The Study of Learning Computer Programming for Students with Medical Fields of Specification-An Analysis via Structural Equation Modeling.

In this study, the authors constructed structural equation models in order to determine the relationship between students' learning attitudes and their computational thinking perspectives and programming empowerment. The purpose is to understand students' perceived competence to use computational thinking effectively, along with their computer programming learning attitude regarding the C++ programming language for one semester (2 hours per week, 36 total learning hours). A total of 495 students specializing in the medical field participated in the study. Structural equation models were constructed according to three adapted scales: the computer programming learning attitude scale, the computational thinking perspectives scale, and the programming empowerment scale. The computer programming learning attitude scale is based on three factors: willingness, negativity, and necessity. The computational thinking perspectives scale also considers three factors: the ability to express, the ability to connect, and the ability to question. The programming empowerment scale is composed of four factors: meaningfulness, impact, creative self-efficacy, and programming self-efficacy. The results showed that a positive learning attitude will positively affect computational thinking perspectives and programming empowerment. However, when students have a negativity attitude, feeling that they are being forced to learn the C++ programming language, their computational thinking perspectives and programming empowerment will be negatively affected. In order to promote students' learning attitude, various teaching strategies, teaching curriculum design, and pedagogy design could be further explored.

CPAP Treatment Improves Pure Tone Audiometry Threshold in Sensorineural Hearing Loss Patients with Sleep-Disordered Breathing.

This article investigates the effects of continuous positive airway pressure (CPAP) on hearing impairment in sensorineural hearing loss (SNHL) patients with sleep-disordered breathing (SDB). This retrospective and observational study took place from September 2016 to February 2021, accumulating 77 subjects with SNHL and SDB (60.7 ± 11.1 years). Of which, 28 received CPAP treatment (63.0 ± 8.5 years). In our methodology, hearing thresholds at low, medium, high, and average frequencies are assessed by pure-tone audiometry at baseline (BL), three (3 m), six (6 m), and 12 (12 m) months. Our results show that the BL of at least three frequencies in all subjects is positively associated with old age, males, smoking, alcohol, coronary artery disease, hypertension, and apnea-hypopnea index [AHI] (all p < 0.05). Moreover, low, medium, and average frequencies are negatively correlated at CPAP-6 m (-5.60 ± 2.33, -5.82 ± 2.56, and -5.10 ± 2.26 dB; all p < 0.05) and CPAP-12 m (-7.97 ± 2.74, -8.15 ± 2.35, and -6.67 ± 2.37 dB; all p < 0.01) against corresponding measures of CPAP-BL. High, medium, and average frequencies positively correlated with age (p < 0.001 for high and average frequencies and <0.01 for medium frequencies). We conclude that in SNHL patients with SDB, hearing thresholds at low and medium frequencies improves under CPAP use after six months, which persists at least to the end of one year.

Confounded by obesity and modulated by urinary uric acid excretion, sleep-disordered breathing indirectly relates to hyperuricaemia in males: A structural equation model.

Sleep-disordered breathing (SDB) causes hypoxic stress and can trigger uric acid (UA) overproduction. We comprehensively investigated whether SDB, interacting with components of metabolic syndrome, hepatic and renal dysfunctions, low physical fitness, sedentary lifestyle, disrupted sleep, and chronic systemic inflammation (CSI), is directly associated with hyperuricaemia. In 528 community-based males (mean [SD] age 46.2 [7.4] years), we cross-sectionally analysed measures of anthropometry; self-reported lifestyle habits; overnight sleep polysomnography data; cardiopulmonary exercise tests; and biomarkers of cardiometabolic, hepatic, and renal functions; and CSI, using structural equation modelling. Objective disrupted sleep, C-reactive protein, low physical fitness, and sedentary lifestyle were not related to UA levels in univariate analysis and were excluded. The latent variables (with corresponding manifest variables) obesity (body mass index, waist-hip ratio), hypertension (post-sleep systolic, diastolic blood pressure), dyslipidaemia (total cholesterol, triglyceride/high-density lipoprotein cholesterol), hepatic dysfunction (alanine aminotransferase, aspartate transaminase), and renal dysfunction (blood urea nitrogen, serum creatinine) were positively; and hyperglycaemia (fasting glucose, glycated haemoglobin) was negatively associated with hyperuricaemia (serum UA), except for SDB (Apnea-Hypopnea Index, percentage of oxygen saturation <90% period against total sleep time, oxygen desaturation index) in the one-stage influence model. In the two-stage model, SDB, closely interacting with obesity, was positively indirectly associated with hyperuricaemia through directly linked renal dysfunction and obesity-linked hypertension, inverse hyperglycaemia, dyslipidaemia, and hepatic dysfunction. In conclusion, structural equation modelling reveals that SDB closely interacts with obesity and is positively but indirectly related to hyperuricaemia in males. This suggests that urinary UA excretion modulates and obesity confounds the SDB-hyperuricaemia relationship.

