INTRODUCTIONWaiting times for kidney transplant are long in Singapore. Healthrelated quality of life (HRQoL) of patients might be affected as a result of the stress of the long wait and the uncertainty of being called to undergo a surgical operation. This study aimed to measure the HRQoL of patients on the kidney transplant waiting list and to identify factors which could impact on the HRQoL scores in this group of patients.

MATERIALS AND METHODSThis was a cross-sectional study of kidney transplant waiting list patients managed at a tertiary renal unit using the SF-36. A SF-36 normative calculator was used to generate HRQoL scores for the Singapore general population matched with the study cohort's age, gender and ethnicity.

RESULTSThere were 265 respondents with a response rate was 81%. Our study shows that HRQoL scores for the kidney transplant waiting list patients were lower than the population norms across all subscales and were clinically significant for General Health, Role Physical, Bodily Pain, Social Functioning and Mental Component Summary scores. Factors such as being Chinese, married, employed and undergoing haemodialysis predicted better HRQoL scores after adjusting for possible confounders. Age, gender, educational level, household income, history of kidney transplant, duration on the transplant waiting list and years on dialysis did not significantly influence SF-36 across all subscales scores.

CONCLUSIONKidney transplant waiting list patients had worse HRQoL compared to the general population. Factors such as ethnicity, marital status, employment status, and type of dialysis treatment significantly influenced patients' perception of their HRQoL.

Cross-Sectional Studies ; Female ; Health Status ; Humans ; Kidney Transplantation ; Male ; Quality of Life ; Surveys and Questionnaires ; Waiting Lists

Research has established the association between lifestyle factors and cognition. However, they are often assessed in isolation overlooking their complex interaction and contribution to the quality of life (QoL). The present study seeks to determine if BMI, lifestyle behaviours (video game use, dietary intake, physical activity, sleep), and cognitive functions could be identified into distinct grouping clusters. In addition, the QoL of the resultant clusters was also examined for differences between them. Data were collected from an online survey (N = 116). Following a 2-step cluster analysis, two distinct clusters were identified with significant differences in video game use and reasoning ability were found between them. Further comparison of demographics and QoL showed a cluster of participants who played more games and had higher cognitive performance exhibited lower QoL compared to the other cluster, specifically in the ‘relationship’ dimension. The findings show video game use and reasoning ability are linked to perceived QoL. Further studies should investigate the interaction between video game use and cognitive functions and how they contribute to perceived QoL.
Cognition