Electronic Adherence Monitoring Research Articles

Electronic monitoring of patient-reported adherence and symptoms on oral chemotherapy at an academic and a community site.

283 Background: Monitoring of toxicity and adherence is often lacking for patients recently started on oral chemotherapy. National guidelines recommend active outreach to patients within a week after treatment start. We developed an online tool to actively reach out to patients newly started on oral chemotherapy at one academic medical center and community practice. Methods: A multi-disciplinary team, including patients, developed an online oral chemotherapy adherence, symptom, and financial toxicity assessment tool within REDCap. We implemented this tool for new oral chemotherapy prescriptions in May 2018 in the gastrointestinal oncology group of an academic medical center and a general community practice. To quantify the impact of this tool on symptom management, we completed a retrospective analysis of patients receiving new oral chemotherapy prescriptions at these same sites, in the 13 months immediately preceding clinical implementation of the online tool, May 2017 to May 2018. Results: In the pre-intervention historical cohort (n = 58) the median time to first symptom assessment by a clinician was 7 days (range 1 – 41 days, SD 7 days), median time to identifying a new or worsening symptom was 10 days (range 1-55 days, SD 10 days), and median time to clinical action regarding a new or worsening symptom was 10 days (range 1-104, SD 20 days). Our first intervention patient used the online tool in May 2018 to report symptoms of “nausea and fatigue,” 4 days after starting oral chemotherapy. This resulted in an oncology clinical nurse calling the patient to review symptom management by phone. Conclusions: The median time to first symptom assessment in our historical control cohort is 7 days, with standard deviation of 7 days, suggesting potential room for improvement. Thus far, the online tool has been completed by one patient. Further data will be reported regarding the uptake of this tool, the tool’s impact on quality measures, and patient reported symptoms, adherence, and financial toxicity.

Actionable Adherence Monitoring: Technological Methods to Monitor and Support Adherence to Antiretroviral Therapy.

Current digital technologies are being used for "actionable adherence monitoring"; that is, technologies that can be used to identify episodes of non-adherence to ART in a timely manner such that tailored interventions based on adherence data can be provided when and where they are needed most. Current digital communication technologies used to monitor ART adherence include electronic adherence monitors (EAMs), digital ingestion monitors, cellular phones, and electronic pharmacy refill tracking systems. Currently available real-time adherence monitoring approaches based on cellular technology allow for the delivery of interventions precisely when and where they are needed. Such technology can potentially enable significant efficiency of care delivery and impact on adherence and associated clinical outcomes. Standard digital advances, such as automated reminders in EAM and electronic pharmacy records, may also achieve improvements with relatively lower cost and easier implementation. Future research is needed to improve the functionality of these approaches, with attention paid to system-level issues through implementation science, as well as acceptability and ethical considerations at the individual level.

Medication adherence is associated with an increased risk of cancer in kidney transplant recipients: a cohort study.

Nonadherence to posttransplant immunosuppressive medication is associated with increased rates of rejection and graft loss, yet it is unknown to what degree ideal adherence is associated with the sequelae of overimmunosuppression. Specifically, we questioned whether excellent adherence increased the posttransplant cancer risk. Between August 1998 and August 2006, 195 consenting kidney transplant recipients had electronic monitoring of theirimmunosuppressive medication adherence. Based on their average quantitative adherence to a single immunosuppressant drug over the first 6 months posttransplant, recipients were grouped into adherence tertiles (highest, >97.9% adherence; middle, 91-97.8%; lowest, <91%). The cumulative incidence of cancer was calculated for patients in each tertile, treating death as a competing risk. The association between adherence and cancer rate was calculated after adjusting for recipient risk factors, using a competing risk proportional hazards model. The median duration of follow-up was 10.1 years. The 10-year estimated cumulative cancer incidence was 59.4% in the most adherent, 36.1% in the middle group and 38.1% in the least adherent group (P = 0.006). Excluding nonmelanocytic skin cancers, cancer incidence remained significantly higher in the highest adherence group (P = 0.002). These data provide additional support for the need to individualize immunosuppression to minimize both rejection and immunosuppressive drug-related complications including cancer.

Ugandan Study Participants Experience Electronic Monitoring of Antiretroviral Therapy Adherence as Welcomed Pressure to Adhere.

