquitous use of smartphones has made distracted walking common, not only on flat ground, but also on stairs. Available information regarding changes in gait performance while walking and using a smartphone in different environments is still lacking. We aimed to investigate the differences in gait behavior and subjective walking confidence while walking up and down stairs and escalators, with and without smartphone use. A field experiment involving 32 female adults was conducted at a subway station. Gait parameters collected included step frequency, acceleration root mean square, step variability, step regularity, and step symmetry. The results showed that walking task, walking environment, and walking direction significantly affected gait performance and walking confidence. Overall, playing games or texting while walking down escalators resulted in the lowest walking confidence and the largest gait performance decrement: slower step frequency; reduced root mean square; decreased step regularity and step symmetry; and increased step variability. Step frequency, step variability, and step regularity significantly correlated with walking confidence. Smartphone use while walking on stairs and escalators significantly affects gait behavior and might increase the risk of falls. Interventions and prevention are needed to increase safety education and hazard warnings for the general population.

Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis

Background

In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices.

Objective

Our study objective is twofold: (1) to propose a set of step-based fall risk parameters that can be obtained independently of the sensor placement by using a ubiquitous step detection method and (2) to evaluate their association with prospective falls.

Methods

A reanalysis was conducted on the 1-week ambulatory inertial data from the StandingTall study, which was originally described by Delbaere et al. The data were from 301 community-dwelling older people and contained fall occurrences over a 12-month follow-up period. Using the ubiquitous and robust step detection method Smartstep, which is agnostic to sensor placement, a range of step-based fall risk parameters can be calculated based on walking bouts of 200 steps. These parameters are known to describe different dimensions of gait (ie, variability, complexity, intensity, and quantity). First, the correlation between parameters was studied. Then, the number of parameters was reduced through stepwise backward elimination. Finally, the association of parameters with prospective falls was assessed through a negative binomial regression model using the area under the curve metric.

Results

The built model had an area under the curve of 0.69, which is comparable to models exclusively built on fixed sensor placement. A higher fall risk was noted with higher gait variability (coefficient of variance of stride time), intensity (cadence), and quantity (number of steps) and lower gait complexity (sample entropy and fractal exponent).

Conclusions

These findings highlight the potential of our method for comprehensive and accurate fall risk assessments, independent of sensor placement. This approach has promising implications for ambulatory gait monitoring and fall risk monitoring using consumer-grade devices.

A Scoping Review of the Validity and Reliability of Smartphone Accelerometers When Collecting Kinematic Gait Data

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful.

Spatiotemporal parameters from remote smartphone-based gait analysis are associated with lower extremity functional scale categories

Objective

Self-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored.

Methods

Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High).

Results

The main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals' self-evaluated function as defined by LEFS categorization (n = 132) and many variables differ between groups. Velocity was found to have the strongest effect size.

Discussion

When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.

Testing the validity and reliability of a new android application-based accelerometer balance assessment tool for community-dwelling older adults

BACKGROUND

Postural instability is a risk factor for falls in older adults. It is possible to detect postural stability using an integrated accelerometer (ACC) sensor in a smartphone. Therefore, a novel ACC-based smartphone application running on the Android operating system called BalanceLab was created and tested.

PURPOSE

This study aimed to determine the validity and reliability of a novel ACC-based Android smartphone application for assessing balance in older adults.

METHODS

Using BalanceLab, 20 older adults completed three balance assessments: the Modified Clinical Test of Sensory Interaction in Balance (MCTSIB), a single-leg stance test (SLST), and a limit of stability test (LOS). The validity of this mobile application was investigated using a three-dimensional (3D) motion analysis system and the Fullerton Advanced Balance (FAB) scale. The test-retest reliability of this mobile application was determined on two separate occasions within one day, at least two hours apart.

RESULTS

The two static balance assessments (the MCTSIB and SLST) demonstrated moderate to excellent correlation with the 3D motion analysis system (r = 0.70-0.91) and the FAB scale (r = 0.67-0.80). However, the majority of the dynamic balance tests (the LOS tests) did not demonstrate any correlation with the 3D motion analysis system or the FAB scale. This novel ACC-based application demonstrated good to excellent test-retest reliability (ICC = 0.76-0.91).

CONCLUSION

A static, but not dynamic, balance assessment tool that uses a novel ACC-based application for Android can be used to measure balance in older adults. This application has moderate to excellent validity and test-retest reliability.

Remote Digital Technologies for the Early Detection and Monitoring of Cognitive Decline in Patients With Type 2 Diabetes: Insights From Studies of Neurodegenerative Diseases

Type 2 diabetes (T2D) is a risk factor for cognitive decline. In neurodegenerative disease research, remote digital cognitive assessments and unobtrusive sensors are gaining traction for their potential to improve early detection and monitoring of cognitive impairment. Given the high prevalence of cognitive impairments in T2D, these digital tools are highly relevant. Further research incorporating remote digital biomarkers of cognition, behavior, and motor functioning may enable comprehensive characterizations of patients with T2D and may ultimately improve clinical care and equitable access to research participation. The aim of this commentary article is to review the feasibility, validity, and limitations of using remote digital cognitive tests and unobtrusive detection methods to identify and monitor cognitive decline in neurodegenerative conditions and apply these insights to patients with T2D.

Benefits and Barriers of Technology for Home Function and Mobility Assessment: Perspectives of Older Patients With Blood Cancers, Caregivers, and Clinicians

PURPOSE

Advances in digital health technology can overcome barriers to measurement of function and mobility for older adults with blood cancers, but little is known about how older adults perceive such technology for use in their homes.

METHODS

To characterize potential benefits and barriers associated with using technology for home functional assessment, we conducted three semistructured focus groups (FGs) in January 2022. Eligible patients came from the Older Adult Hematologic Malignancies Program at Dana-Farber Cancer Institute (DFCI), which includes adults 73 years and older enrolled during their initial consult with their oncologist. Eligible caregivers were 18 years and older and identified by enrolled patients as their primary caregiver. Eligible clinicians were practicing DFCI hematologic oncologists, nurse practitioners, or physician assistants with ≥2 years of clinical experience. A qualitative researcher led thematic analysis of FG transcripts to identify key themes.

