Prevalence and Risk by Age and Sex of Sleep Dysregulation and Depressive Episodes in Bipolar and Depressive Disorders in a Community Survey in Sardinia, Italy
Abstract
:1. Introduction
- Insomnia: difficulty in falling or staying asleep despite adequate opportunities, leading to daytime impairment.
- Sleep-related breathing disorders: abnormal respiration during sleep, detected by polysomnography (PSG).
- Central disorders of hypersomnolence: excessive daytime sleepiness not due to other sleep or circadian rhythm disorders, causing difficulties in staying alert.
- Circadian rhythm sleep–wake disorders: misalignment between the internal circadian rhythm and required sleep timing, causing difficulties in falling asleep or waking up on time.
- Parasomnias: physical actions or verbal expressions during sleep, such as talking, walking, or eating.
- Sleep-related movement disorders: movements that disrupt the ability to fall or stay asleep.
2. Materials and Methods
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sedler, M.J. Medicalization in Psychiatry: The Medical Model, Descriptive Diagnosis, and Lost Knowledge. Med. Health Care Philos. 2016, 19, 247–252. [Google Scholar] [CrossRef] [PubMed]
- Huda, A.S. The Medical Model and Its Application in Mental Health. Int. Rev. Psychiatry 2021, 33, 463–470. [Google Scholar] [CrossRef] [PubMed]
- Vandenbroucke, J.P.; von Elm, E.; Altman, D.G.; Gøtzsche, P.C.; Mulrow, C.D.; Pocock, S.J.; Poole, C.; Schlesselman, J.J.; Egger, M.; MD for the STROBE initiative. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. Ann. Intern. Med. 2007, 147, W163. [Google Scholar] [CrossRef] [PubMed]
- Pearson, M.L.; Selby, J.V.; Katz, K.A.; Cantrell, V.; Braden, C.R.; Parise, M.E.; Paddock, C.D.; Lewin-Smith, M.R.; Kalasinsky, V.F.; Goldstein, F.C.; et al. Clinical, Epidemiologic, Histopathologic and Molecular Features of an Unexplained Dermopathy. PLoS ONE 2012, 7, e29908. [Google Scholar] [CrossRef] [PubMed]
- Parker, G. How Should Mood Disorders Be Modelled? Aust. N. Z. J. Psychiatry 2008, 42, 841–850. [Google Scholar] [CrossRef]
- Parker, G. The DSM Classification of Depressive Disorders: Debating Its Utility. Can. J. Psychiatry 2006, 51, 871–873. [Google Scholar] [CrossRef]
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, 5th ed.; American Psychiatric Association: Arlington, VA, USA, 2013; ISBN 0-89042-554-X. [Google Scholar]
- Angst, J.; Merikangas, K.R.; Cui, L.; Van Meter, A.; Ajdacic-Gross, V.; Rössler, W. Bipolar Spectrum in Major Depressive Disorders. Eur. Arch. Psychiatry Clin. Neurosci. 2018, 268, 741–748. [Google Scholar] [CrossRef]
- Benazzi, F.; Koukopoulos, A.; Akiskal, H.S. Toward a Validation of a New Definition of Agitated Depression as a Bipolar Mixed State (Mixed Depression). Eur. Psychiatry 2004, 19, 85–90. [Google Scholar] [CrossRef]
- Akiskal, H.S.; Akiskal, K.K. In Search of Aristotle: Temperament, Human Nature, Melancholia, Creativity and Eminence. J. Affect. Disord. 2007, 100, 1–6. [Google Scholar] [CrossRef]
- Kalcev, G.; Cossu, G.; Preti, A.; Littera, M.T.; Frau, S.; Primavera, D.; Zaccheddu, R.; Matza, V.; Ermellino, M.; Pintus, E.; et al. Development and Validation of the Questionnaire for Adaptive Hyperactivity and Goal Achievement (AHGA). Clin. Pract. Epidemiol. Ment. Health 2023, 19, e174501792303281. [Google Scholar] [CrossRef]
- Carta, M.G.; Kalcev, G.; Scano, A.; Primavera, D.; Orrù, G.; Gureye, O.; Cossu, G.; Nardi, A.E. Is Bipolar Disorder the Consequence of a Genetic Weakness or Not Having Correctly Used a Potential Adaptive Condition? Brain Sci. 2023, 13, 16. [Google Scholar] [CrossRef] [PubMed]
