Purpose A different pattern of mental health issues was reported during the later stage of the COVID-19 pandemic; however, few studies have examined Malaysians’ knowledge, attitudes, and practices (KAP) prevalent during this time. Patients and Methods A nationwide online cross-sectional study was conducted in Malaysia from June 1, 2021 to June 14, 2021, ie, 18-months from the first reported COVID-19 case in the country. Citizens aged 18 years and above were recruited by means of the snowball sampling method. ANOVA, Pearson correlation, and linear regression tests were used. Results Of the 2168 respondents, most were young adults (62.7%), females (62.4%), tertiary educated individuals (84%), non-health care workers (85.9%), and individuals who knew someone diagnosed with COVID-19 (75.2%). The mean score for knowledge was 10.0 ± 1.52 (maximum score = 12); correct response rate for each question ranged from 54.2% to 99%. The mean score in terms of attitude was 1.3 ± 0.85 (maximum score = 2); 68.7% respondents agreed that control over COVID-19 would finally be achieved; and 62.3% believed that Malaysia could conquer COVID-19. The mean score for practices was 5.1 ± 1.10 (maximum score = 6); 81.5%, 88.1%, and 74.1% respondents avoided crowded places, confined spaces, and conversations in close physical proximity, respectively. Furthermore, 94.2% wore masks when leaving home; 89.0% practiced hand hygiene; and 83.8% adhering to COVID-19 warnings. Small but significant correlations were found between knowledge and attitude ( r = 0.078, p < 0.001) as well as between knowledge and practices ( r = 0.070, p = 0.001). Conclusion Malaysians exhibited sound knowledge but negative attitudes and inadequate practices pertaining to COVID-19 during the pandemic’s later stage. At this phase, unlike at the early stage, the public’s sound knowledge ensured little improvement in their attitudes and practices. Therefore, health education at the later pandemic stage should focus on promoting positive attitudes and developing better practices.

Ultra-wideband radar application for sleep breathing monitoring is hampered by the difficulty of obtaining breathing signals for non-stationary subjects. This occurs due to imprecise signal clutter removal and poor body movement removal algorithms for extracting accurate breathing signals. Therefore, this paper proposed a Sleep Breathing Detection Algorithm (SBDA) to address this challenge. First, SBDA introduces the combination of variance feature with Discrete Wavelet Transform (DWT) to tackle the issue of clutter signals. This method used Daubechies wavelets with five levels of decomposition to satisfy the signal-to-noise ratio in the signal. Second, SBDA implements a curve fit based sinusoidal pattern algorithm for detecting periodic motion. The measurement was taken by comparing the R-square value to differentiate between chest and body movements. Last but not least, SBDA applied the Ensemble Empirical Mode Decomposition (EEMD) method for extracting breathing signals before transforming the signal to the frequency domain using Fast Fourier Transform (FFT) to obtain breathing rate. The analysis was conducted on 15 subjects with normal and abnormal ratings for sleep monitoring. All results were compared with two existing methods obtained from previous literature with Polysomnography (PSG) devices. The result found that SBDA effectively monitors breathing using IR-UWB as it has the lowest average percentage error with only 6.12% compared to the other two existing methods from past research implemented in this dataset.

Background The Spanish chronic obstructive pulmonary disease (COPD) guideline phenotypes patients according to the exacerbation frequency and COPD subtypes. In this study, we compared the patients’ health-related quality of life (HRQoL) according to their COPD phenotypes. Methods This was a cross-sectional study of COPD patients who attended the outpatient clinic of the Serian Divisional Hospital and Bau District Hospital from 23th January 2018 to 22th January 2019. The HRQoL was assessed using modified Medical Research Council (mMRC), COPD Assessment Test (CAT), and St George’s Respiratory Questionnaire for COPD (SGRQ-c). Results Of 185 patients, 108 (58.4%) were non-exacerbators (NON-AE), 51 (27.6%) were frequent exacerbators (AE), and the remaining 26 (14.1%) had asthma-COPD overlap (ACO). Of AE patients, 42 (82.4%) had chronic bronchitis and only 9 (17.6%) had emphysema. Of the 185 COPD patients, 65.9% had exposure to biomass fuel and 69.1% were ex- or current smokers. The scores of mMRC, CAT, and SGRQ-c were significantly different between COPD phenotypes (p <  0.001). There were significantly more patients with mMRC 2–4 among AE (68.6%) (p <  0.001), compared to those with ACO (38.5%) and NON-AE (16.7%). AE patients had significantly higher total CAT (p = 0.003; p <  0.001) and SGRQ-c (both p <  0.001) scores than those with ACO and NON-AE. Patients with ACO had significantly higher total CAT and SGRQ-c (both p <  0.001) scores than those with NON-AE. AE patients had significantly higher score in each item of CAT and component of SGRQ-c compared to those with NON-AE (all p <  0.001), and ACO [(p = 0.003–0.016; p = < 0.001–0.005) except CAT 1, 2 and 7. ACO patients had significantly higher score in each item of CAT and component of SGRQ-c (p = < 0.001–0.040; p <  0.001) except CAT 2 and activity components of SGRQ-c. Conclusions The HRQoL of COPD patients was significantly different across different COPD phenotypes. HRQoL was worst in AE, followed by ACO and NON-AE. This study supports phenotyping COPD patients based on their exacerbation frequency and COPD subtypes. The treatment of COPD should be personalised according to these two factors.

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