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Wang Y, Li B, Zhang C, Buxton OM, Redline S, Li X. Group-based sleep trajectories in children and adolescents: A systematic review. Sleep Med Rev 2024; 75:101916. [PMID: 38461678 DOI: 10.1016/j.smrv.2024.101916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/07/2024] [Accepted: 02/23/2024] [Indexed: 03/12/2024]
Abstract
Sleep is crucial for health and development. Evidence indicates that sleep changes over time and distinct subgroups may experience different longitudinal patterns. This study systematically reviewed the studies that used latent trajectory modeling to investigate sleep trajectories of children and adolescents aged 0-18 years, and summarized the associated determinants and health-related outcomes. We searched PubMed, Embase, CENTRAL, PsycINFO, and Web of Science, identifying 46 articles that met our criteria. To ensure the reliability of the review, only studies rated as good or fair in terms of methodological quality were included, resulting in a total of 36 articles. Group-based trajectories were identified on several sleep dimensions (i.e., sleep duration, general and specific sleep problems, and bed-sharing behavior) and three or four trajectories were reported in most studies. There was a convergence trend across sleep duration trajectories during the first six years of life. Studies on specific sleep problem (i.e., insomnia, night-waking, and sleep-onset difficulties) typically identified two trajectories: consistent, minimal symptoms or chronic yet fluctuating symptoms. Lower socioeconomic status, maternal depression, and night feeding behaviors were the most frequently reported determinants of sleep trajectories. Membership in a group with certain adverse patterns (e.g., persistent short sleep duration) was associated with increased risks of multiple negative health-related conditions, such as obesity, compromised immunity, neurological problems, substance use, or internalizing/externalizing symptoms. Generally, there is potential to improve the quality of studies in this field. Causality is hard to be inferred within the current body of literature. Future studies could emphasize early life sleep, incorporate more assessment timepoints, use objective measures, and employ experimental design to better understand changes of and mechanisms behind the various sleep trajectories and guide targeted interventions for at-risk subpopulations.
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Affiliation(s)
- Yuhang Wang
- Department of Sociology, Tsinghua University, Beijing, China
| | - Buqun Li
- Department of Sociology, Tsinghua University, Beijing, China
| | - Chenggang Zhang
- Department of Sociology, Tsinghua University, Beijing, China
| | - Orfeu M Buxton
- Department of Biobehavioral Health, Pennsylvania State University, PA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Xiaoyu Li
- Department of Sociology, Tsinghua University, Beijing, China.
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Manitsa I, Gregory AM, Broome MR, Bagshaw AP, Marwaha S, Morales-Muñoz I. Shorter night-time sleep duration and later sleep timing from infancy to adolescence. J Child Psychol Psychiatry 2024. [PMID: 38708717 DOI: 10.1111/jcpp.14004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/25/2024] [Indexed: 05/07/2024]
Abstract
BACKGROUND Here, we (a) examined the trajectories of night-time sleep duration, bedtime and midpoint of night-time sleep (MPS) from infancy to adolescence, and (b) explored perinatal risk factors for persistent poor sleep health. METHODS This study used data from 12,962 participants in the Avon Longitudinal Study of Parents and Children (ALSPAC). Parent or self-reported night-time sleep duration, bedtime and wake-up time were collected from questionnaires at 6, 18 and 30 months, and at 3.5, 4-5, 5-6, 6-7, 9, 11 and 15-16 years. Child's sex, birth weight, gestational age, health and temperament, together with mother's family adversity index (FAI), age at birth, prenatal socioeconomic status and postnatal anxiety and depression, were included as risk factors for persistent poor sleep health. Latent class growth analyses were applied first to detect trajectories of night-time sleep duration, bedtime and MPS, and we then applied logistic regressions for the longitudinal associations between risk factors and persistent poor sleep health domains. RESULTS We obtained four trajectories for each of the three sleep domains. In particular, we identified a trajectory characterized by persistent shorter sleep, a trajectory of persistent later bedtime and a trajectory of persistent later MPS. Two risk factors were associated with the three poor sleep health domains: higher FAI with increased risk of persistent shorter sleep (OR = 1.20, 95% CI = 1.11-1.30, p < .001), persistent later bedtime (OR = 1.28, 95% CI = 1.19-1.39, p < .001) and persistent later MPS (OR = 1.30, 95% CI = 1.22-1.38, p < .001); and higher maternal socioeconomic status with reduced risk of persistent shorter sleep (OR = 0.99, 95% CI = 0.98-1.00, p = .048), persistent later bedtime (OR = 0.98, 95% CI = 0.97-0.99, p < .001) and persistent later MPS (OR = 0.99, 95% CI = 0.98-0.99, p < .001). CONCLUSIONS We detected trajectories of persistent poor sleep health (i.e. shorter sleep duration, later bedtime and later MPS) from infancy to adolescence, and specific perinatal risk factors linked to persistent poor sleep health domains.