- Kalcev, G.; Scano, A.; Orrù, G.; Primavera, D.; Cossu, G.; Nardi, A.E.; Carta, M.G. Is a Genetic Variant Associated with Bipolar Disorder Frequent in People without Bipolar Disorder but with Characteristics of Hyperactivity and Novelty Seeking? Clin. Pract. Epidemiol. Ment. Health 2023, 19, e174501792303280. [Google Scholar] [CrossRef]
- Primavera, D.; Aviles Gonzalez, C.I.; Romano, F.; Kalcev, G.; Pinna, S.; Minerba, L.; Scano, A.; Orrù, G.; Cossu, G. Does the Response to a Stressful Condition in Older Adults with Life Rhythm Dysregulations Provide Evidence of the Existence of the “Dysregulation of Mood, Energy, and Social Rhythms Syndrome”? Healthcare 2024, 12, 87. [Google Scholar] [CrossRef]
- Carta, M.G.; Kalcev, G.; Fornaro, M.; Pinna, S.; Gonzalez, C.I.A.; Nardi, A.E.; Primavera, D. Does Screening for Bipolar Disorders Identify a “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS)? A Heuristic Working Hypothesis. J. Clin. Med. 2023, 12, 5162. [Google Scholar] [CrossRef] [PubMed]
- Zelviene, P.; Kazlauskas, E. Adjustment Disorder: Current Perspectives. Neuropsychiatr. Dis. Treat. 2018, 14, 375–381. [Google Scholar] [CrossRef] [PubMed]
- Maercker, A.; Einsle, F.; Köllner, V. Adjustment Disorders as Stress Response Syndromes: A New Diagnostic Concept and Its Exploration in a Medical Sample. Psychopathology 2007, 40, 135–146. [Google Scholar] [CrossRef]
- Strain, J.J.; Klipstein, K.; Newcorn, J.H. Adjustment Disorders. In Psychiatry; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2008; pp. 1702–1717. ISBN 978-0-470-51516-7. [Google Scholar]
- Patten, S.B. Accumulation of Major Depressive Episodes over Time in a Prospective Study Indicates That Retrospectively Assessed Lifetime Prevalence Estimates Are Too Low. BMC Psychiatry 2009, 9, 19. [Google Scholar] [CrossRef] [PubMed]
- Newman, S.C.; Bland, R.C. Incidence of Mental Disorders in Edmonton: Estimates of Rates and Methodological Issues. J. Psychiatr. Res. 1998, 32, 273–282. [Google Scholar] [CrossRef] [PubMed]
- Carta, M.G.; Angst, J. Screening for Bipolar Disorders: A Public Health Issue. J. Affect. Disord. 2016, 205, 139–143. [Google Scholar] [CrossRef]
- Pan, C.; Ye, J.; Wen, Y.; Chu, X.; Jia, Y.; Cheng, B.; Cheng, S.; Liu, L.; Yang, X.; Liang, C.; et al. The Associations between Sleep Behaviors, Lifestyle Factors, Genetic Risk and Mental Disorders: A Cohort Study of 402 290 UK Biobank Participants. Psychiatry Res. 2022, 311, 114488. [Google Scholar] [CrossRef] [PubMed]
- Solelhac, G.; Imler, T.; Strippoli, M.-P.F.; Marchi, N.A.; Berger, M.; Haba-Rubio, J.; Raffray, T.; Bayon, V.; Lombardi, A.S.; Ranjbar, S.; et al. Sleep Disturbances and Incident Risk of Major Depressive Disorder in a Population-Based Cohort. Psychiatry Res. 2024, 338, 115934. [Google Scholar] [CrossRef] [PubMed]
- Cretu, J.B.; Culver, J.L.; Goffin, K.C.; Shah, S.; Ketter, T.A. Sleep, Residual Mood Symptoms, and Time to Relapse in Recovered Patients with Bipolar Disorder. J. Affect. Disord. 2016, 190, 162–166. [Google Scholar] [CrossRef]
- Baglioni, C.; Spiegelhalder, K.; Lombardo, C.; Riemann, D. Sleep and Emotions: A Focus on Insomnia. Sleep Med. Rev. 2010, 14, 227–238. [Google Scholar] [CrossRef] [PubMed]
- Mendlewicz, J. Sleep Disturbances: Core Symptoms of Major Depressive Disorder Rather than Associated or Comorbid Disorders. World J. Biol. Psychiatry 2009, 10, 269–275. [Google Scholar] [CrossRef]
- Murphy, M.; Peterson, M.J. Sleep Disturbances in Depression. Sleep Med. Clin. 2015, 10, 17–23. [Google Scholar] [CrossRef]
- Sateia, M.J. International Classification of Sleep Disorders-Third Edition. Chest 2014, 146, 1387–1394. [Google Scholar] [CrossRef]
- American Academy of Sleep Medicine International Classification of Sleep Disorders, 3rd ed.; Text Revision; American Academy of Sleep Medicine—Association for Sleep Clinicians and Researchers: Darien, IL, USA, 2023.
