Normal limits of home measured spatial gait parameters of the elderly population and their association with health variables

Predicting Post-Fracture Recovery with Smartphone Mobility Data: A Proof-of-Concept Study

BACKGROUND

After a lower-extremity fracture, the patient's priority is to regain function. To date, our ability to measure function has been limited. However, high-fidelity sensors in smartphones continuously measure mobility, providing an expansive pre- and post-injury gait history. We assessed whether pre-injury mobility data, combined with demographic and injury data, reliably predicted post-fracture mobility.

METHODS

We enrolled 107 adult patients (mean age, 45 years; 43% female, 62% White, 36% Black, 1% Asian, 1% more than one race) ≥6 months after the surgical treatment of a lower-extremity fracture. Consenting patients exported their Apple iPhone mobility metrics, including step count, walking speed, step length, walking asymmetry, and double-support time. We integrated these mobility measures with demographic and injury data. Using nonlinear modeling, we assessed whether pre-injury mobility metrics combined with baseline data predicted post-fracture mobility.

RESULTS

All models were well calibrated and had model fits ranging from an adjusted R2 of 0.18 (walking asymmetry) to 0.61 (double-support time). Pre-injury function strongly predicted post-injury mobility in all models. After the injury, the average daily step count increased by 65 steps each week (95% confidence interval [CI], 56 to 75). Weekly gains were significantly greater within 6 weeks after the injury (92 daily steps per week; 95% CI, 58 to 127) than 20 to 26 weeks post-injury (19 daily steps per week; 95% CI, 11 to 27; p < 0.001). Greater pre-injury steps were associated with increased post-injury mobility (301 daily steps post-injury per 1,000 steps pre-injury; 95% CI, 235 to 367). Mean walking speed declined by 0.200 m/s (95% CI, -0.257 to -0.143) from injury to 8 weeks post-injury. From 12 to 26 weeks post-injury, the average walking speed increased by 0.071 m/s (95% CI, 0.044 to 0.097).

CONCLUSIONS

These proof-of-concept findings highlight the value of high-fidelity pre-injury mobility data in predicting recovery. Individualized recovery projections can provide patient-friendly counseling tools and useful clinical insight for surgeons.

LEVEL OF EVIDENCE

Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Step Width Estimation in Individuals With and Without Neurodegenerative Disease via a Novel Data-Augmentation Deep Learning Model and Minimal Wearable Inertial Sensors

Step width is vital for gait stability, postural balance control, and fall risk reduction. However, estimating step width typically requires either fixed cameras or a full kinematic body suit of wearable inertial measurement units (IMUs), both of which are often too expensive and time-consuming for clinical application. We thus propose a novel data-augmented deep learning model for estimating step width in individuals with and without neurodegenerative disease using a minimal set of wearable IMUs. Twelve patients with neurodegenerative, clinically diagnosed Spinocerebellar ataxia type 3 (SCA3) performed over ground walking trials, and seventeen healthy individuals performed treadmill walking trials at various speeds and gait modifications while wearing IMUs on each shank and the pelvis. Results demonstrated step width mean absolute errors of 3.3 0.7 cm and 2.9 0.5 cm for the neurodegenerative and healthy groups, respectively, which were below the minimal clinically important difference of 6.0 cm. Step width variability mean absolute errors were 1.5 cm and 0.8 cm for neurodegenerative and healthy groups, respectively. Data augmentation significantly improved accuracy performance in the neurodegenerative group, likely because they exhibited larger variations in walking kinematics as compared with healthy subjects. These results could enable clinically meaningful and accurate portable step width monitoring for individuals with and without neurodegenerative disease, potentially enhancing rehabilitative training, assessment, and dynamic balance control in clinical and real-life settings.

Relationship between Muscle Strength and Gait Parameters in Healthy Older Women and Men

Maintaining sufficient muscle strength is fundamental to prevent a decline in basic physical functions such as gait, and is therefore a prerequisite for a healthy independent life in older people. However, the relationship between gait parameters and the strength of single muscle groups is reported with inconclusive results. The objective of this study was to analyze the relationship of strength of nine single muscle groups of lower and upper leg muscles as well as handgrip strength for gait parameters in older adults. Sixty-nine independently living older adults participated in the study. Maximum ankle plantar- and dorsiflexion, knee flexion and extension, as well as hip abduction, adduction, flexion, and extension strength, were measured using an isokinetic dynamometer. Additionally, hand grip strength measured via a hand dynamometer was obtained. Walking gait parameters were recorded with a 3D motion capture system on an instrumented treadmill. The relationships between multiple strength and gait variables were analyzed by Pearson's correlation coefficient. Linear regression analyses were performed to identify the predictive ability of muscle strength (normalized to body weight) for gait speed, stride time, stance time, stride length and step width. Multiple significant weak to moderate positive ([r = 0.343, p = 0.047]-[r = 0.538, p = 0.002]) and negative ([r = -0.340, p = 0.046]-[r = 0.593, p = 0.001]) correlations that were unequally distributed between both sexes were detected. Significant regression models explained ([r² = 16.6%, p = 0.015]-[r² = 44.3 %, p = 0.003]) and ([r² = 21.8%, p = 0.022]-[r² = 36.1%, p = 0.044]) of the gait parameter variations for men and women, respectively. The results suggest a sex-specific relevance of single muscle groups for all gait parameters. This may be attributed to anatomical differences and it is important to prevent strength-related changes in gait parameters.

