Usage Patterns Research Articles

A dynamic electric fence planning framework for dockless bike-sharing systems based on inventory prediction

While dockless bike-sharing systems are growing in popularity due to their convenience, indiscriminate parking creates disorder in cities. To address this problem, this paper proposes a novel prediction-based approach for planning dynamic electric fences. These fences can adapt to dynamic usage patterns and efficiently guide parking behavior. First, graded clustering and spatial point integration algorithms are used to determine the locations of electric fence candidates. Second, initial parking and scheduling simulations are developed to calculate the inventory of each candidate. Finally, a spatiotemporal graph neural network is utilized to predict inventory and generate real-time deployment plans for electric fences. Case studies are conducted in two regions in Shanghai, China. Compared to a non-electric fence scenario, extensive experiments show that our framework can satisfy 98.6% of the parking demand and reduce spatial entropy by 15% within a reasonable walking distance. The results contribute to improving urban orderliness and promoting the sustainability of bike-sharing systems.

Scaled codon usage similarity index: A comprehensive resource for crop plants

Over the past three decades species-specific codon usage bias has been used to optimize heterologous gene expression in the target host. However, synthesizing codon optimized gene for multiple species is not achievable due to the prohibitive expense of DNA synthesis. To address this challenge, grouping species with similar codon usage can reduce the need for species-specific codon optimised gene synthesis. We introduced Scaled Codon Usage Similarity (SCUS) index to standardize species similarity assessments based on codon usage profiles. By analysing the SCUS index of 77 plant nuclear genomes from 13 families, we identified codon usage patterns and similarities. We developed an online SCUS index database and a Consensus Relative Synonymous Codon Usage (CRSCU) calculator, available at https://pcud.plantcodon.info. The CRSCU calculator helps determine the most suitable codon usage pattern among two or more species. The SCUS index and CRSCU calculator will facilitate the development of multi-species expression systems, enabling the efficient expression of a single synthetic gene across various crop species. This innovation paves the way for cost-effective and efficient heterologous gene expression across diverse crop species

Revolutionizing library systems with advanced automation: A blueprint for efficiency in academic resource management

The advent of advanced automation technologies has the potential to revolutionize library systems, particularly within academic institutions, where effective resource management is essential. This paper outlines a comprehensive blueprint for integrating automation into library operations to enhance efficiency, accessibility, and user engagement. Key technologies discussed include Radio Frequency Identification (RFID), Artificial Intelligence (AI), and Machine Learning (ML), which streamline processes such as cataloging, circulation, and inventory management. The implementation of RFID facilitates self-service checkouts and real-time inventory tracking, significantly reducing the time spent on manual tasks, thereby allowing library staff to concentrate on enhancing user experiences and curating resources strategically. Furthermore, AI-driven chatbots provide personalized recommendations, improving user satisfaction and ensuring that patrons receive tailored support. This blueprint emphasizes the role of automated digital repositories and intelligent content indexing systems, which accelerate cataloging and ensure timely access to scholarly materials. By automating information retrieval processes, libraries can reduce wait times and improve the overall accessibility of resources. Data analytics are also pivotal, enabling libraries to monitor usage patterns, optimize resource allocation, and forecast future demands, ensuring alignment with academic priorities. Moreover, the paper addresses challenges related to automation implementation, including financial constraints, data privacy concerns, and staff training requirements, proposing solutions such as gradual implementation and interdepartmental collaboration. Ultimately, the transition to advanced automation not only enhances operational efficiency but also boosts academic productivity by making resources more accessible and user-centered. This strategic move toward a digital-first library environment signifies a profound transformation in academic libraries, promoting an agile approach to resource management. As institutions increasingly adopt these technologies, they position themselves at the forefront of educational innovation, effectively integrating academic resources into the digital learning landscape.

Active and Passive Screen Time for Children Aged 5–6

The social situation of development largely determines the cognitive and emotional development of a child. The use of digital devices is an important part of the social situation in development of modern preschool children. By the age of 5–6, children start using digital devices independently and in various modes highlighting importance of understanding their usage patterns. A questionnaire was administered to mothers of 663 5–6-year-old children to investigate this topic. It is shown that passive screen time (time spent watching video content) is almost twice as long as active screen (time spent interactively using digital devices, for example, for videogames, communication, learning, creating). It is also shown that screen time on weekends is twice as much as on weekdays. Weekend screen time reaches three hours exceeds age norms by one hour, indicating insufficient engagement in physical activities, non-digital games, and interpersonal communication. There was no difference in screen time between boys and girls. Screen time is shown to be inversely correlated with maternal education level. It is found that less than 30% of children engage in screen time with their parents.

