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Abstract

Objective

To ascertain the psychological resilience scores and pinpoint the influencing factors in untreated obstructive sleep apnea (OSA) patients.

Methods

This retrospective cross-sectional study recruited OSA patients who initially consulted the otolaryngology department of Jiangsu Province Hospital and underwent polysomnography (PSG). The Connor-Davidson Resilience Scale (CD-RISC) was administered to assess resilience levels. Demographic and clinical data were extracted from patients’ medical records. A generalized linear model (GLM) was employed to explore the factors influencing resilience among OSA patients.

Results

A total of 200 patients fulfilling the OSA diagnostic criteria were enrolled. PSG results indicated that 21 patients (10.5%) had mild OSA, 45 (22.5%) had moderate OSA, and 134 (67%) had severe OSA. Based on lowest oxygen saturation (LSaO2) levels, 28 patients (14%) experienced mild hypoxemia, 42 (21%) moderate hypoxemia, and 130 (65%) severe hypoxemia. Obese patients exhibited significantly elevated apnea-hypopnea index (AHI) values and reduced LSaO2 levels compared to overweight or normal-weight patients (P < 0.01 for all comparisons). CD-RISC scores ranged from 29 to 98, with a mean of 64.63 ± 16.30. GLM analysis revealed marital status, disease duration, and AHI as independent factors influencing the psychological resilience of OSA patients.

Conclusion

The majority of OSA patients seeking initial medical attention exhibit severe conditions. Untreated OSA patients display lower psychological resilience scores. Marital status, duration of illness, and AHI are pivotal factors influencing the psychological resilience of these patients. These findings provide a scientific rationale for developing targeted psychological intervention strategies.

Keywords

Obstructive sleep apnea psychological resilience apnea-hypopnea index body mass index influencing factors

Introduction

Obstructive sleep apnea (OSA) is a prevalent condition characterized by repeated apnea and hypopnea events during sleep due to upper airway collapse.¹ It constitutes a significant health burden, impacting patients’ physical health and posing considerable economic costs to society.² OSA patients exhibit a high incidence of cardiovascular diseases (including systemic hypertension, coronary artery disease, arrhythmias, and ischemic stroke), respiratory disorders (such as COPD and asthma), and metabolic abnormalities (diabetes mellitus, dyslipidemia, and gout).³ Beyond physical health, OSA also adversely affects patients’ mental well-being and social adaptation.^4,5

Psychological resilience, a crucial aspect of mental health, refers to the ability to maintain purpose and persevere in the face of adversity. It involves overcoming life's challenges with perseverance, self-awareness, and internal coherence through personal growth initiatives.⁶ Resilience reflects individuals’ coping strategies and recovery capacity when confronted with setbacks. The Connor-Davidson Resilience Scale (CD-RISC) is a widely used tool for assessing resilience among various populations, with robust psychometric properties that distinguish between high and low resilience levels.⁷

Resilience is recognized as a positive developmental outcome, acquired and internalized over time. These attributes enable individuals to adapt effectively to challenging life circumstances. Given its beneficial impact on negative emotions, such as anxiety, and its cultivable nature, investigating the determinants of psychological resilience among OSA patients is crucial for safeguarding their holistic well-being. However, little is known about the resilience levels or the demographic and disease factors predicting resilience in untreated OSA patients. Therefore, this study aims to conduct a retrospective analysis to explore the current status of resilience in OSA patients and its potential influencing factors.

Materials and methods

This was a retrospective cross - sectional study conducted on patients who presented with snoring symptoms to the Department of Otorhinolaryngology at Jiangsu Province Hospital from January 2022 to January 2023. For these patients, Polysomnography (PSG) examination was routinely recommended for OSA diagnosis, contingent upon their non - objection. Among those undergoing PSG, the CD - RISC scale was routinely utilized to assess psychological resilience, with patients having the option to complete it voluntarily based on their individual situations. All consecutive patients who met the inclusion criteria during the study period were enrolled, without randomization or selective sampling. Inclusion criteria encompassed patients who: (a) were first-time attendees for snoring complaints, (b) had a PSG-derived apnea-hypopnea index (AHI) ≥ 5, and (c) possessed complete medical records and a completed CD-RISC score for psychological resilience assessment. Exclusion criteria comprised: (a) prior snoring consultations, (b) history of surgery or other treatments for OSA, (c) recent use of sleep-altering medications, and (d) incomplete medical records.

