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74 Adherence to Behavioral Interventions is Associated with a Change in Participant Adjustment in a Sample of aMCI Patients
- Ambar R Perez-Lao, Liselotte De Wit, Andrea M Kurasz, Priscilla A Amofa-Ho, Brittany DeFeis, Kailey Langer, Melanie Chandler, Shellie-Anne Levy, Glenn Smith
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 378-379
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Objective:
Behavioral interventions are a non-pharmacological treatment that shows improvement in the everyday functioning of people with Mild Cognitive Impairment (MCI). Multiple studies have focused on examining factors that can reduce or enhance adherence to behavioral interventions. However, few studies use adherence as a predictor of functional changes. The goal of this study was to analyze the association between adherence, age, and education in factor score changes of participant impairment, participant adjustment, and partner adjustment in a sample of participants with amnestic MCI (aMCI) and their study partners.
Participants and Methods:We included fifty-two dyads of a person with aMCI and their study partner with intervention data at baseline and 24-week follow-up from the Physical Exercise and Cognitive Engagement Outcomes for Mild Neurocognitive Disorder (PEACEOFMND) study. At baseline, participants were randomized to one of three behavioral interventions: computerized cognitive training (BrainHQ; n=19), yoga (n=15), or wellness education (n=18). Factors were established from a larger clinical sample that used the same measures as PEACEOFMND. The three-factor latent structure was constructed as the following: 1) participant adjustment combined scores of the Center for Epidemiologic Studies Depression Scale (CES-D), Quality of Life in Alzheimer’s Disease (QoL-AD), and Self-Efficacy for managing MCI scales; 2) partner adjustment included study partner’s scores in CES-D, QoLAD and Caregiving Competence and Mastery Components (CCMC) of the Pearlin scales; 3) participant impairment included participant’s scores in E-Cog memory domain, and study partner’s scores in the Functional Activity Questionnaire (FAQ) and Zarit Burden Interview. We calculated factor changes by obtaining the difference between factor scores at follow-up and baseline. Bayesian correlation analysis was performed to investigate the association between age, education, adherence to the combined behavioral interventions, participant adjustment, participant impairment, and partner adjustment.
Results:The Bayesian correlation results showed moderate evidence (BF10=6.8, Pearson’s r=0.38) supporting a positive correlation between adherence and change in participant adjustment. Additionally, there was moderate evidence (BF10=2.18, Pearson’s r=0.32) supporting a positive correlation between change in participant impairment and participant level of education as well as participant age and change in partner adjustment (BF10=2.8, Pearson’s r=0.33).
Conclusions:Bayesian correlations replicated results from previous analysis using a traditional method, showing that increased adherence to combined behavioral interventions is associated with an increase in participant’s quality of life, self-efficacy, and better mood. Thus, commitment to behavioral intervention completion in aMCI participants is related to overall participant adjustment.
28 Factor Structure of Conventional Neuropsychological Tests and NIH-Toolbox in Healthy Older Adults
- Kailey Langer, Cheshire Hardcastle, Hanna Hausman, Jessica Kraft, Alejandro Albizu, Nicole Evangelista, Emanuel Boutzoukas, Andrew O’Shea, Emily Van Etten, Samantha Smith, Hyun Song, Pradyumna Bharadwaj, Georg Hishaw, Samuel Wu, Steven DeKosky, Gene Alexander, Eric Porges, Michael Marsiske, Ronald Cohen, Adam Woods
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, p. 710
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- Article
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Objective:
The National Institutes of Health-Toolbox cognition battery (NIH-TCB) is widely used in cognitive aging studies and includes measures in cognitive domains evaluated for dimensional structure and psychometric properties in prior research. The present study addresses a current literature gap by demonstrating how NIH-TCB integrates into a battery of traditional clinical neuropsychological measures. The dimensional structure of NIH-TCB measures along with conventional neuropsychological tests is assessed in healthy older adults.
Participants and Methods:Baseline cognitive data were obtained from 327 older adults. The following measures were collected: NIH-Toolbox cognitive battery, Controlled Oral Word Association (COWA) letter and animals tests, Wechsler Test of Adult Reading (WTAR), Stroop Color-Word Interference Test, Paced Auditory Serial Addition Test (PASAT), Brief Visuospatial Memory Test (BVMT), Letter-Number Sequencing (LNS), Hopkins Verbal Learning Test (HVLT), Trail Making Test A&B, Digit Span. Hmisc, psych, and GPARotation packages for R were used to conduct exploratory factor analyses (EFA). A 5-factor solution was conducted followed by a 6-factor solution. Promax rotation was used for both EFA models.
Results:The 6-factor EFA solution is reported here. Results indicated the following 6 factors: working memory (Digit Span forward, backward, and sequencing, PASAT trials 1 and 2, NIH-Toolbox List Sorting, LNS), speed/executive function (Stroop color naming, word reading, and color-word interference, NIH-Toolbox Flanker, Dimensional Change, and Pattern Comparison, Trail Making Test A&B), verbal fluency (COWA letters F-A-S), crystallized intelligence (WTAR, NIH-Toolbox Oral Recognition and Picture Vocabulary), visual memory (BVMT immediate and delayed), and verbal memory (HVLT immediate and delayed. COWA animals and NIH-Toolbox Picture Sequencing did not adequately load onto any EFA factor and were excluded from the subsequent CFA.
Conclusions:Findings indicate that in a sample of healthy older adults, these collected measures and those obtained through the NIH-Toolbox battery represent 6 domains of cognitive function. Results suggest that in this sample, picture sequencing and COWA animals did not load adequately onto the factors created from the rest of the measures collected. These findings should assist in interpreting future research using combined NIH-TCB and neuropsychological batteries to assess cognition in healthy older adults.