Internet of things (IoT) has revolutionized how we utilize technology over the past decade. IoT’s rapid growth affects several fields, including the healthcare sector. As a result, the concept of smart healthcare or electronic healthcare (e-healthcare) has emerged. Smart healthcare promises to enhance people’s lives and wellbeing by monitoring them, offering an efficient connection, improving mobility, gathering medical data, and decreasing hospital and patient costs. IoT in healthcare is still one of the hot and trendy topics that needs in-depth investigation. No recent review has been conducted to elucidate the extent of research in the area, features of published papers, motives, and challenges in enabling IoT in healthcare systems. This study presents a comprehensive systematic review of the screened articles published between 2015 and 2022 pertaining to enabling IoT in healthcare services and applications. A total of 106 papers fulfilled the final inclusion criteria and were analyzed using systematic literature review (SLR). Two procedures were used to review the final articles: First, publications are examined in terms of study designs, publishing journals, and topics/study objectives. In the second approach, motives, challenges, and recommendations for enabling IoT in healthcare systems are explored. This article summarizes published studies on IoT in healthcare systems and its usage in smart healthcare service delivery. Based on the reviewed studies, recommendations for future research to enable the effective application of IoT in healthcare and service delivery are proposed.

The spread of the novel coronavirus COVID-19 resulted in unprecedented worldwide countermeasures such as lockdowns and suspensions of all retail, recreational, and religious activities for the majority of 2020. Nonetheless, no adequate scientific data have been provided thus far about the impact of COVID-19 on driving behavior and road safety, especially in Malaysia. This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. This was accomplished by comparing the driving behaviors of the same drivers in three periods: before COVID-19 lockdown, during COVID-19 lockdown, and after COVID-19 lockdown. Thirty people were previously recruited in 2019 to drive an instrumental vehicle on a 25 km route while recording their driving data such as speed, acceleration, deceleration, distance to vehicle ahead, and steering. The data acquisition system incorporated various sensors such as an OBDII reader, a lidar, two ultrasonic sensors, an IMU, and a GPS. The same individuals were contacted again in 2020 to drive the same vehicle on the same route in order to capture their driving behavior during the COVID-19 lockdown. Participants were approached once again in 2022 to repeat the procedure in order to capture their driving behavior after the COVID-19 lockdown. Such valuable and trustworthy data enable the assessment of changes in driving behavior throughout the three time periods. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers in particular being most affected by the traffic restrictions, driving significantly faster and performing more aggressive steering behaviors during the COVID-19 lockdown than any other time. Furthermore, the locations where the most speeding offenses were committed are highlighted in order to provide lawmakers with guidance on how to improve traffic safety in those areas, in addition to various recommendations on how to manage traffic during future lockdowns.

The Internet of Things (IoT) in healthcare is the newest trend in the healthcare market. IoT allows healthcare providers to expand their reach beyond the usual clinical environment. They are driven to maximize the possibilities of digitally linked healthcare services to improve the user experience, diagnostic accuracy, and communication among healthcare professionals. Sensors, wearables, and health monitors have made healthcare cheaper, faster, and more effective. Despite the privileges of the IoT in healthcare services, the adoption rate of these services is still in the early stages. The aim of this research was to examine the adoption of IoT-enabled healthcare services among Malaysian chronic patients. To achieve this purpose, the study offered an integrated framework to investigate the influence of the identified factors on Behavioural Intention (BI) to adopt IoT healthcare services. The novelty lies in combining the Unified Theory of Acceptance and Use of Technology (UTAUT), the Theory of Organizational Environments for Technology Adoption (TOE), and the Social Exchange Theory (SE). Patients in Malaysia dealing with chronic illnesses were the subjects of an online survey. Eleven predicted predictive constructs' impacts were investigated using partial least square structural equation modeling. The findings revealed that individual and technological factors and their dimensions, significantly affected chronic disease patients’ BI toward IoT-healthcare services adoption. Similar results were observed for the effect of BI on Use Behaviour (UB). Meanwhile, trust partially mediated the effect of individual and technological-related factors on BI.