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition: DSM-IV-TR; American Psychiatric Association: Arlington, VA, USA, 2000; ISBN 978-0-89042-025-6. [Google Scholar]
- Chung, K.-F.; Yeung, W.-F.; Ho, F.Y.-Y.; Yung, K.-P.; Yu, Y.-M.; Kwok, C.-W. Cross-Cultural and Comparative Epidemiology of Insomnia: The Diagnostic and Statistical Manual (DSM), International Classification of Diseases (ICD) and International Classification of Sleep Disorders (ICSD). Sleep Med. 2015, 16, 477–482. [Google Scholar] [CrossRef]
- Forbes, E.E.; Bertocci, M.A.; Gregory, A.M.; Ryan, N.D.; Axelson, D.A.; Birmaher, B.; Dahl, R.E. Objective Sleep in Pediatric Anxiety Disorders and Major Depressive Disorder. J. Am. Acad. Child Adolesc. Psychiatry 2008, 47, 148–155. [Google Scholar] [CrossRef] [PubMed]
- Wu, X.; Zhang, N.; Chao, J.; Liu, Y.; Zhang, B. Sex-Specific in the Association between Depressive Symptoms and Risk of Cognitive Impairment in Chinese Older Adults. Arch. Psychiatr. Nurs. 2024, 52, 69–75. [Google Scholar] [CrossRef]
- Chang, Y.-H.; Yang, C.-T.; Hsieh, S. Social Support Enhances the Mediating Effect of Psychological Resilience on the Relationship between Life Satisfaction and Depressive Symptom Severity. Sci. Rep. 2023, 13, 4818. [Google Scholar] [CrossRef]
- Grygiel, P.; Dolata, R.; Humenny, G.; Muszyński, M. Depressive Symptoms and Loneliness among Early Adolescents: A Psychometric Network Analysis Approach. J. Child Psychol. Psychiatry 2024, 65, 199–214. [Google Scholar] [CrossRef] [PubMed]
- Wang, J. The Longitudinal Relationship between Leisure Activities and Depressive Symptoms among Older Chinese Adults: An Autoregressive Cross-Lagged Analysis Approach. BMC Public Health 2024, 24, 763. [Google Scholar] [CrossRef] [PubMed]
- Rith-Najarian, L.R.; Boustani, M.M.; Chorpita, B.F. A Systematic Review of Prevention Programs Targeting Depression, Anxiety, and Stress in University Students. J. Affect. Disord. 2019, 257, 568–584. [Google Scholar] [CrossRef] [PubMed]
- Buonomo, A.; Benassi, F.; Gallo, G.; Salvati, L.; Strozza, S. In-between Centers and Suburbs? Increasing Differentials in Recent Demographic Dynamics of Italian Metropolitan Cities. Genus 2024, 80, 1. [Google Scholar] [CrossRef]
- Moro, M.F.; Angermeyer, M.C.; Matschinger, H.; Holzinger, A.; Piras, A.P.; Cutrano, F.; Mura, G.; Carta, M.G. Whom to Ask for Professional Help in Case of Major Depression? Help-Seeking Recommendations of the Sardinian Public. Adm. Policy Ment. Health 2015, 42, 704–713. [Google Scholar] [CrossRef] [PubMed]
- Perra, A.; Galetti, A.; Zaccheddu, R.; Locci, A.; Piludu, F.; Preti, A.; Primavera, D.; Di Natale, L.; Nardi, A.E.; Kurotshka, P.K.; et al. A Recovery-Oriented Program for People with Bipolar Disorder through Virtual Reality-Based Cognitive Remediation: Results of a Feasibility Randomized Clinical Trial. J. Clin. Med. 2023, 12, 2142. [Google Scholar] [CrossRef]
- Kroenke, K.; Spitzer, R.L.; Williams, J.B.W. The PHQ-9. J. Gen. Intern. Med. 2001, 16, 606–613. [Google Scholar] [CrossRef] [PubMed]
- Rizzo, R.; Piccinelli, M.; Mazzi, M.A.; Bellantuono, C.; Tansella, M. The Personal Health Questionnaire: A New Screening Instrument for Detection of ICD-10 Depressive Disorders in Primary Care. Psychol. Med. 2000, 30, 831–840. [Google Scholar] [CrossRef]
- Mazzotti, E.; Fassone, G.; Picardi, A.; Sagoni, E.; Ramieri, L.; Lega, I.; Camaioni, D.; Abeni, D.; Pasquini, P. Il Patient Health Questionnaire (PHQ) per lo screening dei disturbi psichiatrici: Uno studio di validazione nei confronti della Intervista Clinica Strutturata per il DSM-IV asse I (SCID-I). Off. J. Ital. Soc. Psychopathol. 2003, 9, 235–242. [Google Scholar]
