Computerized Cognitive and Skills Training in Older People With Mild Cognitive Impairment: Using Ecological Momentary Assessment to Index Treatment-Related Changes in Real-World Performance of Technology-Dependent Functional Tasks.

OBJECTIVES: Cognitive and functional skills training improves skills and cognitive test performance, but the true test of efficacy is real-world transfer. We trained participants with mild cognitive impairment (MCI) or normal cognition (NC) for up to 12 weeks on six technology-related skills using remote computerized functional skills assessment and training (FUNSAT) software. Using ecological momentary assessment (EMA), we measured real-world performance of the technology-related skills over 6 months and related EMA-identified changes in performance to training gains. DESIGN: Randomized clinical trial with post-training follow-up. SETTING: A total of 14 Community centers in New York City and Miami. PARTICIPANTS: Older adults with normal cognition (n = 72) or well-defined MCI (n = 92), ranging in age from 60 to 90, primarily female, and racially and ethnically diverse. INTERVENTION: Computerized cognitive and skills training. MEASUREMENTS: EMA surveys measuring trained and untrained functional skills 3 or more days per week for 6 months and training gains from baseline to end of training. RESULTS: Training gains in completion times across all 6 tasks were significant (p <0.001) for both samples, with effect sizes more than 1.0 SD for all tasks. EMA surveys detected increases in performance for both trained (p <0.03) and untrained (p <0.001) technology-related skills for both samples. Training gains in completion times predicted increases in performance of both trained and untrained technology-related skills (all p <0.001). CONCLUSIONS: Computerized training produces increases in real-world performance of important technology-related skills. These gains continued after the end of training, with greater gains in MCI participants.

Ecological momentary assessment as a measurement tool in depression trials.

We used ecological momentary assessment (EMA) to track symptoms during a clinical trial. Thirty-six participants with major depressive disorder (MDD) and MADRS scores ≥20 were enrolled in a nonrandomized 6-week open-label trial of commercially available antidepressants. Twice daily, a mobile device prompted participants to self-report the 6 items of the HamD6 sub-scale derived from the Hamilton rating scale for depression (HamD17). Morning EMA reports asked "how do you feel now" whereas evening reports gathered a full-day impression. Clinicians who were blinded to the EMA data rated the MADRS, HamD17 and HamD6 at screen, baseline and weeks 2,4, and 6. Hierarchical linear modeling (HLM) examined the course of the EMA assessments and convergence between EMA scores and clinician ratings. HLM analyses revealed strong correlations between AM and PM EMA derived HamD6 scores and revealed significant improvements over time. EMA improvements were significantly correlated with the clinician rated HamD6 scores at endpoint and predicted clinician rated HamD6 score changes from baseline to endpoint (p < .001). There was a large correlation between EMA and clinician derived HamD6 scores at each in-person assessment after baseline. Treatment response defined by EMA matched the clinician rated HamD6 treatment responses in 33 of 36 cases (91.7%). EMA derived symptom scores appear to be efficient and valid measures to track daily symptomatic change in clinical trials and may provide more accurate measures of symptom severity than the episodic "snapshots" that are currently used as clinical outcomes. These findings support further investigation of EMA for assessment in clinical trials.