Medication adherence: A method for designing context-aware reminders

Optimizing Forecasted Activity Notifications with Reinforcement Learning

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.

Adaptive interventions for opioid prescription management and consumption monitoring

OBJECTIVES

While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework.

MATERIALS AND METHODS

Using the framework, we designed Smart Prescription Management (SPM) and Smart Consumption Monitoring (SCM) interventions. The interventions are evaluated using analytical modeling and secondary data on doctor shopping, opioid overdose, prescription quality, and cost components.

RESULTS

SPM was most effective (30-90% improvement, for example, prescriptions reduced from 18 to 1.8 per patient) for extensive doctor shopping and reduced overdose events and mortality. Opioid adherence was improved and the likelihood of addiction declined (10-30%) as the response rate to SCM was increased. There is the potential for significant incentives ($2267-$3237) to be offered for addressing severe OUD.

DISCUSSION

The framework and designed interventions adapt to changing needs and conditions of the patients to become an important part of global efforts in preventing OUD. To the best of our knowledge, this is the first paper on adaptive interventions for preventing OUD by addressing both prescription and consumption.

CONCLUSION

SPM and SCM improved opioid prescription and consumption while reducing the risk of opioid addiction. These interventions will assist in better prescription decisions and in managing opioid consumption leading to desirable outcomes. The interventions can be extended to other substance use disorders and to study complex scenarios of prescription and nonprescription opioids in clinical studies.

A comprehensive mobile health intervention to prevent and manage the complexities of opioid use

OBJECTIVES

The Opioid Use crisis continues to be an epidemic with multiple known influencing and interacting factors. With the need for suitable opioid use interventions, we present a conceptual design of an m-health intervention that addresses the various known interacting factors of opioid use and corresponding evidence-based practices. The visualization of the opioid use complexities is presented as the "Opioid Cube".

METHODS

Following Stage 0 to Stage IA of the NIH Stage Model, we used guidelines and extant health intervention literature on opioid apps to inform the Opioid Intervention (O-INT) design. We present our design using system architecture, algorithms, and user interfaces to integrate multiple functions including decision support. We evaluate the proposed O-INT using analytical modeling.

RESULTS

The conceptual design of O-INT supports the concept of collaborative care, by providing connections between the patient, healthcare professionals, and their family members. The evaluation of O-INT shows a preference for specific functions, such as overdose detection and potential for high system reliability with minimal side effects. The Opioid Cube provides a visualization of various opioid use states and their influencing and interacting factors.

CONCLUSIONS

O-INT is a promising design with a holistic approach to manage opioid use and prevent and treat misuse. With several needed functionalities, O-INT design serves as a decision support system for patients, healthcare professionals, researchers, and policy makers. Together, O-INT and the Opioid Cube may serve as a foundation for development and adoption of highly effective m-health interventions for opioid use.

Mitigating Issues With/of/for True Personalization

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.

Mobile Health Applications and Medication Adherence of Patients With Hypertension: A Systematic Review and Meta-Analysis

INTRODUCTION

Current evidence has revealed the beneficial effects of mobile health applications on systolic and diastolic blood pressure. However, there is still no solid evidence of the underlying factors for these outcomes, and hypertension treatment is performed mainly by medication intake. This study aims to analyze the impacts of health applications on medication adherence of patients with hypertension and understand the underlying factors.

METHODS

A systematic review and meta-analysis were conducted considering controlled clinical trials published, without year filter, through July 2020. The searches were performed in the electronic databases of Scopus, MEDLINE, and BVSalud. Study characteristics were extracted for qualitative synthesis. The meta-analysis examined medication-taking behavior outcomes using the generic inverse-variance method to combine multiple variables.

RESULTS

A total of 1,199 records were identified, of which 10 studies met the inclusion criteria for qualitative synthesis, and 9 met the criteria for meta-analysis with 1,495 participants. The analysis of mean changes revealed significant improvements in medication adherence (standardized mean difference=0.41, 95% CI=0.02, 0.79, I²=82%, p=0.04) as well as the analysis of the values measured after follow-up (standardized mean difference=0.60, 95% CI=0.30, 0.90, I²=77%, p<0.0001). Ancillary improvements were also identified, such as patients' perceived confidence, treatment self-efficacy and self-monitoring, acceptance of technology, and knowledge about the condition and how to deal with health issues.

DISCUSSION

There is evidence that mobile health applications can improve medication adherence in patients with hypertension, with broad heterogeneity between studies on the topic. The use of mobile health applications conceivably leads to ancillary improvements inherent to better medication adherence.

Review, Assess, Classify, and Evaluate (RACE): a framework for studying m-health apps and its application for opioid apps

OBJECTIVE

The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps.

MATERIALS AND METHODS

The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy.

RESULTS

Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps.

DISCUSSION

Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions.

CONCLUSION

With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care.

An analytical model to evaluate reminders for medication adherence

OBJECTIVES

Several interventions have been proposed to improve medication adherence including those using reminders. The performance of reminders, including effectiveness and side effects, varies widely in different settings. We must study this for improving decision making on how, when, and where to use what type of reminders.

METHODS

Analytical modeling is an effective and low-cost method to derive preliminary or intermediate results and insights for further study of interventions for medication adherence. We developed an analytical model that can be used to evaluate the performance of reminders in various settings, including effectiveness, side effects, and healthcare cost savings for medication adherence.

RESULTS

Context-aware reminders perform better than simple reminders for willing patients even when they completely rely on reminders for taking their doses. Simple reminders lead to more side effects than context-aware reminders. Further, context-aware reminders generate more healthcare savings without side effects and a comparable cost of the intervention. The results contribute to an improved understanding of reminders and are used to derive a set of guidelines for patients, healthcare professionals, decision-makers, and mobile app developers.

CONCLUSIONS

The proposed model is a low cost and effective tool to derive results and insights for the use of reminders in different settings to improve medication adherence. Therefore, the model can be utilized as a decision-making tool for deciding whether to pursue an RCT on healthcare interventions. The analytical model can be extended for complex scenarios of multiple interdependent medications, adaptation with patients' condition and behavior, and composite interventions.