How individuals apply and perceive sunscreen: Empirical evidence from Germany.

This study explores how individuals in Germany use and perceive sunscreen, aiming to identify the factors that influence their sunscreen choices. The research focuses on key elements such as convenience, environmental awareness, openness to new technologies, and preferences for different sunscreen products. The findings reveal that the majority of participants prioritize convenience when selecting sunscreen, often choosing products that are easy to apply and carry. Detailed information about the sunscreen's ingredients or health benefits tends to be less important in their decision-making process. Notably, many participants did not consider important factors such as the environmental impact of sunscreen or its effectiveness after reapplication, suggesting a gap in awareness regarding these issues. Additionally, the study investigates the relationship between demographic factors, including age and gender, and sunscreen usage patterns. It was found that these demographic variables do play a role in shaping individuals' attitudes and behaviors regarding sunscreen. For instance, younger participants showed more openness to new technologies and innovative products, while older individuals tended to rely on traditional options. Overall, this research provides valuable insights into how people perceive and use sunscreen in Germany, highlighting the need for further studies to delve deeper into these factors. Understanding these perceptions is crucial for developing effective public health strategies and enhancing product design to better meet the needs and preferences of consumers.

Artificial intelligence for higher education: benefits and challenges for pre-service teachers

IntroductionThe study investigates the integration of artificial intelligence (AI) in higher education (HE) and its impact on pre-service teachers at the University of Latvia (UL) by exploring pre-service teachers' perceptions of the benefits and challenges of AI in both their academic learning and their future professional roles as educators, particularly regarding the promotion of inclusive education.MethodsData was collected via an online survey of 240 pre-service teachers across various disciplines at the UL. The survey included demographic details, AI usage patterns, and perceived benefits and challenges. Responses were analyzed using descriptive statistics, Kruskal-Wallis H tests, Spearman's correlation, and thematic analysis.ResultsLess than half of the participants used AI in their studies, with many expressing ambivalence or opposition toward AI. Benefits included language assistance and accessibility to global knowledge, while challenges involved reduced critical thinking and concerns over plagiarism. Despite recognizing AI's potential to promote inclusivity, most pre-service teachers have not applied it in practice. No significant differences in AI perceptions were found based on age, gender, or study level.DiscussionThe findings highlight a low adoption rate of AI among pre-service teachers and a gap between theoretical recognition of AI's potential and its practical application, particularly for inclusion. The study emphasizes the need for HE institutions to enhance AI literacy and readiness among future teachers.ConclusionAI is underutilized by pre-service teachers in both HE learning and teaching environments, which has implications for teacher preparation programs that better integrate AI literacy and inclusive practices.

Exploring Adolescents’ Social Media Connection and Disconnection: A Latent Class Approach

In industrialized societies characterized by ubiquitous connectivity, many individuals disconnect from their phones or social media to break patterns of habitual use, reduce information overload, alleviate stress, and avoid distractions. Although research has predominantly focused on (young) adults, information on digital disconnection among adolescents remains limited. In response, in the present study, we analyzed survey data from 956 Flemish adolescents in Belgium (<em>M</em><sub>age</sub><em> </em>= 15.10, <em>SD </em>= 1.61, and 64.8% girls) and, using latent class analysis, identified two subgroups: Class 1 labeled as “low adoption of social media apps and disconnection tools,” and Class 2 labeled as “high adoption of social media apps and disconnection tools.” Adolescents in Class 2 were more likely to use social media, specifically social networking, instant messaging, and entertainment apps, and to adopt disconnection tools (e.g., iOS Screentime or the Forest app). Next, drawing on the media practice model, we investigated how sociodemographic and sociopsychological factors contribute to those usage patterns and found that girls, older adolescents, and adolescents with higher scores for depressive symptoms and flourishing were more likely to belong to Class 2. Those findings offer new insights into adolescents’ social media connection and disconnection that can inform interventions to promote healthier smartphone use among adolescents.