Many new technologies monitor patients' and study participants' medical adherence. Some have cautioned that these devices transgress personal autonomy and ethics. But do they? This qualitative study explored how Ugandan study participants perceive the effect of electronic monitoring of their adherence to antiretroviral therapy (ART) on their freedoms to be non-adherent and pursue other activities that monitoring may inadvertently expose. Between August 2014 and June 2015, we interviewed 60 Ugandans living with HIV and enrolled in the Uganda AIDS Rural Treatment Outcomes (UARTO) study, a longitudinal, observational study involving electronic adherence monitors (EAMs) to assess ART adherence. We also interviewed 6 UARTO research assistants. Both direct and indirect content analysis were used to interpret interview transcripts. We found that monitoring created a sense of pressure to adhere to ART, which some participants described as "forcing" them to adhere. However, even participants who felt that monitoring forced them to take medications perceived using the EAM as conducive to their fundamental goal of high ART adherence. Overall, even if monitoring may have limited participants' effective freedom to be non-adherent, participants welcomed any such effect. No participant rejected the EAM on the grounds that it would limit that effective freedom. Reports that monitoring altered behaviors unrelated to pill-taking were rare. Researchers should continue to be vigilant about the ways in which behavioral health monitoring affects autonomy, but should also recognize that even autonomy-limiting monitoring strategies may enable participants to achieve their own goals.

Identifying Tyrosine Kinase Inhibitor Nonadherence in Chronic Myeloid Leukemia: Subanalysis of TAKE-IT Pilot Study

BackgroundThere are inconsistencies in reports on correlates for nonadherence (NA) to tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML). The diagnostic accuracy of subjective adherence measures using electronic monitoring (EM) as the reference standard is yet to be determined. This study aimed to evaluate correlates of TKI NA using EM and test the diagnostic accuracy of subjective adherence measures. Patients and MethodsCML patients receiving a TKI for any duration were enrolled at 4 hematology institutes, and adherence was measured for 4 months. EM adherence was the reference adherence measure, expressed as the percentage of days with the drug taken as prescribed. Subjective adherence was measured using the Basel Assessment of Adherence to Immunosuppressive Medications Scale (BAASIS) self-report and clinician-reported visual analog scale (VAS) at 2 time points. Baseline theory–derived correlates of NA were identified using single and multiple regression analysis. The diagnostic accuracy of BAASIS and clinician-reported VAS was tested against an exploratory EM NA cutoff of < 95%. ResultsThe median EM adherence (n = 55) was 97.5% (range, 48-100%), while the 25th percentile was 92.1%. Lack of membership in a CML patient support group, living alone, and third-line treatment were associated with EM NA on multiple regression analysis. The BAASIS self-report (n = 94) had a sensitivity of 67% and a specificity of 71% for diagnosing NA, while clinician-reported VAS (n = 89) had a sensitivity of 78% and specificity of 42%. ConclusionA quarter of patients had potentially clinically meaningful NA. These NA correlates and the BAASIS provide a basis for identifying nonadherent patients who can be targeted by interventions.

A STANDARDIZED TRIPLE THERAPY COMBINED WITH ELECTRONIC MONITORING OF ADHERENCE NORMALIZES 24 H ABPM IN AT LEAST ONE THIRD OF PATIENTS WITH RESISTANT HYPERTENSION

