Alden L Gross1,2, Brennan R Payne3, Ramon Casanova4,5, Pega Davoudzadeh6, Joseph M Dzierzewski7, Sarah Farias8, Tania Giovannetti9, Edward H Ip4,5, Michael Marsiske10, George W Rebok2, K Warner Schaie11, Kelsey Thomas10, Sherry Willis11, Richard N Jones12,13. 1. a Departments of Epidemiology , Johns Hopkins Center on Aging and Health, Johns Hopkins Bloomberg School of Public Health , Baltimore , Maryland , USA. 2. b Departments of Mental Health , Johns Hopkins Center on Aging and Health, Johns Hopkins Bloomberg School of Public Health , Baltimore , Maryland , USA. 3. c Department of Psychology and The Beckman Institute for Advanced Science and Technology , University of Illinois, Urbana-Champaign , Illinois , USA. 4. d Departments of Biostatistical Sciences , Wake Forest School of Medicine , Winston Salem , North Carolina , USA. 5. e Departments of Social Sciences & Health Policy , Wake Forest School of Medicine , Winston Salem , North Carolina , USA. 6. f Department of Psychology , University of California , Davis , California , USA. 7. g Department of Psychology , Virginia Commonwealth University , Richmond , Virginia , USA. 8. h Department of Neurology , University of California, Davis Medical Center , Sacramento , California , USA. 9. i Department of Psychology , Temple University , Philadelphia , Pennsylvania , USA. 10. j Department of Clinical and Health Psychology , University of Florida , Gainesville , Florida , USA. 11. k Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA; Department of Radiology , Integrated Brain Imaging Center (IBIC), University of Washington , Seattle , WA. 12. l Departments of Psychiatry and Human Behavior , Warren Alpert Medical School, Brown University , Providence , Rhode Island , USA. 13. m Departments of Neurology , Warren Alpert Medical School, Brown University , Providence , Rhode Island , USA.
Abstract
Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. METHODS: The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. RESULTS: Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA < .05; all CFI > .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p < .005). CONCLUSIONS: Empirical data confirm the hypothesized ACTIVE conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from those with the greatest chance of transfer to real-world activities.
Background/Study Context: Conceptual frameworks are analytic models at a high level of abstraction. Their operationalization can inform randomized trial design and sample size considerations. METHODS: The Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) conceptual framework was empirically tested using structural equation modeling (N=2,802). ACTIVE was guided by a conceptual framework for cognitive training in which proximal cognitive abilities (memory, inductive reasoning, speed of processing) mediate treatment-related improvement in primary outcomes (everyday problem-solving, difficulty with activities of daily living, everyday speed, driving difficulty), which in turn lead to improved secondary outcomes (health-related quality of life, health service utilization, mobility). Measurement models for each proximal, primary, and secondary outcome were developed and tested using baseline data. Each construct was then combined in one model to evaluate fit (RMSEA, CFI, normalized residuals of each indicator). To expand the conceptual model and potentially inform future trials, evidence of modification of structural model parameters was evaluated by age, years of education, sex, race, and self-rated health status. RESULTS: Preconceived measurement models for memory, reasoning, speed of processing, everyday problem-solving, instrumental activities of daily living (IADL) difficulty, everyday speed, driving difficulty, and health-related quality of life each fit well to the data (all RMSEA < .05; all CFI > .95). Fit of the full model was excellent (RMSEA = .038; CFI = .924). In contrast with previous findings from ACTIVE regarding who benefits from training, interaction testing revealed associations between proximal abilities and primary outcomes are stronger on average by nonwhite race, worse health, older age, and less education (p < .005). CONCLUSIONS: Empirical data confirm the hypothesized ACTIVE conceptual model. Findings suggest that the types of people who show intervention effects on cognitive performance potentially may be different from those with the greatest chance of transfer to real-world activities.
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