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Inhaled therapies are the cornerstone of effective asthma treatment and adequate inhaled medication adherence (IMA) is critical. mHealth technologies have shown to be promise for asthma self-management, including IMA improvement. InspirerMundi is a mobile application designed to support self-management of patients with asthma. It aims to transform the adherence to treatment into a positive experience through gamification and social interaction while allowing for ubiquitous verified IMA monitoring. The app includes an image-based inhaler usage detection tool, tools for reporting symptoms and burden of the asthma (disease monitoring) and Game & Peer support features. Still, the effectiveness of these tools depends on a regular app use and a real-life assessment of patient engagement and pattern of use is needed. This work evaluates the patient engagement, usage, and acceptance of InspirerMundi app during a real-world multi centre feasibility study. The app use was recommended for a 4-months period to 77 participants with persistent asthma. From those, 72 installed the app, with 67% of them beginning to use both the inhaler usage detection tool and the disease monitoring components within the first week. Over 95% used it more than once, with the period of usage (from first use to last registered monitoring in 2018) being over 30 days for almost 70% of the users, and over 90 days for around 35%. Nevertheless, the usage rate (ratio of the number of days with app usage and the period of use) had a median value of 0.6 and was above 75% for only 35% of the users, revealing room for improvement. In general, the users started to use the app features right after installation and the usage patterns and retention rates indicate that InspirerMundi is well accepted among patients with asthma. [ABSTRACT FROM AUTHOR]

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Poor communication of medication administration instructions is a preventable cause of medication nonadherence. The Universal Medication Schedule (UMS) framework improves adherence by providing a simplified set of dose timing rules. However, this framework does not readily generalize to individuals with varying daily routines. We propose a point-of-care solution for enhancing guideline-based electronic prescribing and personalizing dose schedules. We describe a JSON-based approach to encode and execute standard treatment guidelines to support electronic prescribing as well as an algorithm for optimizing medication administration schedules based on a patient’s daily routine. We evaluated the structure and accuracy of our JavaScript Object Notation (JSON) formalism focusing on Kenya’s hypertension treatment guidelines. Our experiments compare the medication schedules generated by our algorithm with those generated by pharmacists. Our findings show that treatment guidelines can be efficiently represented and executed using the JSON formalism, and that different medication administration schedules can be generated automatically and optimized for patients’ daily routines. [ABSTRACT FROM AUTHOR]

Electronic prescribing, defined as the electronic generation and transmission of a medication order for community-dwelling patients, is presented as an essential technology to improve medication use. The objective of this study was to evaluate a nationwide e-prescribing system in Quebec, Canada. A mixed-method study was conducted from July 2017 until June 2018. A descriptive analysis of e-prescription usage was performed using aggregated usage data, combined with an exploratory descriptive analysis of the e-prescribing system from the perspective of users of two electronic health records (EHR) and pharmacy management systems (PMS) (n=9 prescribers; 8 pharmacy technicians and 11 pharmacists). Overall, the adoption of the system was low, with only 2% of prescriptions being electronically transmitted and retrieved during the study period. Alignment problems were identified on the prescriber’s and receiver’s side, generating safety issues, and hindering the potential for benefits realization. [ABSTRACT FROM AUTHOR]

This study collected data from 2987 people living with HIV in China and employed logistic regression to examine the factors that predict medication adherence among these participants. A total of 2146 (72.1%) participants were receiving antiretroviral therapy (ART). And 1388 patients (64.7%) did not miss any medication dose. Marital status ((OR=1.60, 95% CI: (1.01-2.55)), duration of receiving ART ((OR=2.11, 95% CI: (1.19-3.73)), ever use of synthetic drugs ((OR=2.98, 95% CI: (1.61-5.50)), medication-specific social support ((OR =1.45, 95% CI: (1.01-2.08)), some reasons for missing medication doses, including "busy with other things" (OR=3.13, 95% CI: (1.66-5.91), "Just forgot to take it" (OR=6.38, 95% CI: (2.77-14.71)), and "in a bad mood" (OR=4.54, 95% CI: (2.78-7.41), were significant predictors of medication adherence among HIV-infected individuals who were receiving ART in Guangxi, China. Findings of the current study have practical implications for HIV treatment and interventions that focus on the factors associated with medication adherence. [ABSTRACT FROM AUTHOR]