- Pez, O.; Gilbert, F.; Bitfoi, A.; Carta, M.G.; Jordanova, V.; Garcia-Mahia, C.; Mateos-Alvarez, R.; Prince, M.; Tudorache, B.; Blatier, C.; et al. Validity across Translations of Short Survey Psychiatric Diagnostic Instruments: CIDI-SF and CIS-R versus SCID-I/NP in Four European Countries. Soc. Psychiatry Psychiatr. Epidemiol. 2010, 45, 1149–1159. [Google Scholar] [CrossRef]
- Smith, D.J.; Kyle, S.; Forty, L.; Cooper, C.; Walters, J.; Russell, E.; Caesar, S.; Farmer, A.; McGuffin, P.; Jones, I.; et al. Differences in Depressive Symptom Profile between Males and Females. J. Affect. Disord. 2008, 108, 279–284. [Google Scholar] [CrossRef]
- Serrano, D.; Martí-Lluch, R.; Cárdenas, M.; Solanas, P.; Marrugat, J.; Vilalta-Franch, J.; Garre-Olmo, J. Gender Analysis of the Frequency and Course of Depressive Disorders and Relationship with Personality Traits in General Population: A Prospective Cohort Study. J. Affect. Disord. 2022, 302, 241–248. [Google Scholar] [CrossRef] [PubMed]
- Barros, M.B.d.A.; Medina, L.d.P.B.; Lima, M.G.; de Azevedo, R.C.S.; Sousa, N.F.d.S.; Malta, D.C. Association between Health Behaviors and Depression: Findings from the 2019 Brazilian National Health Survey. Rev. Bras. Epidemiol. 2021, 24, e210010. [Google Scholar] [CrossRef]
- Kibitov, A.A.; Rakitko, A.S.; Kasyanov, E.D.; Rukavishnikov, G.V.; Kozlova, K.A.; Ilinsky, V.V.; Neznanov, N.G.; Mazo, G.E.; Kibitov, A.O. Screening of Depressive Symptoms in a Russian General Population Sample: A Web-Based Cross-Sectional Study. Clin. Pract. Epidemiol. Ment. Health 2021, 17, 205–211. [Google Scholar] [CrossRef] [PubMed]
- Horwath, E.; Cohen, R.S.; Weissman, M.M. Epidemiology of Depressive and Anxiety Disorders. In Textbook in Psychiatric Epidemiology; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2002; pp. 389–426. ISBN 978-0-471-23431-9. [Google Scholar]
- Huang, L.; Tang, S.; Rietkerk, J.; Appadurai, V.; Krebs, M.D.; Schork, A.J.; Werge, T.; Zuber, V.; Kendler, K.; Cai, N. Polygenic Analyses Show Important Differences Between Major Depressive Disorder Symptoms Measured Using Various Instruments. Biol. Psychiatry 2024, 95, 1110–1121. [Google Scholar] [CrossRef] [PubMed]
- Kessler, R.C.; Andrews, G.; Mroczek, D.; Ustun, B.; Wittchen, H.-U. The World Health Organization Composite International Diagnostic Interview Short-Form (CIDI-SF). Int. J. Methods Psychiatr. Res. 1998, 7, 171–185. [Google Scholar] [CrossRef]
- Salvatore, P.; Indic, P.; Khalsa, H.K.; Tohen, M.; Baldessarini, R.J.; Maggini, C. Circadian Activity Rhythms and Psychopathology in Major Depressive Episodes. Psychopathology 2023, 57, 1–9. [Google Scholar] [CrossRef] [PubMed]
- Roloff, T.; Haussleiter, I.; Meister, K.; Juckel, G. Sleep Disturbances in the Context of Neurohormonal Dysregulation in Patients with Bipolar Disorder. Int. J. Bipolar Disord. 2022, 10, 6. [Google Scholar] [CrossRef] [PubMed]
- Skeppar, P.; Adolfsson, R. Bipolar II and the Bipolar Spectrum. Nord. J. Psychiatry 2006, 60, 7–26. [Google Scholar] [CrossRef]
- El-Mallakh, R.S.; Ghaemi, S.N.; Sagduyu, K.; Thase, M.E.; Wisniewski, S.R.; Nierenberg, A.A.; Zhang, H.W.; Pardo, T.A.; Sachs, G. Antidepressant-Associated Chronic Irritable Dysphoria (ACID) in STEP-BD Patients. J. Affect. Disord. 2008, 111, 372–377. [Google Scholar] [CrossRef] [PubMed]
- Primavera, D.; Cossu, G.; Marchegiani, S.; Preti, A.; Nardi, A.E. Does the Dysregulation of Social Rhythms Syndrome (DYMERS) Be Considered an Essential Component of Panic Disorders? Clin. Pract. Epidemiol. Ment. Health 2024, 20, e17450179293272. [Google Scholar] [CrossRef] [PubMed]