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Affiliation(s)
- Ifigeneia Manitsa
- Institute for Mental Health, University of Birmingham, Edgbaston, Birmingham, UK
- School of Psychology, University of Birmingham, Edgbaston, Birmingham, UK
| | - Alice M Gregory
- Department of Psychology, Goldsmiths, University of London, London, UK
| | - Matthew R Broome
- Institute for Mental Health, University of Birmingham, Edgbaston, Birmingham, UK
- School of Psychology, University of Birmingham, Edgbaston, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Edgbaston, Birmingham, UK
- Early Intervention Service, Birmingham Women's and Children's NHS Trust, Birmingham, UK
| | - Andrew P Bagshaw
- School of Psychology, University of Birmingham, Edgbaston, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Edgbaston, Birmingham, UK
| | - Steven Marwaha
- Institute for Mental Health, University of Birmingham, Edgbaston, Birmingham, UK
- School of Psychology, University of Birmingham, Edgbaston, Birmingham, UK
- Specialist Mood Disorders Clinic, Birmingham and Solihull Mental Health Trust, Birmingham, UK
| | - Isabel Morales-Muñoz
- Institute for Mental Health, University of Birmingham, Edgbaston, Birmingham, UK
- School of Psychology, University of Birmingham, Edgbaston, Birmingham, UK
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Yang Y, Li SX, Zhang Y, Wang F, Jiang DJ, Wang SJ, Cao P, Gong QH. Chronotype is associated with eating behaviors, physical activity and overweight in school-aged children. Nutr J 2023; 22:50. [PMID: 37798740 PMCID: PMC10557201 DOI: 10.1186/s12937-023-00875-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/17/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND A later chronotype has been found to be associated with unhealthy habits and diseases, such as an unhealthy diet and metabolic syndrome in adults. Little is known about the association between chronotype, eating habits, physical activity and obesity. Thus, this study aimed to explore the relationships between chronotype, eating behaviors, physical activity, and overweight in Chinese school-aged children. METHODS Data from this study was based on 952 schoolchildren (10-12 y) from six primary schools that participated in China. Anthropometric measurements of height and body weight were performed. Information about sleeping habits, dietary behaviors, and other lifestyle behaviors was gathered using a self-administered questionnaire. Multiple linear regression analysis or multivariable logistic regression model was performed to assess the associations between chronotype, eating behaviors, physical activity, and overweight. RESULTS Nearly 70% (69.9%) of the participants had a self-reported morning chronotype. Multiple linear regression analysis revealed chronotype score was positively associated with physical activities (all P values < 0.001) and sleep duration (all P values < 0.001) and negatively associated with BMI, meal time, eating jet lag and social jet lag (all P values < 0.001). Multivariable logistic regression analysis showed that compared to morning types, non-morning types individuals were more likely to be overweight (OR = 1.593, P value < 0.05), and had more frequent consumption of fast food (OR = 1.616, P value < 0.05), but less frequent consumption of milk (OR = 0.716, P value < 0.05), less time taking part in moderate (OR = 1.356, P value < 0.05) or muscle strengthening (OR = 1.393, 1.877, P value < 0.05) physical activity. CONCLUSIONS This study indicates that early chronotype children are more active, have healthier dietary habits, get more sleep, have shorter social jet lag, and are less likely to be overweight than non-early chronotype children. Our findings suggest that later chronotype may be a potential indicator in the early detection of overweight, unhealthy eating, and physical inactivity behaviors. Chronotype has been found to have an important impact on individual's health. In the present study, we conducted a cross-sectional study to investigate the association between chronotype, eating behaviors, physical activity, and overweight in school-aged children. The findings showed that children with early chronotype is associated with more active, healthier dietary behaviors, longer sleep duration, short social jet lag, and a lower risk of overweight.
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Affiliation(s)
- Yong Yang
- The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, China
| | - Si-Xuan Li
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China
| | - Yan Zhang
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China
| | - Fei Wang
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China
| | - Dan-Jie Jiang
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China
| | - Si-Jia Wang
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China
| | - Peng Cao
- The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, 315020, China
| | - Qing-Hai Gong
- Ningbo Municipal Center for Disease Control and Prevention, Ningbo, Zhejiang, 315010, China.
