Electronic Adherence Monitoring Research Articles

Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence

ABSTRACT Machine Learning (ML) can improve the analysis of complex and interrelated factors that place adherent people at risk of viral rebound. Our aim was to build ML model to predict RNA viral rebound from medication adherence and clinical data. Patients were followed up at the Swiss interprofessional medication adherence program (IMAP). Sociodemographic and clinical variables were retrieved from the Swiss HIV Cohort Study (SHCS). Daily electronic medication adherence between 2008–2016 were analyzed retrospectively. Predictor variables included: RNA viral load (VL), CD4 count, duration of ART, and adherence. Random Forest, was used with 10 fold cross validation to predict the RNA class for each data observation. Classification accuracy metrics were calculated for each of the 10-fold cross validation holdout datasets. The values for each range from 0 to 1 (better accuracy). 383 HIV+ patients, 56% male, 52% white, median (Q1, Q3): age 43 (36, 50), duration of electronic monitoring of adherence 564 (200, 1333) days, CD4 count 406 (209, 533) cells/mm3, time since HIV diagnosis was 8.4 (4, 13.5) years, were included. Average model classification accuracy metrics (AUC and F1) for RNA VL were 0.6465 and 0.7772, respectively. In conclusion, combining adherence with other clinical predictors improve predictions of RNA.

Adherence to Oral Antipsychotics Measured by Electronic Adherence Monitoring in Schizophrenia: A Systematic Review and Meta-analysis.

Poor adherence to oral antipsychotics is common in patients with schizophrenia; nonetheless, there has been no systematic review or meta-analysis on medication adherence measured by electronic adherence monitoring (EAM), considered by many as the 'gold standard' assessment. We systematically searched MEDLINE and Embase to identify studies investigating adherence to oral antipsychotics using EAM in patients with schizophrenia spectrum disorder. There were no exclusion criteria. We looked at the methodology in each study and defined which type of adherence was used in the study. Data on medication adherence, definition of satisfactory adherence (i.e., the threshold set in terms of the percentage of times medication was taken as prescribed), and factors associated with adherence were extracted for the included studies. Further, data on the rates of medication adherence were quantitatively synthesized. A total of 19 studies involving 2184 patients were included. EAM-measured medication adherence was classified into three outcome types: taking adherence, regimen adherence, and timing adherence. The meta-analysis yielded oral antipsychotic adherence rates (defined as a continuous variable) of 71.1%for taking adherence [from seven studies, n = 256, 95% confidence interval (CI) 58.0-84.1], 70.0%for regimen adherence (from five studies, n = 174, 95% CI = 63.6-76.4), and 64.9%for timing adherence (from four studies, n = 212, 95% CI 53.2-76.6), respectively. The proportions of patients with oral antipsychotic adherence, when defined as a dichotomous variable, ranged from 50 to 78.3% for the 70% threshold for satisfactory adherence, 29.8-75.7% for the 75% threshold, and 47.8-75.7% for the 80% threshold. Factors associated with poor medication adherence were greater symptom severity, more frequent dosing regimen, poorer insight, and more negative drug attitude. Oral antipsychotic adherence rates in schizophrenia, defined as a continuous variable and measured by EAM, were in the range of 70%, lower than the 80% threshold used widely to define satisfactory adherence.

Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

IntroductionReal‐time electronic adherence monitoring (EAM) systems could inform on‐going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real‐time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine learning approaches.MethodsWe evaluated an observational cohort of persons living with HIV and treated with antiretroviral therapy (ART) who were monitored longitudinally with standard EAM from 2005 to 2011 and real‐time EAM from 2011 to 2015. Super learner, an ensemble machine learning method, was used to develop a tool for targeting viral load testing to detect viraemia (>1000 copies/ml) based on clinical (CD4 count, ART regimen), viral load and demographic data, together with EAM‐based adherence. Using sample‐splitting (cross‐validation), we evaluated area under the receiver operating characteristic curve (cvAUC), potential for EAM data to selectively defer viral load tests while minimizing delays in viraemia detection, and performance compared to WHO‐recommended testing schedules.ResultsIn total, 443 persons (1801 person‐years) and 485 persons (930 person‐years) contributed to standard and real‐time EAM analyses respectively. In the 2011 to 2015 dataset, addition of real‐time EAM (cvAUC: 0.88; 95% CI: 0.83, 0.93) significantly improved prediction compared to clinical/demographic data alone (cvAUC: 0.78; 95% CI: 0.72, 0.86; p = 0.03). In the 2005 to 2011 dataset, addition of standard EAM (cvAUC: 0.77; 95% CI: 0.72, 0.81) did not significantly improve prediction compared to clinical/demographic data alone (cvAUC: 0.70; 95% CI: 0.64, 0.76; p = 0.08). A hypothetical testing strategy using real‐time EAM to guide deferral of viral load tests would have reduced the number of tests by 32% while detecting 87% of viraemia cases without delay. By comparison, the WHO‐recommended testing schedule would have reduced the number of tests by 69%, but resulted in delayed detection of viraemia a mean of 74 days for 84% of individuals with viraemia. Similar rules derived from standard EAM also resulted in potential testing frequency reductions.ConclusionsOur machine learning approach demonstrates potential for combining EAM data with other clinical measures to develop a selective testing rule that reduces number of viral load tests ordered, while still identifying those at highest risk for viraemia.