Identification of characteristics of foot position and angle during swing phase in fallers using principal component analysis

Identifying the characteristics of fallers is important for preventing falls because such events may reduce quality of life. It has been reported that several variables related to foot positions and angles during gait (e.g., sagittal foot angle and minimum toe clearance) differ between fallers and non-fallers. However, examining such representative discrete variables may not be sufficient to detect crucial information, which may be contained in the large portions of unanalyzed data. Therefore, we aimed to identify the comprehensive characteristics of foot position and angle during the swing phase of gait in non-fallers and fallers using principal component analysis (PCA). Thirty non-fallers and 30 fallers were recruited for this study. We performed PCA to reduce the dimensions of foot positions and angles during the swing phase and obtained principal component scores (PCSs) for each principal component vector (PCV), which were then compared between groups. The results revealed that the PCS of PCV3 in fallers was significantly larger than that in non-fallers (p = 0.003, Cohen's d = 0.80). We reconstructed waveforms of foot positions and angles during the swing phase using PCV3 and our main findings can be summarized as follows. Compared to non-fallers, fallers have a 1) low average foot position in the z-axis (i.e., height) during the initial swing phase 2) small average foot angle in the x-axis (i.e., rotation in the sagittal plane), during the initial swing phase, and 3) large variability in foot position in the y-axis (i.e., anterior/posterior position) during the initial swing phase. We can conclude that these are characteristics of gait related to fallers. Therefore, our findings may be beneficial for evaluating fall risk during gait using a device such as a shoe- or insole-embedded inertial measurement unit.

Stride Length Predicts Adverse Clinical Events in Older Adults: A Systematic Review and Meta-Analysis

Background: This meta-analysis aims to estimate the power of walking stride length as a predictor of adverse clinical events in older adults. Methods: We searched all electronic databases until April 2021 for studies reporting stride length and other spatial gait parameters, including stride velocity, stride width, step width and stride variability, and compared them with clinical outcomes in the elderly. Meta-analyses of odds ratios (ORs) of effects of stride length on clinical outcomes used the generic inverse variance method and random model effects. Clinical outcomes were major adverse events (MAEs), physical disability and mortality. Results: Eleven cohort studies with 14,167 patients (mean age 75.4 ± 5.6 years, 55.8% female) were included in the analysis. At 33.05 months follow up, 3839 (27%) patients had clinical adverse events. Baseline stride length was shorter, WMD −0.15 (−0.19 to −0.11, p < 0.001), and stride length variability was higher, WMD 0.67 (0.33 to 1.01, p < 0.001), in fallers compared to non-fallers. Other gait parameters were not different between the two groups (p > 0.05 for all). Short stride length predicted MAE OR 1.36 (95% CI; 1.19 to 1.55, p < 0.001), physical disability OR 1.26 (95% CI; 1.11 to 1.44, p = 0.004) and mortality OR 1.69 (95% CI; 1.41 to 2.02, p < 0.001). A baseline normalized stride length ≤ 0.64 m was more accurate in predicting adverse clinical events, with summary sensitivity 65% (58–71%), specificity 72% (69–75%) and accuracy 75.5% (74.2–76.7%) compared to stride length variability 5.7%, with summary sensitivity 66% (61–70%), specificity 56% (54–58%) and accuracy 57.1% (55.5–58.6%). Conclusion: The results of this meta-analyses support the significant value of stride length for predicting life-threatening clinical events in older adults. A short stride length of ≤0.64 m accurately predicted clinical events, over and above other gait measures.