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Describing the longitudinal breakfast quality index trajectories in early childhood: results from Melbourne InFANT program. Eur J Clin Nutr 2023; 77:363-369. [PMID: 36494475 DOI: 10.1038/s41430-022-01249-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Breakfast quality in early childhood remains understudied. This study describes the changes in breakfast quality index (BQI) (i.e. trajectory) in early childhood and assesses its associations with obesity outcomes. METHODS Data from children who participated in the Melbourne InFANT Program were used (n = 328). The Melbourne InFANT Program was a 15-month early obesity prevention intervention conducted from 2008 to 2013. Dietary intakes at ages 1.5, 3.5 and 5.0 years were assessed using three parent-proxy reported 24 h recalls. A revised nine-item BQI tool developed based on Australian dietary recommendations for young children was used to calculate BQI scores. Group-based trajectory modelling identified BQI trajectory groups. Multivariable linear and logistic regression examined the associations between identified BQI trajectory groups and obesity outcomes at age 5 years. RESULTS Mean BQI at ages 1.5, 3.5 and 5.0 years was 4.8, 4.8, 2.7 points, respectively. Two BQI trajectory groups were identified, and both showed a decline in BQI. The mean BQI of most children (74%) decreased from 5.0 to 4.0 points from ages 1.5 to 5.0 years (referred as "High BQI" group). The remaining children (26%) had a mean BQI of 4.8 and 1.2 points at age 1.5 and 5.0 years, respectively (referred as "Low BQI" group). The "Low BQI" group appeared to show higher risk of overweight (OR:1.30, 95% CI: 0.60, 2.81, P = 0.66) at age 5 years than the "High BQI" group. No difference in body mass index (BMI) z-score was found between the two groups. CONCLUSIONS Two BQI trajectory groups were identified. Both groups showed a decline in breakfast quality from ages 1.5 to 5.0 years. Our study highlights the need for early health promotion interventions and strategies to improve and maintain breakfast quality across early childhood.
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Gui Y, Deng Y, Sun X, Li W, Rong T, Wang X, Jiang Y, Zhu Q, Liu J, Wang G, Jiang F. Early childhood sleep trajectories and association with maternal depression: a prospective cohort study. Sleep 2022; 45:zsac037. [PMID: 35554573 DOI: 10.1093/sleep/zsac037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 01/12/2022] [Indexed: 09/21/2023] Open
Abstract
STUDY OBJECTIVES To investigate trajectories of early childhood sleep in the first 3 years and their association with maternal depressive symptoms. METHODS Data were from 243 Chinese mother-child dyads. Children's sleep duration and night-waking were assessed using the Brief Infant Sleep Questionnaire (BISQ) at 42 days, 3, 6, 9, 12, 18, 24, and 36 months postpartum. The Center for Epidemiological Survey-Depression Scale (CES-D), Edinburgh Postnatal Depression Scale (EPDS), and Profile of Mood States (POMS) were used to assess maternal depressive symptoms at late pregnancy, 42 days, and 36 months postpartum, respectively. Early childhood sleep trajectories were estimated with group-based trajectory models. The association between early childhood sleep trajectories and maternal depressive symptoms was examined with binary and multinomial logistic regression models and linear regression models. RESULTS Three trajectories of daytime sleep duration ("short", 14.4%; "medium", 60.4%; "long", 25.2%), nighttime sleep duration ("increasing", 17.6%; "stable", 76.3%; "decreasing", 6.1%), and total sleep duration ("short", 21.5%; "medium", 59.9%; "long",18.6%), and two trajectories of night-waking ("resolving", 22.9%; "persistent", 77.1%) were identified. Controlling for confounding factors, maternal depression at 42 days postpartum was associated with higher risks for short daytime sleep duration and persistent night-waking in children. Persistent night-waking in children was associated with increased maternal depressive symptoms at 36 months postpartum. CONCLUSION Early childhood sleep follows distinct trajectories in the first 3 years of life. The trajectories of short daytime sleep duration and persistent night-waking are associated with maternal depression. The findings indicate tailored interventions should target both unfavorable early childhood sleep trajectories and maternal depression.
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Affiliation(s)
- Yiding Gui
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Yujiao Deng
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Xiaoning Sun
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Wen Li
- Department of Pediatrics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Tingyu Rong
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Xuelai Wang
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yanrui Jiang
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qi Zhu
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jianghong Liu
- Department of Family and Community Health, University of Pennsylvania, School of Nursing, Philadelphia, PA, USA
| | - Guanghai Wang
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Fan Jiang
- Pediatric Translational Medicine Institution, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Department of Developmental and Behavioral Pediatrics, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- MOE-Shanghai Key Laboratory of Children's Environmental Health, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
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