Overnight PSG monitoring, lasting t7 hours, was performed using the German SOMNO screene plus system. Sleep respiratory parameters collected included AHI and lowest oxygen saturation (LSaO2). OSA severity was categorized as mild (5 ≤ AHI ≤ 15), moderate (15 < AHI ≤ 30), and severe (AHI > 30).⁸ Hypoxemia severity was classified as mild (LSaO2 ≥ 85%), moderate (80% ≤ LSaO2 < 85%), and severe (LSaO2 < 80%).⁹ Psychological resilience was assessed using the 25-item CD-RISC-25 questionnaire, scored on a 5-point Likert scale (0–4).¹⁰ Total scores ranged from 0 to 100, with higher scores indicating greater resilience.⁷ Resilience was categorized into quartiles based on CD-RISC-25 scores: low (Q1, 0–73), moderate (Q2, 74–82), high (Q3, 83–90), and very high (Q4, 91–100).

Demographic and clinical data, including age, gender, marital status (categorized as unmarried, encompassing divorcees, widows/widowers, and singles, vs. married),¹¹ type of residence (urban or rural), highest educational attainment (categorized as junior high school or below, high school, and college or above), duration of snoring (stratified as nuary 2022–5 years, and >5 years), and body mass index (BMI) were collected from medical records. BMI was classified according to Chinese adult weight standards as normal (BMI < 24 kg/m²), overweight (24 kg/m² ≤ BMI < 28 kg/m²), and obese (BMI ≥ 28 kg/m²).^12,13 This study was conducted in strict accordance with the Helsinki Declaration of 1975 as revised in 2024, received approval from the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (approval number: 2022-SR-280), and all participants provided written informed consent. All patient details have been thoroughly de-identified to ensure that no individual can be identified in any manner.

Statistical analysis was conducted using SPSS software (version 19.0; SPSS Inc., Chicago, IL, USA). Normally distributed numerical variables were presented as the arithmetic mean ± standard deviation (M ± SD). Pearson correlation analysis was utilized to assess correlations among numerical variables. One-way ANOVA (analysis of variance) was employed to compare mean values of relevant parameters across different BMI groups, followed by the least significant difference (LSD) method for pairwise comparisons. A generalized linear model (GLM) analysis was performed to identify independent risk factors for CD-RISC scores in patients with OSA. Polynomial regression was used to test the linearity assumption for continuous variables in the GLM analysis. A P-value <0.05 was considered statistically significant. The reporting of this study conforms to STROBE guidelines.¹⁴

Results

A total of 200 patients, aged between 14 and 72 years (mean age: 40.72 ± 12.41 years), were enrolled in this study, including three minors aged 14, 16, and 17 years. PSG results revealed that the AHI ranged from 5.3 to 102.5 (mean: 40.67 ± 20.15). According to AHI classification, the study population included 21 patients (10.5%) with mild OSA, 45 patients (22.5%) with moderate OSA, and 134 patients (67%) with severe OSA. The LSaO2 during sleep ranged from 22% to 95% (mean: 72.53 ± 11.92%), with 28 patients (14%) experiencing mild hypoxemia, 42 patients (21%) experiencing moderate hypoxemia, and 130 patients (65%) experiencing severe hypoxemia. All enrolled patients met the diagnostic criteria for OSA in China.⁹ Additionally, the BMI ranged from 19.61 to 52.73 (mean: 27.67 ± 4.02), and their CD-RISC scores ranged from 29 to 98 (mean: 64.63 ± 16.30). The baseline characteristics of the study population are presented in detail in Table 1.

Table 1.

Baseline characteristics of the patients included in the study.

