Intraindividual variability in post-stroke cognition and its relationship with activities of daily living and social functioning: an ecological momentary assessment approach.

INTRODUCTION: Ecological momentary assessment (EMA) is a methodological approach to studying intraindividual variation over time. This study aimed to use EMA to determine the variability of cognition in individuals with chronic stroke, identify the latent classes of cognitive variability, and examine any differences in daily activities, social functioning, and neuropsychological performance between these latent classes. METHODS: Participants (N = 202) with mild-to-moderate stroke and over 3-month post-stroke completed a study protocol, including smartphone-based EMA and two lab visits. Participants responded to five EMA surveys daily for 14 days to assess cognition. They completed patient-reported measures and neuropsychological assessments during lab visits. Using latent class analysis, we derived four indicators to quantify cognitive variability and identified latent classes among participants. We used ANOVA and Chi-square to test differences between these latent classes in daily activities, social functioning, and neuropsychological performance. RESULTS: The latent class analysis converged on a three-class model. The moderate and high variability classes demonstrated significantly greater problems in daily activities and social functioning than the low class. They had significantly higher proportions of participants with problems in daily activities and social functioning than the low class. Neuropsychological performance was not statistically different between the three classes, although a trend approaching statistically significant difference was observed in working memory and executive function domains. DISCUSSION: EMA could capture intraindividual cognitive variability in stroke survivors. It offers a new approach to understanding the impact and mechanism of post-stroke cognitive problems in daily life and identifying individuals benefiting from self-regulation interventions.

Using Intervention Mapping and Behavior Change Techniques to Develop a Digital Intervention for Self-Management in Stroke: Development Study.

BACKGROUND: Digital therapeutics, such as interventions provided via smartphones or the internet, have been proposed as promising solutions to support self-management in persons with chronic conditions. However, the evidence supporting self-management interventions through technology in stroke is scarce, and the intervention development processes are often not well described, creating challenges in explaining why and how the intervention would work. OBJECTIVE: This study describes a specific use case of using intervention mapping (IM) and the taxonomy of behavior change techniques (BCTs) in designing a digital intervention to manage chronic symptoms and support daily life participation in people after stroke. IM is an implementation science framework used to bridge the gap between theories and practice to ensure that the intervention can be implemented in real-world settings. The taxonomy of BCTs consists of a set of active ingredients designed to change self-management behaviors. METHODS: We used the first 4 steps of the IM process to develop a technology-supported self-management intervention, interactive Self-Management Augmented by Rehabilitation Technologies (iSMART), adapted from a face-to-face stroke-focused psychoeducation program. Planning group members were involved in adapting the intervention. They also completed 3 implementation measures to assess the acceptability, appropriateness, and feasibility of iSMART. RESULTS: In step 1, we completed a needs assessment consisting of assembling a planning group to codevelop the intervention, conducting telephone surveys of people after stroke (n=125) to identify service needs, and performing a systematic review of randomized controlled trials to examine evidence of the effectiveness of digital self-management interventions to improve patient outcomes. We identified activity scheduling, symptom management, stroke prevention, access to care resources, and cognitive enhancement training as key service needs after a stroke. The review suggested that digital self-management interventions, especially those using cognitive behavioral theory, effectively reduce depression, anxiety, and fatigue and enhance self-efficacy in neurological disorders. Step 2 identified key determinants, objectives, and strategies for self-management in iSMART, including knowledge, behavioral regulation, skills, self-efficacy, motivation, negative and positive affect, and social and environmental support. In step 3, we generated the intervention components underpinned by appropriate BCTs. In step 4, we developed iSMART with the planning group members. Especially, iSMART simplified the original psychoeducation program and added 2 new components: SMS text messaging and behavioral coaching, intending to increase the uptake by people after stroke. iSMART was found to be acceptable (mean score 4.63, SD 0.38 out of 5), appropriate (mean score 4.63, SD 0.38 out of 5), and feasible (mean score 4.58, SD 0.34 out of 5). CONCLUSIONS: We describe a detailed example of using IM and the taxonomy of BCTs for designing and developing a digital intervention to support people after stroke in managing chronic symptoms and maintaining active participation in daily life.

