Micro-Elimination of Hepatitis C among Patients with Kidney Disease by Using Electronic Reminder System—A Hospital-Based Experience

Background: Little is known about the use of an electronic reminder system for HCV screening among patients with kidney disease. In this study, we tried to determine whether reminder systems could improve the HCV screening rate in patients with kidney disease. Methods: Patients with kidney disease were enrolled from August 2019 to December 2020 to automatically screen and order HCV antibody and RNA testing in outpatient departments. Results: A total of 19,316 outpatients with kidney disease were included, and the mean age was 66.5 years. The assessment rate of HCV antibody increased from 53.1% prior to the reminder system to 79.8% after the reminder system (p < 0.001), and the assessment rate of HCV RNA increased from 71% to 82.9%. The anti-HCV seropositivity rate decreased from 7.3% at baseline to 2.5% after the implementation of the reminder system (p < 0.001), and the percentage of patients with detectable HCV RNA among those with anti-HCV seropositivity decreased from 69.1% at baseline to 46.8% (p < 0.001). Conclusions: The feasibility of an electronic reminder system for HCV screening among patients with kidney disease in a hospital-based setting was demonstrated.

Correction: Medication Adherence Reminder System for Virtual Home Assistants: Mixed Methods Evaluation Study (Preprint)

UNSTRUCTURED REMOVE

Vaccination dropout rates among children aged 12-23 months in Democratic Republic of the Congo: a cross-sectional study

Background Overall, 1.8 million children fail to receive the 3-dose series for diphtheria, tetanus and pertussis each year in the Democratic Republic of the Congo (DRC). Currently, an emergency plan targeting 9 provinces including Kinshasa, the capital of the DRC, is launched to reinforce routine immunization. Mont Ngafula II was the only health district that experienced high vaccination dropout rates for nearly five consecutive years. This study aimed to identify factors predicting high immunization dropout rates among children aged 12-23 months in the Mont Ngafula II health district. Methods A cross-sectional household survey was conducted among 418 children in June-July 2019 using a two-stage sampling design. Socio-demographic and perception data were collected through a structured interviewer-administered questionnaire. The distribution of 2017-2018 immunization coverage and dropout rate was extracted from the local health district authority and mapped. Logistic random effects regression models were used to identify predictors of high vaccination dropout rates. Results Of the 14 health areas in the Mont Ngafula II health district, four reported high vaccine coverage, only one recorded low vaccine coverage, and three reported both low vaccine coverage and high dropout rate. In the final multivariate logistic random effects regression model, the predictors of immunization dropout among children aged 12-23 months were: living in rural areas, unavailability of seats, non-compliance with the order of arrival during vaccination in health facilities, and lack of a reminder system on days before the scheduled vaccination. Conclusions Our results advocate for prioritizing targeted interventions and programs to strengthen interpersonal communication between immunization service providers and users during vaccination in health facilities and to implement an SMS reminder system on days before the scheduled vaccination.

Rancang Bangun Sistem Akses Rumah Indekos Menggunakan QR Code Berbasis Mikrokontroler dengan Payment Reminder System pada Aplikasi Android

Rumah indekos merupakan tempat tinggal sementara dan dijadikan sebagai tempat untuk menyimpan barang-barang berharga. Salah satu sarana yang perlu ditingkatkan adalah keamanan pagar dan pintu rumah, karena rumah seringkali menjadi sasaran bagi orang yang ingin berbuat kejahatan, sehingga sistem keamanan sangat dibutuhkan. Salah satu cara yang bisa dilakukan untuk mengamankan pagar dan pintu rumah adalah menggunakan kunci pintu digital. Sistem keamanan pada kunci pintu rumah disaat ini mayoritas ialah sistem keamanan manual berbentuk kunci gembok konvensional. Penelitian ini bertujuan untuk melakukan pengembangan sebuah sistem untuk memberikan tingkat akurasi keamanan yang lebih baik pada pintu kamar dan pagar indekos, dan sistem pengingat waktu pembayaran agar pembayaran indekos lebih terkontrol. Untuk membuka pagar dan pintu kamar penghuni indekos melakukan pemindaian kode QR dengan aplikasi android sehingga dapat mengakses pagar dan pintu kamar tersebut. Penghuni indekos yang statusnya belum membayar uang sewa akan diberi peringatan berupa pop up di aplikasi android. Setelah dilakukan pengujian sistem miniatur rumah indekos, maka didapatkan hasil pengujian yaitu berupa keberhasilan uji fungsionalitas miniatur rumah indekos yang telah dibuat, uji fungsionalitas aplikasi SmartKos pada smartphone android, database firebase, dan keakuratan komunikasi data antara firebase dan NodeMCU berupa delay dengan nilai terburuk pada pengukuran ke-3 dengan nilai 3.89 detik, throughput yang paling baik adalah pada pengukuran ke-3 dengan nilai 43864 bps, packet loss pada penelitian ini memiliki indeks 4 karena memiliki nilai packet loss sebesar 0%.

