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

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis

OBJECTIVE

To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs.

DESIGN

An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition.

SETTING

Home and Community.

PARTICIPANTS

202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances.

RESULTS

Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition.

CONCLUSIONS

The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

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.

Psychosocial factors as main predictors of quality of life 5 years after stroke: a cross-sectional study from a third-level Italian center

Stroke causes a significant reduction in health-related quality of life (HRQoL), and studies addressing its predictors often rely on models with few variables. This study aimed to assess the degree to which health status, health habits, and features of the environment predict HRQoL in stroke survivors with stable clinical condition. WHO Quality of Life questionnaire for old-Age subjects (WHOQOL-AGE) was used to assess HRQoL. We ran a multivariable linear regression to predict WHOQOL-AGE variation, entering measures of health state, bad habits, healthy behaviors, physical environment features, and social support. Patients were stroke survivors with a stable clinical condition, distance from acute event of more than 6 months, and National Institutes of Health Stroke Scale (NIHSS) of 10 or less. A total of 122 participants (47 females, 97 with ischemic stroke) were enrolled, the mean age was 64.1, mean NIHSS 2.9, and mean distance from the acute event was 5.1 years. State anxiety (β = -0.202), trait anxiety (β = -0.232), depression (β = -0.255), social support (β = 0.247), and functional independence (β = -0.210) predicted WHOQOL-AGE variation (Adj. R2 = 0.549). Our results show that psychological symptoms, reduced social network, and functional dependence together have a negative impact on HRQoL. These elements, which are partly stroke-specific, should be taken into account in the recovery process to enhance patients' health outcomes.

Current Technology Developments Can Improve the Quality of Research and Level of Evidence for Rehabilitation Interventions: A Narrative Review

The current important limitations to the implementation of Evidence-Based Practice (EBP) in the rehabilitation field are related to the validation process of interventions. Indeed, most of the strict guidelines that have been developed for the validation of new drugs (i.e., double or triple blinded, strict control of the doses and intensity) cannot-or can only partially-be applied in rehabilitation. Well-powered, high-quality randomized controlled trials are more difficult to organize in rehabilitation (e.g., longer duration of the intervention in rehabilitation, more difficult to standardize the intervention compared to drug validation studies, limited funding since not sponsored by big pharma companies), which reduces the possibility of conducting systematic reviews and meta-analyses, as currently high levels of evidence are sparse. The current limitations of EBP in rehabilitation are presented in this narrative review, and innovative solutions are suggested, such as technology-supported rehabilitation systems, continuous assessment, pragmatic trials, rehabilitation treatment specification systems, and advanced statistical methods, to tackle the current limitations. The development and implementation of new technologies can increase the quality of research and the level of evidence supporting rehabilitation, provided some adaptations are made to our research methodology.