Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.

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Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.

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Objective: To assess the frequency of wrist pain in students due to mobile phone usage, and impact of usage hours and screen size of mobile phones on pain and disability at wrist joint. Methods: A cross-sectional survey was conducted among students studying in different universities of Islamabad and Rawalpindi belonging to both public and private sectors. The study was conducted between May 2018 and March 2019. Sample size was 360 students which were selected through convenience sampling. Data was collected through self-formulated closed ended questionnaire. Patient Rated Wrist Evaluation questionnaire was used to assess pain and disability at wrist joint. Data entry and analysis were done using SPSS 21. Results were analyzed using descriptive statistics. Spearman’s and point-biserial correlation coefficients were applied to determine association between different variables. Results: Point, last month, last 3 months, last 6 months, last year and lifetime frequency were found to be 9%, 18.6%, 29%, 33.3%, 42% and 45.3% respectively. Duration of mobile phone usage was found to be of significant association factor that could lead to wrist pain and disability (p=0.004). Wrist pain was not significantly related to mobile phone screen size (p = 0.488). Conclusion: It appears that wrist pain is common among mobile phone users and an increase in use of mobile phones increased pain and disability of wrist joint. In addition, it seems that screen size of mobile phone has no significant effect on pain and disability of wrist joint. doi: https://doi.org/10.12669/pjms.36.4.1797 How to cite this:Amjad F, Farooq MN, Batool R, Irshad A. Frequency of wrist pain and its associated risk factors in students using mobile phones. Pak J Med Sci. 2020;36(4):---------. doi: https://doi.org/10.12669/pjms.36.4.1797 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: A year after the COVID-19 pandemic vaccination rollout, vaccine elicited immunity is waning and newer variants e.g. The Delta Variants and Omicron have necessitated the administration of booster doses because of the rise in breakthrough infection rate. The objectives of our study are to determine the prevalence of acceptance status of Covid-19 booster dose in the adult population of Pakistan and its association with knowledge and perceived benefits. Methodology: A cross-sectional study was conducted by online self-administered questionnaire shared to the general population of Pakistan. The form was distributed to 100,000 people out of which 461 responded. The questionnaire was based on the Health Belief Model. Frequencies and proportions for categorical variables, and the Chi-squared test was used to examine differences between COVID-19 booster acceptance and perceived barriers in getting a booster dose and between booster acceptance and knowledge on health benefits of a booster dose. Results: 89.4% reported acceptance of the COVID 19 booster dose . The youngest age group of 18-30 years had acceptance prevalence of this group was 85.4% . Participants with respiratory disease (2.6%) had an acceptance prevalence of 12%, along with participants who identified “other” comorbidities (2.4%) that had a 10% acceptance prevalence. Of all subjects who participated 97% (n=447) had been vaccinated and 32.1% (n=148) had received the booster dose. Knowledge was significant at a p-value <0.01 for acceptance of a booster dose. One of the significant perceived barriers and concerns regarding the Covid-19 booster dose according to chi square test results, was being too busy to get the booster dose. Conclusion: Our research has findings which indicate a relatively large percentage of respondents accepting COVID-19 booster vaccination. More efforts are needed to help people register and educate people about the long-term risks.

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