Objective: Poor adherence to drug therapy is one of the most common factor explaining a poor blood pressures (BP) control in patients with apparent resistant hypertension. Few studies have used a standardized treatment coupled to electronic adherence monitoring in patients with apparent resistant hypertension. The objective of this study was to determine the rate of BP control when prescribing a standardized triple therapy associated with electronic monitoring of drug intake for 3 months and assessing BP control using 24 h ambulatory BP monitoring (ABPM). Design and method: Patients with 3 antihypertensive drugs and residual hypertension on 24 hr ABPM (excluding white coat hypertension) were recruited. A single pill combination of olmesartan 40 mg and amlodipine 10 mg was prescribed together with 25 mg chlorthalidone for 3 months. Medications were provided in 2 separated electronic pills boxes (MEMS®) recording the date and time of each opening. Patients were seen at 6 and 12 weeks. At 3 months, we analyzed MEMS® data and performed a second ABPM. Results: 49 patients (36% women) were included: 36 had complete data sets. Mean age was 56.5 ± 11.9 y, BMI was 31.2 ± 5.1 kg/m2. Overall, mean 24 h systolic BP (SBP) decreased from 148 ± 19 mmHg to 129 ± 16 mmHg (p < 0.001) and diastolic BP (DBP) decreased from 89.0 ± 16.1 mmHg to 77.5 ± 10.5 mmHg (p < 0.001). Overall 50 % of patients normalized SBP and 36% normalized both SBP and DBP. Median taking adherence (%) was respectively 92.5 % (interquartile range (IQR) 84.0–100) and 91.9 % (IQR 83.0–100) for the fixed combination and chlorthalidone. When analyzed according to tertiles of adherence, decreases in SBP were respectively 27 ± 16.6 mmHg in tertile 1 (99–100%), 20 ± 31 mmHg in tertile 2 (89–98.9%) and 9.1 ± 15.4 mmHg. in tertile 3 (0–88%) (p = 0.027 for trend). Conclusions: A simplified standardized antihypertensive therapy together with an electronic monitoring of adherence enables to normalize BP in more than 1/3 of patients with apparent resistant hypertension and no white coat effect. BP reduction correlates with the level of adherence. This diagnostic and supportive strategy may prevent from expensive and unnecessary investigations.

Can we increase smokers' adherence to nicotine replacement therapy and does this help them quit?

To examine the effects of five intervention components on smokers' adherence to combined nicotine patch and nicotine gum during a quit attempt and assess whether adherence is related to cessation. Smokers interested in quitting (N = 513; 59% female; 87% White) received nicotine patch plus nicotine gum and participated in a 2x2x2x2x2 randomized factorial experiment (i.e., 32 treatment conditions) evaluating five intervention components: (1) medication adherence counseling versus none; (2) automated medication adherence calls versus none; (3) electronic medication monitoring with feedback and counseling versus e-monitoring alone; (4) 26 versus 8weeks of nicotine patch plus nicotine gum; and (5) maintenance counseling versus none. Adherence was assessed over the first 6weeks post-target quit day via timeline follow-back (nicotine patch) and electronic medication dispenser (gum). In the first 6weeks post-quit day, 12% of participants used no patches or gum, and 40% used the patch every day. Only 1.4% used both patch and gum adherently every day in the 6weeks post-target quit day. E-monitoring counseling increased gum use (from 1.9 to 2.6 pieces/day; p < .001) but did not increase abstinence. More adherent patch and gum use in the first 6weeks were each associated with higher point-prevalence abstinence rates through 1year. This large experiment with electronic monitoring of nicotine gum adherence showed that e-monitoring counseling increased gum use but not abstinence. Adherence to nicotine patch and to gum were each strongly related to abstinence, but it is unclear whether adherence increases abstinence, or relapse causes medication discontinuation. ClinicalTrials.gov NCT01120704.

Asthma across the lifespan: Time for a paradigm shift

We have a unique opportunity to significantly reduce the worldwide burden of asthma in children and affect respiratory outcomes in adults. However, this will require a paradigm shift that is directed at altering the natural history of asthma, reducing asthma exacerbations, and preventing long-term adverse outcomes of childhood asthma. Attention should continue to be directed toward minimizing risk, as well as impairment, with a goal to achieve optimal control. Based on several National Institutes of Health studies conducted over the last 10years, we now have the tools necessary to accomplish this goal. The tools include assessment of lung function over time or defining trajectories of lung growth, the Composite Asthma Severity Index score, a panel of useful biomarkers, the Seasonal Asthma Exacerbation Prediction Index score, and rapidly advancing technology that includes adherence monitoring. Future guideline revisions should consider incorporating recommendations to follow spirometry over time and defining trajectories of lung growth to assess risk for reduced lung growth and early decline, asthma burden by using biomarkers to select and monitor therapy, assessment of social determinants of health, evaluation of risk for seasonal exacerbations, and consideration of electronic adherence monitoring for difficult-to-manage asthma. Guidelines should continue to include a core dedicated to the diagnosis and treatment of intermittent and mild and moderate persistent asthma and include additional sections dedicated to the management of severe asthma.

