Abstract:
The purpose of this cross-sectional descriptive study was to explore the level of caffeine addiction prevalence, mental health, sleep quality, and the factors related to caffeine addiction among employees in Bangkok. We sent online questionnaires to 1,406 employees. Data from 374 people were sent back. According to the exclusion criteria and the completed questionnaires, 321 data (92.51%) were analyzed in this study. All of data came from the employees that drinking coffee. The data were collected from November 2021 to February 2022. Participants completed 9 questionnaires including; demographic information, coffee consumption, caffeine addiction, mental health (Patient Health Questionnaire; PHQ-9), sleep quality (The Pittsburgh Sleep Quality Index; PSQI), nicotine dependence, alcohol use, and drug use. All of the subjects were coffee consumption. The data were analyzed to explore the relationship between mental health, sleep quality, and caffeine addiction by using descriptive statistics, e.g., percentage, mean, standard deviation, min, and max. The inferential statistics were analyzed by Chi-square, independent t-test, Pearsons correlation coefficient, and Logistic regression. The results from 321 subjects showed no mental health symptoms (55.44%), 29.91% were mild level, 13.40% were moderate level, and 1.25% were high level, respectively. On the sleep quality facet, most of the subjects had poor sleep quality (94.08). According to the caffeine addiction questionnaire, 248 subjects refer to the caffeine-addicted group and 73 subjects refer to the non-caffeine-addicted group. The average mental health score correlated with sleep quality (r=0.578, p<0.001). Factors related to caffeine addiction with P < 0.05 was considered as significant difference included female employees, drink coffee 2-4 glasses/day, freshly brewed coffee, cappuccino, drink coffee 4-7 days/week, habitual drinking, drinking for awakening, having poor sleep quality, having symptom of mental health and depression. These factors had positively predicted the caffeine addiction in employees. Therefore, these results suggested the need for action from the related company proactively to pay more attention to factors related to caffeine addiction and the quality of life of employees that might affect job performance.