Association between the heaviness of smoking index (HSI) and quit attempts and quit intentions among cigarette smokers in eight African countries

IntroductionThe increasing smoking population in Africa necessitates intensified tobacco cessation services. Assessing nicotine dependence with tools such as the heaviness of smoking index (HSI) aids in cessation efforts. This study evaluated the association between HSI and quit attempts and quit intentions among African cigarette smokers.MethodsGlobal Adult Tobacco Survey (GATS) data from eight African countries collected between 2011 and 2018 were analysed. The time to first smoke (TTFS) and number of cigarettes per day (CPD) were used to estimate the HSI score and dependence level. The association between HSI dependence level and quit attempts and quit intentions was evaluated using likelihood ratios (LR).ResultsAmong 2,399 daily cigarette users, 1,618 (67.5%) were exclusive cigarette users. The average and median CPDs were 8.27 ± 8.3 and 5 (IQR: 2–18), respectively, with Ethiopia having the highest mean CPD (11.6). In Ethiopia, 51.5% of exclusive and 42% of nonexclusive users had low dependence (HSI < 2), whereas 43.9% and 31.3% of other African countries had low dependence. The association between low dependence and quit attempts was significant only for exclusive cigarette smokers in Ethiopia, with LR of 1.45 (95% CI: 1.08–1.93). The association between low dependence and quit intentions was significant in Ethiopia for both exclusive (LR = 1.42) and nonexclusive (LR = 1.53) users. None of the LR estimates were > 10 or < 0.1.ConclusionThe association between low dependence (HSI < 2) and quit behaviour was limited in African settings, highlighting the complex interplay between usage patterns, dependence assessment, and quit behaviour. A contextual tool for assessing nicotine dependence tailored to African settings is warranted for effective smoking cessation.

GOOGLE WORKSPACE IN ACADEMIC HEALTH INSTITUTIONS: USAGE PROFILE

Introduction: This study investigates the use of Google Workspace in an academic institution in the healthcare sector, highlighting the role of digital technologies in supporting remote teaching during the COVID-19 pandemic. Tools such as Google Classroom, Meet, and Drive were essential for the continuity of learning. Methodology: This descriptive quantitative research analyzed data on the use of Google Workspace between 2021 and 2023. With a sample of 250 participants, including teachers and students, the data were anonymized and processed with Python to identify usage patterns, categorized by device type and geographic location. Results and Discussion: The results showed that 55.4% of the documents in Google Drive were shared, evidencing a collaborative culture. Google Meet stood out in synchronous classes, especially on mobile devices, with longer meetings on computers, suggesting stability and comfort in extended sessions. Documents in Google Docs and PDF formats were the most used, reflecting the accessibility of native formats. Activities were concentrated on weekdays, with peak usage on Mondays. These data reveal that, although technology offers important support for remote learning, its effective use requires ongoing training and support. Conclusion: The study demonstrates the positive impact of Google Workspace on health education, but highlights that the effectiveness of technology depends on well-planned integration and regular training. Digital technology complements teaching, but does not replace the need for hybrid and flexible pedagogical methods that integrate in-person and remote learning, facilitating interaction and accessibility in higher education.

Evolution of Linguistic Markers of Agency, Centrality and Content During Metacognitive Therapy for Psychosis: A Pilot Exploratory Study.

Metacognitive Reflection and Insight Therapy (MERIT) is a form of person-centred psychotherapy that promotes recovery-oriented outcomes by targeting metacognitive capacity. Previous research has shown the feasibility and clinical benefits of MERIT. However, it is not clear whether and how the specific processes targeted by MERIT (e.g., improved sense of agency, self-reflectivity, awareness of others, etc.) are manifested in the patient-therapist communications. In this study, we used natural language processing (NLP) to explore objective linguistic markers of change during MERIT for early psychosis. Five patients underwent 24 sessions each of MERIT, which were recorded and transcribed verbatim. The transcripts underwent NLP to determine the patterns of pronoun use and the frequency of semantic categories related to temporality and mental processing. Mixed linear regressions were used to examine how NLP features evolve over the course of psychotherapeutic sessions. Analyses detected shifts in specific language signals over time including (1) changes in patterns of pronoun usage with more active and central first-person plural pronoun (We); (2) transition in temporal focus of speech from past-focus towards present- and future-focus and (3) increased words representing perceptual and cognitive processes. Our findings suggest NLP can objectively quantify meaningful signals consistent with expected subjective changes in MERIT and potentially other psychotherapeutic interventions.

Prospective Drug Utilization Evaluation Analysis in Outpatient Departments with Ear, Nose, and Throat Outpatients: Incorporating Benchmarks and World Health Organization Indicators.