RESULTS

Twenty-three participants attended the three FGs: eight patients, seven caregivers, and eight oncology clinicians. All participants valued function and mobility assessments and felt that technology could overcome barriers to their measurement. We identified three themes related to potential benefits: making it easier for oncology teams to consider function and mobility; providing standardized, objective data; and facilitating longitudinal data. We also identified four themes related to barriers to home functional assessment: concerns related to privacy and confidentiality, burden of measuring additional patient data, challenges in operating new technology, and concerns related to data improving care.

CONCLUSION

These data suggest that specific concerns raised by older patients, caregivers, and oncology clinicians must be addressed to improve acceptability and uptake of technology used to measure function and mobility in the home.

Validity and reliability of the Apple Health app on iPhone for measuring gait parameters in children, adults, and seniors

This study assessed the concurrent validity and test-retest-reliability of the Apple Health app on iPhone for measuring gait parameters in different age groups. Twenty-seven children, 28 adults and 28 seniors equipped with an iPhone completed a 6-min walk test (6MWT). Gait speed (GS), step length (SL), and double support time (DST) were extracted from the gait recordings of the Health app. Gait parameters were simultaneously collected with an inertial sensors system (APDM Mobility Lab) to assess concurrent validity. Test-retest reliability was assessed via a second iPhone-instrumented 6MWT 1 week later. Agreement of the Health App with the APDM Mobility Lab was good for GS in all age groups and for SL in adults/seniors, but poor to moderate for DST in all age groups and for SL in children. Consistency between repeated measurements was good to excellent for all gait parameters in adults/seniors, and moderate to good for GS and DST but poor for SL in children. The Health app on iPhone is reliable and valid for measuring GS and SL in adults and seniors. Careful interpretation is required when using the Health app in children and when measuring DST in general, as both have shown limited validity and/or reliability.

Evaluation of a smartphone accelerometer system for measuring nonlinear dynamics during treadmill walking: Concurrent validity and test-retest reliability

The accelerometers embedded within smartphones may be a promising tool to capture gait patterns outside the laboratory and for extended periods of time. The current study evaluated the agreement and reliability of gait measures derived from a smartphone accelerometer system, compared to reference motion capture and footswitch systems during treadmill walking. Seventeen healthy young adults visited the laboratory on three separate days and completed three 8-minute treadmill walking trials, during each visit, at their preferred walking speed. The inter-stride interval series was calculated as the time difference between consecutive right heel contacts, located within the signals of the smartphone accelerometer, motion capture, and footswitch systems. The inter-stride interval series was used to estimate common linear gait measures and nonlinear measures, including fractal scaling index, approximate entropy, and sample entropy. Bland Altman plots with 95% limits of agreement and intraclass correlation coefficients assessed agreement and reliability, respectively. The smartphone system was found to be within the acceptable limits of agreement when compared to either reference system. The intraclass correlation coefficients values revealed moderate-to-excellent reliability for the smartphone system, with greater reliability found for linear compared to nonlinear measures and were similar to both reference systems, except for the fractal scaling index. These findings suggest the smartphone accelerometer system is a valid and reliable method for estimating linear and nonlinear gait measures during treadmill walking.

The age-related contribution of cognitive function to dual-task gait in middle-aged adults in Spain: observations from a population-based study

BACKGROUND

Poor dual-task gait performance is associated with a risk of falls and cognitive decline in adults aged 65 years or older. When and why dual-task gait performance begins to deteriorate is unknown. This study aimed to characterise the relationships between age, dual-task gait, and cognitive function in middle age (ie, aged 40-64 years).

METHODS

We conducted a secondary analysis of data from community-dwelling adults aged 40-64 years that took part in the Barcelona Brain Health Initiative (BBHI) study, an ongoing longitudinal cohort study in Barcelona, Spain. Participants were eligible for inclusion if they were able to walk independently without assistance and had completed assessments of both gait and cognition at the time of analysis and ineligble if they could not understand the study protocol, had any clinically diagnosed neurological or psychiatric diseases, were cognitively impaired, or had lower-extremity pain, osteoarthritis, or rheumatoid arthritis that could cause abnormal gait. Stride time and stride time variability were measured under single-task (ie, walking only) and dual-task (ie, walking while performing serial subtractions) conditions. Dual-task cost (DTC; the percentage increase in the gait outcomes from single-task to dual-task conditions) to each gait outcome was calculated and used as the primary measure in analyses. Global cognitive function and composite scores of five cognitive domains were derived from neuropsychological testing. We used locally estimated scatterplot smoothing to characterise the relationship between age and dual-task gait, and structural equation modelling to establish whether cognitive function mediated the association between observed biological age and dual tasks.

FINDINGS

996 people were recruited to the BBHI study between May 5, 2018, and July 7, 2020, of which 640 participants completed gait and cognitive assessments during this time (mean 24 days [SD 34] between first and second visit) and were included in our analysis (342 men and 298 women). Non-linear associations were observed between age and dual-task performance. Starting at 54 years, the DTC to stride time (β=0·27 [95% CI 0·11 to 0·36]; p<0·0001) and stride time variability (0·24 [0·08 to 0·32]; p=0·0006) increased with advancing age. In individuals aged 54 years or older, decreased global cognitive function correlated with increased DTC to stride time (β=-0·27 [-0·38 to -0·11]; p=0·0006) and increased DTC to stride time variability (β=-0·19 [-0·28 to -0·08]; p=0·0002).

INTERPRETATION

Dual-task gait performance begins to deteriorate in the sixth decade of life and, after this point, interindividual variance in cognition explains a substantial portion of dual-task performance.

FUNDING

La Caixa Foundation, Institut Guttmann, and Fundació Abertis.

Biomechanics beyond the lab: Remote technology for osteoarthritis patient data-A scoping review

The objective of this project is to produce a review of available and validated technologies suitable for gathering biomechanical and functional research data in patients with osteoarthritis (OA), outside of a traditionally fixed laboratory setting. A scoping review was conducted using defined search terms across three databases (Scopus, Ovid MEDLINE, and PEDro), and additional sources of information from grey literature were added. One author carried out an initial title and abstract review, and two authors independently completed full-text screenings. Out of the total 5,164 articles screened, 75 were included based on inclusion criteria covering a range of technologies in articles published from 2015. These were subsequently categorised by technology type, parameters measured, level of remoteness, and a separate table of commercially available systems. The results concluded that from the growing number of available and emerging technologies, there is a well-established range in use and further in development. Of particular note are the wide-ranging available inertial measurement unit systems and the breadth of technology available to record basic gait spatiotemporal measures with highly beneficial and informative functional outputs. With the majority of technologies categorised as suitable for part-remote use, the number of technologies that are usable and fully remote is rare and they usually employ smartphone software to enable this. With many systems being developed for camera-based technology, such technology is likely to increase in usability and availability as computational models are being developed with increased sensitivities to recognise patterns of movement, enabling data collection in the wider environment and reducing costs and creating a better understanding of OA patient biomechanical and functional movement data.