Electronic monitoring of adherence to inhaled corticosteroids: an essential tool in identifying severe asthma in children.

International guidelines recommend that severe asthma can only be diagnosed after contributory factors, including adherence, have been addressed. Accurate assessment of adherence is difficult in clinical practice. We hypothesised that electronic monitoring in children would identify nonadherence, thus delineating the small number with true severe asthma.Asthmatic children already prescribed inhaled corticosteroids were prospectively recruited and persistence of adherence assessed using electronic monitoring devices. Spirometry, airway inflammation and asthma control were measured at the start and end of the monitoring period.93 children (62 male; median age 12.4 years) were monitored for a median of 92 days. Median (range) monitored adherence was 74% (21-99%). We identified four groups: 1) good adherence during monitoring 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%. No clinical parameter prior to monitoring distinguished these groups.Electronic monitoring is a useful tool for identifying children in whom a step up in treatment is indicated. Different approaches are needed in those who are controlled when adherent or who are nonadherent. Electronic monitoring is essential in a paediatric severe asthma clinic.

Adherence to Oral Medications During Maintenance Therapy Among Children and Adolescents With Acute Lymphoblastic Leukemia: A Medication Refill Analysis.

Adherence to oral medications during maintenance therapy is essential for pediatric patients with acute lymphoblastic leukemia. Self-reported or electronic monitoring of adherence indicate suboptimal adherence, particularly among particular sociodemographic groups. This study used medication refill records to examine adherence among a national sample of pediatric patients with acute lymphoblastic leukemia. Patients in a national claims database, aged 0 to 21 years with a diagnosis of acute lymphoblastic leukemia and in the maintenance phase of treatment, were included. Medication possession ratios were used as measures of adherence. Overall adherence and adherence by sociodemographic groups were examined. Adherence rates were 85% for 6-mercaptopurine and 81% for methotrexate. Adherence was poorer among patients 12 years and older. Oral medication adherence rates were suboptimal and similar to or lower than previously documented rates using other methods of assessing adherence. Refill records offer a promising avenue for monitoring adherence. Additional work to identify groups most at-risk for poor adherence is needed. Nurses are well positioned to routinely monitor for medication adherence and to collaborate with the multidisciplinary team to address barriers to adherence.

Utility of Different Adherence Measures for PrEP: Patterns and Incremental Value

Measuring PrEP adherence remains challenging. In 2009–2010, the International AIDS Vaccine Initiative randomized phase II trial participants to daily tenofovir disoproxil fumarate/emtricitabine or placebo in Uganda and Kenya. Adherence was measured by electronic monitoring (EM), self-report (SR), and drug concentrations in plasma and hair. Each adherence measure was categorised as low, moderate, or high and also considered continuously; the incremental value of combining measures was determined. Forty-five participants were followed over 4 months. Discrimination for EM adherence by area under receiver operating curves (AROC) was poor for SR (0.53) and best for hair (AROC 0.85). When combining hair with plasma or hair with self-report, discrimination was improved (AROC > 0.9). Self-reported adherence was of low utility by itself. Hair level was the single best PK measure to predict EM-assessed adherence; the other measurements had lower discrimination values. Combining short-term (plasma) and long-term (hair) metrics could be useful to assess patterns of drug-taking in the context of PrEP.

Comparative Long-Term Effect of Three Anti-P2Y12 Drugs after Percutaneous Angioplasty: An Observational Study Based on Electronic Drug Adherence Monitoring.