Drug utilization studies are tools for determining the effectiveness of drug use. The aim of the study was to evaluate drug usage patterns in ear, nose, and throat (ENT) outpatient settings by incorporating established benchmarks and World Health Organization (WHO) indicators. A cross-sectional study on drug utilization evaluation (DUE) was conducted on 800 patients from the ENT outpatient department. We gathered data on currently prescribed medications and identified any discrepancies with a thorough analysis. Continuing educational activities such as "dear doctor" letters and personal consultations were used to rectify any irrational prescribing patterns among physicians. The WHO/International Network for Rational Use of Drugs core drug use indicators, specifically prescribing and patient care indicators, and established benchmarks were applied to encourage rational prescribing. The three most common diagnoses were pharyngitis (51.49%), allergic rhinitis (25.11%), and acute suppurative otitis media (21.17%). Montelukast, in combination with levocetirizine (13.77%) and amoxicillin in combination with clavulanic acid (8.81%), was the most frequently prescribed medication. The average number of drugs per prescription was 4.2% (±1.1), with low usage of generic names (33.0%) and suboptimal reliance on the essential drugs list (76.7%). Furthermore, patient care indicators demonstrated room for improvement, particularly concerning consultation times (6 minutes), dispensing times (30 seconds), and drug labeling practices (0% labeled). The DUE improved the prescribing rate of first-line drugs for five diseases and few aspects of prescribing and patient care indicators. There is an overuse of prescribed drugs, a need for more utilization of generic names, and less than optimal use of the essential drugs list. Additionally, shortcomings in patient care were observed, including issues in consultation, drug dispensing times, and labeling. However, DUE effectively improved WHO patient care metrics and the prescription of first-line drugs warranting its implementation.

Investigating the Use of Machine Learning in Automating Credit Scoring for Microfinance

Machine learning (ML) has emerged as a transformative tool in automating credit scoring, particularly in the domain of microfinance, where access to credit often hinges on unconventional data and rapid decision-making. This research investigates the integration of ML algorithms into credit scoring models for microfinance, focusing on their ability to assess creditworthiness using diverse data sources such as transactional histories, social behavior, and mobile usage patterns. By automating the traditionally manual and subjective processes of credit evaluation, ML offers increased accuracy, scalability, and efficiency, enabling financial institutions to serve underserved populations more effectively. The study explores various ML techniques, including decision trees, random forests, and neural networks, and evaluates their performance in predicting default risk. Challenges such as data privacy, algorithmic bias, and the interpretability of ML models are also addressed. Through case studies and experimental validation, this research aims to provide actionable insights into leveraging ML for credit scoring in microfinance, highlighting its potential to improve financial inclusion and operational efficiency.

Impact of encounters on patient app use: results of a tethered mobile personal health record usage pattern analysis.

The adoption of tethered mobile personal health records provides not only medical information to patients but also various convenience functions related to hospital use, thereby increasing accessibility to healthcare services and promoting patient engagement. We analyse the tethered mobile personal health records app's usage logs to determine how it can contribute to improving medical service accessibility and patient engagement. Log data, that comprised menu type, log time, and date, were collected from the mobile personal health records app of a tertiary referral hospital. Clinical information, including patients' demographics and visit type, was collected from the clinical research warehouse system. The usage log was analysed in terms of the type of visit, service function, and time period. Outpatients accounted for 34% of the total app usage and was the most app-accessed visit type. The most utilized menu functions were lab test and visit schedule for visits or non-visits. For Inpatient and Health check-ups, menu usage patterns showed a focus on lab test results. While investigations and other menu usage showed double peaks in the morning and afternoon, peak usage of lab test results correlated with inpatient blood sampling times, which was around 9 am. App menus to access health information, particularly blood tests, emerged as the most accessed menu. Hence, when compared with blood sampling times and hospital information system usage patterns, encounters occurring in the hospital majorly impacted patient app use. For improved patient engagement, improving lab test function should be the priority.

