## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Statistical Analysis  
Relative reliability was assessed by computing the consistency of the two measurements either between the two repeated assessments in time (test-retest reliability) or between the assessments of the two raters (inter-rater reliability) using the Intraclass Correlation Coefficient (ICC\({}_{2,2}\)) and values <0.5 to indicate poor reliability, 0.51-0.75 moderate reliability, 0.76 to 0.90 good reliability, and >0.90 excellent reliability [43]. For the reliability of the single ordinally scaled items, the weighted kappa coefficient was used with the results to be reported as: <0.0-0.20 \"none\"; 0.21-0.39 \"minimal\"; 0.40-0.59 \"weak\"; 0.60-0.79 \"moderate\"; 0.80-0.90 \"strong\"; >0.90 \"almost perfect\" [44]. The internal consistency reliability wasmeasured with [PERSON]'s alpha coefficient with \"accepted value\" of 0.70, \"good\" internal consistency at values between 0.70 and 0.80, and \"excellent\" internal consistency at values above 0.80 [45]. The single-to-total correlation was used to find how every single item of the scale correlated well with the total FGA\({}_{\text{GR}}\) score. Correlations below 0.30 indicated a low degree of correlation between the item and the remaining items [46]. The Minimal Detectable Change with a 95% confidence threshold (MDC\({}_{95}\)) indicates the smallest change that can be interpreted as a real change. Additionally, it is greater than the expected measurement error and it was computed according to the following formula: MDC\({}_{95}\) = 1.96 \(\times\) SEM \(\times\)\(\surd\)2 [47]. The Standard Error Measurement (SEM) was calculated according to the following formula: SEM = SD\(\surd\)(1 - ICC), where ICC is the coefficient of the test-retest reliability and SD is the standard deviation of the FGA\({}_{\text{GR}}\) total score [48]. Ceiling and floor effects were considered as the percentage score of more than 20% of the participants at the highest and lowest score, accordingly [33]. Concurrent validity was investigated using Pearson's correlation coefficient (rs) for normally distributed data, with values between 0.1 and 0.29 interpreted as \"weak association\", 0.30-0.49 \"low association\", 0.50-0.69 \"moderate association\", 0.70-0.89 \"strong association\", and 0.90-1.00 \"perfect association\" [49]. The discriminant validity was calculated with the \(t\)-test for independent samples between the two groups of low and high concern about falls. For the ability of the FGA\({}_{\text{GR}}\) scale to classify older adults with high risk for falls, a Receiver Operator Characteristic (ROC) curve was used. The TUG cutoff score of 11.1 s was considered as the reference variable, which dichotomized the sample into those with <11.1 s: without risk of falling (\"0\") and those with \(\geq\)11.1 s: with risk of falling (\"1\"). The data point closest to the upper left corner of the curve, which is the value that is balanced between high Sensitivity (Sn) and high Specificity (Sp) was chosen as the optimal cutoff score for prediction, and the Area Under the Curve (AUC) to reflect the measure's accuracy. An AUC \(>\) 0.7 was considered sufficient for discrimination [50]. To determine whether the selected cutoff score could correctly classify falls risk in older adults, the percentage accuracy of the older adults who actually were at risk of falling was calculated using the cutoff score. The Sn, Sp, Positive Likelihood Ratio (LR+), Negative Likelihood Ratio (LR-), Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were also calculated. Sensitivity (Sn) was defined as the proportion of individuals with risk of falling who have a positive result on FGA\({}_{\text{GR}}\), while specificity (Sp) was defined as the proportion of individuals without risk of falling who have a negative result on FGA\({}_{\text{GR}}\)[51].