- Giovanni Carta, M.; Kalcev, G.; Scano, A.; Aviles Gonzalez, C.I.; Ouali, U.; Pinna, S.; Carrà, G.; Romano, F.; Preti, A.; Orrù, G.; et al. The Impact of MDQ Positivity on Quality of Life Impairment: Does It Support the Hypothesis of “Dysregulation of Mood, Energy, and Social Rhythms Syndrome” (DYMERS)? J. Public Health Res. 2023, 12, 22799036231208356. [Google Scholar] [CrossRef]
- Liu, C.; Ye, Z.; Chen, L.; Wang, H.; Wu, B.; Li, D.; Pan, S.; Qiu, W.; Ye, H. Interaction Effects between Sleep-Related Disorders and Depression on Hypertension among Adults: A Cross-Sectional Study. BMC Psychiatry 2024, 24, 482. [Google Scholar] [CrossRef]
- Sullivan, E.C.; James, E.; Henderson, L.-M.; McCall, C.; Cairney, S.A. The Influence of Emotion Regulation Strategies and Sleep Quality on Depression and Anxiety. Cortex 2023, 166, 286–305. [Google Scholar] [CrossRef]
- Fries, G.R.; Saldana, V.A.; Finnstein, J.; Rein, T. Molecular Pathways of Major Depressive Disorder Converge on the Synapse. Mol. Psychiatry 2023, 28, 284–297. [Google Scholar] [CrossRef] [PubMed]
- Carta, M.G.; Ouali, U.; Perra, A.; Ben Cheikh Ahmed, A.; Boe, L.; Aissa, A.; Lorrai, S.; Cossu, G.; Aresti, A.; Preti, A.; et al. Living With Bipolar Disorder in the Time of Covid-19: Biorhythms During the Severe Lockdown in Cagliari, Italy, and the Moderate Lockdown in Tunis, Tunisia. Front. Psychiatry 2021, 12, 634765. [Google Scholar] [CrossRef] [PubMed]
- Alloy, L.B.; Walsh, R.F.L.; Smith, L.T.; Maddox, M.A.; Olino, T.M.; Zee, P.C.; Nusslock, R. Circadian, Reward, and Emotion Systems in Teens Prospective Longitudinal Study: Protocol Overview of an Integrative Reward-Circadian Rhythm Model of First Onset of Bipolar Spectrum Disorder in Adolescence. BMC Psychiatry 2023, 23, 602. [Google Scholar] [CrossRef] [PubMed]
- Tassoni, G.; Cippitelli, M.; Scendoni, R.; Froldi, R.; Buratti, E.; Cerioni, A.; Mietti, G.; Cingolani, M. A Study into the Nature and Extent of Polydrug Use in Driving Recidivism Behavior. Traffic INJ Prev. 2024, 25, 110–115. [Google Scholar] [CrossRef] [PubMed]
- Pascale, A.; Negrin, A.; Laborde, A. Cocaine base paste: Experience from the Montevideo Poison Control Center. Adicciones 2010, 22, 227–231. [Google Scholar] [CrossRef]
- Busch, V.; De Leeuw, J.R.J. Unhealthy Behaviors in Adolescents: Multibehavioral Associations with Psychosocial Problems. Int.J. Behav. Med. 2014, 21, 439–446. [Google Scholar] [CrossRef]
- Carta, M.; Preti, A.; Akiskal, H. Coping with the New Era: Noise and Light Pollution, Hperactivity and Steroid Hormones. Towards an Evolutionary View of Bipolar Disorders. Clin. Pract. Epidemiol. Ment. Health 2018, 14, 33–36. [Google Scholar] [CrossRef] [PubMed]
- Smolensky, M.H.; Sackett-Lundeen, L.L.; Portaluppi, F. Nocturnal Light Pollution and Underexposure to Daytime Sunlight: Complementary Mechanisms of Circadian Disruption and Related Diseases. Chronobiol. Int. 2015, 32, 1029–1048. [Google Scholar] [CrossRef] [PubMed]
- Zhang, C.; Zhu, Z.; Zhao, J.; Li, Y.; Zhang, Z.; Zheng, Y. Ubiquitous Light-Emitting Diodes: Potential Threats to Retinal Circadian Rhythms and Refractive Development. Sci. Total Environ. 2023, 862, 160809. [Google Scholar] [CrossRef]
- Oh, J.H.; Yoo, H.; Park, H.K.; Do, Y.R. Analysis of Circadian Properties and Healthy Levels of Blue Light from Smartphones at Night. Sci. Rep. 2015, 5, 11325. [Google Scholar] [CrossRef] [PubMed]
- Martel, J.; Chang, S.-H.; Chevalier, G.; Ojcius, D.M.; Young, J.D. Influence of Electromagnetic Fields on the Circadian Rhythm: Implications for Human Health and Disease. Biomed. J. 2023, 46, 48–59. [Google Scholar] [CrossRef] [PubMed]