Experience with antiretroviral electronic adherence monitoring among young African American men who have sex with men living with HIV: findings to inform a triaged real-time alert intervention

ABSTRACT We performed a pilot study among young African-American men who have sex with men (AAMSM) of real-time electronic adherence monitoring (EAM) in Chicago to explore acceptability and feasibility of EAM and to inform intervention development. We recruited 40 young AAMSM living with HIV on ART to participate in up to 3 months of monitoring with the Wisepill device. Participants were interviewed at baseline, in response to the first true adjudicated 1-dose, 3-day, and 7-day misses, and at the end of monitoring. Reasons for missing doses and the acceptability and feasibility of electronic monitoring were assessed using mixed methods. The median participant observation time was 90 days (N = 40). For 21 participants with 90 days of follow-up, <90% and <80% adherence occurred in 82% and 79%, respectively in at least one of their monitored months (n = 63 monitored months). The participants generally found the proposed intervention acceptable and useful. Although seven participants said the device attracted attention, none said it led to disclosure of their HIV status. This study found real-time EAM to be generally acceptable and feasible among YAAMSM living with HIV in Chicago. Future work will develop a triaged real-time EAM intervention including text alerts following detection of nonadherence.

Mobile Health Technologies May Be Acceptable Tools for Providing Social Support to Tuberculosis Patients in Rural Uganda: A Parallel Mixed-Method Study.

Background Social support has been shown to mitigate social barriers to medication adherence and improve tuberculosis (TB) treatment success rates. The use of mobile technology to activate social support systems among TB patients, however, has not been well explored. Moreover, studies that tie supportive SMS (Short Message Service) texts to electronic monitoring of TB medication adherence are lacking. Objective To explore TB patients' current access to social support and perceptions of utilizing real-time adherence monitoring interventions to support medication adherence. Methods We purposively selected TB patients who owned phones, had been taking TB medications for ≥1 month, were receiving their treatment from Mbarara Regional Referral Hospital, and reported having ≥1 social supporter. We interviewed these patients and their social supporters about their access to and perceptions of social support. We used STATA 13 to describe participants' sociodemographic and social support characteristics. Qualitative data were analyzed using content analysis to derive categories describing accessibility and perceptions. Results TB patients report requesting and receiving a variety of different forms of social support, including instrumental (e.g., money for transport and other needs and medication reminders), emotional (e.g., adherence counselling), and informational (e.g., medication side effects) support through mobile phones. Participants felt that SMS notifications may motivate medication adherence by creating a personal sense of obligation to take medications regularly. Participants anticipated that limited financial resources and relationship dynamics could constrain the provision of social support especially when patients and social supporters are not oriented about their expectations. Conclusion Mobile telephones could provide alternative approaches to providing social support for TB medication adherence especially where patients do not stay close to their social supporters. Further efforts should focus on optimized designs of mobile phone-based applications for providing social support to TB patients and training of TB patients and social supporters to match their expectations.

Adherence to Antidepressants in Underserved Communities: A Comparison of Electronic Monitoring and Self-report Measures.

Nonadherence to antidepressants is widespread and poses a significant barrier to optimal management and treatment of depression in community settings. The objective of this study was to compare self-reported and electronic monitoring of adherence to antidepressants and to examine the relationship of these measures with depressive symptoms in a medically underserved community. Adherence to antidepressants was measured in 38 primary care patients from the West Philadelphia area using self-report and electronic monitoring (Medication Event Monitoring System caps). Self-report and electronic monitoring of antidepressant adherence showed fair agreement at baseline, slight agreement at 6weeks, and slight agreement at 12weeks. Adherence to antidepressants as assessed by electronic monitors was significantly associated with depression remission at 12weeks [adjusted odds ratio 18.6, 95% confidence interval (1.05, 330.56)]. Compared with electronic monitoring, self-reported adherence tended to overestimate medication adherence to antidepressants. Adherence assessed by electronic monitoring was associated with depression remission.

