Colors influence our daily perceptions and expectations that manifest in a variety of ways. This research has three main objectives: to demonstrate the relationship between the colors of pills and their expected efficacies, to test this effect on a wide variety of demographics, thereby demonstrating their influence on choices made by participants. Finally, to understand the reasoning behind the choices made by participants, and the color associations exhibited. The results of a series of surveys showed clear similarities and differences across various demographics. The strongest and most consistent color associations were those of white with pain relief and red with stimulant efficacies. The color associations found were red with aggression and power, blue with calmness and serenity, white with calm and purity, yellow with energy, and green with environment and health. The findings of this study can help pharmaceutical companies, and medical practitioners, to better make, market, and prescribe pills, depending on the geographical location, ethnicity, and age group of the patient. This may also strengthen the perceived effects of the pills on patients overall by increasing their compliance rates.
Medication should be consumed as prescribed with little to zero margins for errors, otherwise consequences could be fatal. Due to the pervasiveness of camera-equipped mobile devices, patients and practitioners can easily take photos of unidentified pills to avert erroneous prescriptions or consumption. This area of research goes under the umbrella of information retrieval and, more specifically, image retrieval or recognition. Several studies have been conducted in the area of image retrieval in order to propose accurate models, i.e., accurately matching an input image with stored ones. Recently, neural networks have been shown to be effective in identifying digital images. This study aims to provide an enhancement to image retrieval in terms of accuracy and efficiency through image segmentation and classification. This paper suggests three neural network (CNN) architectures: two models that are hybrid networks paired with a classification method (CNN+SVM and CNN+kNN) and one ResNet-50 network. We perform various preprocessing steps by using several detection techniques on the selected dataset. We conduct extensive experiments using a real-life dataset obtained from the National Library of Medicine database. The results demonstrate that our proposed model is capable of deriving an accuracy of 90.8%. We also provide a comparison of the above-mentioned three models with some existing methods, and we notice that our proposed CNN+kNN architecture improved the pill image retrieval accuracy by 10% compared to existing models.
The physical appearance of pills, especially their color, affect expectations of their efficacy in addition to their intended medicinal effect. The purpose of this study is to understand the effects pill colors have on a wide variety of people, testing the hypotheses that the color of the pill affects participants' expectation of the drug's efficacy, and that demographics play a role in the participant's judgement. The 339 participants were recruited from the Rochester Institute of Technology global campuses in Rochester, USA, and Dubai, UAE. The study did not involve individuals taking drugs. Instead, it measured how people perceived the expected effects of pills on six efficacies, by asking the participants to rate the expected efficacy of five colors of pills. While there were some clear patterns and similarities in the results, differences were also apparent across the various demographics considered in this study. It is evident that three out of the six efficacies tested had a strong association with a particular color: stimulant efficacy with red, pain relief with white, and antacid with yellow.Remaining efficacies had weaker associations or varied associations based on different demographics. The patterns that emerged will help pharmaceutical companies, as well as medical practitioners, to better manufacture and prescribe drugs, thus maximizing the effects of the pills on patients overall, and increasing their compliance rates.
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