Aims: Dual platelet inhibition using anti-P2Y12 drugs and aspirin is the standard of care in patients after percutaneous coronary interventions (PCI). Prasugrel and ticagrelor have been shown to be more potent than clopidogrel with less high on-treatment platelet reactivity. Whether differences in long-term adherence to these drugs can partly explain different antiplatelet efficacy has not been studied so far. The objective was to compare the long-term P2Y12 receptor inhibition and drug adherence to different anti-P2Y12 drugs, and to assess the impact of adherence on the pharmacodynamic effect.Methods: Monocentric, prospective, observational study. Stable outpatients treated with clopidogrel 75 mg once daily, prasugrel 10 mg once daily or ticagrelor 90 mg twice daily after PCI with stent implantation were included. Drug adherence was recorded during 6 months using electronic monitoring. Platelet responsiveness was assessed with the vasodilator-stimulated phosphoprotein platelet reactivity index (VASP-PRI) at inclusion, 3 and 6 months.Results: 120 patients had VASP-PRI and adherence data available. At 6-months, mean VASP-PRI (±SD) was 17.7 ± 11.0% with ticagrelor, 29.2 ± 15.5% with prasugrel and 47.2 ± 17.6% with clopidogrel (ANOVA, P < 0.0001).Median [IQR] taking adherence was 96 [82–100]% with ticagrelor, 100 [97–101]% with prasugrel and 100 [99–101]% with clopidogrel (p = 0.0001). Median [IQR] correct dosing was 88 [73–95]% with ticagrelor, 97 [92.5–98]% with prasugrel and 98 [96–99]% with clopidogrel (p = 0.0001).Anti-P2Y12 drug (p ≤ 0.001) and diabetes (p = 0.014) emerged as predictors of poor antiplatelet response after adjusting for age, BMI, sex, and CYP2C19∗2 carriers status.Conclusion: Drug adherence to anti-P2Y12 drugs assessed with electronic monitoring was very high. However, anti-P2Y12 drugs showed significant differences in antiplatelet activity, with newer anti-P2Y12 drugs ticagrelor and prasugrel exerting a stronger P2Y12 receptor inhibition.These data suggest that pharmacokinetic-pharmacodynamic differences between oral anti-P2Y12 drugs are more important than adherence in determining antiplatelet efficacy when adherence to prescription is high.The study was registered (Current Controlled Trials ISRCTN85949729).

Electronic adherence monitoring device performance and patient acceptability: a randomized control trial

ABSTRACTBackground: To investigate the performance and patient acceptability of an inhaler electronic monitoring device in a real-world childhood asthma population.Methods: Children 6 to 15 years presenting with asthma to the hospital emergency department and prescribed inhaled corticosteroids were included. Participants were randomized to receive a device with reminder features enabled or disabled for use with their preventer. Device quality control tests were conducted. Questionnaires on device acceptability, utility and ergonomics were completed at six months.Results: A total of 1306 quality control tests were conducted; 84% passed pre-issue and 87% return testing. The most common failure reason was actuation under-recording. Acceptability scores were high, with higher scores in the reminder than non-reminder group (median, 5th-95th percentile: 4.1, 3.1–5.0 versus 3.7, 2.3–4.8; p < 0.001). Most (>90%) rated the device easy to use. Feedback was positive across five themes: device acceptability, ringtone acceptability, suggestions for improvement, effect on medication use, and effect on asthma control.Conclusions: This study investigates electronic monitoring device performance and acceptability in children using quantitative and qualitative measures. Results indicate satisfactory reliability, although failure rates of 13–16% indicate the importance of quality control. Favorable acceptability ratings support the use of these devices in children.

Investigating the feasibility of text message reminders to improve adherence to nebulized medication in children and adolescents with cystic fibrosis.

BackgroundChildren with cystic fibrosis (CF) often have suboptimal adherence rates to nebulized medication. Adherence barriers cited include forgetting to take the nebulizers, due to busy home and social lives. Text message reminders have been shown to be effective at improving adherence rates in other chronic diseases such as asthma and diabetes.ObjectiveThe objective of this study was to assess the feasibility and efficacy of sending text reminders for a prolonged period of time to children with CF.Materials and methodsChildren with CF aged 5–16 years taking at least one medication via a nebulizer with an electronic adherence monitor were consented for the study. Text message reminders were sent to participants and/or parents via the hospital’s automated text service, up to twice a day, for 6 months. The adherence rates for the 6-month text period were compared to the previous 6 months before the study. Rates were calculated for weekdays, weekends, and school holidays.ResultsSeventeen participants were recruited to the study, with a mean age of 12 years and a mean forced expiratory volume in 1 second (FEV1) of 81% predicted. Fifteen children completed the 6-month text period, and I-neb data were accurately analyzed for 13 participants. The mean adherence rate in the 6 months receiving texts was 80%, compared to 81% in the prior 6 months. Overall adherence rates on weekdays, weekends, and school holidays were equivalent during the 2 time periods. A subgroup of patients with moderate baseline adherence showed increased adherence during the text period, particularly at weekends.ConclusionIt is feasible to send text message reminders to children with CF, and they are amenable to this approach. Although text reminders do not increase rates in patients with existing optimal adherence, they may be of value in patients with more moderate baseline rates.

