This is an observational study of 500 (220 non-fallers and 280 fallers) community-dwelling older adults aged 65-85years. Data were collected between April 2017 – July 2018. Fallers and non-fallers were simultaneously tested with TUG and VFD, to evaluate the discriminatory power of the instruments to detect fallers and non-fallers, and to classify future falls risk with sensitivity, specificity, and predictive values of VFD’s fall index - i.e., the value of intersection point of the stride frequency, and velocity regression lines (E1) measured in velots described in the VFD- and TUG times (measured in seconds).
This study was held at the community health centres, in Igboeze South Local Government Area (LGA), Nsukka senatorial zone, Enugu State, Nigeria comprising 10 towns, namely: Alor-Agu, Unadu, Itchi, Nkalagu-Obukpa, Ibagwa Aka, Iheakpu -Awka, Uhunowerre, Ovoko-Ulo, Ovoko-Agu, and Iheaka. This location was selected following a rise in the number of falls involving resident older adults of the rural communities in Igboeze South LGA, who reported at the Physiotherapy unit of the University of Nigeria Teaching Hospital’s rural outpost in Obukpa, Nsukka. Therefore, a rural health education outreach programme on risk factors for falls and falls prevention strategies among older adults in Igboeze South LGA was planned by the research team. The health outreach programme involved the community health committees and the traditional rulers of the communities within Igboeze South LGA. The traditional rulers, therefore, convened community health fora in their respective community health centres, and the town criers were mobilized to disseminate information to all clans and villages, for at least four market days to ensure a wide publicity. In addition, various age-grade groups were also invited by the traditional rulers, through their leaders to the health programme. Out of 2880 rural community dwellers who attended the health fora, eighty-eight (880) were community-dwelling older adults aged ≥65years and above. This group were subsequently targeted and were invited to participate in this study.
In a previous study of mobility by Sosnoff et al , individuals classified as fallers demonstrated diminished mobility scores for all assessments when compared to non-fallers. There was an apparent difference in walking coordination and walking speed, and the differences between fallers and non-fallers were of moderate magnitude based on effect sizes for TUG (d = −0.45) and timed walking speed (d = −0.46) over a 25-foot distance (i.e., 7.62 metres), which is similar to 7 meters used in this study. Using the Power analysis of 80% to detect a difference between means at an effect size of 0.46, with a significance level (alpha) of 0.05 (one-tailed), a sample size of 840 was mathematically determined. Considering the likelihood of attrition, 10% of the calculated sample (84) was required, however only 40 (about 5%) were available and were included in the study making a total of 880 older adults. The participants flow through the study is presented in Figure 1. Criteria for participation included only those who are: -
- ≥65 years or older, and
- able to walk independently and
- those who have a Mini-Mental Score Examination (MMSE) score higher than 23 .
- not blind
Based on their self-reported history of locomotive falls, participants were categorized into 2 groups, namely: i. fallers, and ii. non-fallers. The fallers were operationalized in the context of this study as individuals who satisfied the fall frequency requirement of at least two falls within 18 months. The fall frequency was provided by the participants and confirmed by participants’ relatives/neighbors. Nevertheless, 120 participants who were unable to do so, were excluded from the study. Non-fallers were older adults who have not fallen during the past 12 months . However, all those who had fallen once were screened for co-morbidities of fall, and educated on fall prevention strategies. They were given some exercise regimens to strengthen their muscles and placed on the watch list for follow-up visitations. All participants provided written informed consent following approval by the Institutional Human Ethics Research Board. The study process involved three stages: obtaining informed consent, anthropometric assessment, and measurement of the risk of fall using the TUG and VFD, respectively.
With the help of four trained research assistants, the 880 older adults who participated in the health forum were screened for eligibility to participate in the study. One hundred and sixty were found ineligible, while the remaining 720 were invited to participate in the study. Out of this number, 500 (280 self-reported fallers and 220 self-identified non-fallers) individuals accepted and 220 declined to participate. The 500 willing participants were requested to provide their fall history.
