Enhanced Gait Variability Index in older Asian Adults and Increased Physiological Fall Risk: Results from the Yishun Study

There is no consensus on a composite gait variability (GV) score as an overall GV for the various spatial and temporal gait parameters. This had dampened the reporting and acceptance of GV, with little work on this topic in Asian population. This cross-sectional study derived an Enhanced Gait Variability Index (EGVI) and reference values in an Asian population and evaluated its validity as an indicator of physiological fall risk. It was conducted in a large residential town of Yishun in Singapore with community-dwelling adults aged 21–90 years of age categorized into 3 groups—reference group, low fall-risk and high fall-risk. Physiological Profile Assessment (PPA) score of ≥ 2.0 was used to differentiate high fallrisk and low fall-risk groups. EGVI were derived from five spatiotemporal parameters: step length (cm), step time (s), stance time (s), single support time (s) and stride velocity (cm/s), with those participants aged less than 65 years old as reference group. Our Asian population showed greater overall gait variability compared to an European cohort. This Asian EGVI displayed a non-linear relationship with both ageing and gait speed—significant changes in the EGVI were observed for those older than 60 years of age and in those whose habitual gait speed was lesser than 120 cm/s. The EGVI discriminated between older adults with and without high fall risk and showed weak to moderate correlation with a number of the functional mobility and balance tests in both high and low fall risk groups. We derived an Asian EGVI with reference values and validated its ability to discriminate fall risk among older adults.

The challenge has been to identify an assessment metric that is independent of gait changes that are affected by covariates such as age and gender, yet sensitive in predicting future falls, especially among the community dwelling older adults. Gait variability may offer a viable solution.
Gait variability (GV) refers to the change in spatiotemporal characteristics between steps. Sensitive to age-related mobility deficits and pathological processes, GV changes have been used to predict falls [12][13][14], cognitive decline [15] and dementia [16]. With the advancement in technology, it is now possible to quantify both macro and micro levels of gait changes without the use of expensive equipment. The use of wearable technologies such as smartwatches and accelerometers to monitor gait within the community-dwelling population have opened up opportunities to identify individuals at risk of falls [17] and cognitive impairment [18]. However, the use and reporting of GV have not gained wide acceptance due partly to the lack of consensus regarding the best approach to quantify them [19] as well as the lack of a composite score as a single representation of the various spatiotemporal parameters of gait [20]. A composite measure for GV could enable comparison between populations, or allow clinicians to track GV changes in association with different pathological processes.
As such, investigators have developed summary measures of kinematics variables for GV, e.g., Gillette Gait Index (GGI) [21] and the Gait Deviation Index (GDI) [22]. Gouelle et al. (2013) [20] developed and validated the Gait Variability Index (GVI) as an alternative to the GDI and the GGI. The GVI is a conglomerate variability measure of nine spatiotemporal parameters [20]. More recently, Gouelle  The work on GVI and EGVI have thus far been limited to diseased populations [24,25], but there is potential for the use of this composite index as an indicator for mobility deficits, fall-risk and cognitive impairment in community-dwelling older adults. Furthermore, GVI/EGVI has not been studied in Asians, and is without Asian reference values.
The main objective of this study was to derive EGVI reference values in an Asian population and to evaluate its ability to discriminate between different physiological fall risk.

Setting
Community-dwelling adults (≥21 years) were recruited from a large north-eastern residential town of Yishun in Singapore, with a residential population of 220,320 (50.6% females), with 12.2% older adults (≥65 years). This is similar to the overall Singapore residential population [26] of 4,026,210 (51.1% females), with 14.4% older adults (≥65 years).

