Association of sleep quality with sociodemographic factors and lifestyle in adolescents from southern Brazil

Lima, Tiago Rodrigues de; Silva, Diego Augusto Santos

doi:10.1007/s12519-018-0136-8

Cited by 19 publications

(38 citation statements)

References 34 publications

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“…Contrastingly, self-employment, being a non-regular employee, and drinking alcohol more than 3 times per week were inversely associated with poor mental health conditions. In general, these results were consistent with the results obtained from previous cross-sectional studies that addressed the risk factors of poor mental health conditions, with unemployment and low levels of disposable income being cited in two [33,35], short sleep duration being cited in three [38,[42][43][44][45], and lack of physical exercise being cited in five studies [15,19,40]. Thus, it is confirmed that these factors are those most associated with effects on mental health conditions in terms of cross-sectional and longitudinal direction.…”

Section: Plos Onesupporting

confidence: 91%

“…Regarding exercise, the lack of physical activity could be a risk factor for poor mental health conditions [ 39 ], and some studies report that physical activity could play a significant role in promoting better mental health outcomes [ 15 , 19 , 39 – 41 ]. Moreover, one previous study reported that adolescents who had low aerobic fitness were more likely to report poorer sleep quality [ 42 ]. Based on these findings, there is a need to promote regular practice of physical activities in order to improve quality of sleep and mental health conditions, mainly because it helps reduce stress levels.…”

Section: Discussionmentioning

confidence: 99%

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Impact of socioeconomic- and lifestyle-related risk factors on poor mental health conditions: A nationwide longitudinal 5-wave panel study in Japan

Nagasu

Yamamoto

2020

PLoS ONE

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The association of socioeconomic status and lifestyle behaviours on mental health appears well-established in the literature, as several studies report that better socioeconomic status such as higher levels of disposable income and employment as well as practising healthy lifestyles can enhance mental well-being. However, the reliance on cross-sectional correlations and lack of adequate statistical controls are possible limitations. This study aims to add the evidence of longitudinal association to the literature by using Japanese representative longitudinal household panel data. We employed panel data analytical techniques such as the random-effects conditional logistic regression (RE-CLR) and the fixed-effects conditional logistic regression (FE-CLR) models with possible time variant confounders being controlled. Our sample was comprised of 14,717 observations of 3,501 individuals aged 22-59 years for five waves of the Japanese Household Panel Survey. We confirmed many of the factors associated with mental health reported in existing studies by analysing crosssectional data. These significant associations are also longitudinal (within) associations estimated by the FE-CLR models. Such factors include unemployment, low household income, short nightly sleeping duration, and lack of exercise. However, we also found that several factors such as disposable income, living alone, and drinking habits are not significantly associated with mental health in the FE-CRL models. The results imply the reverse causality that poor mental health conditions cause lower disposal income, possibly due to the inability to exhibit higher productivity, but an increase in disposal income would not necessarily improve mental health conditions. In this case, aggressive policy interventions to increase the disposal income of people of lower socioeconomic backgrounds would not necessarily be effective to minimize health inequalities.

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Section: Plos Onesupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Impact of socioeconomic- and lifestyle-related risk factors on poor mental health conditions: A nationwide longitudinal 5-wave panel study in Japan

Nagasu

Yamamoto

2020

PLoS ONE

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“…We adjusted for several sociodemographic and family characteristics linked with adolescent sleep and physical activity behavior 49–51 , including sex (male vs. female), age, race (Black, Hispanic, other or missing, vs. White), mother’s education (college or graduate school, high school or equivalent, some college, vs. less than high school), family structure (biological mother, biological mother with new partner, biological father only or with new partner or other primary caregiver, vs. biological mother and biological father), and household income to national poverty threshold (<49%, 50–99%, 100–199%, 200–299%, vs. >300%). We also adjusted for body mass index measured during the home visit (BMI; age percentile).…”

Section: Methodsmentioning

confidence: 99%

Bidirectional, Daily Temporal Associations between Sleep and Physical Activity in Adolescents

