An Exploration of Sedentary Behavior Patterns in Community-Dwelling People With Stroke: A Cluster-Based Analysis

A Mendelian randomization-based study of the causal relationship between leisure sedentary behavior and delirium

BACKGROUND

Delirium is an acute or subacute change in mental status caused by various factors. We evaluated the causal relationship between leisure sedentary behaviors (LSBs) and delirium.

METHODS

A two-sample Mendelian randomization (MR) study was performed to evaluate the causal relationship between sedentary behaviors (time spent watching television, time spent using computer, and time spent driving) and delirium. Statistical information for the associations between single nucleotide polymorphisms (SNPs) and the traits of interest was obtained from independent consortia that focused on European populations. The dataset for LSBs was acquired from genome-wide association studies (GWAS) comprising a substantial sample size: 437887 samples for time spent watching television, 360,895 for time spent using computer, and 310,555 for time spent driving. A GWAS with 1269 delirium cases and 209,487 controls was used to identify genetic variation underlying the time of LSBs. We used five complementary MR methods, including inverse variance weighted method (IVW), MR-Egger, weighted median, weighted mode, and simple mode.

RESULTS

Genetically predicted time spent watching television (odds ratio [OR]: 2.921, 95 % confidence interval [CI]: 1.381-6.179) demonstrated significant association with delirium (P = 0.005), whereas no significant associations were observed between time spent using computer (OR: 0.556, 95 % CI: 0.246-1.257, P = 0.158) and time spent driving (OR: 1.747, 95 % CI: 0.09-3. 40, P = 0.713) and delirium. Sensitivity analyses supported a causal interpretation, with limited evidence of significant bias from genetic pleiotropy. Moreover, our MR assumptions appeared to be upheld, enhancing the credibility of our conclusions.

LIMITATIONS

Larger sample sizes are needed to validate the findings of our study.

CONCLUSION

Time spent watching television is a significant risk factor for delirium. Reducing television time may be an important intervention for those at higher risk of delirium.

Clinical phenotypes of patients with acute stroke: a secondary analysis

INTRODUCTION

Stroke is a leading cause of mortality worldwide and a major cause of disability having a high burden on patients, society, and caregiving systems. This study was conducted to investigate the presence of clusters of in-hospital patients with acute stroke based on demographic and clinical data. Cluster analysis reveals patterns in patient characteristics without requiring knowledge of a predefined patient category or assumptions about likely groupings within the data.

METHODS

We performed a secondary analysis of open-access anonymized data from patients with acute stroke admitted to a hospital between December 2019 to June 2021. In total, 216 patients (78; 36.1% men) were included in the analytical dataset with a mean (SD) age of 60.3 (14.4). Many demographic and clinical features were included in the analysis and the Barthel Index on discharge was used for comparing the functional recovery of the identified clusters.

RESULTS

Hierarchical clustering based on the principal components identified two clusters of 109 and 107 patients. The clusters were different in the Barthel Index scores on discharge with the mean (SD) of 39.3 (29.3) versus 62.6 (29.4); t (213.87) = -5.818, P <0.001, Cohen's d (95%CI) = -0.80 (-1.07, -0.52). A logistic model showed that age, systolic blood pressure, pulse rate, D-dimer blood level, low-density lipoprotein, hemoglobin, creatinine concentration, the National Institute of Health Stroke Scale value, and the Barthel Index scores on admission were significant predictors of cluster profiles (all P ≤0.029).

CONCLUSION

There are two clusters in hospitalized patients with acute stroke with significantly different functional recovery. This allows prognostic grouping of hospitalized acute stroke patients for prioritization of care or resource allocation. The clusters can be recognized using easily measured demographic and clinical features.

Agreement between the activPAL accelerometer and direct observation during a series of gait and sit-to-stand tasks in people living with cervical dystonia

Background

Accelerometers are commonly used for the assessment of PA; however, these devices have not been validated in people with dystonia who experience movement limitations. To properly understand movement behaviors and deliver accurate exercise prescription in this population, the validity of these devices must be tested.

Objective

This study aimed to validate step count and postural transitions detected by the activPAL accelerometer (AP) against direct observation (DO) during two functional assessments: the 30-s sit-to-stand (30STS) and 6-min usual-pace walk tests. Methods: A total of 11 participants with cervical dystonia (CD) (male/female n = 5/6; mean age = 61 years; BMI = 24 kg/m2) performed the 6-min usual pace walking and 30STS while wearing the activPAL. A trained observer counted steps and observed the number of sit-to-stands.

Results

The average step count detected with AP and DO was 651.8 (218-758) and 654.5 (287-798) respectively. The average transitions detected were 11 (4-16) and 12 (4-17) respectively. Both methods showed good agreement and there was a statistically significant and strong correlation between the two methods, i.e., transitions (r = 0.983, p = 0.0001), and step counts (r = 0.9841, p = 0.0001).

