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BackgroundIndividuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. ObjectiveThis study aims to understand patients’ preferences for mobile health (mHealth) technology and how that technology can be integrated into patients’ routines, especially with regard to medication use. MethodsWe conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication–taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. ResultsWe conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants’ mean glycated hemoglobin A1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. ConclusionsThe participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

BackgroundAdherence to evidence-based therapies such as medications and exercise remains poor among patients after a myocardial infarction (MI). Text message reminders have been shown to improve rates of adherence to medication and exercise, but the existing studies have been of short duration. ObjectiveTwo single-center randomized controlled pilot trials were conducted to evaluate the impact of text message reminders over 12 months on adherence to cardiac medications and exercise among patients receiving cardiac rehabilitation after hospitalization for MI. MethodsIn the medication adherence trial, 34 patients were randomized to receive usual care alone or usual care plus daily text message reminders delivered at the time of day at which medications were to be taken. In the exercise adherence trial, 50 patients were randomized to receive usual care alone or usual care plus 4 daily text messages reminding them to exercise as directed. ResultsThe text message reminders led to a mean 14.2 percentage point improvement in self-reported medication adherence over usual care (P

Background Persistence to P2Y12 inhibitors after myocardial infarction (MI) remains low. Out-of-pocket cost is cited as a factor affecting medication compliance. We examined whether a copayment intervention affected 1-year persistence to P2Y12 inhibitors and clinical outcomes. Methods and Results In an analysis of ARTEMIS (Affordability and Real-World Antiplatelet Treatment Effectiveness After Myocardial Infarction Study), patients with MI discharged on a P2Y12 inhibitor were stratified by baseline out-of-pocket medication burden: low ($0-$49 per month), intermediate ($50-$149 per month), and high (≥$150 per month). The impact of the voucher intervention on 1-year P2Y12 inhibitor persistence was examined using a logistic regression model with generalized estimating equations. We assessed the rates of major adverse cardiovascular events among the groups using a Kaplan-Meier estimator. Among 7351 MI-treated patients at 282 hospitals, 54.2% patients were in the low copay group, 32.0% in the middle copay group, and 13.8% in the high copay group. Patients in higher copay groups were more likely to have a history of prior MI, heart failure, and diabetes compared with the low copay group (all P

Background Hospitals commonly provide a short-term supply of free P2Y12 inhibitors at discharge after myocardial infarction, but it is unclear if these programs improve medication persistence and outcomes. The ARTEMIS (Affordability and Real-World Antiplatelet Treatment Effectiveness After Myocardial Infarction Study) trial randomized hospitals to usual care versus waived P2Y12 inhibitor copayment costs for 1-year post-myocardial infarction. Whether the impact of this intervention differed between hospitals with and without pre-existing medication assistance programs is unknown. Methods and Results In this post hoc analysis of the ARTEMIS trial, we examined the associations of pre-study free medication programs and the randomized copayment voucher intervention with P2Y12 inhibitor persistence (measured by pharmacy fills and patient report) and major adverse cardiovascular events using logistic regression models including a propensity score. Among 262 hospitals, 129 (49%) offered pre-study free medication assistance. One-year P2Y12 inhibitor persistence and major adverse cardiovascular events risks were similar between patients treated at hospitals with and without free medication programs (adjusted odds ratio 0.93, 95% CI, 0.82-1.05 and hazard ratio 0.92, 95% CI, 0.80-1.07, respectively). The randomized copayment voucher intervention improved persistence, assessed by pharmacy fills, in both hospitals with (53.6% versus 44.0%, adjusted odds ratio 1.45, 95% CI, 1.20-1.75) and without (59.0% versus 48.3%, adjusted odds ratio 1.46, 95% CI, 1.25-1.70) free medication programs (Pinteraction=0.71). Differences in patient-reported persistence were not significant after adjustment. Conclusions While hospitals commonly report the ability to provide free short-term P2Y12 inhibitors, we did not find association of this with medication persistence or major adverse cardiovascular events among patients with insurance coverage for prescription medication enrolled in the ARTEMIS trial. An intervention that provided copayment assistance vouchers for 1 year was successful in improving medication persistence in hospitals with and without pre-existing short-term medication programs. Registration URL: https://www.clini​caltr​ials.gov/. Unique identifier: NCT02406677.

