Background: The American Society of Anesthesiologists (ASA) score is generated based on patients’ clinical status. Accurate ASA classification is essential for the communication of perioperative risks and resource planning. Literature suggests that ASA classification can be automated for consistency and time-efficiency. To develop a rule-based algorithm for automated ASA classification, this study seeks to establish consensus in ASA classification for clinical conditions encountered at a tertiary women’s hospital. Methods: Thirty-seven anesthesia providers rated their agreement on a 4-point Likert scale to ASA scores assigned to items via the Delphi technique. After Round 1, the group’s collective responses and individual item scores were shared with participants to improve their responses for Round 2. For each item, the percentage agreement (‘agree’ and ‘strongly agree’ responses combined), median (interquartile range/IQR), and SD were calculated. Consensus for each item was defined as a percentage agreement ≥ 70%, IQR  1.0, and SD < 1.0. Results: All participants completed the study and none had missing data. The number of items that reached consensus increased from 25 (51.0%) to 37 (75.5%) in the second Delphi round, particularly for items assigned ASA scores of III and IV. Nine items, which pertained to alcohol intake, asthma, thyroid disease, limited exercise tolerance, and stable angina, did not reach consensus even after two Delphi rounds. Conclusions Delphi consensus was attained for 37 of the 49 study items (75.5%), facilitating their incorporation into a rule-based clinical support system designed to automate the prediction of ASA classification.

OBJECTIVES: This study aimed to evaluate the determinants of health-related quality of life (HRQoL) among workers in Singapore. METHODS: We analysed data from a cross-sectional study of 464 participants from 4 companies in Singapore. Physical and mental components of HRQoL were assessed using the Short-Form 36 version 2.0 survey. A generalized linear model was used to determine factors associated with the physical component summary (PCS) and mental component summary (MCS) scores of HRQoL. RESULTS: The overall mean PCS and MCS scores were mean±standard deviation 51.6±6.7 and 50.2±7.7, respectively. The scores for subscales ranged from 62.7±14.7 for vitality to 83.5±20.0 for role limitation due to emotional problems. Ethnicity, overweight/obesity, and years working at the company were significantly associated with physical HRQoL, and age and stress at work were significantly associated with mental HRQoL. Moreover, sleep quality was significantly associated with both physical and mental HRQoL. CONCLUSIONS These findings could help workplaces in planning strategies and initiatives for employees to maintain a worklife balance that encompasses their physical, emotional, and social well-being.

OBJECTIVES: This study aimed to evaluate the determinants of health-related quality of life (HRQoL) among workers in Singapore. METHODS: We analysed data from a cross-sectional study of 464 participants from 4 companies in Singapore. Physical and mental components of HRQoL were assessed using the Short-Form 36 version 2.0 survey. A generalized linear model was used to determine factors associated with the physical component summary (PCS) and mental component summary (MCS) scores of HRQoL. RESULTS: The overall mean PCS and MCS scores were mean±standard deviation 51.6±6.7 and 50.2±7.7, respectively. The scores for subscales ranged from 62.7±14.7 for vitality to 83.5±20.0 for role limitation due to emotional problems. Ethnicity, overweight/obesity, and years working at the company were significantly associated with physical HRQoL, and age and stress at work were significantly associated with mental HRQoL. Moreover, sleep quality was significantly associated with both physical and mental HRQoL. CONCLUSIONS These findings could help workplaces in planning strategies and initiatives for employees to maintain a worklife balance that encompasses their physical, emotional, and social well-being.

The development of underground workspaces is a strategic effort towards healthy urban growth in cities with ever-increasing land scarcity. Despite the growth in underground workspaces, there is limited information regarding the impact of this environment on workers’ health. The Health Effects of Underground Workspaces (HEUW) study is a cohort study that was set up to examine the health effects of working in underground workspaces. In this paper, we describe the rationale for the study, study design, data collection, and baseline characteristics of participants. The HEUW study recruited 464 participants at baseline, of whom 424 (91.4%) were followed-up at 3 months and 334 (72.0%) at 12 months from baseline. We used standardized and validated questionnaires to collect information on socio-demographic and lifestyle characteristics, medical history, family history of chronic diseases, sleep quality, health-related quality of life, chronotype, psychological distress, occupational factors, and comfort levels with indoor environmental quality parameters. Clinical and anthropometric parameters including blood pressure, spirometry, height, weight, and waist and hip circumference were also measured. Biochemical tests of participants’ blood and urine samples were conducted to measure levels of glucose, lipids, and melatonin. We also conducted objective measurements of individuals’ workplace environment, assessing air quality, light intensity, temperature, thermal comfort, and bacterial and fungal counts. The findings this study will help to identify modifiable lifestyle and environmental parameters that are negatively affecting workers’ health. The findings may be used to guide the development of more health-promoting workspaces that attempt to negate any potential deleterious health effects from working in underground workspaces.