Physical and Psychological Impacts on COVID-19 Hospitalized Patients of 3rd Wave

Background: COVID shelters and many emergency centers were established as a countermeasure to control this pandemic that hit the word by the end of 2019. Due to unavailability of medical care, along with physical health issues, these patients suffer with mental health related issues. Aims: This study aimed to explore the both, physical and psychological impacts upon the patients diagnosed with COVID-19 and admitted in intensive care units of hospitals of Pakistan during the third wave. Methods: This cross-sectional survey was performed during the peak time of COVID-19 for the duration of two months i.e. April & May 2021. After getting ethical approval from Shifa International Hopsital (Ref# 070-021), permission was sorted from public and private hospitals of Pakistan. 183 conscious patients diagnosed with COVID and currently admitted in intensive care units were randomly selected from hospitals of Islamabad and Rawalpindi. Written consent was taken from patients and their caregivers after they were briefed regarding the importance of the study. PHQ-15 was used to assess somatic symptoms related to COVID-19 whereas DASS-21 was used to assess level of depression, anxiety and stress among patients. Results: Of 183 hospitalized patients of COVID-19 in intensive care units, 170 (92.9%) participants showed mild to severe level of somatic symptoms on PHQ-15. Shortness of breath, feeling hearth race, back pain, stomach pain, low energy and sleeping difficulties were the most common somatic complaints reported by patients. The statistics of DASS-21 showed that 51 (27.86%) participants had mild to severe level of depression, 74 (40.4%) had mild to profound level of anxiety and 96 (52.45%) reported mild to profound level of stress. Conclusion: This study portrayed a better understanding and confirms the physical and psychological impacts upon hospitalized COVID-19 patients, therefore highlighting the need of both physical and mental health interventions to minimize these impacts

An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric.publishedVersio

Utjecaj COVID-a na funkcionalni status hospitaliziranih bolesnika

Originating with unexplained symptoms from Wuhan, city of China, COVID-19 being a global pandemic causing tremendous morbidity and mortality, has proved to be the biggest challenge of the 20th century. This study aimed to explore the functional impacts of COVID-19 upon those patients who were diagnosed with this disease and were admitted in hospitals. This cross-sectional survey included 183 COVID-19 diagnosed patients from COVID-19 isolation wards of public and private hospitals of Islamabad and Rawalpindi. After getting ethical permission from Institutional Review Board of Shifa International Hospital (Ref # 070-21), this survey was conducted for the time period of 6 months from December 2020 to May 2021. Through convenient sampling, 183 patients with the age range of 25 to 55 years with no already diagnosed psychological complaints were assessed for eligibility briefed regarding the study purpose and then were asked for their voluntary participation. The Functional Status Scale for the Intensive Care Unit (FSS-ICU) was used to assess the functional status impacted due to COVID-19 during hospitalization. Frequencies and percentages were calculated through SPSS-21. On FSS-ICU, out of 183 COVID-19, 11 (6%) patients reported that they were dependent, 18 (9.8%) required maximum assistance, 32 (17.5%) required moderate assistance, 27 (14.8%) required minimal, 24 (13.1%) required supervision to complete their tasks, 28 (15.3%) required assistive devices, whereas 43 (23.5%) were totally independent. Results indicated a temporal impact of COVID-19 upon functional status of hospitalized patients in intensive care units, therefore highlighting the need of physiotherapeutic and psychotherapeutic interventions.Pojavom neobjašnjivih simptoma u Wuhanu, Kini, globalna pandemija COVID-19, pokazala se najvećim izazovom 20. stoljeća uzrokujući ogroman pobol i smrtnost. Cilj istraživanja bio je istražiti funkcionalne učinke COVID-19 na bolesnike kojima je dijagnosticirana ova bolest i koji su hospitalizirani u zdravstvenim ustanovama. Ovo poprečno istraživanje obuhvatilo je 183 bolesnika s dijagnozom COVID-19, a koji su smješteni u izolacijske odjele javnih i privatnih bolnica u Islamabadu i Rawalpindiju. Nakon dobivanja dopusnice Etičkog povjerenstva Bolnice Shifa (br. # 070-21), istraživanje je provedeno u razdoblju od 6 mjeseci, od prosinca 2020. do svibnja 2021. Prikladnim uzorkovanjem, ukupno je 183 bolesnika u dobi od 25 do 55 godina bez prethodno dijagnosticiranih psiholoških tegoba, analizirano radi ispunjavanja kriterija istraživanja. Nakon što su ispitanici bili upoznati sa svrhom istraživanja, zamoljeni su za dobrovoljno sudjelovanje. Skala za određivanje funkcionalnog statusa bolesnika u jedinici intenzivnog liječenja (engl. Functional Status Scale - Intensive Care Unit, FSS-ICU) korištena je za procjenu utjecaja COVID-19 na funkcionalni status bolesnika tijekom hospitalizacije. Frekvencije i postoci izračunati su koristeći SPSS-21. Na FSS-ICU ljestvici, od 183 bolesnika s COVID-19, 11 (6%) je ovisno o tuđoj pomoći, 18 (9,8%) treba maksimalnu pomoć, 32 (17,5%) treba umjerenu pomoć, 27 (14,8%) treba minimalnu pomoć, 24 (13,1%) treba nadzor kako bi izvršili svoje zadatke, 28 (15,3%) treba pomoćna sredstva, dok je 43 (23,5%) bilo potpuno neovisno. Dobiveni rezultati ukazali su na vremenski utjecaj COVID-19 na funkcionalni status hospitaliziranih bolesnika u jedinicama intenzivnog liječenja te stoga naglašavaju potrebu fizioterapeutskih i psihoterapijskih intervencija

An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods

Recent advances in image processing and machine learning methods have greatly enhanced the ability of object classification from images and videos in different applications. Classification of human activities is one of the emerging research areas in the field of computer vision. It can be used in several applications including medical informatics, surveillance, human computer interaction, and task monitoring. In the medical and healthcare field, the classification of patients’ activities is important for providing the required information to doctors and physicians for medication reactions and diagnosis. Nowadays, some research approaches to recognize human activity from videos and images have been proposed using machine learning (ML) and soft computational algorithms. However, advanced computer vision methods are still considered promising development directions for developing human activity classification approach from a sequence of video frames. This paper proposes an effective automated approach using feature fusion and ML methods. It consists of five steps, which are the preprocessing, feature extraction, feature selection, feature fusion, and classification steps. Two available public benchmark datasets are utilized to train, validate, and test ML classifiers of the developed approach. The experimental results of this research work show that the accuracies achieved are 99.5% and 99.9% on the first and second datasets, respectively. Compared with many existing related approaches, the proposed approach attained high performance results in terms of sensitivity, accuracy, precision, and specificity evaluation metric