- Mortazavi, S.A.R.; Mortazavi, S.M.J. Women with Hereditary Breast Cancer Predispositions Should Avoid Using Their Smartphones, Tablets and Laptops at Night. Iran. J. Basic Med. Sci. 2018, 21, 112–115. [Google Scholar] [CrossRef]
- Skwarło-Sońta, K. Achieving Healthy Aging in the Light-Polluted World. In Sleep and Clocks in Aging and Longevity; Jagota, A., Ed.; Springer International Publishing: Cham, Switzerland, 2023; pp. 445–459. ISBN 978-3-031-22468-3. [Google Scholar]
- Jeon, B.; Baek, J. Menstrual Disturbances and Its Association with Sleep Disturbances: A Systematic Review. BMC Women’s Health 2023, 23, 470. [Google Scholar] [CrossRef] [PubMed]
- Lin, Y.; Hu, Y.; Guo, J.; Chen, M.; Xu, X.; Wen, Y.; Yang, L.; Lin, S.; Li, H.; Wu, S. Association between Sleep and Multimorbidity in Chinese Elderly: Results from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Sleep Med. 2022, 98, 1–8. [Google Scholar] [CrossRef]
- Feinsilver, S.H.; Hernandez, A.B. Sleep in the Elderly: Unanswered Questions. Clin. Geriatr. Med. 2017, 33, 579–596. [Google Scholar] [CrossRef]
- Kim, J.H.; Duffy, J.F. Circadian Rhythm Sleep-Wake Disorders in Older Adults. Sleep Med. Clin. 2018, 13, 39–50. [Google Scholar] [CrossRef]
- Feinsilver, S.H. Normal and Abnormal Sleep in the Elderly. Clin. Geriatr. Med. 2021, 37, 377–386. [Google Scholar] [CrossRef] [PubMed]
- Kocevska, D.; Lysen, T.S.; Dotinga, A.; Koopman-Verhoeff, M.E.; Luijk, M.P.C.M.; Antypa, N.; Biermasz, N.R.; Blokstra, A.; Brug, J.; Burk, W.J.; et al. Sleep Characteristics across the Lifespan in 1.1 Million People from the Netherlands, United Kingdom and United States: A Systematic Review and Meta-Analysis. Nat. Hum. Behav. 2021, 5, 113–122. [Google Scholar] [CrossRef] [PubMed]
- O’Connor, E.A.; Whitlock, E.P.; Beil, T.L.; Gaynes, B.N. Screening for Depression in Adult Patients in Primary Care Settings: A Systematic Evidence Review. Ann. Intern. Med. 2009, 151, 793–803. [Google Scholar] [CrossRef]
- El-Den, S.; Chen, T.F.; Gan, Y.-L.; Wong, E.; O’Reilly, C.L. The Psychometric Properties of Depression Screening Tools in Primary Healthcare Settings: A Systematic Review. J. Affect. Disord. 2018, 225, 503–522. [Google Scholar] [CrossRef] [PubMed]
- Katona, C.; Livingston, G.; Manela, M.; Leek, C.; Mullan, E.; Orrell, M.; D’Ath, P.; Zeitlin, D. The Symptomatology of Depression in the Elderly. Int. Clin. Psychopharmacol. 1997, 12, S19. [Google Scholar] [CrossRef] [PubMed]
- Kessler, R.C.; Birnbaum, H.; Bromet, E.; Hwang, I.; Sampson, N.; Shahly, V. Age Differences in Major Depression: Results from the National Comorbidity Survey Replication (NCS-R). Psychol. Med. 2010, 40, 225–237. [Google Scholar] [CrossRef] [PubMed]
Age | Men | Women |
---|---|---|
>75 | 58 (8.0%) | 92 (11.9%) |
60–74 | 132 (18.1%) | 150 (20.2%) |
45–59 | 185 (25.4%) | 190 (25.5%) |
30–44 | 211 (30.0%) | 206 (27.7%) |
16–29 | 142 (19.5%) | 136 (17.6%) |
Total | 728 | 774 |
Age | Men | Total PHQ9 Men | ANOVA 1way within Men by Age Classes | Women | Total PHQ9 Women | ANOVA 1way within Women by Age Classes | Men vs. Women |
---|---|---|---|---|---|---|---|
>75 | 58 (8.0%) | 3.58 ± 3.56 | Pivot | 92 (11.9%) | 5.41 ± 4.83 | Pivot | df 1148 F = 6.197 p = 0.014 |
60–74 | 132 (18.1%) | 2.93 ± 3.52 | df 1188 F = 1.365 p = 0.244 | 150 (20.2%) | 3.71 ± 3.85 | df 1240 F = 9.239 p = 0.003 | df 1280 F = 3.122 p = 0.078 |
45–59 | 185 (25.4%) | 2.87 ± 3.02 | df 1241 F = 2.235 p = 0.136 | 190 (25.5%) | 4.66 ± 4.44 | df 1280 F = 1.714 p = 0.192 | df 1373 F = 20.730 p < 0.0001 |