Objective: This study investigates patients' medication‐taking routines and the feasibility of harnessing habit formation through context cues and rewards to improve medication adherence. Methods: Semistructured qualitative interviews with patients with gout from an urban health care system were conducted to explore typical medication‐taking behavior, experiences using electronic pill bottles, barriers to adherence, existing context cues, and potential cues and rewards for habit‐forming behavior. Medication‐taking patterns were recorded for six weeks using electronic pill bottles before interviews to inform discussion. Transcribed interviews were analyzed to generate themes using codes developed by the study team, with representative quotations selected as illustrations. Results: We conducted interviews with 15 individuals (mean age 60.6 [SD 20.3] years, three women [20%], and nine White patients [60%]). Pill bottle–recorded adherence to urate‐lowering therapy (ULT) was high (mean 0.91 [SD 0.10]), and one patient was experiencing an active gout flare. Five key themes emerged: (1) reasons for nonadherence, (2) internal and external motivations for adherence, (3) structured routines around taking medications, (4) rewards for good medication adherence, and (5) the role of pill cap technology in medication‐taking. Conclusion: The importance of a predictable, structured routine in which participants could incorporate their medication‐taking behavior emerged as a key factor that promoted consistent adherence. Further, identifying context cues and reminders seemed to promote incorporation of medication‐taking into routines. Therefore, habit‐based interventions that use context cues to establish routines around medication‐taking may be a feasible strategy to improve adherence in patients with chronic conditions such as gout. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions High‐risk medication use is associated with an increased risk of adverse events, but little is known about its chronic utilization by key demographic groups. We aimed to study the associations between age, sex, and race/ethnicity with new chronic use of high‐risk medications in older adults.In this retrospective cohort study, we analyzed data from older adults aged ≥65 years enrolled in a national health insurer who started a high‐risk medication between 2017 and 2022 across 16 high‐risk medication classes. We used generalized estimating equations to estimate the associations between sociodemographic classifications and the onset of chronic high‐risk medication use after initiation (≥90 days' supply across ≥2 fills within 180 days). We adjusted the analyses for sociodemographic and clinical patient characteristics and added three‐way interaction terms for race/ethnicity, sex, and age to explore whether the outcome varied across different subgroups of race/ethnicity, age, and sex.Across 2,751,069 patients (mean age: 74 years [SD = 7], 72% White, 60% Female), 406,075 (15%) became new chronic users of ≥1 high‐risk medication. Compared to White older adults, Asian (RR = 0.81, 95% CI: 0.79–0.84), Black (RR = 0.92, 95% CI: 0.90–0.94), and Hispanic (RR = 0.85, 95% CI: 0.83–0.86) older adults had a lower risk of becoming new chronic users. Men had a higher risk compared to women (RR = 1.09, 95% CI: 1.08–1.10). Age was not significantly associated with new chronic high‐risk medication use (≥75 years: RR = 1.00, 95% CI: 1.00–1.01). We observed differences across some medication classes, like benzodiazepines, first‐generation antihistamines, and antimuscarinics for which non‐White older adults were at a higher risk. The joint presence of specific age, sex, and race/ethnicity characteristics decreased the risk of becoming a new chronic user (e.g., Hispanic/Female/65–74 years: RR = 0.96, 95% CI: 0.94–0.99).New chronic high‐risk medication use varied across older adults by sociodemographic characteristics, suggesting the need to individualize medication optimization approaches and better understand how systematic barriers in access to health care may influence differences in high‐risk medication use in older adults. [ABSTRACT FROM AUTHOR]

Background: High‐risk medications like benzodiazepines, sedative hypnotics, and antipsychotics are commonly prescribed for hospitalized older adults, despite guidelines recommending avoidance. Prior interventions have not fully addressed how physicians make such prescribing decisions, particularly when experiencing stress or cognitive overload. Simulation training may help improve prescribing decision‐making but has not been evaluated for overprescribing. Methods: In this two‐arm pragmatic trial, we randomized 40 first‐year medical resident physicians (i.e., interns) on inpatient general medicine services at an academic medical center to either intervention (a 40‐minute immersive simulation training) or control (online educational training) groups. The primary outcome was the number of new benzodiazepine, sedative hypnotic, or antipsychotic orders for treatment‐naïve older adults during hospitalization. Secondary outcomes included the same outcome by all providers, being discharged on one of the medications, and orders for related or control medications. Outcomes were measured using electronic health record data over each intern's service period (~2 weeks). Outcomes were evaluated using generalized estimating equations, adjusting for clustering. Results: In total, 522 treatment‐naïve older adult patients were included in analyses. Over follow‐up, interns prescribed ≥1 high‐risk medication for 13 (4.9%) intervention patients and 13 (5.0%) control patients. The intervention led to no difference in the number of new prescriptions (Rate Ratio [RR]: 0.85, 95%CI: 0.31–2.35) versus control and no difference in secondary outcomes. In secondary analyses, intervention interns wrote significantly fewer "as‐needed" ("PRN") order types for the high‐risk medications (RR: 0.29, 95%CI: 0.08–0.99), and instead tended to write more "one‐time" orders than control interns, though this difference was not statistically significant (RR: 2.20, 95%CI: 0.60–7.99). Conclusions: Although this simulation intervention did not impact total high‐risk prescribing for hospitalized older adults, it did influence how the interns prescribed, resulting in fewer PRN orders, suggesting possibly greater ownership of care. Future interventions should consider this insight and implementation lessons raised. Trial Registration: Clinicaltrials.gov(NCT04668248). [ABSTRACT FROM AUTHOR]

Purpose: To measure the impact of beliefs, expectations, side effects, and their combined effects on the risk for medication nonpersistence. Patients and methods: Using a cross-sectional design, individuals from Saskatchewan, Canada who started a new antihypertensive, cholesterol-lowering, or antihyperglycemic medication were surveyed about risk factors for nonpersistence including: (a) beliefs measured by a composite score of three questions asking about the threat of the condition, importance of the drug, and harm of the drug; (b) incident side effects attributed to treatment; and (c) expectations for side effects before starting treatment. Descriptive statistics and logistic regression models were used to quantify the influence of these risk factors on the outcome of nonpersistence. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated. Results: Among 3,029 respondents, 5.8% (n=177) reported nonpersistence within four months after starting the new drug. After adjustment for numerous covariates representing sociodemographics, health-care providers, medication experiences and beliefs, both negative beliefs (OR: 7.26, 95%CI: 4.98– 10.59) and incident side effects (OR: 8.00, 95%CI: 5.49– 11.68) were associated with the highest odds of nonpersistence with no evidence of interaction. In contrast, expectations for side effects before starting treatment exhibited an important interaction with incident side effects following treatment initiation. Among respondents with incident side effects (n=741, 24.5%), the risk for early nonpersistence was 11.5% if they indicated an expectation for side effects before starting the medication compared to 23.6% if they did not (adjusted OR: 0.38, 95%CI: 0.25– 0.60). Conclusion: Expectations for side effects may be a previously unrecognized but important marker of the probability to persist with treatment. A high percentage of new medication users appeared unprepared for the possibility of side effects from their new medication making them less resilient if side effects occur. [ABSTRACT FROM AUTHOR]