Medication adherence: A method for designing context-aware reminders

Singh, Neetu; Varshney, Upkar

doi:10.1016/j.ijmedinf.2019.103980

Cited by 13 publications

(5 citation statements)

References 76 publications

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“…These include communication with and notification to patients, observing consumption behavior, providing individual/group education and support, analyzing patterns of opioid consumption, and providing cognitive behavior therapy. To make these interventions more effective we add context-awareness and refer to these as smart interventions (Singh & Varshney, 2019b). The interventions can be in the form of a mobile app implementing reminders, monitoring, and support functions.…”

Section: Design Approachmentioning

confidence: 99%

Smart Interventions for Opioid Abuse

Singh,

Varshney

2024

International Journal of Healthcare Information Systems and Informatics

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The number of people in the US with opioid abuse exceeds two million, and the total cost is approximately $100B per year. There is a need for smart interventions that can lead to better outcomes for patients and reduce the need for healthcare resources. In this study, the authors present three smart interventions for patients: (a) mobile reminders, (b) electronic monitoring, and (c) composite intervention. More specifically, the authors present a design approach for smart interventions and operationalize the interventions. They have developed an analytical model for evaluating interventions. Interventions are cost-effective for higher values of intervention effectiveness, hospital, and emergency room cost. However, with quality-of-life (QoL) improvement, cost-effectiveness improves significantly. The authors also explored the use of financial incentives for increasing the adoption of interventions. These results will help patients, healthcare professionals, decision-makers, and family members to choose the most suitable intervention to address opioid abuse.

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Section: Design Approachmentioning

confidence: 99%

Smart Interventions for Opioid Abuse

Singh,

Varshney

2024

International Journal of Healthcare Information Systems and Informatics

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“…Enabling the user to customize a reminder is a means of prompting the user at their preferred time. However, there is the danger that timeslots chosen by the user aren't effective in terms of engaging in the desired activity; hence a context-aware reminder that learns the user's interaction behavior may perhaps be more effective (Singh and Varshney, 2019 ).…”

Section: Personalized Persuasive Software Featuresmentioning

confidence: 99%

Mitigating Issues With/of/for True Personalization

Oinas-Kukkonen

Pohjolainen

Agyei

2022

Front. Artif. Intell.

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A common but false perception persists about the level and type of personalization in the offerings of contemporary software, information systems, and services, known as Personalization Myopia: this involves a tendency for researchers to think that there are many more personalized services than there genuinely are, for the general audience to think that they are offered personalized services when they really are not, and for practitioners to have a mistaken idea of what makes a service personalized. And yet in an era, which mashes up large amounts of data, business analytics, deep learning, and persuasive systems, true personalization is a most promising approach for innovating and developing new types of systems and services—including support for behavior change. The potential of true personalization is elaborated in this article, especially with regards to persuasive software features and the oft-neglected fact that users change over time.

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“…Technologies have been developed to support their daily life, work, study, or research, such as personal web libraries [ 2 ], Tiimo [ 3 ], T-BOT and Q-BOT [ 3 , 4 ]. But people with very busy schedules, such as nurses, caregivers, or people with problems of cognition, loss of memory, or dementia [ 5 ] can forget the schedule that has been arranged, so a reminder system is required in this area [ 6 , 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Forecasted Activity Notifications with Reinforcement Learning

Fikry

Inoue

2023

Sensors

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In this paper, we propose the notification optimization method by providing multiple alternative times as a reminder for a forecasted activity with and without probabilistic considerations for the activity that needs to be completed and needs notification. It is important to consider various factors when sending notifications to people after obtaining the results of the forecasted activity. We should not send notifications only when we have forecasted results because future daily activities are unpredictable. Therefore, it is important to strike a balance between providing useful reminders and avoiding excessive interruptions, especially for low probabilities of forecasted activity. Our study investigates the impact of the low probability of forecasted activity and optimizes the notification time with reinforcement learning. We also show the gaps between forecasted activities that are useful for self-improvement by people for the balance of important tasks, such as tasks completed as planned and additional tasks to be completed. For evaluation, we utilize two datasets: the existing dataset and data we collected in the field with the technology we have developed. In the data collection, we have 23 activities from six participants. To evaluate the effectiveness of these approaches, we assess the percentage of positive responses, user response rate, and response duration as performance criteria. Our proposed method provides a more effective way to optimize notifications. By incorporating the probability level of activity that needs to be done and needs notification into the state, we achieve a better response rate than the baseline, with the advantage of reaching 27.15%, as well as than the other criteria, which are also improved by using probability.

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Medication adherence: A method for designing context-aware reminders

Cited by 13 publications

References 76 publications

Smart Interventions for Opioid Abuse

Smart Interventions for Opioid Abuse

Mitigating Issues With/of/for True Personalization

Optimizing Forecasted Activity Notifications with Reinforcement Learning

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