Smartphone assessment uncovers real-time relationships between depressed mood and daily functional behaviors after stroke.

INTRODUCTION: The impact of depressed mood in daily life is difficult to investigate using traditional retrospective assessments, given daily or even within-day mood fluctuations in various contexts. This study aimed to use a smartphone-based ambulatory assessment to examine real-time relationships between depressed mood and functional behaviors among individuals with stroke. METHODS: A total of 202 participants with mild-to-moderate stroke (90% ischemic, 45% female, 44% Black) completed an ecological momentary assessment five times per day for 2 weeks by reporting their depressed mood and functional behaviors regarding where, with whom, and what activity was spent. RESULTS: Participants spent 28% of their wake-up time participating in passive leisure activities but spent the least time in physical (4%) and vocational (9%) activities. Depressed mood was concurrently lower when participants engaged in social activities (ß = -0.023 ± 0.011) and instrumental activities of daily living (ß = -0.061 ± 0.013); spent time with family members (ß = -0.061 ± 0.014), spouses (ß = -0.043, ± 0.016), friends (ß = -0.094, ± 0.021), and coworkers (ß = -0.050 ± 0.021); and were located in restaurants (ß = -0.068 ± 0.029), and homes of family members (ß = -0.039 ± 0.020) or friends (ß = -0.069 ± 0.031). Greater depressed mood was associated with worse ratings in satisfaction, performance, and engagement of activities in concurrent (ßs = -0.036 ± 0.003, -0.053 ± 0.003, -0.044 ± 0.003) and time-lagged models (ßs = -0.011 ± 0.004, -0.012 ± 0.004, -0.013 ± 0.004). DISCUSSION: Smartphone-based ambulatory assessment can elucidate functional behaviors and associated mood after stroke. Findings support behavioral activation treatments to schedule social and instrumental activities for stroke survivors to reduce their depressed mood.

Ecological Momentary Assessment of Real-World Functional Behaviors in Individuals With Stroke: A Longitudinal Observational Study.

OBJECTIVE: To validate and characterize real-world functional behaviors in individuals after stroke. DESIGN: Longitudinal observational study using ecological momentary assessment (EMA) as a real-time assessment of functional behaviors in natural contexts. Wilcoxon rank-sum tests, Fisher exact tests, and Spearman correlations were used to analyze data. SETTING: Community. PARTICIPANTS: Individuals with mild to moderate stroke (N=212). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Individuals were assessed 5 times daily for 14 days with EMA surveys to determine what, with whom, and where individuals were doing activities and appraise mental, somatic, and cognitive symptoms. Individuals also completed standardized assessments during laboratory visits, including Lawton Instrumental Activities of Daily Living Scale, Activity Card Sort, Patient-Reported Outcome Measurement Information System, and Quality of Life in Neurological Disorders. RESULTS: Most individuals (median age, 60 years; 55% male) were ischemic stroke (90%) and had mild stroke severity (median National Institutes of Health Stroke Scale, 2). A total of 14,140 EMA surveys were analyzed. Individuals were home 78% of the time; primarily participated in passive, unproductive activities (27%), especially watching television and resting; and participated least in physical activities (4%). EMA was sensitive to indicators of poststroke disability; unemployed individuals reported fewer vocational activities but more activities of daily living (ADL) and passive activities than employed counterparts. Users of mobility devices and individuals with cognitive problems spent significantly less time on vocational activities and more on ADL than nonusers and those without cognitive problems. Our data supported the validity of EMA methods in stroke, with small to moderate correlations of EMA with in-laboratory measures of daily functioning (r=-0.30 to 0.35, P<.05) and very large correlations between EMA and in-laboratory measures of symptoms, especially those measuring same constructs (r=-0.64 to 0.79, P<.0001). CONCLUSIONS: Our findings reveal that EMA tracked poststroke functioning precisely. EMA may be beneficial in examining poststroke functional recovery, in monitoring patients for home-based interventions, and for longitudinal research.