Hierarchical Clustering Reveals Multilevel Structure for Multifactorial Assessment in Community-Dwelling Older Adults

Multifactorial falls assessment is recommended for older adults at elevated risk but can generate numerous potentially overlapping measurements, increasing testing burden despite potentially diminishing informational returns as correlated measures are added. Characterizing how measures covary may inform subsequent efforts to evaluate more efficient assessment procedures. Community-dwelling older adults (n = 298) completed a single-visit assessment including the Physiological Profile Assessment (PPA), preferred- and maximal-speed walking, Montreal Cognitive Assessment (MoCA), and Modified Falls Efficacy Scale (MFES). Hierarchical agglomerative clustering was applied to 51 variables. Dendrogram fidelity, cluster separation, and resampling stability were evaluated using the cophenetic correlation coefficient (CCC), silhouette width, Jaccard coefficients, and adjusted Rand index (ARI). The dendrogram showed strong fidelity to the correlation-distance structure (CCC = 0.937). Among the examined partitions, k = 2 had the highest mean silhouette width (0.333), indicating weak separation, and demonstrated high resampling stability. This partition grouped MoCA and MFES items with visual, strength, and maximal balance-range measures, separately from gait, postural sway, reaction-time, sensory, and coordinated-stability measures. At k = 3, the latter grouping remained unchanged while MoCA items separated from the MFES and physiologic measures; however, this finer, hypothesis-guided partition had lower separation and lower stability for the MoCA-dominant cluster. These findings describe the covariance organization of this multifactorial assessment battery and provide candidate groupings for future research on measurement redundancy, information retention, and reproducibility across datasets.