Gait Characteristics Analyzed with Smartphone IMU Sensors in Subjects with Parkinsonism under the Conditions of "Dry" Immersion

Parkinson's disease (PD) is increasingly being studied using science-intensive methods due to economic, medical, rehabilitation and social reasons. Wearable sensors and Internet of Things-enabled technologies look promising for monitoring motor activity and gait in PD patients. In this study, we sought to evaluate gait characteristics by analyzing the accelerometer signal received from a smartphone attached to the head during an extended TUG test, before and after single and repeated sessions of terrestrial microgravity modeled with the condition of "dry" immersion (DI) in five subjects with PD. The accelerometer signal from IMU during walking phases of the TUG test allowed for the recognition and characterization of up to 35 steps. In some patients with PD, unusually long steps have been identified, which could potentially have diagnostic value. It was found that after one DI session, stepping did not change, though in one subject it significantly improved (cadence, heel strike and step length). After a course of DI sessions, some characteristics of the TUG test improved significantly. In conclusion, the use of accelerometer signals received from a smartphone IMU looks promising for the creation of an IoT-enabled system to monitor gait in subjects with PD.

The validity and reliability of the OneStep smartphone application under various gait conditions in healthy adults with feasibility in clinical practice

Objective

Primary purpose of this study was to determine the validity and reliability of the OneStep smartphone application in healthy adults. Secondary purpose was to determine the feasibility of measuring gait dysfunction, limitation in spatiotemporal characteristics, longitudinally in patients following total hip or knee arthroplasty.

Methods

First objective, 20 healthy adults (mean age, 42.3 ± 19.7 years; 60% males; mean body mass index, 29.0 ± 5.2 kg/m²) underwent gait analysis under four gait conditions (self-selected gait speed, fixed gait speed at 0.8 m/s, fixed gait speed at 2.0 m/s and self-selected gait speed with dual task) for the validity and reliability of the smartphone to the motion laboratory. Reliability was determined by intraclass correlation coefficients. Validity was determined by Pearson correlations. Agreement was assessed by the Bland–Altman method. Second objective, 12 additional patients with total hip or knee arthroplasty (mean age, 58.7 ± 6.5 years; 58% males; mean body mass index, 28.9 ± 5.8 kg/m²) were measured at 2- and 10 weeks postoperatively. The smartphone application was used to evaluate change in gait dysfunction over time within the patients’ own environment using paired t test.

Results

The smartphone application demonstrated moderate-to-excellent intraclass correlation coefficients for reliability between-system (ICC range, 0.56–0.99), -limb (ICC range, 0.62–0.99) and -device (ICC range, 0.61–0.96) for gait analysis of healthy adults. Pearson correlations were low-to-very high between methods (r range, 0.45–0.99). Bland–Altman analysis revealed relative underestimation of spatiotemporal variables by the smartphone application compared to the motion system. For patients following total hip or knee arthroplasty, gait analysis using the OneStep application demonstrated significant improvement (p < 0.001, Cohen’s d > 0.95) in gait dysfunction between 2- and 10 weeks postoperatively.

Conclusion

The smartphone application can be a valid, reliable and feasible alternative to motion laboratories in evaluating deficits in gait dysfunction in various environments and clinical settings.

Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis

This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.

System Comparison for Gait and Balance Monitoring Used for the Evaluation of a Home-Based Training

There are currently no standard methods for evaluating gait and balance performance at home. Smartphones include acceleration sensors and may represent a promising and easily accessible tool for this purpose. We performed an interventional feasibility study and compared a smartphone-based approach with two standard gait analysis systems (force plate and motion capturing systems). Healthy adults (n = 25, 44.1 ± 18.4 years) completed two laboratory evaluations before and after a three-week gait and balance training at home. There was an excellent agreement between all systems for stride time and cadence during normal, tandem and backward gait, whereas correlations for gait velocity were lower. Balance variables of both standard systems were moderately intercorrelated across all stance tasks, but only few correlated with the corresponding smartphone measures. Significant differences over time were found for several force plate and mocap system-obtained gait variables of normal, backward and tandem gait. Changes in balance variables over time were more heterogeneous and not significant for any system. The smartphone seems to be a suitable method to measure cadence and stride time of different gait, but not balance, tasks in healthy adults. Additional optimizations in data evaluation and processing may further improve the agreement between the analysis systems.

Application of EMGB to Study Impacts of Public Green Space on Active Transport Behavior: Evidence from South Korea

Public green spaces (e.g., parks, green trails, greenways) and motivations to engage in active transport are essential for encouraging walking and cycling. However, how these key factors influence walker and cyclist behavior is potentially being increasingly influenced by the use of smart apps, as they become more ubiquitous in everyday practices. To fill this research gap, this work creates and tests a theoretically integrated study framework grounded in an extended model of goal-directed behavior, including public green space and motivation with perceived usefulness of smart apps. In order to accomplish the purpose of this study, we conducted an online survey of Korean walkers (n = 325) and cyclists (n = 326) between 10 and 25 July 2021 and applied partial least squares, structural equation, and multi-group analysis to validate the research model. Results revealed that active transport users' awareness of public green space positively influences attitude toward (γ = 0.163), as well as behavioral intention of (γ = 0.159), walking and cycling. Additionally, motivation (extrinsic and intrinsic) greatly influences attitude (γ = 0.539) and behavioral intention (γ = 0.535). Subjective norms (γ = 0.137) and positive (γ = 0.466) and negative anticipated emotions (γ = 0.225) have a significant impact on the desire that leads to behavioral intention. High and low perceived smart app usefulness also significantly moderates between public green space and attitude (t-value = 25.705), public green space and behavioral intention (t-value = 25.726), motivation and attitude (t-value = -25.561), and motivation and behavioral intention (t-value = -15.812). Consequently, the findings are useful to academics and practitioners by providing new knowledge and insights.

