Physical function, psychosocial adaptation, and hardiness post stroke

Introduction: Stroke is a chronic condition that significantly impacts on morbidity and mortality (Balanda et al. 2010). Globally, the complexity of stroke is well documented and more recently, in Ireland, as part of the National Survey of Stroke Survivors (Horgan et al. 2014). There are a number of factors that are known to influence adaptation post stroke. However, there is a lack of research to explain the variability in how survivors adapt post stroke. Hardiness is a broad personality trait that leads to better outcome. This study investigated the influence of hardiness and physical function on psychosocial adaptation post stroke. Methods: A quantitative cross-sectional, correlational, exploratory study was conducted between April and November 2013. The sample consisted of stroke survivors (n=100) who were recruited from three hospital outpatient departments and completed a questionnaire package. Results: The mean age of participants was 76 years (range 70-80), over half (56%) of the participants achieved the maximum score of 20 on the Barthel Index indicating independence in activities of daily living. The median number of days since stroke onset was 91 days (range 74-128). The total mean score and standard deviation for hardiness was 1.89 (0.4) as measured by the Dispositional Resilience Scale, indicating medium hardiness (possible range 0-3). Psychosocial adaptation was measured using the Psychosocial Adjustment to Illness Scale, the total weighted mean and standard deviation was 0.54 (0.3) indicating a satisfactory level of psychosocial adaptation (possible range 0-3). A hierarchical multiple linear regression was performed which contained 6 independent variables (hardiness, living arrangement, and length of hospital stay, number of days since stroke onset, physical function and self-rated recovery). Findings demonstrated that physical function (p<0.001) and hardiness (p=0.008) were significantly related to psychosocial adaptation. Altogether, 65% of the variation in psychosocial adaptation can be explained by the combined effect of the independent variables. Physical functioning had the highest unique contribution (11%) to explain the variance in psychosocial adaptation while self-rated recovery, hardiness, and living arrangements contributed 3% each. Conclusion: This research provides important information regarding factors that influence psychosocial adaptation post stroke at 3 months. Physical function significantly contributed to psychosocial adaptation post stroke. The personality trait of hardiness provides insight into how behaviour influenced adaptation post stroke. While hardiness also had a strong relationship with psychosocial adaptation, further research is necessary to fully comprehend this process.

Exploring the relationship between patterns of physical activity, sedentary behaviour and cardiometabolic health in middle-aged Irish adults

Background and Study Rationale Being physically active is a major contributor to both physical and mental health. More specifically, being physically active lowers risk of coronary heart disease, high blood pressure, stroke, metabolic syndrome (MetS), diabetes, certain cancers and depression, and increases cognitive function and wellbeing. The physiological mechanisms that occur in response to physical activity and the impact of total physical activity and sedentary behaviour on cardiometabolic health have been extensively studied. In contrast, limited data evaluating the specific effects of daily and weekly patterns of physical behaviour on cardiometabolic health exist. Additionally, no other study has examined interrelated patterns and minute-by-minute accumulation of physical behaviour throughout the day across week days in middle-aged adults. Study Aims The overarching aims of this thesis are firstly to describe patterns of behaviour throughout the day and week, and secondly to explore associations between these patterns and cardiometabolic health in a middle-aged population. The specific objectives are to: 1 Compare agreement between the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and GENEActiv accelerometer-derived moderate-to-vigorous (MVPA) activity and secondly to compare their associations with a range of cardiometabolic and inflammatory markers in middle-aged adults. 2 Determine a suitable monitoring frame needed to reliably capture weekly, accelerometer-measured, activity in our population. 3 Identify groups of participants who have similar weekly patterns of physical behaviour, and determine if underlying patterns of cardiometabolic profiles exist among these groups. 4 Explore the variation of physical behaviour throughout the day to identify whether daily patterns of physical behaviour vary by cardiometabolic health. Methods All results in this thesis are based on data from a subsample of the Mitchelstown Cohort; 475 (46.1% males; mean aged 59.7±5.5 years) middle-aged Irish adults. Subjective physical activity levels were assessed using the IPAQ-SF. Participants wore the wrist GENEActiv accelerometer for 7 consecutive days. Data was collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised based on validated cut-offs. Data on cardiometabolic and inflammatory markers was collected according to standard protocol. Cardiometabolic outcomes (obesity, diabetes, hypertension and MetS) were defined according to internationally recognised definitions by World Health Organisation (WHO) and Irish Diabetes Federation (IDF). Results The results of the first chapter suggest that the IPAQ-SF lacks the sensitivity to assess patterning of activity and guideline adherence and assessing the relationship with cardiometabolic and inflammatory markers. Furthermore, GENEActiv accelerometer-derived MVPA appears to be better at detecting relationships with cardiometabolic and inflammatory markers. The second chapter examined variations in day-to-day physical behaviour levels between- and within-subjects. The main findings were that Sunday differed from all other days in the week for sedentary behaviour and light activity and that a large within-subject variation across days of the week for vigorous activity exists. Our data indicate that six days of monitoring, four weekdays plus Saturday and Sunday, are required to reliably estimate weekly habitual activity in all activity intensities. In the next chapter, latent profile analysis of weekly, interrelated patterns of physical behaviour identified four distinct physical behaviour patterns; Sedentary Group (15.9%), Sedentary; Lower Activity Group (28%), Sedentary; Higher Activity Group (44.2%) and a Physically Active Group (11.9%). Overall the Sedentary Group had poorer outcomes, characterised by unfavourable cardiometabolic and inflammatory profiles. The remaining classes were characterised by healthier cardiometabolic profiles with lower sedentary behaviour levels. The final chapter, which aimed to compare daily cumulative patterns of minute-by-minute physical behaviour intensities across those with and without MetS, revealed significant differences in weekday and weekend day MVPA. In particular, those with MetS start accumulating MVPA later in the day and for a shorted day period. Conclusion In conclusion, the results of this thesis add to the evidence base regards an optimal monitoring period for physical behaviour measurement to accurately capture weekly physical behaviour patterns. In addition, the results highlight whether weekly and daily distribution of activity is associated with cardiometabolic health and inflammatory profiles. The key findings of this thesis demonstrate the importance of daily and weekly physical behaviour patterning of activity intensity in the context of cardiometabolic health risk. In addition, these findings highlight the importance of using physical behaviour patterns of free-living adults observed in a population-based study to inform and aid health promotion activity programmes and primary care prevention and treatment strategies and development of future tailored physical activity based interventions.