Variables	Values
Age (years) (M±SD)	40.72±12.41
Gender (n, %）
Male	179 (89.50%）
Female	21 (10.50%）
Education level (n, %）
College or above	132 (66.00%）
High school	29 (14.50%）
≤Junior high school	39 (19.50%）
Urban/rural status (n, %）
Urban area	156 (78.00%）
Rural area	44 (22.00%）
Marital status (n, %）
Married	164 (82.00%）
Not married	36 (18.00%）
Duration of illness (years)(n, %）
>5	88 (44.00%）
1–5	86 (43.00%）
≤1	26 (13.00%）
BMI	27.67±4.02
AHI	40.67±20.15
LSaO2	72.53±11.92
CD-RISC Score	64.63±16.30
Resilience distribution (n, %)
Low	140 (70.00%)
Moderate	30 (15.00%)
High	14 (7.00%)
Very high	16 (8.00%)

BMI: body mass index; AHI: apnea-hypopnea index; LSaO2: lowest oxygen saturation; CD-RISC: Connor-Davidson Resilience Scale; M ± SD: mean ± standard deviation; resilience distribution: low (CD-RISC Score, 0–73), moderate (CD-RISC Score, 74–82), high (CD-RISC Score, 83–90), and very high (CD-RISC Score, 91–100).

Patients were categorized into three groups based on their BMI: normal weight, overweight, and obese. The results of PSG tests and CD-RISC scores for patients within these BMI categories are presented in Table 2. According to Table 2, no significant differences were observed in CD-RISC scores among patients of different BMI categories. However, significant differences were noted in AHI and LSaO2 levels. Further pairwise comparisons revealed that the LSaO2 level in obese patients was significantly lower than that in both the overweight and normal weight groups (P < 0.001 for both comparisons). Additionally, the LSaO2 level in the overweight group was also significantly lower compared to the normal weight group (P = 0.019). In terms of AHI, obese patients exhibited significantly higher values than those in the overweight and normal weight groups (P < 0.001 for both comparisons). Although the AHI in the overweight group was numerically higher than that in the normal weight group, this difference did not reach statistical significance (P = 0.104). These findings suggest that obese patients tend to have more severe OSA conditions.

Table 2.

Sleep respiratory parameters and CD-RISC scores (M ± SD) of OSA patients stratified by BMI categories.

BMI categories	BMI＜24	24 ≤ BMI＜28	BMI ≥28	F	P
BMI categories	Normal (n = 30）	Overweight (n = 84）	Obese (n = 86）	F	P
LSaO2	80.83 ± 8.52	75.43 ± 9.20	66.8 ± 12.63	24.29	0 . 001
AHI	29.3 ± 17.14	35.8 ± 18.75	49.3 ± 19.03	17.72	0.001
CD-RISC Score	66.4 ± 17.87	65.8 ± 16.10	62.3 ± 15.90	1.202	0.303

BMI: body mass index; AHI: apnea-hypopnea index; LSaO2: lowest oxygen saturation; CD-RISC: Connor-Davidson Resilience Scale; M ± SD: mean ± standard deviation.

A GLM was utilized to investigate the independent risk factors influencing the CD-RISC scores of patients with OSA, with CD-RISC scores serving as the dependent variable and other clinical characteristics as independent variables. The results of this analysis are presented in Table 3. According to Table 3, marital status, duration of illness, and AHI emerged as independent risk factors affecting the CD-RISC scores of patients with OSA (P = 0.019, P = 0.026, P = 0.001, respectively). Specifically, being married, when compared to being unmarried, was identified as a protective factor that enhances patients’ resilience (P = 0.019). Conversely, having an illness duration exceeding 5 years, in comparison to a duration of less than 1 year, served as a negative factor influencing the resilience of patients with OSA. Additionally, an increase in AHI was also found to be a negative factor impacting patients’ resilience. Further correlation analysis revealed a significant negative association between AHI and CD-RISC scores in OSA patients (r = -0.297, P < 0.001), reflecting a small effect size with a weak but detectable relationship.

Table 3.