Walking-in-Place Characteristics-Based Geriatric Assessment Using Deep Convolutional Neural Networks

The world population is aging, and this phenomenon is expected to continue for the next decades. This study aimed to propose a simple and reliable method that can be used for daily in-home monitoring of frailty and cognitive dysfunction in the elderly based on their walking-in-place characteristics. Fifty-four community-dwelling elderly people aged 65 years or older participated in this study. The participants were categorized into the robust and the non-robust groups according to the FRAIL scale. The mini-mental state examination was used to classify the cognitive impairment and the non-cognitive impairment groups. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the foot, shank, thigh, and posterior pelvis while each participant was walking in place for 20 seconds. The walking-in-place spectrograms were acquired by applying time-frequency analysis to the lower body movement signals measured in one stride. Four-fold cross-validation was applied to 80% of the total samples and the remaining 20% were used as test data. The deep convolutional neural network-based classifiers trained with the walking-in-place spectrograms enabled to categorize the robust and the non-robust groups with 94.63% accuracy and classify the cognitive impairment and the non-cognitive impairment groups with 97.59% accuracy. This study suggests that the walking-in-place spectrograms, which can be obtained without spacious experimental space, cumbersome equipment, and laborious processes, are effective indicators of frailty and cognitive dysfunction in the elderly.

High Intensity Exercise for Walking Competency in Individuals with Stroke: A Systematic Review and Meta-Analysis

OBJECTIVE

To assess the effects of high intensity exercise on walking competency in individuals with stroke.

DATA SOURCES

A systematic electronic searching of the PubMed, EMBASE, Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), CINAHL (EBSCOhost), and SPORTSDiscus (EBSCOhost) was initially performed up to June 25, 2019.

STUDY SELECTION

Randomized controlled trials or clinical controlled trials comparing any walking or gait parameters of the high intensity exercise to lower intensity exercise or usual physical activities were included. The risk of bias of included studies was assessed by the Cochrane risk of bias tool. The quality of evidence was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluation) system.

DATA EXTRACTION

Data were extracted by 2 independent coders. The mean and standard deviation of the baseline and endpoint scores after training for walking distance, comfortable gait speed, gait analysis (cadence, stride length, and the gait symmetry), cost of walking, Berg Balance Scale , Time Up&Go (TUG) Test and adverse events were extracted.

DATA SYNTHESIS

A total of 22 (n = 952) studies were included. Standardized mean difference (SMD), weighted mean difference (WMD), and odds ratios (ORs) were used to compute effect size and subgroup analysis was conducted to test the consistency of results with different characteristics of exercise and time since stroke. Sensitivity analysis was used to assess the robustness of the results, which revealed significant differences on walking distance (SMD = .32, 95% CI, .17-.46, P < .01, I² = 39%; WMD = 21.76 m), comfortable gait speed (SMD = .28, 95% CI, .06-.49, P = .01, I² = 47%; WMD = .04 m/s), stride length (SMD = .51, 95% CI, .13-.88, P < .01, I² = 0%; WMD = .12 m) and TUG (SMD = -.36, 95% CI, -.72 to .01, P = .05, I² = 9%; WMD = -1.89 s) in favor of high intensity exercise versus control group. No significant differences were found between the high intensity exercise and control group in adverse events, including falls (OR = 1.40, 95% CI, .69-2.85, P = .35, I² = 11%), pain (OR = 3.34, 95% CI, .82-13.51, P = .09, I² = 0%), and skin injuries (OR = 1.08, 95% CI, .30-3.90, P = .90, I² = 0%).

CONCLUSIONS

This systematic review suggests that high intensity exercise could be safe and more potent stimulus in enhancing walking competency in stroke survivors, with a capacity to improve walking distance, comfortable gait speed, stride length, and TUG compared with low to moderate intensity exercise or usual physical activities.

The spatial parameters of gait and their association with falls, functional decline and death in older adults: a prospective study

Association between spatial gait parameters and adverse health outcomes in the elderly has not been sufficiently studied. The goal of this study is to evaluate whether the stride length or the step width predict falls, functional loss and mortality. We conducted a prospective cohort study on a probabilistic sample of 431 noninstitutionalized, older-than-64-years subjects living in Spain, who were followed-up for five years. In the baseline visit, spatial gait parameters were recorded along with several control variables, with special emphasis on known medical conditions, strength, balance and functional and cognitive capacities. In the follow-up calls, vital status, functional status and number of falls from last control were recorded. We found that a normalized-to-height stride length shorter than 0.52 predicted recurrent falls in the next 6 months with 93% sensitivity and 53% specificity (AUC: 0.72), and in the next 12 months with 81% sensitivity and 57% specificity (AUC: 0.67). A normalized stride length <0.5 predicted functional loss at 12 months with a sensitivity of 79.4% and specificity of 65.6% (AUC: 0.75). This predictive capacity remained independent after correcting for the rest of risk factors studied. Step-with was not clearly related to functional loss or falls. Both shorter normalized stride length (OR1.56; AUC: 0.62; p < 0.05) and larger step width (OR1.42; AUC: 0.62; p < 0.05) were associated with risk of death at 60 months; however, none of them remained as independent predictor of death, after correcting for other risk factors. In summary, spatial gait parameters may be risk markers for adverse outcomes in the elderly. Step length is independently associated with functional loss and falls at one year, after correction for numerous known risk factors.