Medication adherence works as the key mediator between practice and patient outcomes. However, in actual application, inadequate medication adherence is a pervasive cause of poor health outcomes. Many interventions have been developed to help promote patients' healthy behavior in taking medication. In this paper, we discuss the theoretical background that supports a novel intervention which includes not only rewards but also punishments through the use of mobile phone to increase medication adherence rate. We also design multiple scenarios of the intervention to illustrate further its applications based on the theories. Moreover, the comparison of different scenarios provides a deeper understanding of the cost-effectiveness and targeted patient type for each scenario. We contribute to mobile health practice by designing and fully examining a novel intervention, and we extend related theories to explain the application of intervention. [ABSTRACT FROM AUTHOR]

Inhaled controller medications are the cornerstone of asthma treatment and adherence is of critical importance to achieve good clinical results. However, suboptimum adherence has been the rule rather than the exception in patients with asthma. Two of the key challenges to change this scenario are: 1) development of easy-to-disseminate objective measures of inhaled drugs adherence, and 2) the promotion of behavior changes within the patients' routine daily-life. The knowledge that gaining and maintaining a person's attention is the first step in promoting behavior change and that smartphones and virtual community gaming can have a relevant role in enhancing motivation led to the concept of a mobile app. Inspirers is an attempt to transform the process of adherence to treatment into a positive experience through gamification and social interaction, while allowing for ubiquitous verified monitoring of treatment adherence. The main purpose of this work is to present the Inspirers features, development phases and forthcoming developments. We first describe the current version of Inspirers focusing on two major features: innovative inhaler usage detection based on imaging processing and the gamification approach based on social/peer support. Then we present the highly iterative development process implemented, which incorporated input/feedback from patients and physicians throughout. Finally, we discuss the forthcoming developments, mainly the integration of a feature related to symptoms monitoring; the generalization of the inhaler usage detection tool, now working with one type of inhaler, to other inhalers and oral treatments; and the implementation of feasibility and validation studies. [ABSTRACT FROM AUTHOR]

We explored how drug switching impacts adherence measures for common chronic oral medications. Switching between ingredients with the same indication was detected within a 30- day grace period. The proportion of days covered (PDC) and adherent status (cutoff 0.8) for each ingredient was calculated and compared between different censoring approaches: censoring drug switching (PDCswitch), censoring the end of dispensing (PDCend) and fixed 365-day period (PDC365). Overall, 854,380 (15.9%) patients in the Optum ClinFormatics (Optum) and 150,785 (22.0%) patients in the MarketScan Multi-state Medicaid (MDCD) had at least one switch within one year. Compared with PDC365 in Optum, PDCswitch means were higher: 0.85 vs. 0.41 for antihypertensive, 0.82 vs. 0.46 for antihyperglycemics and 0.84 vs. 0.33 for antihyerlipidemia. Further, the percentages of adherent patients were higher: 95.8% vs. 17.9% for antihypertensive, 85.5% vs. 18.9% for antihyperglycemics and 72.1% vs. 5.3% for antihyerlipidemia. Significant and modest changes were observed between PDCswitch and PDCend. [ABSTRACT FROM AUTHOR]

Non-adherence to medication is a major concern in healthcare as it can lead to negative health consequences and higher costs. Providing medication reminders is one type of approach to address this issue. Conventional medication reminders rely on fixed uniform style mechanisms to deliver their messages, which can result in user disengagement. It is desirable to provide a more personal and individually customised type of reminder which mimics a human carer. This paper describes an interactive avatar-based reminder application to be used on a mobile device, with the ultimate aim to increase adherence. The design of the prototype application is presented. This prototype is built with PhoneGap using JavaScript, HTML and CSS, and incorporates speech recognition and text-to-speech functionality by using existing plugins. The application will be deployed initially to evaluate the perceived satisfaction and usefulness of the application. [ABSTRACT FROM AUTHOR]

