Emerging Technology for Healthy Lifestyle of the Middle-Age and Elderly: A Scoping Review.

Background: Emerging technology research focusing on promoting healthy lifestyles for the middle-aged and elderly is paramount in recent literature. However, limited evidence is available for the middle-aged population. This paper reviews how emerging technologies can help in promoting a healthy lifestyle for the middle-aged and elderly. Methods: A scoping literature review method was employed. Articles were extracted from online databases published within 2010-2021. Overall, 3,152 articles related to the topic were obtained and 2979 articles were archived via different search procedures. Moreover, 173 articles that met the inclusion criteria underwent qualitative synthesize for conclusive inferences. Results: Most studies focused on people aged 60 and up, leaving the middle-aged population under-studied and unprepared to age. Older adults have high technology anxiety and resistance to change. Limited studies are available to support technology-based healthy lifestyle promotion for middle-aged people. The emerging technologies that are useful in promoting healthy lifestyle behavior among middle-aged people include: robotics, virtual reality, wearables, artificial intelligence, smart textiles, as well as centralized health information systems. Conclusion: This review sets as a pace-setter for future research on how emerging technologies can aid in the development of healthy lifestyles for the middle-aged and elderly population, allowing them to live a quality life as they age.

Self-Management Apps for People With Epilepsy: Systematic Analysis.

BACKGROUND: Patients with epilepsy (PWEs) are motivated to manage and cope with their disorder themselves (ie, self-management [SM] is encouraged). Mobile health (mHealth) apps have multiple features that have a huge potential to improve SM of individuals with chronic disorders such as epilepsy. OBJECTIVE: This study aimed to review all freely available apps related to the SM of PWEs and to determine the SM domains covered in these apps. METHODS: We performed a search of apps on Google Play and App Store using the keywords "epilepsy" or "seizures" from May to August 2018. Apps were included if they were free and in English language. We excluded apps with installation-related issues and not related to epilepsy self-management (eSM). RESULTS: A total of 22 eSM apps were identified in our search: 6 of these run only on iOS, 7 only on Android, and 9 run on both operating systems. Of the 11 domains of SM, seizure tracking and seizure response features were covered by most apps (n=22 and n=19, respectively), followed by treatment management (n=17) and medication adherence (n=15). Three apps (Epilepsy Journal, Epilepsy Tool Kit, and EpiDiary) were installed more than 10,000 times, with features focused specifically on a few domains (treatment management, medication adherence, health care communication, and seizure tracking). Two apps (Young Epilepsy and E-Epilepsy Inclusion) covered more than 6 SM domains but both had lower installation rates (5000+ and 100+, respectively). CONCLUSIONS: Both Android and iOS mHealth apps are available to improve SM in epilepsy, but the installation rate of most apps remains low. The SM features of these apps were different from one another, making it difficult to recommend a single app that completely fulfills the needs of PWEs. The common features of the apps evaluated included seizure tracking and seizure response. To improve the efficacy and availability of these apps, we propose the following: (1) involve the stakeholders, such as physicians, pharmacists, and PWEs, during the development of mHealth apps; (2) assess the efficacy and acceptance of the apps objectively by performing a usability analysis; and (3) promote the apps so that they benefit more PWEs.

Prescription drug monitoring programs in the US: A systematic literature review on its strength and weakness.

Prescription Drug Monitoring Program (PDMP) is an electronic database that tracks the prescriptions of controlled drugs with its aims to combat the incidence of drug abuse. Although the establishment of PDMP in the US was since 2003, evidence of the impact of PDMP's strength and weakness towards its implementation is still scarce. A systematic literature review according to Preferred Reporting Items for Systematic Review (PRISMA) standard was conducted to investigate the influence of PDMP's strength in combating the incidence of drug abuse and also to review the weaknesses of PDMP that prohibit its implementation. Results from this study reveal that the implementation of PDMP has mitigated the issue of drug abuse and has increased work efficiency among healthcare practitioners. However, the implementation rate of this system is low due to its weaknesses such as limited internet access and limited access to the PDMP system. Therefore, efforts to overcome the weaknesses of PDMP need to be instituted to ensure the healthcare system could fully optimize PDMP's benefits.

A novel association rule mining approach using TID intermediate itemset.

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.