2514. Real-time Antiretroviral Electronic Adherence Monitoring In Young African American Men Who Have Sex With Men

BackgroundAntiretroviral therapy adherence remains a challenge, particularly for young African American men who have sex with men (YAAMSM). We enrolled 40 YAAMSM for 3 months of electronic adherence monitoring (EAM). These data may be useful in developing an antiretroviral EAM intervention that responds to missed doses with real-time text messages.MethodsYAAMSM (age 18–34 years) living with HIV and taking ART participated in a quantitative and qualitative study that included ART adherence monitoring with a Wisepill electronic monitoring device for up to 3 months. Interviews were performed during April 2017–April 2019 at baseline and follow-up. Monitoring data were reviewed to determine timing and patterns of missing their first true adjudicated miss for durations of 1 dose, 3 consecutive days, and 7 consecutive days. Follow-up qualitative interviews included exploring acceptability of monitoring.ResultsThe median age was 28 years and median participant observation time was 90 days (interquartile range 88–90 days) (n = 40 participants). Among those with at least 2 weeks follow-up and adjudication (n = 32), 100% missed at least 1 day. Most (82%) of these participants were <80% adherent in at least one of their monitored months. One dose and 3-day misses did not cluster (e.g., no disproportion on weekends). Most (88%) first missed doses occurred during the first 9 days monitored and most (69%) of the 13 who missed 3 consecutive days missed within the first monitored month. Four participants missed 7 consecutive days. Among 31 with a follow-up interview, 28 (90%) felt receiving a text because of device monitoring would affect their medication taking in the future. Illustrative quotes included, “It made me more responsible” and “…it makes you want to do it right.”ConclusionMost YAAMSM living with HIV in this study had adherence below the target threshold of >80%. These data support development of a text message responsive real-time electronic adherence monitoring intervention approach.Disclosures All authors: No reported disclosures.

Using digital technology for home monitoring, adherence and self-management in cystic fibrosis: a state-of-the-art review

Digital healthcare is a rapidly growing healthcare sector. Its importance has been recognised at both national and international level, with the WHO recently publishing its first global strategy for digital...

Use of remote symptom monitoring with breast cancer survivors using patient reported outcome measures through Epic Mychart.

208 Background: Electronic patient-reported outcome (PRO) monitoring has been found to improve quality of life and overall survival in patients with metastatic cancer patients during active treatment. There has been limited data using PROs to enhance clinical outcomes in cancer survivorship. This pilot study of breast cancer survivors assessed utilization and satisfaction of proactive PRO symptom and adherence monitoring through an EHR patient portal (EPIC MyChart). Methods: Eighty breast cancer survivors who had completed surgery, chemotherapy, and/or radiation and received a survivorship care plan were randomized 1:1 either to usual care or PRO monitoring. The PRO group received questions from the NIH PROMIS toolkit through EPIC MyChart monthly between visits for 6 months. The triage nurse received an EPIC Inbasket alert if the survivor reported moderate to severe symptoms, had not taken their medication(s), or reported they would like to speak to a nurse. Participants in the control group were assessed for sadness, anxiety and satisfaction with provider communication at their usual follow visits. Results: Of 239 surveys sent out, 214 (90%) were completed. Eighty-five (36%) surveys triggered Inbasket alerts to the nurse. The most common nursing response to alerts was calling the patient for counseling/education. Compared to control, the PRO monitoring group exhibited no difference in satisfaction at 3 months, but had significantly higher scores at 6 months for satisfaction with communication (P=0.005) and monitoring (P=0.002). Anxiety scores did not differ between groups. Conclusions: Remote electronic monitoring of symptoms and adherence through the EHR bet was feasible with high completion rates, improved satisfaction, and did not increase anxiety in breast cancer survivors. Follow up work is assessing PRO monitoring to improve efficiency of following cancer survivors, and implementation of survivorship care plans.

Beyond Survival in Solid Organ Transplantation: A Summary of Expert Presentations from the Sandoz 6th Standalone Transplantation Meeting, 2018.

Beyond Survival in Solid Organ Transplantation: A Summary of Expert Presentations from the Sandoz 6th Standalone Transplantation Meeting, 2018.

