Designing mHealth Apps to Incorporate Evidence-Based Techniques for Prolonging User Engagement.

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.

A New Approach to Enhancing Engagement in eHealth Apps.

This viewpoint presents a 3-phase conceptual model of the process of user engagement with eHealth apps. We also describe how knowledge gleaned from psychosocial, behavioral, and cognitive science can be incorporated into this model to enhance user engagement with an eHealth app in each phase of the engagement process.

Understanding the elements of a holistic wound assessment.

Wounds have become a significant public health challenge and consume a large amount of healthcare resources. Wounds can have severe negative effects on patients' quality of life, causing psychological and social distress, and may lead to significant periods of lost employment, resulting in financial loss. For nurses to manage wounds effectively, they need to be competent in undertaking holistic wound assessments. This article supports nurses' understanding by providing information on various types of wounds and their differing aetiologies. It also outlines the elements of a wound assessment and discusses some of the challenges that may be encountered during this process.

Use of technology for care coordination initiatives for patients with mental health issues: a systematic literature review.

This systematic literature review investigates the use of technology for the coordination and management of mental health care with an emphasis on outcomes. Searches of MEDLINE/PubMed, Scopus, and EMBASE were conducted between January 1, 2003, and January 4, 2018, to identify articles that assessed patient outcomes associated with care coordination, evaluated technology to improve care, or discussed management of mental health care using technology. A total of 21 articles were included in a qualitative review based on the recommendations set forth by the PRISMA statement. Among the various health technologies, electronic health records were most commonly used for care coordination, with primary care being the most frequent setting. Care coordination was shown to provide easier patient access to health care providers and to improve communication between caregiver and patient, especially in cases where geographic location or distance is a challenge. Barriers to coordinated care included, but were not limited to, insufficient funding for health information technology, deficient reimbursement plans, limited access to technologies, cultural barriers, and underperforming electronic health record templates. In conclusion, many studies showed the benefit of coordinated and collaborative care through the use of technology; however, further research and development efforts are needed to continue technological innovation for advanced patient care.

Expert Consensus Survey on Digital Health Tools for Patients With Serious Mental Illness: Optimizing for User Characteristics and User Support.

BACKGROUND: Digital technology is increasingly being used to enhance health care in various areas of medicine. In the area of serious mental illness, it is important to understand the special characteristics of target users that may influence motivation and competence to use digital health tools, as well as the resources and training necessary for these patients to facilitate the use of this technology. OBJECTIVE: The aim of this study was to conduct a quantitative expert consensus survey to identify key characteristics of target users (patients and health care professionals), barriers and facilitators for appropriate use, and resources needed to optimize the use of digital health tools in patients with serious mental illness. METHODS: A panel of 40 experts in digital behavioral health who met the participation criteria completed a 19-question survey, rating predefined responses on a 9-point Likert scale. Consensus was determined using a chi-square test of score distributions across three ranges (1-3, 4-6, 7-9). Categorical ratings of first, second, or third line were designated based on the lowest category into which the CI of the mean ratings fell, with a boundary >6.5 for first line. Here, we report experts' responses to nine questions (265 options) that focused on (1) user characteristics that would promote or hinder the use of digital health tools, (2) potential benefits or motivators and barriers or unintended consequences of digital health tool use, and (3) support and training for patients and health care professionals. RESULTS: Among patient characteristics most likely to promote use of digital health tools, experts endorsed interest in using state-of-the-art technology, availability of necessary resources, good occupational functioning, and perception of the tool as beneficial. Certain disease-associated signs and symptoms (eg, more severe symptoms, substance abuse problems, and a chaotic living situation) were considered likely to make it difficult for patients to use digital health tools. Enthusiasm among health care professionals for digital health tools and availability of staff and equipment to support their use were identified as variables to enable health care professionals to successfully incorporate digital health tools into their practices. The experts identified a number of potential benefits of and barriers to use of digital health tools by patients and health care professionals. Experts agreed that both health care professionals and patients would need to be trained in the use of these new technologies. CONCLUSIONS: These results provide guidance to the mental health field on how to optimize the development and deployment of digital health tools for patients with serious mental illness.

Predicting Future High-Cost Schizophrenia Patients Using High-Dimensional Administrative Data.

BACKGROUND: The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions. OBJECTIVES: The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods. METHODS: This was a retrospective study using a payer administrative database. The study cohort consisted of 97,862 patients diagnosed with schizophrenia (ICD9 code 295.*) from January 2009 to June 2014. Training (n = 34,510) and study evaluation (n = 30,077) cohorts were derived based on 12-month observation and prediction windows (PWs). The target was average total cost/patient/month in the PW. Three models (baseline, intermediate, final) were developed to assess the value of different variable categories for cost prediction (demographics, coverage, cost, health-care utilization, antipsychotic medication usage, and clinical conditions). Scalable orthogonal regression, significant attribute selection in high dimensions method, and random forests regression were used to develop the models. The trained models were assessed in the evaluation cohort using the regression R2, patient classification accuracy (PCA), and cost accuracy (CA). The model performance was compared to the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) model. RESULTS: At top 10% cost cutoff, the final model achieved 0.23 R2, 43% PCA, and 63% CA; in contrast, the CMS-HCC model achieved 0.09 R2, 27% PCA with 45% CA. The final model and the CMS-HCC model identified 33 and 22%, respectively, of total cost at the top 10% cost cutoff. CONCLUSION: Using advanced feature selection leveraging detailed health care, medication utilization features, and supervised machine learning methods improved the ability to predict and identify future high-cost patients with schizophrenia when compared with the CMS-HCC model.