30–44 | 211 (30.0%) | 2.87 ± 3.44 | df 1267 F = 1.909 p = 0.168 | 206 (27.7%) | 4.16 ± 3.86 | df 1296 F = 5.773 p = 0.017 | df 1415 F = 9.825 p = 0.002 |
16–29 | 142 (19.5%) | 2.66 ± 2.42 | df 1198 F = 4.458 p = 0.036 | 136 (17.6%) | 4.03 ± 3.72 | df 1226 F = 6.004 p = 0.015 | df 1276 F = 13.358 p < 0.0001 |
Age | Men | Item 3 PHQ9 Men | ANOVA 1way within Men by Age Classes | Percentage of Total PHQ9 Score | Women | Item 3 PHQ9 Women | ANOVA 1way within Women by Age Classes | Percentage of Total PHQ9 Score | Men vs. Women |
---|---|---|---|---|---|---|---|---|---|
>75 | 58 (8.0%) | 0.68 ± 0.67 | Pivot | 19.0% | 92 (11.9%) | 1.19 ± 1.02 | Pivot | 22% | df 1, 148 F = 11.387 p = 0.001 |
60–74 | 132 (18.1%) | 0.67 ± 0.91 | df 1188 F = 0.006 p = 0.940 | 22.8% | 150 (20.2%) | 0.71 ± 0.88 | df 1240 F = 15.011 p < 0.0001 | 19.1% | df 1, 280 F = 0.141 p = 0.708 |
45–59 | 185 (25.4%) | 0.50 ± 0.77 | df 1241 F = 2.560 p = 0.111 | 17.4% | 190 (25.5%) | 0.81 ± 1.02 | df 1338 F = 9.314 p = 0.002 | 17.4% | df 1, 373 F = 10.990 p = 0.001 |
30–44 | 211 (30.0%) | 0.42 ± 0.73 | df 1267 F = 5.972 p = 0.015 | 14.6% | 206 (27.7%) | 0.63 ± 0.83 | df 1301 F = 25.267 p < 0.0001 | 15.1% | df 1, 415 F = 7.536 p = 0.006 |
16–29 | 142 (19.5%) | 0.52 ± 0.81 | df 1198 F = 1.767 p = 0.185 | 19.5% | 136 (17.6%) | 0.59 ± 0.91 | df 1226 F = 21.625 p < 0.0001 | 14.6% | df 1, 280 F = 0.141 p = 0.708 |
Age | Men | Men Depressive Episodes (PHQ > 9) | Within Men by Age Classes, Chi-square with Yates Correction if Needed. | Women | Women Depressive Episodes (PHQ > 9) | Within Women by Age Classes, Chi-Square with Yates Correction if Needed. | Women vs. Men (Chi-Square with Yates Correction if Needed) |
---|---|---|---|---|---|---|---|
>75 | 58 (8.0%) | 3.4% (2/58) | Pivot | 92 (11.9%) | 17.4% (16/92) | Pivot | |
60–74 | 132 (18.1%) | 6.1% (8/132) | 0.152, p = 0.697 OR = 0.55 (CI95% 0.1–2.7) | 150 (20.2%) | 9.3% (14/150) | 3.409, p = 0.105 OR = 2.04 (CI95% 0.9–4.4) | 1.997, p = 0.158 OR = 1.77 (CI95% 0.8–3.9) |
45–59 | 185 (25.4%) | 4.3% (8/185) | 0.086, p = 0.769 OR = 0.79 (CI95% 0.2–3.8) | 190 (25.5%) | 10.5% (20/190) | 2.653, p = 0.065 OR = 1.78 (CI95% 0.9–3.6) | 6.549, p = 0.010 OR = 5.89 (CI95% 1.3–26.6) |
30–44 | 211 (30.0%) | 5.7% (12/211) | 0.462, p = 0.497 OR = 0.59 (CI95% 0.2–3.8) | 206 (27.7%) | 11.1% (23/206) | 2.168, p = 0.141 OR = 1.67 (CI95% 0.8–3.7) | 4.068, p = 0.044 OR = 2.08 (CI95% 1.0–4.3) |
16–29 | 142 (19.5%) | 1.4% (2/142) | 0.143, p = 0.705 OR = 2.50 (CI95% 0.3–18.1) | 136 (17.6%) | 6.6% (9/136) | 7.462, p = 0.006 OR = 3.18 (CI95% 1.3–7.5) | 4.960, p = 0.026 OR = 4.96 (CI95% 1.0–4.3) |
Total | 4.4% (32/728) | 10.6% (82/774) | 20.052, p < 0.0001 OR = 2.57 (CI95% 1.7–3.9) | ||||
High divergences | 60–74 vs. 16–29 | 4.210, p = 0.040 OR = 4.5 (CI95% 1.0–21.7) | 30–44 vs. 16–29 | 1.997, p = 0.141 OR = 1.77 (CI95% 0.8–3.9) |
Age | Men | Men Sleep Disorders (PHQ Item 3 > 1) | Within Men by Age Classes, Chi-Square with Yates Correction if Needed. | Women Sleep Disorders (PHQ Item 3 > 1) | Within Women by Age Classes, Chi-square with Yates Correction if Needed. | Women vs. Men (Chi-Square with Yates Correction if Needed) |
---|---|---|---|---|---|---|
>75 | 58 (8.0%) | 6.9% (4/58) | Pivot | 35.9% (33/92) | Pivot | 16.070, p < 0.0001 OR = 7.55 (CI95% 2.5–22.7) |
60–74 | 132 (18.1%) | 14.3% (19/132) | 2.656; p = 0.103 OR = 0.40 (CI95% 0.1–1.2) | 14.7% (22/150) | 14.598, p < 0.0001 OR = 3.24 (CI95% 1.7–6.1) | 0.004; p = 0.940 OR = 1.02 (CI95% 0.5–2.0) |