Action Based Integrated Tracking Approach to Foster the Collaboration and Achieve Operational Excellence

In rapidly growing data-intensive workflow processes, one of the major challenges for engineers is to carry out the high-priority tasks aligned with the engineering and business needs of the organization. This paper presents an implementation methodology adopted to solve this problem and create a holistic action tracking system that is firmly integrated into the engineering practice and fosters improved communication within various engineering disciplines. The first step in creating the comprehensive action tracking workflow was to identify the engineering workflows that end with an actionable item. Afterward, a framework was created that integrates these action items with the corresponding workflow as per the organization structure active directory. Once these action items are created, the inbuilt reminder system notifies the user of the upcoming tasks based on the priority assigned to these items. The escalation mechanism also ensures that the manager gets a notification when actions passed the due date. Once the action items are closed, the action assignee and assigner both get a notification to close the action loop. One of the most significant benefits of such a process is that all the outcomes of the engineering workflows, such as the requirement to retest the well for production tests, allowable approval requirements, injection improvement recommendations, etc., are directly fed into the integrated action tracking mechanism. This way, the engineers do not need to use a separate system for assigning and tracking the actions. Once these actions are fed into an integrated action tracking system, the system maintains, expedites, and escalates the actions by itself. Another benefit realized by this action tracking system was that the organization structure was internally utilized within the online platform. Therefore, it helps in dynamically assigning the organization structure and creating an escalation item to the right person at the right time so that the integrity of the overall operation remains uncompromised. The business intelligence tool is integrated with the action tracking system to support monitoring and decision-making surveillance exercises of reservoir monitoring plans and allowable monitoring, for example, utilizing the inbuilt planning and performance dashboards. Another major objective achieved in this closed-loop action tracker was the improved system-based communication among various engineering disciplines as the actions are decided based on common business objectives and agreement within the digital platform One of the well-known gaps in modern production optimization solutions is integrating the engineering solutions with the actionable item. This system bridges this gap and provides a way forward for improved collaboration and fostering communication among various disciplines.

The Development of a Smart Reminder System to Promote Adherence to Technology-Based Intervention and Assessment

The overarching aim of the National Institute on Aging funded Adherence Promotion with Person-centered Technology (APPT) Project is to promote adherence to technology-based solutions designed to enhance the early detection and treatment of cognitive decline. The goal is to build and evaluate adaptive, tailored, and integrated technology-based adherence support systems for mobile software platforms that assess and train cognitive skill. The symposium describes the various steps of the development process of the APPT smart adherence support system. N. Charness will present an overview of the APPT project, its aims, and the clinical trials designed to assess the effectiveness of the APPT smart reminder system compared to typical reminder systems. S. Chakraborty will present detailed analyses of past cognitive intervention data to inform understanding of who is likely at risk for poor adherence and how adherence lapses might be predicted in advance to provide just-in-time adherence support. D. Carr will present an exploration of motivating factors for participants to engage in research, and these motivations will be tapped to help develop motivational messages for the APPT adherence support system to be used in the two planned clinical trials. M. Dieciuc will provide additional insights into motivations for engaging in home-based cognitive assessment and training derived from a focus group study. Finally, S. Zhang will describe the results of an initial pilot study examining the effectiveness of motivational reminder messages that match vs. mismatch participants’ own motivations. All results inform the design of the APPT system to maximize adherence.

Factors That Motivate Older Adults to Participate in Research: Typologies and Implications

A key challenge for scholars who study aging is identifying a pool of research volunteers willing to participate. Toolkits and strategies acknowledge the differences in recruitment needed for older adults relative to younger adults, but there is little information about variations among older adult research volunteers. Based on a community sample of older adults age 60+, this study evaluates differences across seven specific motivators across three broad categories: values/altruism, personal growth/improvement, and immediate gratification. We then identify and evaluate four typologies of older adult volunteers based on the combinations of motivations the older adults in our sample identify as important to participation in research studies. Based on these analyses, we describe how our results might inform recruitment and retention practices in aging studies. Further, we will discuss how these results will help shape our technology-based reminder system with a greater understanding of motivations.

The Effect of Reminder Message Tailoring on Cognitive Intervention Adherence

To examine the potential impact of tailored messaging on adherence and attitudes toward text message reminders, a pilot study conducted in advance of the APPT randomized controlled trial systematically manipulated the match between text message content and participants’ self-reported motivations to participate in a cognitive intervention study. Older adults (n=40) were asked to engage in cognitive training, in the form of gamified neuropsychological tests, 30 minutes a day for 10 consecutive days, and adherence was tracked remotely over time. Critically, each day text message reminders alternated between messages consistent or inconsistent with participants’ previously reported motivations for entering the study. This talk presents results, derived from multilevel modeling, that explore the effectiveness of this simple and cost-effective message tailoring approach for facilitating adherence and engendering positive attitudes toward the reminder system, and implications for programs requiring long-term adherence.

Machine Learning Approaches to Understanding and Predicting Patterns of Adherence

In cognitive training of older adults, adherence is a major challenge, but appropriate just-in-time adaptive interventions can improve adherence. To understand adherence patterns and predictors of adherence lapses, we aggregated data from two previous trials (N &gt; 230) involving home-based cognitive interventions. This dataset, detailing 40,000 intervention interactions, contains information about intervention engagement and measures of objective and subjective cognitive performance, demographics, technology proficiency, and attitudes. Exploratory analyses were conducted to understand patterns and predictors of faltering adherence, using classification models, together with feature selection to remove redundant variables. Adherence behaviors in a week were predictive of quitting the following week. Game parameters such as the time of play were weak indicators of future playing patterns, whereas game success was a strong predictor of adherence. These and other useful observations will be incorporated in the design and development of the smart reminder system to be deployed in the APPT project.