Real-world compensatory strategy use in community-dwelling mid-life and older adults: An evaluation of quality

• Published in: Clinical neuropsychologist Vol. 38; no. 2; pp. 429 - 452
• Main Authors: Beech, Brooke F., Sumida, Catherine A., Schmitter-Edgecombe, Maureen
• Format: Journal Article
• Language: English
• Published: England Routledge 17.02.2024
• Subjects: Aged; Aging; Cognition; Compensation; ecological validity; everyday functioning; Humans; Independent Living; Memory, Episodic; Neuropsychological Tests; prospective memory; aging; ecological validity; prospective memory; Compensation; everyday functioning
• ISSN: 1385-4046; 1744-4144; 1744-4144
• DOI: 10.1080/13854046.2023.2209927

Objective: Older adults often spontaneously engage in compensatory strategies (CS) to support everyday task completion, but factors that influence success of chosen CS remain unclear. This study examines whether real-world prospective memory (PM) task completion is better predicted by CS count or a CS quality rating. Method: Seventy mid-life and older adult participants were presented four novel, real-world PM tasks via remote assessment and encouraged to use their typical CS. The examiner captured detailed information about planned CS at task presentation (T1) and utilized CS at follow-up testing (T2). From this information, count (CS Count; quantity of CS) and quality (CS Quality; rating of CS thoroughness and utility) scores were coded separately for the planned and utilized CS. PM task performance accuracy was also coded (PM Accuracy). Results: Hierarchical regressions revealed planned CS Count and Quality did not predict PM Accuracy. In contrast, the utilized CS Quality predicted a significant amount of PM Accuracy variance over and above CS Count, global cognition, and age (R 2 = .47, ΔR 2 = .24, ΔF = 29.36, p < .001, f 2 = .45). Furthermore, utilized CS Quality accounted for a similar amount of variance in PM Accuracy when utilized CS Count was removed from the model. Conclusions: This study's CS coding system can capture and quantify the quality of strategies, which uniquely predicts real-world PM performance. This coding system may provide researchers with a nuanced CS measure and lead to improved CS interventions designed to support everyday PM performance, such as targeted CS trainings.