GME analysis of factors influencing resilience scores among patients with OSA.

Variables	B [95% CI ]	Wald	P
Gender	–	2.461	0.117
Male(ref.)	0^a
Female	−5.540 [−12.463, 1.382]	2.461	0.117
Education level	–	1.140	0.566
≤Junior High School (ref.)	0^a
High School	−1.489 [−8.774, 5.796 ]	0.161	0.689
College or Above	1.929 [−4.780, 8.638 ]	0.318	0.573
Urban/Rural Status	–	0.085	0.771
Rural Area(ref.)	0^a
Urban Area	0.888 [−5.080, 6.856 ]	0.085	0.771
Marital Status	–	5.457	0 . 019
Not Married(ref.)	0^a
Married	6.854 [1.103, 12.605 ]	5.457	0.019
Duration of Illness （years）	–	7.306	0.026
<1(ref.)	0^a
1–5	−5.532 [−12.343, 1.278]	2.535	0.111
＞5	−9.341 [−16.268, −2.415]	6.987	0.008
Age (Years)	–	2.815	0.245
≤44(ref.)	0^a
45–59	−2.379 [−8.043, 3.286 ]	0.677	0.411
≥60	−7.663 [−16.846, −1.520 ]	2.675	0.102
BMI	–	0.023	0.988
BMI＜24(ref.)	0^a
24 ≤ BMI ＜ 28	0.484 [−5.991, 6.959 ]	0.021	0.884
BMI ≥ 28	0.272 [−6.528, 7.071]	0.006	0.938
LSaO2 (%)	–	0.984	0.611
LSaO2 ≥ 85(ref.)	0^a
80 ≤ LSaO2 ＜ 85	−0.441 [−7.946, 7.063 ]	0.013	0.908
LSaO2 ＜ 80	2.523 [−5.414, 10.461]	0.388	0.533
AHI	−0.283 [−0.420, −0.146]	16.286	0.001

GME: generalized linear model; a: reference values; B: estimated regression coefficient； CI: confidence interval; BMI: body mass index; AHI: apnea-hypopnea index; LSaO2: lowest oxygen saturation; ref.: reference variable.

Discussion

OSA has a profound impact on both physical and mental health, and its untreated state is associated with an increased risk of developing multiple complications^.¹ However, awareness among the general population regarding the hazards and severity of OSA remains inadequate.¹⁵ In this study, among patients presenting for their initial consultation, 67% met the criteria for severe OSA, and 65% fulfilled the standards for severe hypoxemia. These findings suggest that the majority of OSA patients seeking treatment present with severe conditions, highlighting the need for adequate attention and further therapeutic interventions.

Obesity has long been recognized as a significant factor associated with OSA, with a positive correlation between BMI and disease severity observed in both genders.¹⁶ Our study investigated variations in OSA severity among patients with different BMI classifications and found that obese patients exhibited significantly higher AHI values and lower LSaO2 levels compared to those who were overweight or of normal weight. These findings suggest that obesity is a crucial risk factor for the exacerbation of OSA.^8,17 Obesity may lead to anatomical changes in the upper airway, such as pharyngeal narrowing and posterior displacement of the tongue base, which increase the risk of airway collapse and obstruction during sleep.¹⁸ Additionally, obesity can exacerbate OSA by affecting the regulatory function of the respiratory center and increasing the burden on respiratory muscles.¹⁹ Therefore, for obese patients with OSA, it is essential to adopt corresponding therapeutic measures to improve sleep quality and prevent the occurrence of complications. Moreover, controlling weight and improving lifestyle habits may help alleviate the severity of OSA and enhance patients’ quality of life.