The reliability of gait parameters captured via instrumented walkways: a systematic review and meta-analysis

INTRODUCTION

Electronic pressure-sensitive walkways are commonly available solutions to quantitatively assess gait parameters for clinical and research purposes. Many studies have evaluated their measurement properties in different conditions with variable findings. In order to be informed about the current evidence of their reliability for optimal clinical and scientific decision making, this systematic review provided a quantitative synthesis of the test-retest reliability and minimal detectable change of the captured gait parameters across different test conditions (single and cognitive dual-task conditions) and population groups.

EVIDENCE ACQUISITION

A literature search was conducted in PubMed, Embase, and Scopus until November 2021 to identify articles that examined the test-retest reliability properties of the gait parameters captured by pressure-sensitive walkways (gait speed, cadence, stride length and time, double support time, base of support) in adult healthy individuals or patients. The methodological quality was rated using the Consensus-Based Standards for the Selection of Health Measurement Instruments Checklist. Data were meta-analyzed on intraclass correlation coefficient to examine the test-retest relative reliability. Quantitative synthesis was performed for absolute reliability, examined by the weighted average of minimal detectable change values.

EVIDENCE SYNTHESIS

A total of 44 studies were included in this systematic review. The methodological quality was adequate in half of the included studies. The main finding was that pressure-sensitive walkways are reliable tools for objective assessment of spatial and temporal gait parameters both in single-and cognitive dual-task conditions. Despite few exceptions, the review identified intraclass correlation coefficient higher than 0.75 and minimal detectable change lower than 30%, demonstrating satisfactory relative and absolute reliability in all examined populations (healthy adults, elderly, patients with cognitive impairment, spinocerebellar ataxia type 14, Huntington's disease, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, spinal cord injury, stroke or vestibular dysfunction).

CONCLUSIONS

Current evidence suggested that, despite different populations and testing protocols used in the included studies, the test-retest reliability of the examined gait parameters was acceptable under single and cognitive dual-task conditions. Further high-quality studies with powered sample sizes are needed to examine the reliability findings of the currently understudied and unexplored pathologies and test conditions.

Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis

BACKGROUND

Use of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS).

METHODS

Ten young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks.

RESULTS

Twenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions.

CONCLUSION

The present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.

Impact of walking states, self-reported daily walking amount and age on the gait of older adults measured with a smart-phone app: a pilot study

BACKGROUND

Smartphones provide a cost-effective avenue for gait assessment among older adults in the community. The purpose of this study is to explore the impact of walking state, self-reported daily walking amount, and age on gait quality, using a smartphone application.

METHODS

One hundred older adult individuals from North China, aged 73.0 ± 7.7 years, voluntarily participated in this study. They performed three walking tests: normal walking, fast walking, and visually impaired walking. Three-dimensional acceleration data for gait were obtained using the smartphone app Pocket Gait. This study used multivariate analysis of variance (MANOVA) to explore the effects of the walking state, self-reported daily walking amount, and age on the step frequency, root mean square (RMS) acceleration, step time variability, regularity, and symmetry.

RESULTS

The walking state, self-reported daily walking amount, and age had statistically significant effects on gait quality. Compared with normal walking, the step frequency, RMS acceleration, variability, and regularity were greater in the fast-walking state, and simulated visually impaired walking did not significantly affect gait quality. Relatively older individuals had a significant decline in gait quality compared to (relatively) younger older adult individuals. Compared with older adults who walked less than 1 km a day, older adults who walked more had better gait quality.

CONCLUSIONS

The walking state, self-reported daily walking amount, and age have a significant effect on the gait quality of older adults. Walking with pigmented sunglasses can be used as a training intervention to improve gait performance. Older adult people who walk less than 1 km/day have worse gait quality compared with their counterparts.

Smartphone-based inertial sensors technology - Validation of a new application to measure spatiotemporal gait metrics

BACKGROUND

Smartphones are increasingly recognized as the future technology for clinical gait assessment.

RESEARCH QUESTION

To determine the concurrent validity of gait parameters obtained using the smartphone technology and application in a group of patients with musculoskeletal pathologies.

METHODS

Patients with knee, lower back, hip, or ankle pain were included in the study (n = 72). Spatiotemporal outcomes were derived from the walkway and the smartphone simultaneously. Pearson's correlations and limits of agreement (LoA) determined the association between the two methods.

RESULTS

Cadence and gait cycle time showed excellent correlation and agreement between the smartphone and the walkway (cadence: r = 0.997, LoA=1.4%, gait cycle time: r = 0.996, LoA = 1.6%). Gait speed, double-limb support and left and right step length demonstrated strong correlations and moderate agreement between methods (gait speed: r = 0.914, LoA=15.4%, left step length: r = 0.842, LoA = 17.0%, right step length: r = 0.800, LoA=16.4%). The left and right measures of single-limb support and stance percent showed a consistent 4% bias across instruments, yielding moderate correlation and very good agreement between the smartphone and the walkway (r = 0.532, LoA = 9% and r = 0.460, LoA=9.8% for left and right single-limb support; r = 0.463, LoA = 5.1% and r = 0.533, LoA = 4.4% for left and right stance).

SIGNIFICANCE

The examined application appears to be a valid tool for gait analysis, providing clinically significant metrics for the assessment of patients with musculoskeletal pathologies. However, additional studies should examine the technology amongst patients with severe gait abnormalities.

Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders

The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

Validity and Reliability of a Smartphone App for Gait and Balance Assessment

Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20–69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants’ postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson’s correlations (r_p) and limits of the agreement (LoA%). The within-session test–retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ r_p ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed.

