A Systematic Review on Pill and Medication Dispensers from a Human-Centered Perspective

Introduction: Clinical trials are fundamental to advancing all areas of medicine. Despite their importance, trials are often expensive and time-consuming due to the need for extensive human resources, with limitations in cohort sizes and potential biases from loss of follow-up. Smart pill bottles (SPBs) offer a promising innovation by automating data collection, which could reduce costs and improve the granularity and accuracy of data. This technology may provide a more efficient alternative to traditional methods, streamlining data acquisition in clinical research. Objectives: This proof-of-concept study aims to assess the feasibility of using smart pill bottles (SPBs) to collect data on opioid consumption in a postoperative setting, comparing their cost-efficiency and data quality to traditional methods. We hypothesize that SPBs will be readily adopted by users and enable the collection of highly granular data with fewer missing data points, while reducing the costs associated with human resource-based data collection. Material and Methods: This single-center, single-arm trial will enroll 69 patients aged 18 and above undergoing major abdominal surgery via laparotomy. Following recruitment, patients will complete web-based questionnaires assessing pain, comorbidities, and quality of life. Postoperatively, patients will receive an SPB, the Thess Therapy Smart System manufactured by Thess Corporate (France) and provided by AppMed Inc. (Canada) to monitor opioid consumption at home for up to 90 days. At the end of the study period, participants will use the web-based platform to complete the same questionnaires, an opioid compliance checklist and a product satisfaction survey. The primary outcome will be the percentage of patients who successfully use the SPB throughout the study period. Secondary outcomes will include the extent of automated data collection, data granularity, project costs, the incidence of persistent opioid consumption, and patient satisfaction with the SPB. Trial registration: clinicaltrials.gov (July 25th, 2024). Unique protocol ID: 2025-3801. NCT number:NCT06522698.

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Sea Horse Optimization–Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition

Amirthalingam,

Alatawi,

Chellamani

et al. 2024

Sensors

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Medication adherence is an essential aspect of healthcare for patients and is important for achieving medical objectives. However, the lack of standard techniques for measuring adherence is a global concern, making it challenging to accurately monitor and measure patient medication regimens. The use of sensor technology for medication adherence monitoring has received much attention lately since it makes it possible to continuously observe patients’ medication adherence behavior. Sensor devices or smart wearables utilize state-of-the-art machine learning (ML) methods to analyze intricate data patterns and provide predictions accurately. The key aim of this work is to develop a sensor-based hand gesture recognition model to predict medication activities. In this research, a smart sensor device-based hand gesture prediction model is developed to recognize medication intake activities. The device includes a tri-axial gyroscope, geometric, and accelerometer sensors to sense and gather data from hand gestures. A smartphone application gathers hand gesture data from the sensor device, which is then stored in the cloud database in a .csv format. These data are collected, processed, and classified to recognize the medication intake activity using the proposed novel neural network model called Sea Horse Optimization–Deep Neural Network (SHO-DNN). The SHO technique is implemented to update the biases and weights and the number of hidden layers in the DNN model. By updating these parameters, the DNN model is improved in classifying the samples of hand gestures to identify the medication activities. The research model demonstrates impressive performance, with an accuracy of 98.59%, sensitivity of 97.82%, precision of 98.69%, and an F1 score of 98.48%. Hence, the proposed model outperformed the most available models in all the aforementioned aspects. The results indicate that this model is a promising approach for medication adherence monitoring in healthcare applications, instilling confidence in its effectiveness.

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A Systematic Review on Pill and Medication Dispensers from a Human-Centered Perspective

Cited by 4 publications

References 68 publications

Home Pill Dispenser Operating Technologies: Automation Mechanisms Comparison

Home Pill Dispenser Operating Technologies: Automation Mechanisms Comparison

Optimizing Research Data Acquisition with Smart Pill Bottles (SPBs), the ORDAS Trial: A Feasibility and Implementation Study Protocol

Sea Horse Optimization–Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition

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