Objectively Measured Disrupted Sleep Is Independently and Directly Associated With Low Exercise Capacity in Males: A Structural Equation Model.

STUDY OBJECTIVES: We investigated the interaction between objective sleep disturbance and obesity, sedentary lifestyle, and lung dysfunction and whether it is negatively associated with cardiorespiratory fitness. METHODS: In this community cohort study of 521 men (age 46.6 ± 7.5 years), measures of anthropometry, pulmonary function, overnight sleep polysomnography, and cardiopulmonary exercise testing were processed stepwise using structural equation modeling (SEM). RESULTS: A univariate correlation analysis was used to group the corresponding variables (in parentheses) into the following eligible latent variables for lower exercise capacity: obesity (body mass index, waist-to-hip ratio), irregular exercise, impaired lung function (predicted values of forced expiratory volume in the first second, forced vital capacity, maximal ventilatory volume, and lung diffusion capacity for carbon monoxide), disrupted sleep (total sleep time, percentage of slow-wave sleep, sleep efficiency), and sleep-disordered breathing (apnea-hypopnea index, lowest oxygen saturation, percentage of total period of oxygen saturation < 90%). Advanced SEM analyses produced a well-fitted final confirmatory model that obesity (direct strength ßd = .366, P < .001), irregular exercise (ßd = .274, P < .001), and impaired lung function (ßd = .152, P < .001), with their mutual interactions, as well as disrupted sleep (ßd = .135, P = .001) were independently and directly associated with low exercise capacity. By contrast, sleep-disordered breathing (ßd = 0, P = .215) was related to low exercise capacity indirectly through obesity into the mutual interaction cycle of obesity, irregular exercise, and impaired lung function. Sleep-disordered breathing was robustly and mutually correlated with obesity (mutual relationship index = .534, P < .001). CONCLUSIONS: Objectively measured disrupted sleep is directly and independently associated with low exercise capacity; however, sleep-disordered breathing is indirectly mediated by obesity and mutual interactions among obesity, lung dysfunction, and sedentary lifestyle and is linked to low exercise capacity. Our findings indicate that individuals with limited exercise capacity without definite causes should undertake a sleep study, particularly in those describing symptoms of sleep-disordered breathing or insomnia.

Higher stroke incidence in the patients with pancreatic cancer: A nation-based cohort study in Taiwan.

No study has investigated the role of pancreatic cancer in the stroke risk using population data. We used claims data obtained from a universal health insurance program of Taiwan to evaluate the stroke risk in pancreatic cancer patients.From the catastrophic disease registry of the insurance, we identified 7479 patients with pancreatic malignancy without stroke history from 2000 to 2009. The comparison cohort consisted of 29,916 individuals identified from 1 million insured people without cancer and stroke history, matching with the cancer cohort by propensity score. We followed each selected individual until stroke was diagnosed or until being censored for death or withdrawal from insurance, or for a maximum of 3 follow-up years, or the end of 2011.The pancreatic cancer cohort had a 2.3-fold greater incident stroke than comparisons had (28.5 vs 12.3 per 1000 person-years), with an adjusted hazard ratio (aHR) of 2.74 (95% confidence interval (CI) = 2.31-3.24) after controlling for covariates, or a subdistribution hazard ratio (SHR) of 2.04 (95% CI = 1.74-2.40) accounting for the competing risk of deaths. During the follow-up period, stroke events occurred constantly in comparisons, but declined rapidly in the cancer cohort. The pancreatic cancer cohort had a stroke incidence of 46.6 per 1000 person-years within 6 months postdiagnosis, with an aHR of 4.37 (95% CI = 3.45-5.54) and a SHR of 3.87 (95% CI = 3.08-4.86), relative to comparisons.Our study suggests that patients with pancreatic cancer are at an elevated risk of stroke, patients deserve sufficient follow-up care, particularly in the first 6 months after the diagnosis of the cancer, and for those with comorbidities.