Using real-world clinical data from the Indiana Network for Patient Care, we analyzed the associations between nonadherence to oral antihyperglycemic agents (OHA) and subsequent diabetes-related hospitalization and all-cause mortality for patients with type 2 diabetes. OHA adherence was measured by the annual proportion of days covered (PDC) for 2008 and 2009. Among 24,067 eligible patients, 35,507 annual PDCs were formed. Over 90% (n=21,798) of the patients had a PDC less than 80%. In generalized linear mixed model analyses, OHA non-adherence is significantly associated with diabetes related hospitalizations (OR: 1.2; 95% CI [1.1,1.3]; p<0.0001). Older patients, white patients, or patients who had ischemic heart disease, stroke, or renal disease had higher odds of hospitalization. Similarly, OHA non-adherence increased subsequent mortality (OR: 1.3; 95% CI [1.02, 1.61]; p<0.0001). Patient age, male gender, income and presence of ischemic heart diseases, stroke, and renal disease were also significantly associated with subsequent allcause death. [ABSTRACT FROM AUTHOR]

In this paper, we present a user-friendly mobile application that automatically generates alarm signals to remind a user to take medication. This application is able to automatically process a prescription of multiple medications and provide a visual reminder, as well as an audio reminder in the user's chosen language/dialect. This application can help to promote medication adherence among elderly patients. [ABSTRACT FROM AUTHOR]

This study was conducted with 344 clients of federally funded HIV-clinics within the state of New Mexico and posited that the social ecological model of public health is an essential framework for explaining medication adherence and self-efficacy for people living with HIV (PLWH). Three levels of the PLWH client experience were considered in this analysis: individual, interpersonal, and organizational. Analyses examined the strongest predictors of medication adherence and self-efficacy. Drug use and critical appraisal were the strongest predictors of medication adherence, while depression and isolation were the strongest predictors of medication self-efficacy. Findings demonstrated the usefulness of the social ecological framework and supported the importance of examining the constructs of medication adherence and self-efficacy separately. Finally results provided direction for intervention and future research within the HIV-clinic setting. [ABSTRACT FROM AUTHOR]

Home-based pill dispensers are one of many devices that are designed to aid medication management and improve adherence. Such devices are common in the United States of America but not widely exploited in Asia. We designed a home-based pill dispenser known as MedMate. Users' perceived acceptance of a new technology such as MedMate affects its actual usage. Thus, we created a paper prototype of MedMate to get feedbacks from users early in the development cycle. We conducted a user study consisting of a questionnaire adapted from the Technology Acceptance Model (TAM), followed by an interview to gain more in-depth understanding of the users' perceived acceptance and more importantly their concerns regarding MedMate. The results of the user study show that the participants were positive about its usefulness and ease-of-use. More than two third of the participants were willing to accept and use the pill dispenser in their homes but with some concerns. Their main concerns include affordability, portability, reliability, safety and security, privacy, maintenance, durability, efficiency of reminder, accessibility and capability in handling different forms of medication. These findings resonate with our prior studies on the acceptance of other home-based medical devices such as mobile ECG-measuring devices. Thus, most of these factors can be extended for consideration when designing other home-based medical devices. [ABSTRACT FROM PUBLISHER]

Over 3 million hospitalizations and 17 million ER visits occur in Canada each year. A substantial proportion of these encounters are preventable and attributable to medication non-adherence. Non-adherence to medication changes made at discharge increases the risk of adverse events post-discharge. A mobile application was developed to empower patients to better manage their medications post-discharge. A pilot randomized controlled trial will be conducted to assess the application’s usability and efficacy in decreasing non-adherence to medication changes made at discharge. [ABSTRACT FROM AUTHOR]

We developed a HeartGuardian app and explored its effects supporting people with CVD on lipid control and medication adherence. Fifty-seven patients were enrolled, 29 in the intervention group and 28 in the control group. The 12-week intervention resulted in a moderate improvement in lipid level and greater improvement in medication adherence (82.14% vs 37.93%, P = 0.001). These outcomes translate into significant differences in occurrence of major adverse cardiac events (28.75% vs 72.43%, P = 0.001). [ABSTRACT FROM AUTHOR]