Comparison of six methods to estimate adherence in an ART-na\xefve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?

BackgroundIncomplete adherence to antiretroviral therapy (ART) results in virologic failure and resistance. It remains unclear which adherence measure best predicts these outcomes. We compared six patient-reported and objective adherence measures in one ART-naïve cohort in South Africa.MethodsWe recruited 230 participants from a community ART clinic and prospectively collected demographic data, CD4 count and HIV-RNA at weeks 0, 16 and 48. We quantified adherence using 3-day self-report (SR), clinic-based pill count (CPC), average adherence by pharmacy refill (PR-average), calculation of medication-free days (PR-gaps), efavirenz therapeutic drug monitoring (TDM) and an electronic adherence monitoring device (EAMD). Associations between adherence measures and virologic and genotypic outcomes were modelled using logistic regression, with the area under the curve (AUC) from the receiver operator characteristic (ROC) analyses derived to assess performance of adherence measures in predicting outcomes.ResultsAt week 48 median (IQR) adherence was: SR 100% (100–100), CPC 100% (95–107), PR-average 103% (95–105), PR-gaps 100% (95–100) and EAMD 86% (59–94), and efavirenz concentrations were therapeutic (>1 mg/L) in 92%. EAMD, PR-average, PR-gaps and CPC best predicted virological outcome at week 48 with AUC ROC of 0.73 (95% CI 0.61–0.83), 0.73 (95% CI 0.61–0.85), 0.72 (95% CI 0.59–0.84) and 0.64 (95% CI 0.52–0.76) respectively. EAMD, PR-gaps and PR-average were highly predictive of detection of resistance mutations at week 48, with AUC ROC of 0.92 (95% CI 0.87–0.97), 0.86 (0.67–1.0) and 0.83 (95% CI 0.65–1.0) respectively. SR and TDM were poorly predictive of outcomes at week 48.ConclusionEAMD and both PR measures predicted resistance and virological failure similarly. Pharmacy refill data is a pragmatic adherence measure in resource-limited settings where electronic monitoring is unavailable.Trial registration The trial was retrospectively registered in the Pan African Clinical Trials Registry, number PACTR201311000641402, on the 13 Sep 2013 (www.pactr.org). The first participant was enrolled on the 12th July 2012. The last patient last visit (week 48) was 15 April 2014

Real-time electronic adherence monitoring plus follow-up improves adherence compared with standard electronic adherence monitoring.

The impact of real-time electronic monitoring on antiretroviral therapy adherence warrants further study. We conducted an analysis of cohort participants that initially involved standard electronic adherence monitoring (EAM), followed by real-time EAM and home visits for sustained at least 48-h adherence interruptions. Immediately after switching between the two types of EAM, mean adherence among 112 participants increased from 84% to 93% and remained elevated for 6 months (P < 0.001). Real-time EAM is a promising approach for improving adherence.

Long-term adherence to topical psoriasis treatment can be abysmal: a 1-year randomized intervention study using objective electronic adherence monitoring.

Most people with psoriasis have limited disease that could be treated with topicals, but topical efficacy is limited by low short-term adherence. Psoriasis is a chronic disease, and long-term adherence is an even bigger problem. To determine how well medication is used in the long-term topical treatment of psoriasis and to assess the potential of an internet-based reporting intervention to improve treatment adherence and outcomes. An investigator-blinded, prospective study evaluated topical fluocinonide adherence in 40 patients with mild-to-moderate psoriasis over 12 months. Subjects were randomized in a 1 : 1 ratio to standard-of-care or internet-based reporting group. Adherence was objectively monitored with Medication Event Monitoring System® caps. Fifty per cent of subjects discontinued the treatment. Greater adherence was seen in the intervention group compared with the standard-of-care group (50% vs. 35%, P = 0·08). Psoriasis Area and Severity Index improved more in the intervention group at month 1 (1·61 vs. -0·12, P = 0·003), month 3 (2·50 vs. 0·79, P = 0·025) and month 12 (3·32 vs. 0·34, P = 0·038) than in the standard-of-care group. This study likely underestimates the challenge of long-term adherence, as adherence tends to be better in research studies than in clinical practice. This study also did not fully account for primary nonadherence. Adherence to topical treatment is low in the short term and decreased further in the long term, a considerable challenge for dermatologists to address. A reporting intervention may be one of the ways we can improve our patients' treatment outcomes.