45–59 | 185 (25.4%) | 9.7% (18/185) | 0.430; p = 0.512 OR = 0.69 (CI95% 0.2–2.1) | 24.2% (46/190) | 4.178, p = 0.041 OR = 1.75 (CI95% 1.1–3.0) | 13.887, p < 0.0001 OR = 2.96 (CI95% 1.6–5.3) |
30–44 | 211 (30.0%) | 7.6% (16/211) | 2.064; p = 0.151 OR = 0.44 (CI95% 0.1–1.4) | 12.6% (26/206) | 21.647, p < 0.0001 OR = 3.87 (CI95% 2.1–7.0) | 2.921; p = 0.087 OR = 1.95 (CI95% 0.9–3.4) |
16–29 | 142 (19.5%) | 9.1% (13/142) | 0.270; p = 0.603 OR = 0.73 (CI95% 0.2–2.4) | 12.6%, (17/136) | 15.065, p < 0.0001 OR = 3.58 (CI95% 1.8–7.0) | 0.807; p = 0.369 OR = 1.42 (CI95% 0.7–3.0) |
Total | 9.6% (70/728) | 18.7% (144/774) | 24.812, p < 0.0001 OR = 2.15 (CI95% 1.6–2.9) | |||
High divergences | 60–74 vs. 30–44 | 4.111; p = 0.043 OR = 0.2.05 (CI95% 1.0–4.1) | 45–59 vs. 16–29° and 30–44° | 6.972, p = 0.008 OR = 2.23 (CI95% 1.2–4.9) 8.924. p = 0.008 OR = 2.21 (CL95% 1.3–3.7) |
Age | Men | Men’s Depressive Episodes without Sleep Disturbance (PHQ It. 3 < 2) | Men by Age (Fisher Exact Test) p | Men Sleep Disturbance Without Depressive Episode | Men by Age (Fisher Exact Test) p | Women | Women Depressive Episodes without Sleep Disturbance (PHQ It. 3 < 2) | Women by Age (Fisher Exact Test) p | Women Sleep Disturbance Without Depressive Episode | Women by Age (Fisher Exact Test) p |
---|---|---|---|---|---|---|---|---|---|---|
>75 | 58 (8.0%) | 3.4% (2/58) | Pivot | 20.7% (12/58) | Pivot | 92 (11.9%) | 0% (0/92) | Pivot | 18.5% (17/92) | Pivot |
60–74 | 132 (18.1%) | 1.5% (2/132) | 0.587 | 9.8% (9/132) | 0.010 | 150 (20.2%) | 2.7% (4/150) | 0.301 | 8% (12/150) | 0.023 |
45–59 | 185 (25.4%) | 1.1% (2/185) | 0.242 | 6.5 (12/185) | 0.004 | 190 (25.5%) | 1.0% (2/190) | 0.999 | 14.7% (18/190) | 0.036 |
30–44 | 211 (30.0%) | 2.8% (6/211) | 0.683 | 4.7% (4/211) | 0.0001 | 206 (27.7%) | 4.3% (9/206) | 0.061 | 5.8% (12/206) | 0.001 |
16–29 | 142 (19.5%) | 0.70 (1/142) | 0.202 | 8.4% (12/142) | 0.028 | 136 (17.6%) | 2.2% (3/136) | 0.275 | 8.0% (11/136) | 0.024 |
Total | 9/32 (28.1%—Prevalence 1.2) | 49/70 (70%—Prevalence 6.7) | 18/82 (21.9%—Prevalence 2.3) | 70/144 (48.6%—Prevalence 9.0) | ||||||
Men vs Women | Chi sq- = 0.485 p = 0.486 OR = 1.4 (0.5–3.5) | Chi sq- = 8.729 * p = 0.003 OR = 2.5 (1.3–4.5) | Chi sq- = 2.752 ** p = 0.097 OR = 0.7 (0.5–1.1) | |||||||
High Discr. | >75 vs. others Fisher p = 0.153 ** | 60–74 vs. 30–44 Fisher p = 0.103 | >75 vs. others Fisher p = 0.103 ** | 45–59 vs. 16–29 Fisher p = 0.699 |
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Congiu, P.; Carta, M.G.; Perra, A.; Cantone, E.; Lorrai, S.; Pintus, E.; Tusconi, M.; Cossu, G.; Redolfi, S.; Sancassiani, F. Prevalence and Risk by Age and Sex of Sleep Dysregulation and Depressive Episodes in Bipolar and Depressive Disorders in a Community Survey in Sardinia, Italy. J. Clin. Med. 2024, 13, 4870. https://doi.org/10.3390/jcm13164870
Congiu P, Carta MG, Perra A, Cantone E, Lorrai S, Pintus E, Tusconi M, Cossu G, Redolfi S, Sancassiani F. Prevalence and Risk by Age and Sex of Sleep Dysregulation and Depressive Episodes in Bipolar and Depressive Disorders in a Community Survey in Sardinia, Italy. Journal of Clinical Medicine. 2024; 13(16):4870. https://doi.org/10.3390/jcm13164870
Chicago/Turabian StyleCongiu, Patrizia, Mauro Giovanni Carta, Alessandra Perra, Elisa Cantone, Stefano Lorrai, Elisa Pintus, Massimo Tusconi, Giulia Cossu, Stefania Redolfi, and Federica Sancassiani. 2024. "Prevalence and Risk by Age and Sex of Sleep Dysregulation and Depressive Episodes in Bipolar and Depressive Disorders in a Community Survey in Sardinia, Italy" Journal of Clinical Medicine 13, no. 16: 4870. https://doi.org/10.3390/jcm13164870