Is unemployment associated with inefficient sleep habits? A cohort study using objective sleep measurements

Abstract

Unemployed people could be at risk of developing inefficient sleep habits by spending excessive time in bed, as they lack a structuring activity. This could impact their mental health and reintegration into labour. This study aims to analyse possible associations between employment status and sleep parameters using actigraphy. Subjects (148 employed and 50 unemployed) were drawn from a German population-based cohort. Sleep parameters were measured with the SenseWear Bodymedia Pro 3 armband. Comparison of means concerning sleep duration, sleep efficiency, time of sleep and sleep fragmentation was performed separately for week days and weekends. Multiple linear regression analysis was performed to analyse group differences controlling for covariates. Finally, we defined cut-off scores for each sleep variable, and analysed the distribution of subjects above and below these values. Unemployed people did not sleep significantly longer than employed people. However, on week days, they displayed night sleep efficiency reduced by on average > 5% points, they lay down for 28 min longer, had later mid sleep time (38 min) and sleep offset (55 min), as well as more frequent awakenings after sleep onset accounting for being awake 28 min longer (all p ≤ 0.005). Sleep in unemployed subjects compared with employed subjects aged 41-64 years was less efficient, more fragmented and shifted to a later point of the night. Results support prior findings that unemployment has a negative influence on sleep quality. Unemployed individuals could benefit from intervention programmes aiming at the adoption of healthier sleep habits.

Mismatch of Biological and Social Time in Cocaine-Addicted Men

“…Consequently, the shift from early to late chronotype, as reported by a higher number of CUD patients than control participants, requires further verification from a longitudinal design. Secondly, we did not measure sleep quality, which has been suggested to be influenced by unemployment [70] and has even been shown to impact on the likelihood of drug use, including the use of cocaine [43, 44]. Finally, employment status may influence people’s lives in many ways, psychologically and socially.…”

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

“…We then measured bias across attributes including age, sex at birth, race, family income, health insurance, and employment to identify subgroups where the tool underperformed. We studied these specific attributes because of known behavioral differences across demographic and SES subgroups 23 , 24 , 30 – 32 that could impact the reliability of the developed AI tool. Finally, we interpreted why the tool underperformed by identifying inconsistencies between the AI tool and sensed-behaviors predicting depression across subgroups.…”

Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data

“…We then measured bias across attributes including age, sex at birth, race, household income, health insurance and employment to identify subgroups where the tool underperformed. We studied these speci c attributes because of known behavioral differences across demographic and SES subgroups 23,24,[30][31][32] that could impact the reliability of the developed AI tool. Finally, we interpreted why the tool underperformed by identifying inconsistencies between the AI tool and sensed-behaviors predicting depression across subgroups.…”