The CD-RISC score is widely used to evaluate an individual's psychological resilience, particularly their capacity to adapt and recover from life's pressures and challenges.²⁰ It is generally accepted that higher levels of psychological resilience are associated with lower BMI, and several studies have reported a negative correlation between BMI and psychological resilience.^21,22 However, a cross-sectional study conducted in a community-based population revealed a significant association between greater psychological resilience and higher BMI, highlighting the intricate relationship between psychological resilience and weight management.²³ In our study, an unexpected finding was that there were no statistically significant differences in the CD-RISC scores among patients categorized into different BMI groups. This finding may imply that body weight alone does not directly determine one's psychological resilience and suggests that the psychological resilience of patients with OSA may be influenced by a multitude of other factors.²⁴ Furthermore, the GLM analysis conducted in this study also confirms that BMI is not an independent risk factor affecting patients’ psychological resilience.

Currently, there exists a scarcity of pertinent references concerning the psychological resilience status of patients with OSA. In this study, we observed that the mean psychological resilience score among patients with OSA was 64.63, significantly lower than that reported in the general population.²⁵ Further analysis revealed that the vast majority (approximately 70%) of the patients with OSA in this study exhibited low levels of psychological resilience. Considering that resilience is a modifiable trait that can be enhanced through targeted interventions,⁷ it is imperative to explore the factors contributing to reduced resilience in patients with OSA. Further analysis using GLM identified marital status, disease duration, and AHI as factors associated with the psychological resilience of these patients. It is important to emphasize that these findings represent associations rather than causal relationships.

Firstly, marriage may confer a sense of stability, emotional support, and belonging.²⁶ Compared to non-married individuals, being married appears to be associated with higher levels of psychological resilience. This association is likely attributed to the emotional sustenance, stable social ties, and shared health responsibility inherent in marital relationships—each playing a critical role in managing psychological stresses associated with chronic conditions like OSA. Emotional support from spouses buffers acute psychological stress, while stable social networks mitigate social isolation; concurrently, shared health responsibility fosters adaptive coping strategies. These interdependent mechanisms collectively explain why marriage functions as a protective factor for greater psychological resilience.

OSA represents a prevalent clinical challenge that can complicate the progression of mental disorders.²⁷ Certain symptoms of OSA overlap with those of depression and can also result in substantial cognitive decline.²⁷ The AHI serves as a metric for assessing the severity of OSA, where a higher AHI score signifies more severe OSA conditions among patients.^28,29 Correlation analysis revealed an inverse relationship between AHI and patients’ psychological resilience, indicating that as the severity of OSA intensifies, patients’ psychological resilience diminishes. This finding further underscores the adverse effects of severe OSA on patients’ mental health. In individuals with OSA, a heightened AHI value may suggest a higher frequency of apnea and hypopnea events, leading to diminished sleep quality, excessive daytime sleepiness, and cognitive impairment, which collectively exacerbate the psychological burden on these patients.

The impact of disease duration on patients’ psychological resilience is a significant factor that cannot be overlooked. Patients diagnosed with OSA endure disrupted sleep patterns, leading to inadequate nocturnal rest. This, in turn, exacerbates both physical and mental fatigue.³⁰ The management of OSA over an extended period frequently coincides with both physical and mental exhaustion, potentially disrupting numerous facets of their professional and personal lives. Furthermore, inadequate sleep has been associated with an augmentation in negative affect, a decrement in positive affect, and heightened difficulties in regulating emotional responses.³¹ As adverse emotional states accumulate and psychological trauma intensifies, they inevitably compromise patients’ psychological resilience, subsequently diminishing their capacity to overcome challenges. This creates a vicious cycle where the decrement in resilience further exacerbates their difficulties. Therefore, early identification and prompt initiation of treatment for OSA are imperative to mitigate the detrimental effects on both physical and mental health.³²

This study is accompanied by several limitations that merit consideration. Firstly, the single-center, retrospective design with a modest sample size inherently limits the generalizability of findings and clinical interpretability. Additionally, while multiple variables were incorporated into the models, the statistical analysis did not apply multiple testing corrections—a methodological consideration that may warrant further exploration in future studies. Secondly, psychological resilience, as a dynamic psychological construct measured via self-reported scales, is subject to both social desirability bias inherent in subjective assessment and confounding by unmeasured social/familial environmental factors, necessitating cautious interpretation of resilience-related findings. Thirdly, the gender imbalance characterized by the overrepresentation of male patients raises concerns about selection bias and may limit generalizability to female OSA populations. Given these limitations, further research is needed to explore psychological resilience and its influencing factors among OSA patients across demographic and clinical strata.