Master

Nye

Lee

et al. 2019

Sci Rep

103

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This study evaluated the daily, temporal associations between sleep and daytime physical activity and sedentary behavior among adolescents from the Fragile Families & Child Wellbeing Study. A sub-sample of the cohort at age 15 (N = 417) wore actigraphy monitors for one week during the school year from which we derived daily minutes in sedentary and moderate-to-vigorous physical activity (MVPA) and nighttime sleep measures. Multilevel models tested temporal associations of nightly sleep onset, offset, duration, and sleep maintenance efficiency, with daily MVPA and sedentary behavior. More MVPA than an individual’s average was associated with earlier sleep onset (p < 0.0001), longer duration (p = 0.03), and higher sleep maintenance efficiency (p < 0.0001). On days with more sedentary behavior than an individual’s average, sleep onset and offset were delayed (p < 0.0001), duration was shorter (p < 0.0001), and sleep maintenance efficiency was higher (p = 0.0005). Conversely, nights with earlier sleep onset predicted more next-day sedentary behavior (p < 0.0001), and nights with later sleep offset and longer sleep duration were associated with less MVPA (p < 0.0001) and less sedentary time (p < 0.0001, p = 0.004) the next day. These bidirectional associations between sleep and physical activity suggest that promoting MVPA may help to elicit earlier bedtimes, lengthen sleep duration, and increase sleep efficiency, critical for healthy adolescent development.

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“…For instance, while adults may encounter sleep problems due to anxiety or depression, adolescents may have sleep problems attributable to a delayed sleep cycle [28]. Furthermore, adolescent sleep patterns may indeed have gender differences, with adolescent girls having a greater tendency toward sleep quality problems than adolescent boys [29, 30]. For instance, female adolescents tend to have more nocturnal awakenings and more nightmares than their male counterparts [31, 32].…”

Section: Introductionmentioning

confidence: 99%

Relationship of sleep quality, smartphone dependence, and health-related behaviors in female junior college students

et al. 2019

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Introduction Smartphone usage has become commonplace and impact on sleep quality among adolescents. Adolescent girls have a greater tendency toward sleep quality problems. However, relationship of sleep quality, smartphone dependence, and health-related behaviors in female junior college students has not been studied. Objectives This study had the two goals: to investigate the relationship between female college students' sleep quality, smartphone dependence, and health-related behaviors, and to identify predictors of sleep quality. Methods This study employed a cross-sectional research approach to gather 409 subjects at a junior college in southern Taiwan, and used a structured questionnaire to collect data. The questionnaire consisted of four parts: basic demographic data, the Pittsburgh Sleep Quality Index, assessment of smartphone dependence, and the Health Promoting Lifestyle Profile (HPLP). Logistic regression analysis was employed to check for any association between sleep quality and smartphone dependence or HPLP. Results Sleep quality was significantly associated with degree of smartphone dependence, total HPLP score, and scores on the four HPLP subscales of nutritional behavior, self-actualization, interpersonal support, and stress management behavior. The lower the subjects' degree of smartphone dependence was, the better their sleep quality was. Furthermore, the degree of smartphone dependence and total HPLP score were significant predictors of sleep quality. Conclusions Smartphone dependence is associated with poor sleep quality among female college students. Improving health-related behaviors (nutritional behavior, self-actualization, interpersonal support, and stress management behavior) can also promote improvement in sleep quality.

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Association of sleep quality with sociodemographic factors and lifestyle in adolescents from southern Brazil

Abstract: Adolescents who were most likely to report poor sleep quality were female, those whose mothers had a higher educational level and had low aerobic fitness. Students with sedentary risk behavior were less likely of reporting poor sleep quality.

Cited by 19 publications

References 34 publications

Impact of socioeconomic- and lifestyle-related risk factors on poor mental health conditions: A nationwide longitudinal 5-wave panel study in Japan

Impact of socioeconomic- and lifestyle-related risk factors on poor mental health conditions: A nationwide longitudinal 5-wave panel study in Japan

Bidirectional, Daily Temporal Associations between Sleep and Physical Activity in Adolescents

Relationship of sleep quality, smartphone dependence, and health-related behaviors in female junior college students

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