The instruments used in the study included the weighing (Hana Bathroom) scale, stadiometer, measuring tape and stopwatch (Hanhart, Germany). The Hanhart stopwatch is impact resistant, dustproof and water resistant, with diamond-turned metal case, 55mm diameter, built-in strap ring, time intervals 1/5 secs, display 30 mins, a resolution of 0.20 sec and has a measuring capacity 00:30:00 hr: min: sec. These instruments were used to measure the body weight, height, distance to walk/cover and walk time, respectively. To eliminate inter-observer variability, only one investigator was assigned the task of evaluating the participants for the primary outcome measures. The primary outcome measures were the TUG and the VFD.
An instrument for Determining Cognitive status:
Mini-mental state examination was used to assess global cognition level, which comprised items concerning attention, language, following commands and figure copying, orientation, registration and recall for all participants. The cutoff score was 23 , and none of the Participants fell below the cutoff. A score of 19–23 point suggests mild dementia, whereas scores above 23 suggest normal cognition.
Primary outcome measures
TUG test: Timed-Up-and-Go test’ was done with the participants sitting correctly in the chair with an armrest, with an approximate seat height of 46 cm, and arm height 65 cm. Participants were instructed that on hearing the command “go” they were to get up and walk at a comfortable and safe pace to a line on the floor 3 meters away, turn, return to the chair and sit down again. The timing started immediately the participant got up from the chair and stopped when the participant has seated again with the back resting on the back of the chair. However, each participant was required to walk through the test once before being timed in order to become acquainted with the procedure. The participants were required to perform the test three times, and the fastest time of the three was used in this study. Participants were also allowed to wear their regular footwear and use their customary walking aids (none, cane, walker), but no physical assistance was given. A TUG time is a time in seconds that participants needed to complete the test. Longer time indicates worse balance and mobility performance. Times under 10 seconds are suggestive of completely free and independent individuals; however, times ≥ 13.5 seconds is the cutoff point for fallers .
VFD: The participants were required to walk a 7-meter distance (measured out on the ground using the measuring tape) barefoot or in their normal shoes after the purpose of the study was explained to them. They were requested to walk the distance at five self-selected speeds: ordinary, very slow, slow, fast and very fast, in that order. For each speed, the number of steps and time taken to complete the distance were obtained (using a Stop-Watch-Hanbart Germany), and used to calculate the mean values of stride length (L), stride frequency (F), and velocity (V), for each participant as indicated in equations 1-4 [10, 11]. For each participant recruited, data were collected over a 23-minute time frame.
The regression lines of L, F and V are known as L-line, F-line, and V-line, respectively [10, 11]. These parameters were adapted to describe the VFD [16-19]. Ordinarily, the VFD consists of the graphical regression plots of the three basal gait parameters (stride length, frequency, and velocity) expressed in five self-selected speeds. The five self-selected speeds of walking, varying from very slow, slow, normal, fast and very fast speeds, were serially numbered, 1-5, and assigned arbitrary units - velots. The numbers were used for the X-axis, while the numerical values of velocity, stride length, and stride, were used on the Y-axis. These lines make up the primary features of the VFD [10, 11]. The point of equality for the numerical values of velocity and stride frequency (E1) marked the upper limit of very slow speed and a speed transition to the path of minimal energy trajectory [16-19]. Similarly, the point of equality for the numerical values of velocity and stride length (E2) marked the upper limit of normal speed and a speed transition to fast walking speed. Eke-Okoro  demonstrated that 3.5 velots is the value of E1 on the VFD of fallers, which discriminated them from non-fallers(≤3.5velots) and therefore has a diagnostic utility. Consequently, he predicted that the critical point for the onset of fall is E1≥3.5 velots.
Information on age and gender were obtained by self-report. Co-morbidities (defined as a history of dizziness, vestibular disease, diabetes, alcoholism, and arthritis), were ascertained by self-report questionnaires. Alcohol consumption was dichotomized, and defined as weekly alcohol consumption of ≥11 units for men and ≥8 units for women Body mass index (BMI), was determined as weight in kilograms divided by the square of height in meters, and categorized according to the National Institutes of Health obesity standards <18.5 = underweight, 18.5-24.9 = normal weight, 25.0-29.9 = overweight, and >30 = obese .