Promoting transitions of care, safety, and medication adherence for patients taking fingolimod in community pharmacies.

The development and dissemination of a specialty pharmacy service to optimize fingolimod therapy management are described. Fingolimod was the first oral therapy developed to counter relapsing-remitting multiple sclerosis. Pharmacovigilance measures and individualized support are strongly recommended due to associated safety concerns. The Fingolimod Patient Support Program (F-PSP) was developed and disseminated within a community pharmacy network. The F-PSP aims to ensure responsible use of fingolimod and patient empowerment by promoting medication adherence and patient safety through a person-centered and integrated care approach. It complements basic pharmacy services through 2 interventions: medication adherence support and pharmacovigilance tailored to fingolimod. The adherence intervention combines motivational interviewing with longitudinal electronic medication adherence monitoring. The pharmacovigilance component consists of informing patients of fingolimod recommendations, reminding patients of recommended medical tests, and tracking and monitoring symptoms, especially those of potential serious adverse fingolimod reactions. A secure Web platform guides the pharmacist in conducting interviews and enables collection of patient-reported outcome data. A transition care pharmacist proposes program participation to all patients initiated on fingolimod, performs enrollment, and coordinates transfers to patient-designated community pharmacies for ongoing care. The F-PSP enables support of individual patients, and also provides real-world data, helping to bridge the gap between practice and research. The F-PSP is intended to be a generic model of a specialty pharmacy service that is transferable to any other healthcare context, specialty drug or disease.

Use of remote symptom monitoring with breast cancer survivors using patient-reported outcome (PRO) measures in MyChart.

e23125 Background: Electronic patient-reported outcome monitoring has been found to improve quality of life and overall survival when used with metastatic cancer patients during treatment (Basch, et al., 2017). There has been limited data on using PROs to enhance clinical outcomes in cancer survivorship. This pilot study of breast cancer survivors assessed utilization and satisfaction of electronic proactive symptom and adherence monitoring. Methods: Eighty breast cancer survivors who had completed surgery, chemotherapy, and/or radiation and had received their survivorship care plan were randomized to either usual care or to the PRO monitoring group. The PRO group received questions from the NIH PROMIS toolkit through their patient portal (EPIC) monthly for 6 months. The triage nurse received an alert if the survivor reported on the questionnaire that they had moderate to severe symptoms, had not taken their medication, or would like to speak to a nurse. Participants in the control arm were assessed for sadness, anxiety and satisfaction with provider communication at their usual follow-up visits. Results: Response rates for surveys ranged from 47to 75%. Patients in the PRO monitoring group exhibited no difference in satisfaction at 3 months but had significantly higher scores at 6 months for communication (P=0.005) and monitoring (P= 0.002). Anxiety scores were not significantly different among the usual care group versus the frequent remote monitoring group. Conclusions: Electronic PROs monitoring between clinic visits improved satisfaction with communication and follow-up without increasing anxiety in breast cancer survivors. The pilot data indicate continued development of efficient PROs monitoring can be an important tool to transform the survivorship care plan from a document to an active process of proactively monitoring symptoms and adherence. Support: UG1CA189823. Reference: Basch, E., Deal, A. M., Dueck, A.C., et al. (2017). Overall Survival Results of a Trial Assessing Patient-Reported Outcomes for Symptom Monitoring During Routine Cancer Treatment. Jama, 318 (2), 197. Study Number: A23_Pilot2

Impact Of "eHealth" in Allergic Diseases and Allergic Patients.

The emergence of new technology enables allergists and patients to compile data and receive feedback regarding key symptoms, risk behavior, and/or management. The term "eHealth" refers to a diverse group of tools that use computerized technologies to improve both the efficacy and the efficiency of the health care industry. eHealth comprises a variety of technologies, as follows: mobile devices (mHealth) in medical care, including electronic diaries, wearable sensors, and adherence monitoring; health informatics (eg, electronic health records, computerized physician order entry, clinical decision support); telemedicine, which is the use of information and communication technologies for the management of diseases and medical education; social media platforms, and the analysis of information acquired through these platforms using "big data" technologies. In this review, we summarize the latest findings on the use of eHealth technology and the relevance of eHealth to allergic conditions.

Tenofovir-Diphosphate as a Marker of HIV Pre-exposure Prophylaxis Use Among East African Men and Women.

