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Introduction: Globally, hypertension is considered a public health problem affecting over 1.3 billion individuals of which 60% need reminders to take medication to achieve adherence, optimal blood pressure and avert complications. Literature shows electronic short message reminders (eSMS) improved medication adherence., Objective: To determine the effect of e-SMS reminders on medication adherence among hypertensive patients in Katsina State., Methods: A quasi-experimental study was employed to determine the effect of e-SMS reminders on medication adherence among 252 control and 252 intervention groups selected using a multistage sampling technique. e-SMS reminders were sent to the intervention group only. Data was collected using a pretested interviewer-administered questionnaire, and analysed using IBM-SPSS-25, while medication adherence was assessed using the 8-item Modified Morisky Adherence Scale., Results: The commonest respondents' age group was 40-49 years (23.8%) and Male-Female ratio was 0.8:1 Majority were Hausas (97.6%) by tribe and Muslims (98%). Adherence in the study group was 59.5% and 75.9% (P<0.001) that of the control group was 63.1% and 66% at pre and post-intervention stages respectively. Factors associated with adherence in the intervention group were sex, occupation, insurance coverage, and being in the 4th wealth quintile. e-SMS reminder increased adherence in the intervention groups by >16% (P<0.001) In the control group, males were 3.4 times more likely to adhere to medication than females (aOR 3.4; 95% CI 1.4-8.3) while in the intervention group, business tycoons were 3 times more likely to adhere to medication than other occupations (aOR 3.7; 95% CI 1.006-13.75;)., Conclusion: e-SMS reminders significantly increased adherence to the intervention. Determinants of adherence were occupation, male sex, insurance coverage, and being in the 4th wealth quintile. It is recommended that the State Hospital Management Board integrate e-SMS reminders as part of hypertension treatment protocols due to their potential to improve medication adherence., Competing Interests: The Authors declare that no competing interest exists, (Copyright © 2024 by West African Journal of Medicine.)

Lack of an effective early sign language learning framework for a hard-of-hearing population can have traumatic consequences, causing social isolation and unfair treatment in workplaces. Alphabet and digit detection methods have been the basic framework for early sign language learning but are restricted by performance and accuracy, making it difficult to detect signs in real life. This article proposes an improved sign language detection method for early sign language learners based on the You Only Look Once version 8.0 (YOLOv8) algorithm, referred to as the intelligent sign language detection system (iSDS), which exploits the power of deep learning to detect sign language-distinct features. The iSDS method could overcome the false positive rates and improve the accuracy as well as the speed of sign language detection. The proposed iSDS framework for early sign language learners consists of three basic steps: (i) image pixel processing to extract features that are underrepresented in the frame, (ii) inter-dependence pixel-based feature extraction using YOLOv8, (iii) web-based signer independence validation. The proposed iSDS enables faster response times and reduces misinterpretation and inference delay time. The iSDS achieved state-of-the-art performance of over 97% for precision, recall, and F1-score with the best mAP of 87%. The proposed iSDS method has several potential applications, including continuous sign language detection systems and intelligent web-based sign recognition systems., Competing Interests: The authors declare there are no competing interests., (©2024 Alamri et al.)