Cancer patients assigned for abdominal surgery are often given exercise programmes (prehabilitation) prior to surgery, which aim to improve fitness in order to reduce pre-operative risk. However, only a small proportion of patients are able to partake in supervised hospital-based prehabilitation because of inaccessibility and a lack of resources, which often makes it difficult for health professionals to accurately monitor and provide feedback on exercise and activity levels. The development of a simple tool to detect the type and intensity of physical activity undertaken outside the hospital setting would be beneficial to both patients and clinicians. This paper aims to describe the key exercises of a prehabilitation programme and to determine whether the types and intensity of various prehabilitation exercises could be accurately identified using Fourier analysis of 3D accelerometer sensor data. A wearable sensor with an inbuilt 3D accelerometer was placed on both the ankle and wrist of five volunteer participants during nine prehabilitation exercises which were performed at low to high intensity. Here, the 3D accelerometer data are analysed using fast Fourier analysis, where the dominant frequency and amplitude components are extracted for each activity performed at low, moderate, and high intensity. The findings indicate that the 3D accelerometer located at the ankle is suitable for detecting activities such as cycling and rowing at low, moderate, and high exercise intensities. However, there is some overlap in the frequency and acceleration amplitude components for overland and treadmill walking at a moderate intensity.
Physical fitness and level of activity are considered important factors for patients with cancer undergoing major abdominal surgery. Cancer patients with low fitness capacity are at greater risk of postoperative complications, longer hospital stays, and mortality. One of the main challenges facing both healthcare providers and patients is to improve the patient’s physical fitness within the available short period (four to six weeks) prior to surgery. Supervised and unsupervised physical prehabilitation programs are the most common recommended methods for enhancing postoperative outcomes in patients undergoing abdominal surgery. Due to obstacles such as geographical isolation, many patients have limited access to medical centers and facilities that provide onsite prehabilitation programs. This article presents a review of the literature and the development of a model that can remotely monitor physical activities during the prehabilitation period. The mixed prehabilitation model includes the identification of fundamental parameters of physical activities (type, intensity, frequency, and duration) over time. A mathematical model has been developed to offer a solution for both the healthcare provider and patients. This offers the opportunity for physicians or physiotherapists to monitor patients performing their prescribed physical exercises in real time. The model that has been developed is embedded within the internet of things (IoT) system, which calculates the daily and weekly efforts made by the patients and automatically stores this in a comma-separated values (CSV) file that medical staff can access. In addition, this model allows the patient to compensate for missed prescribed activity by adding additional efforts to meet the prehabilitation requirements. As a result, healthcare staff are provided with feedback on patient engagement in prescribed exercise during the period of the prehabilitation program.
Over the past two decades, there has been a notable and swift advancement in the field of healthcare with regards to the Internet of Things (IoT). This progress has brought forth a substantial prospect for healthcare services to enhance performance, transparency, and cost effectiveness. Internet of Things gateways, such as local computational facilities, mobile devices, or custom miniature computational embedded electronics like the Raspberry Pi (RPi), are crucial in facilitating the required processing and data compression tasks as well as serving as front-end event detectors. Numerous home-based healthcare monitoring systems are currently accessible; however, they have several limitations. This paper examines the role of the Raspberry Pi gateway in the healthcare system, specifically in the context of pre-operative prehabilitation programs (PoPPs). The IoT remote monitoring system employed a Microduino integrated with various supporting boards as a wearable device. Additionally, a Raspberry Pi was utilised as a base station or mobile gateway, while ThingSpeak served as the cloud platform. The monitoring system was developed with the purpose of assisting healthcare personnel in real time, remotely monitoring patients while engaging in one or more of the nine typical physical activities that are often prescribed to individuals participating in a prehabilitation program. Furthermore, an alert notification system was designed to notify the clinician and patient if the values were abnormal (i.e., the patient had not been active for many days). The integration of IOT and Raspberry Pi technology into a pre-operative prehabilitation program yielded a promising outcome with a success rate of 78%. Consequently, this intervention is expected to facilitate the resolution of challenges encountered by healthcare providers and patients, including extended waiting periods and constraints related to staffing and infrastructure.
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