Background: Controlled pharmacokinetic (PK) studies in United States populations have defined categories of tenofovir-diphosphate (TFV-DP) in dried blood spots (DBS) for various pre-exposure prophylaxis (PrEP) adherence targets. It is unknown how these categories perform in other populations. Therefore, we evaluated the sensitivity and specificity of these PK-derived categories compared to daily medication electronic adherence monitoring (MEMS) data among East African men and women using daily PrEP.Methods: Participants were enrolled as members of HIV serodiscordant couples as part of an open-label PrEP study in Kenya and Uganda. Blood samples were taken at quarterly visits and stored as DBS, which were analyzed for TFV-DP concentrations.Results: Among 150 samples from 103 participants, MEMs data indicated that 87 (58%) took ≥4 doses and 62 (41%) took ≥6 per week consistently over the 4 weeks prior to sample collection. Sensitivities of DBS TFV-DP levels were 62% for the ≥4 doses/week category (≥700 fmol/punch TFV-DP) and 44% for the ≥6 doses/week category (≥1050 fmol/punch TFV-DP); specificities were 86 and 94%, respectively. There were no statistically significant differences in these sensitivities and specificities by gender.Conclusion: In this sample of East African PrEP users, categories of TFV-DP concentrations developed from directly observed PrEP use among United States populations had high specificity but lower than expected sensitivity. Sensitivity was lowest when MEMS data indicated high adherence (i.e., ≥6 doses/week). PrEP studies and implementation programs should carefully consider the sensitivity and specificity of the TFV-DP levels used for adherence feedback.

Measuring Antiretroviral Adherence Among Young People Living with HIV: Observations from a Real-Time Monitoring Device Versus Self-report.

This report compares self-report (SR) antiretroviral (ARV) adherence data to adherence data collected via Wisepill, a real-time electronic monitoring (EM) device, among young people living with HIV (YPLH) in the southern United States. Participants (n = 66; ages 16 to 26) were followed for 14weeks. Descriptive analyses were used to compare SR to EM data. Correlations and a linear regression were conducted to explore factors possibly associated with SR-EM discrepancies. We also examined associations between various levels of SR and EM adherence and viral suppression/non-suppression at 14weeks. Rates of SR adherence were maintained between 87% and 92% while rates of EM adherence declined from 64% to 34%. YPLH who were ARV-experienced, had lower treatment motivation, and reported more frequent recent marijuana use, had a greater discrepancy between their SR and EM adherence levels compared to other YPLH. Higher rates of SR and EM adherence were independently associated with a decline in viral load. A sensitivity analysis also revealed that SR adherence was a better predictor of viral non-suppression, whereas EM adherence was a better predictor of viral suppression. These measurement approaches are discussed in the context of providing clinical care to YPLH.

Clustering functional data with application to electronic medication adherence monitoring in HIV prevention trials.

Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. In this paper we focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network(HPTN) 069 study for identifying subgroups with distinct adherence behavior over the study period.

Is There a Threshold for Medication Adherence? Lessons Learnt From Electronic Monitoring of Drug Adherence.

Medication adherence is a well-recognized problem in the management of patients with chronic diseases needing a long-term pharmacotherapy. While fighting against non-adherence, an important question frequently arises, i.e., how much adherence is enough to obtain the full treatment benefits? Most studies having attempted to answer this question have used large pharmacy records and health care databases to quantify the percentage of days covered by the prescribed treatment and hence deduce a threshold below which there appears to be fewer benefits from therapy. In the present paper, the use of data obtained using electronic monitoring of adherence is discussed as another means to assess adherence thresholds with a particular emphasis on hypertension. The data show that even with the use of electronic monitoring of adherence, which provides a comprehensive dosing history, it is extremely difficult to define an adherence threshold in hypertension. This is due to many factors that need to be taken into account, including not only the pattern of patients’ adherence and their clinical and environmental characteristics, but also the pharmacological characteristics of the prescribed drugs, the severity of the disease and many others. To determine adherence cut-offs more precisely, specific protocols should be designed to answer the question in various clinical conditions. These protocols should be conducted in well-defined patients’ groups, they should use the most reliable methods to measure adherence providing if possible a detailed dosing history perhaps combined with drugs levels in blood or urine. These studies should also choose the best methods to measure clinical endpoints, such as ambulatory blood pressure monitoring or home blood pressure in the case of hypertension. One important aspect is that datasets should be solid and large enough to be able to analyze adherence data as a continuous variable using newly developed mathematical models including new metrics catching the complexity of adherence. The rapid development of new technologies like devices, connectivity, and analytics, will probably provide new solutions to improve our ability to define valid and clinically useful adherence thresholds in various therapeutic areas.