Adherence to medications is a key performance indicator and behavioral outcome in healthcare. Electronic healthcare databases represent rich data sources for estimating adherence in both research and practice. To build a solid evidence base for adherence management across clinical settings, it is necessary to standardize adherence estimation and facilitate its appropriate use. We present the recent development and oportunities offered by AdhereR, an R package for visualisation of medication histories and computation of adherence. [ABSTRACT FROM AUTHOR]

Recent news have reported an increasing number of people victims of drug-induced deaths. The reason is the fact that many patients do not have medical background or they might simply neglect certain information about the medicine they take. With a careful analysis of the requirements involved in taking medicines and by consulting physicians, it was possible to propose an intelligent medicine cabinet concept; designed to be as simple as using a conventional medicine cabinet, increasing its usability and acceptance by the construction of an electronic solution. Radio Frequency Identification was used in this work to get information from the user, medicines and prescriptions automatically, allowing a guided medication process. The proposed concept described in this paper is an attempt to facilitate people's lives to take medicines, improving medication adherence and managing related information. [ABSTRACT FROM PUBLISHER]

Research Question The motivation potential (MP) scale measures health seekers' motivation for following through on directions provided by their health care providers. The prediction of health behaviors, especially medication adherence, remains a challenge for health care researchers and clinicians. Many existing measures fall short of offering consistent, parsimonious prediction of health behavior. Composed of two three-item subscales (desirability motivation potential; feasibility motivation potential), the MP scale seeks to remedy these clinical challenges by offering a concise, powerful predictor of health behavior. While prior work offered preliminary evidence of the MP scale's sound measurement properties, the present work assesses whether the scale offers improved prediction of medication adherence, an important health behavior, across chronic disease contexts of diabetes and hypertension. Method and Data The two studies assessed the motivation potential (MP) scale's effectiveness as predictor of medication adherence across two different chronic medical conditions. The first study (n = 407) investigated medication adherence for diabetic patients. The second study (n = 299) evaluated the MP scale as a predictor of medication adherence for hypertension (high blood pressure) patients. Both studies included two measures of medication adherence (MARS and SRMA) to promote generalizability (Menckeberg et al. 2008; Morisky et al. 2008). Finally, adaptations for measures of self-efficacy (Skaff et al. 2003) and anxiety (Marteau and Bekker 1992) were included to further benchmark the predictive validity of the two MP subscales. The samples for both studies were matched to US chronic disease prevalence across age groups. Summary of Findings Through comparisons with contemporary socio-cognitive predictors including the beliefs about medicines questionnaire (Horne et al. 1999), self-efficacy (Skaff et al. 2003), and medication-related anxiety (Marteau and Bekker 1992), the MP scale, driven largely by its feasibility subscale, compares favorably to existing measures in its prediction of medication adherence. Across diabetes and hypertension patients, the MP scale, driven largely by the motivation potential feasibility subscale, explains nearly 30% of the variance in self-reported medication adherence across chronic conditions. In the present two studies, these results outpace the performance of other bestin-class contemporary predictors involving beliefs and affect. Importantly, the results from both studies also suggest that the feasibility subscale accounts for unique variance beyond these other predictors. Indeed, in many cases, the inclusion of the feasibility scale dropped these other predictors to non-significance in the full HLR model. Key Contributions Offering evidence of predictive ability, these two studies serve as a starting point in demonstrating the rigor and practicality of the motivation potential (MP) scale across chronic disease contexts. The two MP subscales combined offer an improvement in overall prediction and brevity that reduces response burden for patients while still providing guidance to clinicians on the form of interventions required (e.g., feasibility or desirability-related). With initial evidence now established that the motivation potential scale delivers improved prediction across chronic disease contexts, future research should push forward to generalize the effectiveness of the MP scale in predicting medication adherence across a wider array of chronic and acute medical conditions. Moreover, studies of the MP scale for non-medication related health behaviors such as sticking with a medically-initiated diet, exercising, or extinguishing a health-detrimental habit share a place in exploring the generalizability of the scale and determining its performance across contexts. Connecting consumer insight and behavioral medicine, the MP scale offers an opportunity for promoting prediction and intervention for health behaviors that directly impact quality of care and patient wellbeing. [ABSTRACT FROM AUTHOR]