STAAR: a randomised controlled trial of electronic adherence monitoring with reminder alarms and feedback to improve clinical outcomes for children with asthma

BackgroundSuboptimal adherence to inhaled steroids is common in children with asthma and is associated with poor disease control, reduced quality of life and even death. Previous studies using feedback of...

Tangled Up in Blue: Unraveling the Links Between Emotional Distress and Treatment Adherence in Type 2 Diabetes.

We conducted comprehensive assessments of emotional distress to examine relations with diabetes medication adherence over time. Ethnically and socioeconomically diverse adults treated for type 2 diabetes completed validated self-reports (SRs) for diabetes distress and depression, were administered semistructured depression interviews, and provided blood samples for A1C. Medication adherence among 104 participants was electronically monitored (EM) over the subsequent 3 months; validated SRs of medication adherence were also obtained. Hierarchical linear regression evaluated independent effects of diabetes distress and depression on adherence. Mean ± SD 3-month medication adherence was 76.1% ± 25.7% for EM and 83.7% ± 21.9% for SR. Higher levels of SR (P < 0.001) and interview-based (P < 0.05) depressive symptom severity (P < 0.05) and diabetes-related distress (P < 0.01) showed a significant bivariate association with EM and SR nonadherence. Regression models showed baseline diabetes distress was a significant independent predictor of EM (β = -0.29; P = 0.001) and SR adherence (β = -0.24; P < 0.02) at follow-up. SR depression was an independent predictor of EM and SR adherence and reduced the effects of diabetes distress to nonsignificance. Subsequent models indicated this effect was driven by somatic rather than cognitive-affective symptoms of depression. Results were consistent but weaker for interview-based depressive symptoms. Findings support diabetes-related distress and depression symptom severity as risk factors for type 2 diabetes medication nonadherence. Somatic symptoms captured by depression measures, but not cognitive-affective symptoms, independently predict nonadherence and should be further investigated as a potential link between emotional distress and nonadherence.

Comparison of Population Pharmacokinetics Based on Steady-State Assumption Versus Electronically Monitored Adherence to Lopinavir, Atazanavir, Efavirenz, and Etravirine: A Retrospective Study.

Population pharmacokinetic (PopPK) analyses often rely on steady state and full adherence to prescribed dosage regimen assumptions from data gathered during therapeutic drug monitoring (TDM). Nonadherence is common in chronic diseases such as HIV. This study evaluates the impact of adherence measurement by electronic monitoring on PopPK parameter estimation and individual concentration profile predictions, and also the influence of adherence issues on the clinical interpretation of a concentration measurement. Published PopPK models for lopinavir, atazanavir, efavirenz, and etravirine were applied to estimate PK parameters and individual concentrations in 140 HIV patients taking part in a medication adherence program using 2 dosing data sets. The first set included the last dose reported by the patient with steady-state and full adherence assumptions; the second set used detailed electronic dosing history. PopPK parameter estimates and individual predictions were compared between the 2 dosing entries. Clearance estimates and likewise predicted concentrations did not markedly differ between the 2 dosing histories. However, certain patterns of nonadherence such as sparse missed doses or consecutive missed doses lead to suboptimal drug exposure. The interpretation based on self-reported information would have concluded on a wrongly appropriate individual exposure. PopPK analysis assuming steady state with full adherence produced similar results to those based on detailed electronic dosing history reconciled with patients' allegations. Self-reported last dose intake appeared reliable for concentration predictions and therapeutic drug monitoring interpretation for most patients followed at the medication adherence program. Yet, clinicians should be aware that concentration predictions based on self-reported last dose intake might be overestimated in case of undetected patterns of nonadherence, increasing the risk of forthcoming therapeutic failure.