Objective Sleep Measures in Inpatients With Subacute Stroke Associated With Levels and Improvements in Activities of Daily Living.

OBJECTIVE: To investigate whether objective polysomnographic measures of prevalent sleep problems such as sleep-disordered-breathing (SDB) and insomnia are associated with activities of daily living levels in inpatients at rehabilitation units. DESIGN: Retrospective and observational study. SETTING: Single rehabilitation center. PARTICIPANTS: Inpatients with subacute stroke (N=123) (61.6±13.1 years; 23.8±3.4 kg/m2; 33% women; 90.5±36.7 days post-stroke) underwent a 1-night polysomnographic study and a 1-month inpatient rehabilitation program. MAIN OUTCOME MEASURES: Admission and discharge Barthel Index (BI) scores and its change scores. RESULTS: One hundred three (92%) patients had moderate-to-severe SDB (46.7±25.1 events/h in the apnea-hypopnea index), and 24 (19.5%) patients had acceptable continuous positive airway pressure adherence. Diverse values were found for total sleep time (259±71 min), sleep efficiency (69.5%±19.3%), sleep latency (24.3±30.9 min), and wakefulness after sleep onset (93.1±74.2 min). Admission BI scores and the BI change scores were 33.8±23.2 and 10.1±9.2, respectively. The National Institutes of Health Stroke Scale (NIHSS, 10.2±5.6), available in 57 (46%) patients, was negatively associated with admission levels and gains in BI change scores (P<.001, =0.002, respectively) in a univariate analysis. In regression models with backward selection, excluding NIHSS score, both age (P=.025) and wakefulness after sleep onset (P<.001) were negatively associated (adjusted R2=0.260) with admission BI scores. Comorbidity of hypertension; sleep latency percentage of stage 1, non-rapid eye movement sleep; and desaturation events ≥4% (P<.001, 0.001, 0.021, and 0.043, respectively; adjusted R2=0.252) were negatively associated with BI score gains. CONCLUSIONS: Based on objective sleep measures, insomnia rather than SDB in inpatients with subacute stroke was associated negatively with admission levels of activity of daily living and its improvement after a 1-month rehabilitation course.

Depression and family support in breast cancer patients.

BACKGROUND: Breast cancer is the most common cancer in women. Among the survivors, depression is one of the most common psychiatric comorbidities. This paper reports the point prevalence of major depressive disorder among breast cancer patients and the association between family support and major depressive disorder. METHODS: Clinical data were collected from a breast cancer clinic of a general hospital in central Taiwan. Participants included 300 patients who were older than 18 years and diagnosed with breast cancer. Among these individuals, we used Mini International Neuropsychiatric Interview (a structural diagnostic tool for psychiatric disorders) to ascertain if they had major depressive disorder. We also used the Family Adaptability, Partnership, Growth, Affection, and Resolve score to assess the family support. RESULTS: The point prevalence of major depressive disorder among breast cancer patients was 8.33%, and this was positively associated with insomnia, psychiatric family history, pain severity, and radiotherapy and negatively associated with menopause, cancer duration, hormone therapy, and family support. Family support (adjusted odds ratio =0.87, 95% CI: 0.78-0.98) was found to be an associated factor for major depressive disorder in breast cancer patients after controlling for potential risk factors. CONCLUSION: Major depressive disorder is a common comorbidity among breast cancer patients. Family support is an important associated factor for these patients. Health care professionals should evaluate mood problems and family support while treating these patients.

