Participants in this cross-sectional study were recruited from two secondary health care institutions and from the population who lived independently in community. A convenience sample of older adults was utilized for this study. All participants were community-dwelling adults and were eligible to participate if they were aged 60 and older and reported being able to ambulate 12 meters without an assistive device. Exclusion criteria were a Lithuanian version of the Mini-Mental State Examination (MMSE) (24) score < 21, Parkinson’s disease, recent stroke, terminal illness, or unwillingness to participate. Eligible subjects provided signed written informed consent based on the principles expressed in the Declaration of Helsinki (25). Ethical approval was obtained from the Vilnius Regional Ethics Board for Biomedical Research. Data collection occurred between May 2019 and February 2020. Initially, 165 subjects were invited, of whom 7 met the exclusion criteria (Parkinson’s disease = 2, recent stroke = 5), and 25 were rejected due to incomplete measurement. Data from 133 participants (86 women and 46 man) with an average age 75.1 ± 8 years were analysed.
The research team conducted face-to-face interviews using structured questionnaires to record the required characteristics, such as age, sex, years of formal education, self-reported chronic diseases, number of prescribed and over-the-counter medications, self-reported history of falls in the previous 12 months, and use of an assistive device (yes/no). Height was obtained using a tape measure, weight was measured using a bathroom scale (Clatronic PW 3368, Clatronic®, Kempen, Germany), and BMI was calculated based on height and weight. Interviewer-administered questionnaires included MMSE and the Falls Efficacy Scale-International (FES-I) (26).
Assessment of frailty criteria
Frailty was assessed using the five components proposed by Fried et al. (2). Weight loss was determined by self-reported unintentional weight loss of > 4.54 kg over the past year. Weakness was evaluated by a grip strength measurement using a hydraulic hand dynamometer (Jamar®, Sammons PrestonRolyan, Bolingbrook, IL, USA). Three measures were performed, and the arithmetic mean was used to identify this criterion. Weakness was defined according to sex and the BMI cut-offs used by Fried et al. Exhaustion was evaluated by two statements of the Centre for Epidemiologic Studies Depression Scale(CES-D) questionnaire (27): ‘I felt everything I did was an effort’ and ‘I could not get going’. The frequency of ‘occasionally’ or ‘most of the time’ as a reply to either of these statements was considered as an indication of exhaustion. Slowness was defined by a walking speed of 4 m distance at the usual pace measured by a stopwatch and stratified by gender and height using the cut-offs defined by Fried et al. Low physical activity was determined using the Physical Activity Scale for Elderly (PASE) (28). PASE scores less than 64 for men and less than 52 for woman were used to indicate a positive response of low physical activity.
Participants were scored one point for each criterion found, totalling a score that could range from 0 to 5. Frailty level was categorized following Fried et al. (2): robust = no criteria; prefrail = one or two criteria, and frail = three or more criteria.
Physical performance tests
The timed up and go test (TUG) (29) is widely used for the identification of older adults at a high risk of falling. Savva et al. (2013) (30) proposed that the TUG test is a sensitive and specific measure of frailty. In our study, participants were told to sit on a chair (seat height, 46 cm). Participants were then instructed to stand, to walk at their normal pace for a distance of 3 meters, to turn at the endpoint, to walk back the same distance, and sit on a chair. Total time starting from standing up to full sitting down was recorded. The time of one trial was taken as the TUG test score.
The participants were then evaluated using the dynamic gait index (DGI) (31) for the assessment of the gait in response to changing tasks, such as turning the head while walking, stepping over the obstacle, climbing the stairs, and others. This index consists of eight tasks. Each task is scored from 0 to 3 points, with 0 being the worst and 3 being the best performance. The maximum score is 24 points. A result less than 19 points indicates impaired gait and a risk of falling.
Sensor-Based assessment of gait
We used a total of six wireless inertial sensors (Shimmer Research, Dublin, Ireland) attached by straps on the thighs, shins, and feet (Fig. 1).
Each sensor includes triaxial accelerometer, gyroscope, and magnetometer and is able to measure linear acceleration, angular velocity, and magnetic heading in three dimensions. The data from sensors was acquired via a Bluetooth wireless connection at a sampling frequency of 256 Hz. Participant walked a distance of 4 m (13 feet) at the self-selected usual pace. Data from three trials was used. From all the data obtained from the inertial sensors, we selected shank angular velocity and foot linear acceleration to determine heel-strike and toe-off characteristic points. This data was filtered using a Butterworth second order low pass filter with an 8 Hz cut-off frequency and an additional least square method 25th order filter with a 10 Hz cut-off frequency for composite foot acceleration data. A gait event detection algorithm was made by picking toe-off points from the angular velocity data (32) and heel strike points from composite foot acceleration data (33). Gait parameters were calculated based on these gait events. The following quantitative gait parameters were calculated: stance phase time, swing phase time, stride time, on right and left leg accordingly, double support time, and cadence (steps/min).
Demographic and clinical characteristics were compared between frailty groups using one-way analysis of variance (ANOVA) for continuous variables and Pearson’s chi-squared test for categorical variables. One-way ANOVA was used to compare the frailty group scores on physical performance tests and gait parameters derived from sensor data. The presence of overall statistically significant results in the ANOVA was followed with post-hoc Tukey analysis to identify significant pairwise associations. Effect sizes were calculated as Cohen’s d. The guidelines (34) for interpreting this value are: 0.02 = small effect, 0.5 = moderate effect, and 0.8 = large effect.
Multinomial logistic regression (35), with the robust group as the reference, was then used to investigate the gait parameters that discriminate the three frailty levels. The dependent variable was frailty, modelled as two indicator variables of prefrail and frail referenced to the category of robust. The independent variables were TUG time, DGI score, and eight different sensor-based gait parameters. The independent variables assessed had Cohen’s d effect sizes ≥ 0.8 for both prefrail versus robust and frail versus robust. Each of the independent variables was fitted in a separate univariate logistic regression model for a total of ten models. Each model estimated the odds ratios for prefrail relative to robust and frail relative to robust. Linear regression diagnostics were performed to evaluate multicollinearity and normality. There were no major deviations from normality and multicollinearity.
We used receiver operating characteristic (ROC) curves to calculate the area under curve (AUC) to estimate the predictive validity of each parameter. The cut-off values were calculated based on the Youden index. Sensitivity and specificity were calculated based on the cut-off values. All analysis was performed using IBM SPSS for Windows software, version 20.0. Statistical significance was set at a p value less than 0.05.