Conclusion

In this observational study, the majority of newly diagnosed OSA patients may present with severe conditions, warranting considerable attention. OSA may not only cause severe physical problems but also significantly affect patients’ mental health. Our findings suggest that marital status, illness duration, and AHI may be key factors influencing the psychological resilience of OSA patients. These results may deepen our understanding of the psychological characteristics of OSA patients and offer a scientific foundation for developing targeted psychological intervention strategies.

Footnotes

Acknowledgments

The authors are grateful to all staff in the department of Otolaryngology. They would like to express our profound gratitude to the copyright holder of the Connor-Davidson Resilience Scale (CD-RISC) and Dr Jonathan Davidson for granting us permission to utilize this scale in the present study.

ORCID iDs

Rui Lin

Ren-Yu Guo

Ping Wang

Wen-Cheng Zhou

Human ethics and consent to participate declarations

This study was approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (located in Nanjing, China; approval number: 2022-SR-280; date of approval: April 26, 2022), and all participants provided written informed consent. The authors of this study affirm that the research was conducted in accordance with the principles outlined in the Declaration of Helsinki.

Consent for publication

Not applicable.

Author contributions

Wen-Cheng Zhou analyzed the results and wrote the manuscript. Ren-Yu Guo, Ping Wang, and Rui Lin collaborated in the acquisition, analysis, or interpretation of data.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

References

Arachchige

Steier

. Beyond usual care: a multidisciplinary approach towards the treatment of obstructive sleep apnoea. Front Cardiovasc Med 2021; 8: 747495.

Evans

Sulyman

Froymovich

. The goals of treating obstructive sleep apnea. Otolaryngol Clin North Am 2020; 53: 319–328.

Bonsignore

Baiamonte

Mazzuca

, et al. Obstructive sleep apnea and comorbidities: a dangerous liaison. Multidiscip Respir Med 2019; 14: 8.

Lee

Jung

. Obstructive sleep apnea related to mental health, health-related quality of life and multimorbidity: a nationwide survey of a representative sample in Republic of Korea. PLoS One 2023; 18: e287182.

Rumble

Okoyeh

Benca

. Sleep and women’s mental health. Psychiatr Clin North Am 2023; 46: 527–537.

Sisto

Vicinanza

Campanozzi

, et al. Towards a transversal definition of psychological resilience: a literature review. Medicina (Kaunas) 2019; 55: 45.

Connor

Davidson

. Development of a new resilience scale: the Connor-Davidson resilience scale (CD-RISC). Depress Anxiety 2003; 18: 76–82.

Liu

Zhang

, et al. Pathophysiological mechanisms and therapeutic approaches in obstructive sleep apnea syndrome. Signal Transduct Target Ther 2023; 8: 18.

Subspecialty Group Of Laryngopharyngology EBOC, Subspecialty Group Of Laryngopharyngology SOOH, Subspecialty Group Of Voice SOOH, Shanghai Sixth People S Hospital Affiliated To Shanghai Jiao Tong University School Of Medicine SKLO. Guideline for the diagnosis and surgical treatment of adult obstructive sleep apnea(2024): a protocol. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2024; 59: 114–121.

10.

Boskailo

Franjic

Juric

, et al. Resilience and quality of life of patients with breast cancer. Psychiatr Danub 2021; 33: 572–579.

11.

Wister

Ferris

, et al. Resilience among older adults with multimorbidity using the Connor-Davidson scale in the Canadian Longitudinal Study on aging: health behaviour, socio-economic, and social support predictors. BMC Public Health 2024; 24: 2567.

12.

Liu

Zhao

Liu

, et al. Association between body mass index and medication-overuse headache among individuals with migraine: a cross-sectional study. Obes Facts 2024; 17: 286–295.

13.

Chen

. The guidelines for prevention and control of overweight and obesity in Chinese adults. Biomed Environ Sci 2004; 17: 1–36.