The importance of how research participants think they are perceived: results from an electronic monitoring study of antiretroviral therapy in Uganda

ABSTRACTNovel monitoring technologies in HIV research, such as electronic adherence monitors (EAMs), have changed the nature of researcher-participant interactions. Yet little is known about how EAMs and the resulting interaction between researchers and participants affect research participation and the data gathered. We interviewed participants and research assistants (RAs) in an observational cohort study involving EAMs for HIV antiretroviral therapy (ART) in Uganda. We qualitatively explored interviewees’ views about ethical issues surrounding EAMs and assessed data with conventional and directed content analysis. Participants valued their relationships with RAs and were preoccupied with RAs’ perceptions of them. Participants were pleased when the EAM revealed regular adherence, and annoyed when it revealed non-adherence that contradicted self-reported pill-taking behavior. For many, the desire to maintain a good impression incentivized adherence. But some sought to creatively conceal non-adherence, or refused to use the EAM to avoid revealing non-adherence to RAs. These findings show that participants’ perceptions of the study staff's perceptions of them affected the experience of being monitored, study participation, and ultimately the data gathered in the study. Investigators in monitoring-based research should be aware that social interactions between participants and study staff could affect both the practical and ethical conduct of that research.

Electronic Monitoring of Adherence to Inhaled Corticosteroids: An Essential Tool in Identifying Severe Asthma in Children

To study if electronic monitoring could be used to separate patients with severe asthma with poor control. In addition, the purpose was to observe if electronic monitoring would lead to the optimization of standard asthma therapy.This was a prospective observational cohort of children aged 5 to 17 years. The study included 108 children with asthma recruited from the outpatient department of Royal Brompton Hospital in London, United Kingdom.All children had an asthma diagnosis by standard protocols. Children were classified into 3 groups: severe therapy-resistant asthma, difficult asthma, and mild-to-moderate asthma. Baseline and end–of–monitoring period assessments included an asthma control test, lung function, fractional exhaled nitric oxide, asthma-related quality of life, and medication adherence using a smart inhaler.Data for 93 children who completed the study were analyzed. The median days of monitoring was 92. Good adherence was found in 42% of patients who took ≥80% of medications. Suboptimal adherence was noted in 58% of patients (27% took 60%–79% of medications, and 31% took <60% of medications). No significant differences were seen between the adherence groups for the asthma control test, fractional exhaled nitric oxide, forced expiratory volume in 1 second, the exacerbation rate, or oral corticosteroid use. Four groups were identified after the data analysis: (1) good adherence with improved control (24%; likely previous poor adherence), (2) good adherence with poor control (18%; severe therapy-resistant asthma), (3) poor adherence with good control (26%; likely overtreated), and (4) poor adherence with poor control (32%).This study revealed the importance of accurate adherence monitoring in children with asthma. Suboptimal adherence was noted in 58% of patients despite knowing that adherence was being monitored. The authors of this study identified patients with true severe asthma despite good adherence to therapy, suggesting that patients would merit further phenotyping of their asthma. In the study, researchers also confirmed the inadequacy of self-reported adherence and the limitation of prescription records.Not surprisingly, 58% of the study patients had suboptimal adherence. This represents real-world data and the difficulty with asthma adherence despite education, monitoring, and routine follow-up. This study also reveals that electronic monitoring could be helpful in identifying poor adherence as a cause of asthma poor control. Therefore, for some, improved education may be more beneficial than prescribing more medications.

Feasibility and Acceptability of Real-Time Antiretroviral Adherence Monitoring among Depressed Women Living with HIV in the Deep South of the US.

This study presents feasibility and acceptability data on the use of a real-time wireless electronic adherence monitor (EAM), among African American women living with HIV with co-occurring depression, residing in remote areas of the Southeastern United States. EAM and self-report ART adherence was monitored over an average of 14.8weeks among 25 participants who were recruited at four HIV clinics in Alabama. Intra-class correlation showed a low degree of concordance between EAM and self-report (ICC = 0.33, 95% bootstrap CI 0.13, 0.59). 83% of data collected via EAM was transmitted in real-time. Due to technological failures, 11.4% were not transmitted in real-time, but were later recovered, and 5.7% were lost entirely. Acceptability was examined through surveys and qualitative interviews. Results suggest that EAM monitoring is acceptable and feasible in a rural US setting; however, technological difficulties, such as loss of connectivity may impede the device's usefulness for just-in-time adherence interventions.