Participants
Random sampling was employed to obtain a representative sample of approximately 300 male and 300 female participants, filling quotas of 20-40 participants in each sex-and age-group (10-year age-groups between 21-60; 5-year age-groups after 60). Older adults (above 75 years old) were also additionally recruited through community and senior activity centres. Participants were excluded if they had physical disabilities that limited their activities of daily living; diagnosed with either cognitive impairment or any neuromuscular disorders; or suffering from more than five poorly controlled co-morbidities or chronic illness. Ethics approval was obtained from the National Healthcare Group Domain Specific Review Board (DSRB-2017/00212) and written consent was obtained from all participants.

Methods
A 6m instrumented walkway system, GAITRite® (CIR systems, USA, 120 Hz sampling rate) was used for the gait analysis. Participants were instructed to initiate their gait 1m before and end 1m after the walkway system, to account for any gait related accelerations or decelerations, respectively. Participants were instructed to walk barefoot at their selfselected (habitual) gait speed. After a practice trial, three valid trials were recorded. A trial was considered valid if at least 6 alternate footfalls were captured within the sensor platform. Spatiotemporal parameters ( Figure   1  Prior to the gait assessments, participants performed two common functional mobility tests: the Timed-up and Go (TUG) [27] and the Five Times Sit to Stand Test (5XSS). Participants performed the TUG twice and the mean value was used for analysis. As for the 5XSS, participants were provided a single practice trial, after which the actual test was performed. Additionally, they also performed the short-Physiological Profile Assessment (PPA) [28]. The short-PPA has been validated as an indicator of fall risk in older adults [28][29][30]. It consists of five sub-tests: Participants aged 65 and above and with a fall risk score of 2.0 and above were categorized as the "High Risk" (HR) group and the rest as "Low Risk" (LR) group [28]. Those

EGVI Calculation
Alternative parameters [20], pn, which describes the intra-trial The EGVI was calculated using a modified macro (an Excel version was also provided as a supplementary material by Gouelle et al. (2013) [20]) that was again implemented in R Studio (Version 3.6.1).
Modifications were based on Gouelle et al. (2018) [23], and they primarily pertained to addressing issues related to (a) magnitude (b) direction (c) and redundancy. The details of the calculation and modifications are presented elsewhere [20,23]. We only highlight the main steps involved in the derivation of the EGVI here.
First, the mean sum of product, s HP , was calculated based on the five spatiotemporal parameters (step time, step length, stance time, single support time and stride velocity) of 215 healthy participants (aged 21 to 65 and gait speed ≥ 100.0 cm/s) by matrix multiplication (see Gouelle et al., 2013 [20] for more details) of the weighted coefficient, cn, and the alternative parameters, pn. The s HP for this group was 18.05, which was close to Gouelle et al. 2013 [20]. Then, the sum of product of each participant, s α , was computed (again by matrix multiplication) and the absolute distance, d α,HP , between this participant (s α ) and the healthy control group (s HP ) was calculated.  Table 1 shows the profile of the participants grouped according to age and PPA fall risk. Twenty participants who did not complete all three of the PPA subtasks were excluded from data analysis. Complete gait data was available in 511 subjects and has been presented in detail elsewhere [31]. The HR group was significantly older than the LR group. They were also significantly shorter, although there were no differences in their weight and BMI. There was a significant difference in fall risk score (PPA) between the two groups-the mean PPA score of the HR was in the 'Marked' range whereas it was in the "Normal" range for the LR. Gait speed of HR group was also significantly slower than the LR group. EGVI of the HR group was significantly greater than the LR group. The LR group had better performance in all functional mobility and balance assessments than the HR group.

The Asian EGVI
The raw EGVI of our reference group (1.28 ± 0.64) was significantly lower (p < 0.001) than that reported by Gouelle  3 HR group were significantly at greater fall risk than the LR group; 4 HR group walked significantly slower than the LR group; 5 EGVI for HR group was significantly greater than the LR group; 6 HR group elicited significantly greater angle than the LR group; 7 HR group were significantly weaker than the LR group; 8 HR group swayed significantly greater than the LR group; 9 HR group took significantly longer than the LR group; 10 HR group took significantly longer than the LR group.