Current socio-cognitive approaches to predicting medication adherence such as the belief in medicines questionnaire (BMQ) struggle to consistently predict when patients will cease taking their medications as directed. This work presents the motivation potential (MP) scale and its two subscales as an alternative approach to predicting medication adherence, remedying theoretical and practical shortcomings of existing measures. The MP scale measures a patient's motivation for the specific behavior of taking medication exactly as directed by a health care provider through desirability (MPD subscale) and feasibility (MPF subscale) evaluations of this behavior. In a study with diabetic patients, the present work develops, refines, and validates the MP scale for use as a tool in predicting medication adherence. Research Question Addressing limitations in adherence prediction, the present work benchmarks the predictive effectiveness of the motivation potential (MP) scale against the belief in medicines questionnaire (Horne et al. 1999) for diabetic patients. Informed by contemporary goal theory (Fishbach et al. 2009) and expectancy-value frameworks (Brehm et al. 1983), this study tested whether the MP scale parsimoniously captures much of the variance in related motivational models. Moreover, it serves as a starting point for assessing the MP scale as a clinical tool for health care providers to directly assess adherence motivation in their patients. Combining a more motivationally-sensitive level of measurement and a streamlined approach to constructs, the MP scale was expected to display valid, reliable psychometric properties while outperforming the belief in medicines questionnaire (BMQ) in predicting medication adherence, laying the groundwork for developing and executing novel, theoretically-informed interventions that surpass the effectiveness of current approaches (Krueger et al. 2005). Method and Data A study of diabetic patients taking medication to treat their condition was conducted to develop and validate the motivation potential (MP) scale. The diabetes epidemic increasingly stands out as one of the largest public health challenges of our time (McKinlay and Marceau 2000). Medication adherence directly affects health outcomes for diabetic patients (Pladevall et al. 2004), making it an ideal context to develop and validate the motivation potential (MP) scale. Through a combination of exploratory factor analysis, reliability analysis, confirmatory factor analysis, and hierarchical linear regression, this study provides initial evidence of the MP scale's convergent, discriminant, and predictive validity. Participants in the study were 475 (229 female; Mage = 37.26; range: 18-76; 46.7% married; 77% Caucasian; 50.5% with household income < $45k) diabetic patients recruited from an online panel. On average, participants had been treated for diabetes for 5.38 years with 50.5% insulin dependent. The survey instrument given to participants consisted of a series of screening/demographic questions, the belief in medicines questionnaire (Horne et al. 1999), twenty possible motivation potential scale items, a measure of goal commitment (Hollenbeck et al. 1989), and a measure of selfreported medication adherence (Horne et al. 1999). Summary of Findings This study provided initial evidence supportive of the motivation potential scale, refined to two three-item subscales, as a valid instrument capable of improved adherence prediction. From a measurement perspective, the two subscales, desirability and feasibility, demonstrated excellent convergent validity and acceptable discriminant/criterion validity in their relationships with related constructs such as commitment and the four BMQ subscales. Building towards support for predictive validity and utility, the MPD and MPF subscales accounted for a substantial portion of the variance in self-reported adherence (r2 = . 27) when compared to substantially lengthier BMQ subscales (r2 = .18). In its most stringent test, the BMQ subscales also predicted medication adherence variance even when controlling for the four BMQ subscales (which drop to non-significance) and an extensive set of socio-demographic and health-related factors. Key Contributions Consistently predicting medication adherence remains an elusive first step in the chain of knowledge upon which effective interventions can be built. The refined, six-item motivation potential (MP) scale matches the benefits of improved prediction with reduced response burden for patients. Taking the findings into account, the two motivation potential subscales demonstrate comparatively robust ability to predict total and unique variance in selfreported medication adherence. Starting in the context of diabetic patients and their medication adherence, the stage is set for future studies that generalize the validity and effectiveness of the motivation potential scale in predicting medication adherence and other health behaviors across medical conditions. Integrating theory and pressing needs of health care practice, the motivation potential scale offers a new direction forward for transforming understanding and prediction of patient adherence (Scammon et al. 2011). [ABSTRACT FROM AUTHOR]