Gait Analysis Using Accelerometry Data from a Single Smartphone: Agreement and Consistency between a Smartphone Application and Gold-Standard Gait Analysis System

Spatio-temporal parameters of human gait, currently measured using different methods, provide valuable information on health. Inertial Measurement Units (IMUs) are one such method of gait analysis, with smartphone IMUs serving as a good substitute for current gold-standard techniques. Here we investigate the concurrent validity of a smartphone placed in a front-facing pocket to perform gait analysis. Sixty community-dwelling healthy adults equipped with a smartphone and an application for gait analysis completed a 2-min walk on a marked path. Concurrent validity was assessed against an APDM mobility lab (APDM Inc.; Portland, OR, USA). Bland–Altman plots and intraclass correlation coefficients (agreement and consistency) for gait speed, cadence, and step length indicate good to excellent agreement (ICC_2,1 > 0.8). For right leg stance and swing % of gait cycle and double support % of gait cycle, results were moderate (0.52 < ICC_2,1 < 0.62). For left leg stance and swing % of gait cycle left results show poor agreement (ICC_2,1 < 0.5). Consistency of results was good to excellent for all tested parameters (ICC_3,1 > 0.8). Thus we have a valid and reliable instrument for measuring healthy adults’ spatio-temporal gait parameters in a controlled walking environment.

Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations

Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

Validation of quantitative gait analysis systems for Parkinson's disease for use in supervised and unsupervised environments

BACKGROUND

Gait impairments are among the most common and impactful symptoms of Parkinson's disease (PD). Recent technological advances aim to quantify these impairments using low-cost wearable systems for use in either supervised clinical consultations or long-term unsupervised monitoring of gait in ecological environments. However, very few of these wearable systems have been validated comparatively to a criterion of established validity.

OBJECTIVE

We developed two movement analysis solutions (3D full-body kinematics based on inertial sensors, and a smartphone application) in which validity was assessed versus the optoelectronic criterion in a population of PD patients.

METHODS

Nineteen subjects with PD (7 female) participated in the study (age: 62 ± 12.27 years; disease duration: 6.39 ± 3.70 years; HY: 2 ± 0.23). Each participant underwent a gait analysis whilst barefoot, at a self-selected speed, for a distance of 3 times 10 m in a straight line, assessed simultaneously with all three systems.

RESULTS

Our results show excellent agreement between either solution and the optoelectronic criterion. Both systems differentiate between PD patients and healthy controls, and between PD patients in ON or OFF medication states (normal difference distributions pooled from published research in PD patients in ON and OFF states that included an age-matched healthy control group). Fair to high waveform similarity and mean absolute errors below the mean relative orientation accuracy of the equipment were found when comparing the angular kinematics between the full-body inertial sensor-based system and the optoelectronic criterion.

CONCLUSIONS

We conclude that the presented solutions produce accurate results and can capture clinically relevant parameters using commodity wearable sensors or a simple smartphone. This validation will hopefully enable the adoption of these systems for supervised and unsupervised gait analysis in clinical practice and clinical trials.

Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson's Disease

Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions.

Simple Smartphone-Based Assessment of Gait Characteristics in Parkinson Disease: Validation Study

Background

Parkinson disease (PD) is a common movement disorder. Patients with PD have multiple gait impairments that result in an increased risk of falls and diminished quality of life. Therefore, gait measurement is important for the management of PD.

Objective

We previously developed a smartphone-based dual-task gait assessment that was validated in healthy adults. The aim of this study was to test the validity of this gait assessment in people with PD, and to examine the association between app-derived gait metrics and the clinical and functional characteristics of PD.

Methods

Fifty-two participants with clinically diagnosed PD completed assessments of walking, Movement Disorder Society Unified Parkinson Disease Rating Scale III (UPDRS III), Montreal Cognitive Assessment (MoCA), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D) rating scale tests. Participants followed multimedia instructions provided by the app to complete two 20-meter trials each of walking normally (single task) and walking while performing a serial subtraction dual task (dual task). Gait data were simultaneously collected with the app and gold-standard wearable motion sensors. Stride times and stride time variability were derived from the acceleration and angular velocity signal acquired from the internal motion sensor of the phone and from the wearable sensor system.

Results

High correlations were observed between the stride time and stride time variability derived from the app and from the gold-standard system (r=0.98-0.99, P<.001), revealing excellent validity of the app-based gait assessment in PD. Compared with those from the single-task condition, the stride time (F_1,103=14.1, P<.001) and stride time variability (F_1,103=6.8, P=.008) in the dual-task condition were significantly greater. Participants who walked with greater stride time variability exhibited a greater UPDRS III total score (single task: β=.39, P<.001; dual task: β=.37, P=.01), HAM-A (single-task: β=.49, P=.007; dual-task: β=.48, P=.009), and HAM-D (single task: β=.44, P=.01; dual task: β=.49, P=.009). Moreover, those with greater dual-task stride time variability (β=.48, P=.001) or dual-task cost of stride time variability (β=.44, P=.004) exhibited lower MoCA scores.

Conclusions

A smartphone-based gait assessment can be used to provide meaningful metrics of single- and dual-task gait that are associated with disease severity and functional outcomes in individuals with PD.

How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data

BACKGROUND

Low-cost sensor devices are often limited in terms of sample rate. Based on signal periodicity, the Nyquist theorem allows determining the minimum theoretical sample rate required to adequately capture cyclical events, such as pelvic movement in trotting horses.

OBJECTIVES

To quantify the magnitude of errors arising with reduced sample rates when capturing biological signals using the example of pelvic time-displacement series and derived minima and maxima used to quantify movement asymmetry in lame horses.

STUDY DESIGN

Data comparison.

METHODS

Root mean square (RMS) errors between the 'reference' time-displacement series, captured with a validated inertial sensor at 100 Hz sample rate, and down-sampled time-series (8 Hz to 50 Hz) are calculated. Accuracy and precision are determined for maxima and minima derived from the time-displacement series.

RESULTS

Average RMS errors are <2 mm at 50 Hz sample rate, <4 mm at 40 Hz, <7 mm between 25 and 35 Hz, and increase to up to 20 mm at 20 Hz and below. Accuracy for maxima and minima is generally below 1mm. Precision is 1 mm at 50 Hz sample rate, 3 mm at 40Hz and ≥9 mm at 20 Hz and below.

MAIN LIMITATIONS

Only sample rate, no other sensor parameters were investigated.

CONCLUSIONS

Sample rate related errors for inertial sensor derived time-displacement series of pelvic movement are <2mm at 50 Hz, a rate that many low-cost loggers, smartphones or wireless sensors can sustain hence rendering these devices valid options for quantifying parameters relevant for lameness examinations in horses.