14.

von Elm

Altman

Egger

, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med 2007; 147: 573–577.

15.

Rasool

Alenezi

Alanazi

, et al. Knowledge of obstructive sleep apnea among the general population in Arar, Northern Region of Saudi Arabia. Cureus 2024; 16: e51529.

16.

Maierean

Vulturar

Chetan

, et al. The management of obstructive sleep apnea patients during the COVID-19 pandemic as a public health problem-interactions with sleep efficacy and mental health. Int J Environ Res Public Health 2023; 20: 4313.

17.

Hirani

Smiley

. A scoping review of sleep apnea: where do we stand? Life (Basel) 2023; 13: 87.

18.

Isono

. Obesity and obstructive sleep apnoea: mechanisms for increased collapsibility of the passive pharyngeal airway. Respirology 2012; 17: 32–42.

19.

Orozco

Rodriguez

Palma

, et al. Obesity hypoventilation syndrome, literature review. Sleep Adv 2024; 5: 33.

20.

Afshari

Nourollahi-Darabad

Chinisaz

. Demographic predictors of resilience among nurses during the COVID-19 pandemic. Work 2021; 68: 297–303.

21.

Olson

Howard

McCaffery

, et al. Psychological resilience in older adults with type 2 diabetes from the look AHEAD trial. J Am Geriatr Soc 2023; 71: 206–213.

22.

Uzun

Kara

Sirin

, et al. Examination of relationship factors between psychological resilience and social support in adolescent obesity. Arch Pediatr 2023; 30: 277–282.

23.

Zheng

Zhuang

Zhu

, et al. Association between psychological resilience and body mass index in a community-based population: a cross-sectional study. Obes Sci Pract 2024; 10: e761.

24.

Zhu

Zhang

Zhuang

, et al. Association between sleep duration and psychological resilience in a population-based survey: a cross-sectional study. J Educ Health Promot 2024; 13: 43.

25.

Liu

, et al. Impact of childhood maltreatment on adult resilience. BMC Psychiatry 2023; 23: 637.

26.

Zeng

, et al. Professional identity and its associated psychosocial factors among physicians from standardized residency training programs in China: a national cross-sectional study. Front Med (Lausanne) 2024; 11: 1413126.

27.

Szaulinska

Antosik-Wojcinska

Paszkowska

, et al. Hypersomnia and cognitive impairment in a patient with bipolar disorder—a case report. Psychiatr Pol 2017; 51: 483–494.

28.

Soria

Aguado

Juarez

, et al. Obstructive sleep apnea and oxygenation in very old adults: a propensity-score match study. J Am Med Dir Assoc 2024; 25: 105023.

29.

Thorisdottir

Hrubos-Strom

Karhu

, et al. Verbal memory is linked to average oxygen saturation during sleep, not the apnea-hypopnea index nor novel hypoxic load variables. Sleep Med 2024; 123: 29–36.

30.

Carratu

Karageorgiou

Bonfitto

, et al. Long-term evaluation of mental fatigue by Maastricht Questionnaire in patients with OSAS treated with CPAP. Monaldi Arch Chest Dis 2007; 67:1–9.

31.

Pattison

Tolson

Barnes

, et al. Improved depressive symptoms, and emotional regulation and reactivity, in individuals with obstructive sleep apnea after short- and long-term CPAP therapy use. Sleep Med 2023; 111: 13–20.

32.

Arredondo

Udeani

Panahi

, et al. Obstructive sleep apnea in adults: what primary care physicians need to know. Cureus 2021; 13: e17843.

Retrospective cross-sectional analysis of factors influencing resilience in newly diagnosed patients with obstructive sleep apnea

Abstract

Objective

Methods

Results

Conclusion

Keywords

Introduction

Materials and methods

Results

Discussion

Conclusion

Footnotes

Acknowledgments

ORCID iDs

Human ethics and consent to participate declarations

Consent for publication

Author contributions

Funding

Declaration of conflicting interests

Data availability

References