Assessments
The EGVI for both the HR and LR groups were moderately and positively correlated with the results of functional mobility-TUG and 5XSS tests. The overall PPA fall risk score was also weakly correlated with EGVI, although more strongly for the HR group. The KES was negatively correlated with EGVI for both groups (Table 3).

DISCUSSION
This study derived and described Asian EGVI reference values and investigated its validity as an indicator of physiological fall risk among older adults. Our results showed that this Asian community dwelling population had greater overall gait variability compared with a European cohort. The EGVI of our cohort did not have a linear relationship with age and habitual gait speed. The EGVI was almost constant till the group in their 60s, after which it increased steadily.
Furthermore, people with slower habitual gait had a greater EGVI or composite gait variability. The PPA is a valid and reliable measure for falls risk and outcome for interventions to reduce falls [28]. We demonstrated the validity of EGVI to discriminate between older adults with high and low PPA fall risk. Interestingly, only a single alternative parameter pstep_length_mean was significantly associated with fall risk score.
The EGVI also had weak to moderate correlation with tests of functional mobility and balance performance (including TUG) [27] in both the high and low fall risk groups, further substantiating its validity as a GV composite index for GV in older Asian population.
Our control participants showed significantly greater values in six alternative gait parameters (see Figure 1), pn, used in the calculation of intra-trial gait variability. This showed a greater overall GV in ours compared to a Western population. Differences in gait parameters between Asian and the Western populations have been previously reported [32] but with no definitive conclusion on GV differences.
Differences were reported mainly in stride length and walking speed and was attributed to the smaller stature of the Asian subjects [32]. However, these physical differences were unlikely to have any effect on GV. It is plausible that the differences in our study could have been due to the different age range or the proportion of women included in the control populations since both age [6,33] and gender [34] have been associated with gait variability.
The EGVI of our population was affected by age as well as gait speed. Balasubramanian et al. (2015) [35] reported that the GVI decreased with advancing age but did not include those who were younger than 50 years old. This was not surprising since variability in most of the gait spatiotemporal parameters increases with age [6], especially among older adults [33][34][35][36]. Furthermore, the slower the self-selected gait speed of the older adults, the greater was the EGVI. This again was not surprising since gait variability increases with reduced gait speed [37].
However, the increase in the EGVI among those who were greater than 60 years old were likely due to age-related muscle weakness and loss of flexibility, rather than due singly to slower gait [38,39].

Study Limitations
The strengths of this study are its population-based nature and thoroughness of data collection. This study also has limitations. Firstly, while we used a well validated physiological fall risk assessment and other functional mobility tests, we did not investigate actual falls. We were also not able to validate the local EGVI with a more varied or diseased population as this was part of a normative study of a generally healthy population.

Future Recommendations
The use of EGVI as a GV composite should not be only applied to fallrisk indicator as gait speed alone could be just as useful. In addition to physiological changes, EGVI may also be associated with cognitive changes. Thus, we recommend that future studies should investigate not only the validity of our Asian EGVI reference with a population which has more serious functional mobility issues but also those with cognitive impairment. There has been active research in the areas of cognition and gait variability, and the EGVI might provide a meaningful composite score that could be easily applied in the populations experiencing cognitive decline and impairment. This study showed that the EGVI can be an easily referenced composite index for clinicians to longitudinally monitor the mobility deficits, fall-risk and cognitive impairment in community-dwelling older adults.

CONCLUSIONS
We presented and discussed EGVI reference values for an Asian population. We also validated this reference with an older population with a higher fall risk and established the validity of EGVI as a composite GV index in an older population. Our Asian EGVI reference values should be further validated in and applied to various clinical populations with gait limitations, cognitive impairment and higher risk of falls, including individuals with stroke, dementia and Parkinson disease etc.

DATA AVAILABILITY
The dataset of the study is available from the authors upon reasonable request.