Comparison of Subjective and Objective Assessments on Improvement in Gait Function after Carotid Endarterectomy

The purpose of the present study was to determine whether objective gait test scores obtained using a tri-axial accelerometer can detect subjective improvement in gait as determined by the patient after carotid endarterectomy (CEA). Each patient undergoing CEA for ipsilateral internal carotid artery stenosis determined whether their gait was subjectively improved at six months after CEA when compared with preoperatively. Gait testing using a tri-axial accelerometer was also performed preoperatively and six months postoperatively. Twelve (15%) of 79 patients reported subjectively improved gait. Areas under the receiver operating characteristic curve for differences between pre- and postoperative test values in stride time, cadence, and ground floor reaction for detecting subjectively improved gait were 0.995 (95% confidence interval (CI), 0.945-1.000), 0.958 (95%CI, 0.887-0.990), and 0.851 (95%CI, 0.753-0.921), respectively. Cut-off points for value differences in detecting subjectively improved gait were identical to mean -1.7 standard deviation (SD) for stride time, mean +1.6 SD for cadence, and mean +0.4 SD for ground floor reaction of control values from normal subjects. Objective gait test scores obtained using the tri-axial accelerometer can detect subjective gait improvements after CEA. When determining significant postoperative improvements in gait using a tri-axial accelerometer, optimal cut-off points for each test value can be defined.

Dual-task gait speed assessments with an electronic walkway and a stopwatch in older adults. A reliability study

BACKGROUND/OBJECTIVES

Slow gait speed prospectively predicts elevated risk of adverse events such as falls, morbidity, and mortality. Additionally, gait speed under a cognitively demanding challenge (dual-task gait) predicts further cognitive decline and dementia incidence. This evidence has been mostly collected using electronic walkways; however, not all clinical set ups have an electronic walkway and comparability with simple manual dual-gait speed testing, like a stopwatch, has not yet been examined. Our main objective was to assess concurrent-validity and reliability of gait speed assessments during dual-tasking using a stopwatch and electronic walkway in older adults with mild and subjective cognitive impairment (MCI and SCI).

DESIGN

Cross-sectional, reliability study.

SETTING

Clinic based laboratory at an academic hospital in London, ON, Canada.

PARTICIPANTS

237 walk tests from 34 community-dwelling participants (mean age 71.84 SD 5.38; 21 female - 62%, 13 male - 38%) with SCI and MCI. were included from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS-ND) study.

INTERVENTION

Each participant performed seven walk tests: three single gait walks at their normal pace, three dual-task walks (walking and counting backwards by one, by sevens, and naming animals), and one fast walk.

MEASUREMENTS

Gait speed (cm/s) for each walk was measured simultaneously with an electronic walkway (Zeno Mat®) and a handheld stopwatch (Ultrak chronometer®). Dual-task cost (DTC) was calculated for the three individual dual-task walks as [((single gait speed - dual-task gait speed) / single gait speed) ∗ 100]. Level of agreement between the two measurement methods was analyzed using Pearson correlations, paired t-tests, and Bland-Altman plots.

RESULTS

Gait speed was consistently lower when measured with the stopwatch than with the electronic walkway (mean speed difference: 10.6 cm/s ± 5.1, p < 0.001). Calculating DTC, however, yielded very similar results with both methods (mean DTC difference: 0.19 ± 1.18, p = 0.872). The higher the DTC, the closer the measurement between methods.

CONCLUSION

Assessing and calculating DTC with a stopwatch is simple, accessible and reliable. Its validity and reliability were high in this clinical sample of community older adults with SCI and MCI.

Improvement in gait function after carotid endarterectomy is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex: a 123I-iomazenil SPECT study

OBJECTIVE

Carotid endarterectomy (CEA) often restores cerebral perfusion and neurotransmitter receptor function, which is seen on early and late images, respectively, on brain I-iomazenil single-photon emission computed tomography (SPECT). The reliability of gait-related parameters obtained using a triaxial accelerometer, a portable device for gait assessment, has been confirmed with test-retest measurements. The purpose of the present prospective cohort study was to determine whether improvement in gait function after CEA is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex.

METHODS

Gait testing using a triaxial accelerometer was performed preoperatively and 6 months postoperatively in 64 patients undergoing CEA for ipsilateral internal carotid artery stenosis (≥70%). I-iomazenil SPECT was also performed with scanning within 30 min (early images) and at 180 min (late images) after tracer administration before and after surgery. SPECT data were analyzed using a three-dimensional stereotactic surface projection, and motor (Brodmann 4) and premotor (Brodmann 6) cortexes in each hemisphere were combined and defined as the motor-related cortex.

RESULTS

Based on preoperative and postoperative gait testing, seven patients (11%) showed postoperative improved gait. Logistic regression analysis revealed that postoperative increase in I-iomazenil uptake in the motor-related cortex ipsilateral to surgery on early [95% confidence interval (CI), 4.32-365.21; P = 0.0477) or late (95% CI, 9.45-1572.57; P = 0.0173) images was an independent predictor of postoperative improved gait.

CONCLUSIONS

Improvement in gait function after CEA is associated with postoperative recovery in perfusion and neurotransmitter receptor function in the motor-related cerebral cortex.

Validity and reliability of wearable inertial sensors in healthy adult walking: a systematic review and meta-analysis

BACKGROUND

Inertial measurement units (IMUs) offer the ability to measure walking gait through a variety of biomechanical outcomes (e.g., spatiotemporal, kinematics, other). Although many studies have assessed their validity and reliability, there remains no quantitive summary of this vast body of literature. Therefore, we aimed to conduct a systematic review and meta-analysis to determine the i) concurrent validity and ii) test-retest reliability of IMUs for measuring biomechanical gait outcomes during level walking in healthy adults.

METHODS

Five electronic databases were searched for journal articles assessing the validity or reliability of IMUs during healthy adult walking. Two reviewers screened titles, abstracts, and full texts for studies to be included, before two reviewers examined the methodological quality of all included studies. When sufficient data were present for a given biomechanical outcome, data were meta-analyzed on Pearson correlation coefficients (r) or intraclass correlation coefficients (ICC) for validity and reliability, respectively. Alternatively, qualitative summaries of outcomes were conducted on those that could not be meta-analyzed.

