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The COVID-19 pandemic has great effects for economies internationally. This study studies the interconnection between COVID-19 metrics and Pakistan's premier stock exchange, the Karachi Stock Exchange (KSE) with the object of identifying the most effective machine learning (ML) model for predicting KSE developments in the pandemic. Our investigation periods the peak COVID-19 period from March 1, 2020, to November 26, 2021, applying data from both the KSE 100 index and COVID-19 associated variables. Five various ML methods were applied involving Linear Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Regression Tree (Rtree), and Support Vector Machine (SVM) and measured their performance employing critical accuracy metrics such as Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared (R 2 ). The outcomes discover that the RF model outperformed its equivalents realizing an R 2 of 0.91 with k = 5. These results conflict with a previous study that supported a negative impact of COVID-19 on improved stock markets. The visions from this study can assist investors in managing strategic investment decisions and assist policymakers in making measures to reduce the pandemic's effects on the stock market., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier Ltd.)

Background: A considerable body of research has demonstrated that reducing sitting time benefits health. Therefore, the current study aimed to explore the prevalence of sedentary behavior (SB) and its patterns., Methods: A total of 6975 university students (49.1% female) were chosen randomly to participate in a face-to-face interview. The original English version of the sedentary behavior questionnaire (SBQ) was previously translated into Arabic. Then, the validated Arabic version of the SBQ was used to assess SB. The Arabic SBQ included 9 types of SB (watching television, playing computer/video games, sitting while listening to music, sitting and talking on the phone, doing paperwork or office work, sitting and reading, playing a musical instrument, doing arts and crafts, and sitting and driving/riding in a car, bus or train) on weekdays and weekends., Results: SBQ indicated that the total time of SB was considerably high (478.75 ± 256.60 and 535.86 ± 316.53 (min/day) during weekdays and weekends, respectively). On average, participants spent the most time during the day doing office/paperwork (item number 4) during weekdays (112.47 ± 111.11 min/day) and weekends (122.05 ± 113.49 min/day), followed by sitting time in transportation (item number 9) during weekdays (78.95 ± 83.25 min/day) and weekends (92.84 ± 100.19 min/day). The average total sitting time of the SBQ was 495.09 ± 247.38 (min/day) and 58.4% of the participants reported a high amount of sitting time (≥ 7 hours/day). Independent t-test showed significant differences (P ≤ 0.05) between males and females in all types of SB except with doing office/paperwork (item number 4). The results also showed that male students have a longer daily sitting time (521.73 ± 236.53 min/day) than females (467.38 ± 255.28 min/day). Finally, 64.1% of the males reported a high amount of sitting time (≥ 7 hours/day) compared to females (52.3%)., Conclusion: In conclusion, the total mean length of SB in minutes per day for male and female university students was considerably high. About 58% of the population appeared to spend ≥7 h/day sedentary. Male university students are likelier to sit longer than female students. Our findings also indicated that SB and physical activity interventions are needed to raise awareness of the importance of adopting an active lifestyle and reducing sitting time., (© 2024. The Author(s).)