Trends and Determinants of Palliative Care Utilization Among Patients With Metastatic Upper Tract Urothelial Carcinoma in the National Cancer Database

Objective To identify patterns of palliative care usage and identify determinants of palliative intervention using a large contemporary cohort of patients with metastatic upper tract urothelial carcinoma (mUTUC). Methods: The National Cancer Database was queried from 2004 to 2020 for patients with mUTUC. Patients with a prior malignancy, non-mUTUC, and missing follow up or palliative care information were excluded. Demographics and baseline characteristics were compared between patients with mUTUC who received palliative care and those that did not. Trends in annual palliative care usage were assessed via logistic regression. Univariate and multivariate logistic regression models were used to identify predictors of receipt of palliative care. Results: Four thousand and forty-four patients with mUTUC were included in the final cohort, among which 908 received palliative care (22.5%) and 3136 did not (77.5%). We found that the utilization of palliative care increased significantly from 2004 (15.0%) to 2019 (23.1%), P &lt; .001. Additionally, on multivariate analysis we found that a recent year of diagnosis, receipt of a prior nonsurgical treatment paradigm, and an overall survival &lt;6 months were independent predictors of palliative intervention, all P &lt; .001. On the other hand, undergoing treatment at a minority serving hospital and older age were associated with lower likelihood of receiving palliative care, both P &lt; .001. Conclusions: There is a low but increasing trend of utilization of palliative care among patients with mUTUC. Expansion of palliative care services, particularly among older patients and those at minority-serving hospitals, remains a key opportunity to improve quality of life and enhance patient-centered care among those with mUTUC.

Smartphone use patterns and the impact on accommodation and convergence system of the eyes among Malaysian teenagers.

To determine the smartphone use patterns and effects of smartphone use on accommodation and convergence system of the eyes among Malaysian teenagers. A total of 62 participants aged between 13 and 17y were involved. A self-administered questionnaires containing 12 items was used to evaluate the smartphone usage patterns. This was followed by an eye examination, involving a battery of accommodation and convergence assessments before and after the smartphone use. The data analysis comprised descriptive statistics, paired t-test, and correlation coefficients. The use of smartphones is at a high level and at an optimal distance daily, with more than 6h a day watching video films, games, and completing school projects. Majority of the participants not reported eye strain factors and eye prescription changes with the use of digital devices. The use of a smartphone continuously for 30min was found to significantly decrease amplitude of accommodation, accommodative facility, and positive relative accommodation (P<0.001). Meanwhile, the lag of accommodation parameters and negative relative accommodation increased with the use of smartphones significantly (P<0.001). The near point of convergence (NPC) and distance and near negative fusional vergence decreased significantly (P<0.001). The NPC parameter was found to have a weak negative association with the frequency of smartphone use (R=-0.276, P<0.05). Frequent and continuous use of smartphones have increased visual stress and resulted in weakness of accommodation and vergence functions. Therefore, frequent break is mandatory when using a smartphone and appropriate visual hygiene, the 20-20-20 rule (every 20min, view something 20 feet away for 20s) are required during smartphone use to maintain visual function.

Deciphering Codon Usage Patterns in the Mitochondrial Genome of the Oryza Species

Rice (Oryza) is a genus in the Gramineae family, which has grown widely all over the world and is a staple food source for people’s survival. The genetic information of rice has garnered significant attention in recent years, prompting numerous researchers to conduct extensive investigations in this field. But rice mitochondrial codon usage patterns have received little attention. The present study systematically analyzed the codon usage patterns and sources of variance in the mitochondrial genome sequences of five rice species by the CodonW and R software programs. Our results revealed that the GC content of codons in rice mitochondrial genome genes was determined to be 43.60%. Notably, the individual codon positions exhibited distinct GC contents: 48.00% for position 1, 42.65% for position 2, and 40.16% for position 3. These findings suggest the preference of the rice mitochondrial genome for codons ending in A or U. A weak codon bias was observed, with the effective number of codons (ENC) varying between 40.02 and 61.00, with an average value of 54.34. Subsequently, we identified 25 identical high-frequency codons in five rice mitochondrial genomes, with 11 codons ending in A and 12 codons ending in U. The regression lines in the neutrality plot exhibited slopes of less than 0.5 in five rice species, indicating a predominant role of natural selection, while mutation pressure remained relatively insignificant. In the PR2-plot analysis, most of the genes were located in the right half of the plot, indicating that the third base of the synonymous codon was preferred to end in G than C. Additionally, the ENC plot and ENC ratio analysis unveiled that codon preferences in the rice mitochondrial genome were predominantly influenced by natural selection rather than mutational pressure. The analysis of correspondence revealed distinct variations in the codon usage pattern across five rice mitochondrial genomes. Based on the RSCU values of species, a cluster tree was inconsistent with the mitochondrial genetic data, indicating that RSCU data could not be used as a basis for classification at the species level in the Oryza genus. These results will help decide the specific types of natural selection pressures influencing codon usage and improve the expression of exogenous genes in rice mitochondrial genomes by optimizing their codons.