RESULTS

A total of 82 articles, assessing the validity or reliability of over 100 outcomes, were included in this review. Seventeen biomechanical outcomes, primarily spatiotemporal parameters, were meta-analyzed. The validity and reliability of step and stride times were found to be excellent. Similarly, the validity and reliability of step and stride length, as well as swing and stance time, were found to be good to excellent. Alternatively, spatiotemporal parameter variability and symmetry displayed poor to moderate validity and reliability. IMUs were also found to display moderate reliability for the assessment of local dynamic stability during walking. The remaining biomechanical outcomes were qualitatively summarized to provide a variety of recommendations for future IMU research.

CONCLUSIONS

The findings of this review demonstrate the excellent validity and reliability of IMUs for mean spatiotemporal parameters during walking, but caution the use of spatiotemporal variability and symmetry metrics without strict protocol. Further, this work tentatively supports the use of IMUs for joint angle measurement and other biomechanical outcomes such as stability, regularity, and segmental accelerations. Unfortunately, the strength of these recommendations are limited based on the lack of high-quality studies for each outcome, with underpowered and/or unjustified sample sizes (sample size median 12; range: 2-95) being the primary limitation.

Innovations in research and clinical care using patient-generated health data

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

A Mobile Phone-Based Gait Assessment App for the Elderly: Development and Evaluation

Background

Gait disorders are common among older adults. With an increase in the use of technology among older adults, a mobile phone app provides a solution for older adults to self-monitor their gait quality in daily life.

Objective

This study aimed to develop a gait-monitoring mobile phone app (Pocket Gait) and evaluate its acceptability and usability among potential older users.

Methods

The app was developed to allow older adults to track their gait quality, including step frequency, acceleration root mean square (RMS), step regularity, step symmetry, and step variability. We recruited a total of 148 community-dwelling older adults aged 60 years and older from two cities in China: Beijing and Chongqing. They walked in three ways (single task, dual task, and fast walking) using a smartphone with the gait-monitoring app installed and completed an acceptability and usability survey after the walk test. User acceptability was measured by a questionnaire including four quantitative measures: perceived ease of use, perceived usefulness, ease of learning, and intention to use. Usability was measured using the System Usability Scale (SUS). Interviews were conducted with participants to collect open-ended feedback questions.

Results

Task type had a significant effect on all gait parameters, namely, step frequency, RMS, step variability, step regularity, and step symmetry (all P values <.001). Age had a significant effect on step frequency (P=.01), and region had a significant effect on step regularity (P=.04). The acceptability of the gait-monitoring app was positive among older adults. Participants identified the usability of the system with an overall score of 59.7 (SD 10.7) out of 100. Older adults from Beijing scored significantly higher SUS compared with older adults from Chongqing (P<.001). The age of older adults was significantly associated with their SUS score (P=.048). Older adults identified improvements such as a larger font size, inclusion of reference values for gait parameters, and inclusion of heart rate and blood pressure monitoring.

Conclusions

This mobile phone app is a health management tool for older adults to self-manage their gait quality and prevent adverse outcomes. In the future, it will be important to take factors such as age and region into consideration while designing a mobile phone–based gait assessment app. The feedback of the participants would help to design more elderly-friendly products.

The Coefficient of Variation of Step Time Can Overestimate Gait Abnormality: Test-Retest Reliability of Gait-Related Parameters Obtained with a Tri-Axial Accelerometer in Healthy Subjects

The aim of this study was to investigate whether variation in gait-related parameters among healthy participants could help detect gait abnormalities. In total, 36 participants (21 men, 15 women; mean age, 35.7 ± 9.9 years) performed a 10-m walk six times while wearing a tri-axial accelerometer fixed at the L3 level. A second walk was performed ≥1 month after the first (mean interval, 49.6 ± 7.6 days). From each 10-m data set, the following nine gait-related parameters were automatically calculated: assessment time, number of steps, stride time, cadence, ground force reaction, step time, coefficient of variation (CV) of step time, velocity, and step length. Six repeated measurement values were averaged for each gait parameter. In addition, for each gait parameter, the difference between the first and second assessments was statistically examined, and the intraclass correlation coefficient (ICC) was calculated with the level of significance set at p < 0.05. Only the CV of step time showed a significant difference between the first and second assessments (p = 0.0188). The CV of step time also showed the lowest ICC, at <0.50 (0.425), among all parameters. Test-retest results of gait assessment using a tri-axial accelerometer showed sufficient reproducibility in terms of the clinical evaluation of all parameters except the CV of step time.

Self-Reported Head Trauma Predicts Poor Dual Task Gait in Retired National Football League Players

OBJECTIVE

Symptomatic head trauma associated with American-style football (ASF) has been linked to brain pathology, along with physical and mental distress in later life. However, the longer-term effects of such trauma on objective metrics of cognitive-motor function remain poorly understood. We hypothesized that ASF-related symptomatic head trauma would predict worse gait performance, particularly during dual task conditions (ie, walking while performing an additional cognitive task), in later life.

METHODS

Sixty-six retired professional ASF players aged 29 to 75 years completed a health and wellness questionnaire. They also completed a validated smartphone-based assessment in their own homes, during which gait was monitored while they walked normally and while they performed a verbalized serial-subtraction cognitive task.

RESULTS

Participants who reported more symptomatic head trauma, defined as the total number of impacts to the head or neck followed by concussion-related symptoms, exhibited greater dual task cost (ie, percentage increase) to stride time variability (ie, the coefficient of variation of mean stride time). Those who reported ≥1 hit followed by loss of consciousness, compared to those who did not, also exhibited greater dual task costs to this metric. Relationships between reported trauma and dual task costs were independent of age, body mass index, National Football League career duration, and history of musculoskeletal surgery. Symptomatic head trauma was not correlated with average stride times in either walking condition.

INTERPRETATION

Remote, smartphone-based assessments of dual task walking may be utilized to capture meaningful data sensitive to the long-term impact of symptomatic head trauma in former professional ASF players and other contact sport athletes. ANN NEUROL 2020;87:75-83.

Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System

Various gait parameters can be used to assess the risk of falling in older adults. However, the state-of-the-art systems used to quantify gait parameters often come with high costs as well as training and space requirements. Gait analysis systems, which use mobile and commercially available cameras, can be an easily available, marker-free alternative. In a study with 44 participants (age ≥ 65 years), gait patterns were analyzed with three different systems: a pressure sensitive walkway system (GAITRite-System, GS) as gold standard, Motognosis Labs Software using a Microsoft Kinect Sensor (MKS), and a smartphone camera-based application (SCA). Intertrial repeatability showed moderate to excellent results for MKS (ICC(1,1) 0.574 to 0.962) for almost all measured gait parameters and moderate reliability in SCA measures for gait speed (ICC(1,1) 0.526 to 0.535). All gait parameters of MKS showed a high level of agreement with GS (ICC(2,k) 0.811 to 0.981). Gait parameters extracted with SCA showed poor reliability. The tested gait analysis systems based on different camera systems are currently only partially able to capture valid gait parameters. If the underlying algorithms are adapted and camera technology is advancing, it is conceivable that these comparatively simple methods could be used for gait analysis.

Are cost-effective technologies feasible to measure gait in older adults? A systematic review of evidence-based literature

BACKGROUND

Unrestricted by time and place, innovative technologies seem to provide cost-effective solutions for gait assessment in older adults.

OBJECTIVE

The objective of this study is to provide an overview of gait assessment for older adults by investigating critical gait characteristics of older adults, discussing advantages and disadvantages of the current gait assessment technologies, as well as device applicability.

METHODS

The Preferred Reporting Item for Systematic Reviews and Meta-Analysis (PRISMA) guidelines were followed during the review. Inclusion criteria were: (1) Sample consisting of adults older than 60 years; (2) qualitative, quantitative, or mixed-method researches using one or more specific gait assessment technologies; and (3) publication in English between 2000 and 2018.

RESULTS

In total, twenty-one studies were included. Gait speed, stride length, frequency, acceleration root mean square, step-to-step consistency, autocorrelation, harmonic ratio were reported in the existing literatures to be associated with falls. The enrolled studies address the use of pedometer, wearable accelerometer-based devices, Kinect, Nintendo Wii Balance Board as cost-effective gait assessment technologies.

CONCLUSIONS

Gait parameters and assessment approaches for older adults are diverse. Cost-effective technologies such as a wearable accelerometer-based device, Kinect, and the Nintendo Wii Balance Board provide potential alternatives for gait assessment with acceptable validity and reliability compared with sophisticated devices. The popularity and development of cost-effective devices have made large-scale data collection for gait assessment possible in the daily environment. Further study could involve older adults and their family members/caregivers in use of these technologies to design elderly-friendly products.

Investigating Gains in Neurocognition in an Intervention Trial of Exercise (IGNITE): Protocol

Despite the ubiquity of normal age-related cognitive decline there is an absence of effective approaches for improving neurocognitive health. Fortunately, moderate intensity exercise is a promising method for improving brain and cognitive health in late life, but its effectiveness remains a matter of skepticism and debate because of the absence of large, comprehensive, Phase III clinical trials. Here we describe the protocol for such a randomized clinical trial called IGNITE (Investigating Gains in Neurocognition in an Intervention Trial of Exercise), a study capable of more definitively addressing whether exercise influences cognitive and brain health in cognitively normal older adults. We are conducting a 12-month, multi-site, randomized dose-response exercise trial in 639 cognitively normal adults between 65 and 80 years of age. Participants are randomized to (1) a moderate intensity aerobic exercise condition of 150 min/week (N = 213), (2) a moderate intensity aerobic exercise condition at 225 min/week (N = 213), or (3) a light intensity stretching-and-toning control condition for 150 min/week (N = 213). Participants are engaging in 3 days/week of supervised exercise and two more days per week of unsupervised exercise for 12 months. A comprehensive cognitive battery, blood biomarkers and battery of psychosocial questionnaires is assessed at baseline, 6 and 12-months. In addition, brain magnetic resonance imaging, physiological biomarkers, cardiorespiratory fitness, physical function, and positron emission tomography of amyloid deposition are assessed at baseline and at the 12-month follow-up. The results from this trial could transform scientific-based policy and health care recommendations for approaches to improve cognitive function in cognitively normal older adults.

Test-retest reliability of a smartphone app for measuring core stability for two dynamic exercises

Background

Recently, there has been growing interest in using smartphone applications to assess gait speed and quantify isometric core stability exercise intensity. The purpose of this study was to investigate the between-session reliability and minimal detectable change of a smartphone app for two dynamic exercise tests of the lumbopelvic complex.

Methods

Thirty-three healthy young and active students (age: 22.3 ± 5.9 years, body weight: 66.9 ± 11.3 kg, height: 167.8 ± 10.3 cm) participated in this study. Intraclass correlation coefficient (ICC), coefficient of variation (%CV), and Bland–Altman plots were used to verify the reliability of the test. The standard error of measurement (SEM) and the minimum detectable difference (MDD) were calculated for clinical applicability.

Results

The ICCs ranged from 0.73 to 0.96, with low variation (0.9% to 4.8%) between days of assessments. The Bland–Altman plots and one-sample t-tests (p > 0.05) indicated that no dynamic exercise tests changed systematically. Our analyses showed that SEM 0.6 to 1.5 mm/s-2) and MDD (2.1 to 3.5 mm/s-2).

Conclusion

The OCTOcore app is a reliable tool to assess core stability for two dynamic exercises. A minimal change of 3.5 mm/s-2 is needed to be confident that the change is not a measurement error between two sessions.

The Football Players' Health Study at Harvard University: Design and objectives

The Football Players Health Study at Harvard University (FPHS) is a unique transdisciplinary, strategic initiative addressing the challenges of former players’ health after having participated in American style football (ASF). The whole player focused FPHS is designed to deepen understanding of the benefits and risks of participation in ASF, identify risks that are potentially reversible or preventable, and develop interventions or approaches to improve the health and wellbeing of former players. We are recruiting and following a cohort of former professional ASF players who played since 1960 (current n = 3785). At baseline, participants complete a self‐administered standardized questionnaire, including initial reporting of exposure history and physician‐diagnosed health conditions. Additional arms of the initiative are addressing targeted studies, including promising primary, secondary, and tertiary interventions; extensive in‐person clinical phenotyping, and legal and ethical concerns of the play. This paper describes the components of the FPHS studies undertaken and completed thus far, as well as those studies currently underway or planned for the near future. We present our initiatives herein as a potential paradigm of one way to proceed (acknowledging that it is not the only way). We share what we have learned so that it may be useful to others, particularly in regard to trying to make professional sports meet the needs of multiple stakeholders ranging from players to owners, to fans, and possibly even to parents making decisions for their children.