Scaling exponent values as an ordinary function of the ratio of very low frequency to high frequency powers in heart rate variability over various sleep stages.

PURPOSE: To assess the physiological meanings of the detrended fluctuation analysis (DFA) slope α and its relationship to spectral measures in heart rate variability, this study investigated changes of α and its corresponding spectral measures over various night-sleep stages. METHODS: The overall DFA α and natural-logarithm-transformed power values of the spectral parameters ln[high-frequency (HF)], ln[low-frequency (LF)], and ln[very-low-frequency (VLF)], and their relationship from one 5-min proper electrocardiography segment in each of pre-sleep-wakefulness (AWK), non-rapid eye movement stage 2 (N2), slow-wave (N3), the first and the latest rapid-eye movement sleep (REM1, and REM2), were computed in 93 otherwise healthy males (44.1 ± 7.7 years.) with wide-ranged apnea-hypopnea, periodic-limb movement and arousal indices (19.0 ± 20.9, 4.7 ± 9.9, and 10.7 ± 18.2 h, respectively). RESULTS: While ln(HF) dipped from AWK, N2, and N3 to REM1 then rebounded to the origin level at REM2, ln(VLF) dipped from AWK to N2, N3 trough, and then surged to levels surpassing AWKs and N2s at REM1 and REM2. ln(LF/HF), ln(VLF/HF), and α dipped from AWK and N2 to N3 trough, surged to levels surpassing AWKs, and N2s at REM1 then became attenuated at REM2. By general linear modeling, the relationship between α and the corresponding spectral values can be seen over various stages as α = b 0 + 0.147 × ln(VLF/HF) (R (2) = 0.766), regardless of age and sleep-sympathoexcitatory episodes. CONCLUSION: The REM sleep attenuations appeared in ln(HF) and its derivatives, such as ln(LF/HF), ln(VLF/HF), and the overall DFA slope α values. The quantitative function of ln(VLF/HF) describes the α values constantly for overnight sleep stages, and it is not affected by age, LF, PLM, and AHI. Our findings therefore suggest that in sleep studies with spectral HRV measures, ln(VLF/HF) as a surrogate of the overall DFA slope α should be calculated at the same time.

Evaluation of candidate measures for home-based screening of sleep disordered breathing in Taiwanese bus drivers.

BACKGROUND: Sleepiness-at-the-wheel has been identified as a major cause of highway accidents. The aim of our study is identifying the candidate measures for home-based screening of sleep disordered breathing in Taiwanese bus drivers, instead of polysomnography. METHODS: Overnight polysomnography accompanied with simultaneous measurements of alternative screening devices (pulse oximetry, ApneaLink, and Actigraphy), heart rate variability, wake-up systolic blood pressure and questionnaires were completed by 151 eligible participants who were long-haul bus drivers with a duty period of more than 12 h a day and duty shifting. RESULTS: 63.6% of professional bus drivers were diagnosed as having sleep disordered breathing and had a higher body mass index, neck circumference, systolic blood pressure, arousal index and desaturation index than those professional bus drivers without evidence of sleep disordered breathing. Simple home-based candidate measures: (1) Pulse oximetry, oxygen-desaturation indices by ≥3% and 4% (r = 0.87~0.92); (2) Pulse oximetry, pulse-rising indices by ≥7% and 8% from a baseline (r = 0.61~0.89); and (3) ApneaLink airflow detection, apnea-hypopnea indices (r = 0.70~0.70), based on recording-time or Actigraphy-corrected total sleep time were all significantly correlated with, and had high agreement with, corresponding polysomnographic apnea-hypopnea indices [(1) 94.5%~96.6%, (2) 93.8%~97.2%, (3) 91.1%~91.3%, respectively]. Conversely, no validities of SDB screening were found in the multi-variables apnea prediction questionnaire, Epworth Sleepiness Scale, night-sleep heart rate variability, wake-up systolic blood pressure and anthropometric variables. CONCLUSIONS: The indices of pulse oximetry and apnea flow detection are eligible criteria for home-based screening of sleep disordered breathing, specifically for professional drivers.