Exploring potential car trips for long-distance school escorting using smart card data and a household travel survey

Encouraging students to commute by the metro can effectively reduce household car use caused by long-distance commuting to school. This article focuses on the frequency of metro use by groups of students commuting to school based on the assumption that students who use the metro may occasionally be driven to school by their parents. For the 1st time, we propose a school metro commuter identification process that considers the potential behaviour of escorted students, and we study the potential car trips for long-distance school escorting in Nanjing (China) using Smart Card Data (SCD) and a household travel survey from Nanjing. 3 clusters of students who use the metro for commutes to school are identified by frequency of use for possible escorting behaviour based on the commuting day. As possible factors influencing the 3 frequency groups, usage pattern of the metro, entry time, travel duration and the school–housing relationship are extracted from SCD. Furthermore, a multinomial logistic regression model is used to examine the significant factors that influence the grouping of students. The results show that students who use the metro occasionally for a long commuting distance to school are more likely to be escorted to and from school by their parents, especially to school. The later the entry time is to the metro, the more likely that students are to be escorted to school. Additionally, a long school–housing travel duration/distance significantly contributes to parents’ car trips for commuting. The results of this article are valuable for transport policy to reduce car use for long-distance school trips.

Exploring the impact of device ownership, usage patterns, and technology proficiency on mobile learning among higher education students: a multi-faceted study

This study explores the impact of device ownership, frequency of use, and technology proficiency on integrating mobile technologies (IMT) in learning by students in higher education institutions (HEIs) in Ghana. The objective is to understand how these factors are influenced by demographic variables such as age, gender, and program of study. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 457 students selected with a disproportionate stratified sampling technique across multiple HEIs offering STEM programs to analyse these relationships. The results demonstrated that technology proficiency significantly enhances the integration of mobile technologies into learning. It was noted that device ownership alone was insufficient to improve technology proficiency; frequent and effective usage was more essential. Additionally, gender and program of study moderated the effects of device ownership and frequency of use on technology proficiency. These findings highlight the importance of promoting effective usage patterns and advancing technology proficiency to integrate mobile technologies in educational settings better. It is recommended that HEIs focus on providing training programs to staff to integrate more learning activities that promote frequent use of mobile technologies in academic activities to enhance students' technology proficiency.

Understanding the Function of Past Participles in Complex Sentences

This article delves into the intricacies of past participles and their function in complex sentences within the English language. It aims to provide a comprehensive understanding of how past participles are used to create nuance and depth in both written and spoken English. The discussion begins with an examination of the formation and structure of past participles, distinguishing between regular and irregular verbs and their respective participle forms. The article then explores the critical role of past participles in forming perfect tenses, passive voice constructions, and participle clauses, illustrating each function with detailed examples and practical exercises. In addition, common mistakes and misconceptions are addressed, providing clear explanations to help learners differentiate between past participles and past tense forms. Effective teaching strategies are outlined, offering educators practical methods and classroom activities to reinforce learning. The conclusion emphasizes the importance of mastering past participles for achieving advanced language proficiency and encourages further study and practice. By exploring various sentence structures, examples, and common usage patterns, this article offers valuable insights for linguists, educators, and advanced English learners.

Contact lens adoption and associated knowledge among the general population of South Gujarat, India

: Contact lenses have emerged as a popular alternative to spectacles, offering users enhanced vision correction, cosmetic appeal, and improved quality of life. However, awareness of contact lens care, hygiene, and complications remains a concern, particularly in regions with limited access to proper education on lens use. This study aimed to assess the level of knowledge, awareness, and practices related to contact lens use among the general population of South Gujarat, India.: A cross-sectional study was conducted using a structured online questionnaire administered to 1,500 participants. The questionnaire covered demographics, awareness of contact lens types, usage patterns, hygiene practices, and knowledge of potential complications. Data were analyzed using SPSS software, with chi-square tests used to determine statistical significance.: The study revealed that 60% of participants had some awareness of contact lenses, but only 30% demonstrated "extreme" awareness. Most participants used contact lenses for vision correction (65%), followed by cosmetics (30%). Adherence to recommended hygiene practices was variable, with only 56% consistently using contact lens solutions. Furthermore, 21% of users experienced discomfort while wearing lenses, and the dropout rate within the first six months was 28%.: There are significant gaps in knowledge and adherence to contact lens care, especially among younger users. Educational interventions focusing on hygiene practices, replacement schedules, and proper lens care are crucial to reducing complications and improving user experience.