Participants’ characteristics, as well as means and SD values for continuous variables and percentages for categorical variables, were determined within both males and females. Data were principally analyzed for males and females combined, but analyses were repeated for males and females separately. Differences in gait parameters between fallers and non-fallers were determined with and without adjustment for the potential confounding effects of history of diseases of orthopedic or neurological nature; use of alcohol within 72 hours of gait recording [39-41], use of sedatives within 72 hours of gait recording [39, 41], chronic co-morbidities (history of dizziness, vestibular disease, cardiovascular risk factors, and diabetes), alcoholism, gender and the use of anti-anxiety drugs [41-45]. Normality of distribution of the basal gait parameters (stride velocity, stride frequency and stride length), was confirmed by the Kolmogorov-Smirnov test. Mean differences in VFD performance (absolute and adjusted) and physical characteristics between fallers and non-fallers were determined using independent-sample t-tests. Data collected for this study were presented in tables. Descriptive statistics and independent t-test were used to analyze the biodata, anthropometric indices, and gait variables. McNemar test was used to check for the diagnostic accuracy of the VFD relative to the TUG, while weighted Kappa was used to check for the level of agreement between the TUG and VFD in screening out fallers from non-fallers. For all kappa statistics, κ values were interpreted as follows: below 0 as less than chance, 0.01–0.20 as slight, 0.21–0.40 as fair, 0.41–0.60 as moderate, 0.61–0.80 as good and above 0.80 as very good levels of agreement. Sensitivity and specificity of the VFD and TUG were also determined. The estimated population midpoints and 95% confidence intervals were calculated for: i. prevalence of the condition; ii. test sensitivity (conditional probability that the test will be positive if the condition is present); iii. test specificity (conditional probability that the test will be negative if the condition is absent); iv. predictive values of the test (probabilities for true positive, true negative, false positive, and false negative); and, v. positive and negative likelihood ratios.
After stratification on the basis of sex, weighted linear regression analysis was applied to explore the relation of E1 and gait speed as a performance-based physical measure. The distributions of E1 in both men and women were right-skewed. Consequently, it was considered appropriate to use natural-log-transformed values, which gave the best-fitting model for analysis in which the E1 values were treated as continuous variables. For both males and females, standard-deviation scores of E1 were obtained from the formula (Xi-Xm) ÷ SD, where Xi was the natural-log-transformed E1 in the individual male/female subject, Xm is the mean natural-log-transformed E1 in the male/female subjects, and SD the standard deviation of the natural-log-transformed E1 in the male/female subjects. With this calculation., it was possible to determine the change in the gait speed for each increment of 1SD in the natural-log-transformed E1. The relations of E1 to gait speed were also estimated with a quartile-based analysis by dividing E1 values into quartiles with subjects in the lowest quartile as the reference group. Comorbidities were assessed by referring to the self‐reported physician's diagnosis and included dizziness, vestibular disease, diabetes, alcoholism, and arthritis. An extended-model approach was applied for covariates adjustments: Model 1 = Age, body mass index categories, smoking status, alcohol consumption, and use of walking devices; Model 2 = Model 1 + co-morbidities (dizziness, vestibular disease, diabetes, alcohol consumption, and arthritis); Model 3 = Model 2 + markers of cardiovascular risk (natural-log-transformed levels of the use of anti-anxiety drugs). BMI was also controlled in the association between E1 and gait velocity (Model 4) in order to observe the possible change of association.
A 5% significance level was principally used to identify statistically significant associations, but a Bonferroni correction was also applied to enable identification of significant associations after allowance for multiple comparisons. Receiver operating characteristic (ROC) curves were calculated to analyze the diagnostic validity of the VFD. ROC computes the true positive and false positive for each test value and plots them on a curve. The area under the ROC curve (AUC) could be interpreted as a measure of classification quality of the test. The AUC values range from 0 and 1, with higher values indicating better classification accuracy. As much as its value is closer to 0.5, the poorer the accuracy of the test is because the value of 0.5 corresponds to a random classification. XLSTAT-BIOMED statistical software was used for this analysis, which computes the p-value with a logistic regression model. A p-value <.05 implies that the logistic regression classifies the fallers based on the empirical data better than by chance. The 95% confidence interval (CI) estimates the interval of the population parameter out of the study data. For any diagnostic or screening tool the lower bound of the 95% CI of the ROC curve, should be greater than 0.5 if not, the risk that the real population estimate is not better than a random classification is too high.