Conclusion

There is a good agreement between activPAL and direct observation for step counts and transitions between sitting and standing in people living with CD.

Misuse of multinomial logistic regression in stroke related health research: A systematic review of methodology

Multinomial logistic regression (MLR) is often used to model the association between a nominal outcome variable and one or more covariates. The results of MLR are interpreted as relative risk ratios (RRR) and warrant a more coherent interpretation than ordinary logistic regression. Some authors compare the results of MLR to ordinal logistic regression (OLR), irrespective of the fact that these estimate different quantities. We aim to investigate the time trends in the use and misuse of MLR in studies including stroke patients, specifically the extent to which (1) the results are denoted as anything other than RRR, (2) comparisons are made of results with results of OLR and (3) results have been interpreted coherently. Secondarily, we examine the use of model validation techniques in studies with predictive aims. We searched EMBASE and PubMed for articles using MLR on populations of stroke patients. Identified studies were screened, and information pertaining to our aims was extracted. A total of 285 articles were identified through a systematic literature search, and 68 of these were included in the review. Of these, 60 articles (88%) did not denote exponentiated coefficients of MLR as relative risk ratios but rather some other measure. Additionally, 63 articles (93%) interpreted the results of MLR in a non-coherent manner. Two articles attempted to compare MLR results with those of OLR. Nine studies attempted to use MLR for predictive means, and three used relevant validation techniques. From these findings, it is clear that the interpretation of MLR is often suboptimal.

Multidimensional analysis of sedentary behaviour and participation in Spanish stroke survivors (Part&Sed-Stroke): a protocol for a longitudinal multicentre study

INTRODUCTION

Stroke survivors usually experience long-lasting functional, emotional and social consequences that might contribute to sedentary behaviour and participation restrictions, which are important targets to address during rehabilitation. However, the trajectory and inter-relationship between these factors are unknown.

METHODS AND ANALYSIS

Part&Sed is a research project based on an observational study design with 6 and 12 months of follow-ups in stroke survivors. In addition, a qualitative analysis of the impact of the stroke on the stroke survivor, validation of the Satisfaction with Daily Occupation-Occupational Balance assessment tool and analysis of the reliability of the Fitbit Inspire 2 activity tracker wristband will be carried out. Participants will be chronic stroke survivors with independent walking capacity. Sociodemographic and clinical data, physical activity, ambulation, sleep, quality of life, anxiety and depression, community participation, and occupational satisfaction and balance, as well as data provided by the activity tracker wristband, will be collected. In addition, if the participant has a primary caregiver, the caregiver will also be monitored. A minimum of 130 participants will be recruited to conduct a random-effects multiple regression model. Mixed models for repeated measures will assess the variation over time of the different variables associated with participation and sedentary behaviour. Psychometric properties (eg, internal consistency, construct validity, test-retest reliability) of the Satisfaction with Daily Occupation-Occupational Balance will be determined. Additionally, intraclass correlation coefficients and minimum detectable change will be calculated to assess intrasubject reliability of physical activity and sleep parameters recorded by the Fitbit Inspire 2. The qualitative analysis process will be carried out using the analysis proposed by Giorgi.

ETHICS AND DISSEMINATION

The study received ethical approval from the Spanish Regional Ethics Committee 'Comité de Ética de la Investigación de la Comunidad de Aragón' (PI21/333). The results will be made available via peer-reviewed publications, international conferences and official channels.

Predictors of non-stepping time in people with chronic stroke

BACKGROUND

Sedentary time is an independent construct from active time. Previous studies have examined variables associated with sedentary time to inform behavior change programs; however, these studies have lacked data sets that encompass potentially important domains.

OBJECTIVES

The purpose of this study was to build a more comprehensive model containing previously theorized important predictors of sedentary time and new predictors that have not been explored. We hypothesized that variables representing the domains of physical capacity, psychosocial, physical health, cognition, and environmental would be significantly related to sedentary time in individuals post-stroke.

METHODS

This was a cross-sectional analysis of 280 individuals with chronic stroke. An activity monitor was used to measure sedentary (i.e. non-stepping) time. Five domains (8 predictors) were entered into a sequential linear regression model: physical capacity (6-Minute Walk Test, assistive device use), psychosocial (Activities Specific Balance Confidence Scale and Patient Health Questionnaire-9), physical health (Charlson Comorbidity Index and body mass index), cognition (Montreal Cognitive Assessment), and environmental (Area Deprivation Index).

RESULTS

The 6-Minute Walk Test (β = -0.39, p < .001), assistive device use (β = 0.15, p = .03), Patient Health Questionnaire-9 (β = 0.16, p = .01), and body mass index (β = 0.11, p = .04) were significantly related to non-stepping time in individuals with chronic stroke. The model explained 28.5% of the variability in non-stepping time.

CONCLUSIONS

This work provides new perspective on which variables may need to be addressed in programs targeting sedentary time in stroke. Such programs should consider physical capacity, depressive symptoms, and physical health.