Expert Consensus Survey on Medication Adherence in Psychiatric Patients and Use of a Digital Medicine System.

BACKGROUND: There is an unmet need to objectively assess adherence problems that are a common cause of unexplained or unexpected suboptimal outcome. A digital medicine system (DMS) has been developed to address this need in patients with serious mental illness. OBJECTIVE: To conduct a quantitative expert consensus survey to (1) assess relative importance of causes of suboptimal outcomes, (2) examine modalities used to assess adherence, (3) provide guidance on when and how to use the DMS in clinical practice once available, and (4) suggest interventions for specific reasons for nonadherence. METHODS: A panel of 58 experts in psychiatry completed a 23-question survey (October 13 through December 23, 2013) and rated their responses on a 9-point Likert scale. A χ² test of score distributions was used to determine consensus (P < .05). RESULTS: The panel rated adherence as the most important factor in suboptimal outcomes and yet the least likely to be assessed accurately. All predefined uses of the DMS received high mean first-line ratings (≥ 7.4). The experts recognized the utility of the DMS in managing adherence problems, identified clinical situations appropriate for DMS, and assessed potential benefits and challenges of this technology. Consensus was reached on first-line interventions for 10 of 11 reasons for nonadherence. CONCLUSIONS: The results provide a guide to clinicians on the evaluation of suboptimal outcomes, when and how to use the DMS, and the most appropriate interventions to address detected adherence problems.

Why do psychiatric patients stop antipsychotic medication? A systematic review of reasons for nonadherence to medication in patients with serious mental illness.

BACKGROUND: Antipsychotic medication reduces the severity of serious mental illness (SMI) and improves patient outcomes only when medicines were taken as prescribed. Nonadherence to the treatment of SMI increases the risk of relapse and hospitalization and reduces the quality of life. It is necessary to understand the factors influencing nonadherence to medication in order to identify appropriate interventions. This systematic review assessed the published evidence on modifiable reasons for nonadherence to antipsychotic medication in patients with SMI. METHODS: Articles published between January 1, 2005, and September 10, 2015, were searched on MEDLINE through PubMed. Abstracts were independently screened by 2 randomly assigned authors for inclusion, and disagreement was resolved by another author. Selected full-text articles were divided among all authors for review. RESULTS: A qualitative analysis of data from 36 articles identified 11 categories of reasons for nonadherence. Poor insight was identified as a reason for nonadherence in 55.6% (20/36) of studies, followed by substance abuse (36.1%, 13/36), a negative attitude toward medication (30.5%, 11/36), medication side effects (27.8%, 10/36), and cognitive impairments (13.4%, 7/36). A key reason directly associated with intentional nonadherence was a negative attitude toward medication, a mediator of effects of insight and therapeutic alliance. Substance abuse was the only reason consistently associated with unintentional nonadherence, regardless of type and stage of SMI. DISCUSSION: Although adherence research is inherently biased because of numerous methodological limitations and specific reasons under investigation, reasons for nonadherence consistently identified as significant across studies likely reflect valid existing associations with important clinical implications. CONCLUSION: This systematic review suggests that a negative attitude toward medication and substance abuse are consistent reasons for nonadherence to antipsychotic medication among people with SMI. Adherence enhancement approaches that specifically target these reasons may improve adherence in a high-risk group. However, it is also important to identify drivers of poor adherence specific to each patient in selecting and implementing intervention strategies.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

BACKGROUND: Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system. METHODS: Data were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system. Cox models were applied to test the associations between delivery of services and re-incarceration. Additionally, machine learning was used to train and validate a predictive model to examine effects of non-modifiable risk factors (age, past arrests, mental health diagnosis) and modifiable risk factors (outpatient, medical and case management services, and use of a jail diversion program) on re-arrest outcome. RESULTS: An association was found between past arrests and admission to crisis stabilization services in this population (N = 10,307). Delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Predictive models linked non-modifiable and modifiable risk factors and outcomes and predicted the probability of re-arrests with fair accuracy (area under the receiver operating characteristic curve of 0.67). CONCLUSIONS: By modeling the complex interactions between risk factors, service delivery, and outcomes, systems of care might be better enabled to meet patient needs and improve outcomes.

Usability of a novel digital medicine system in adults with schizophrenia treated with sensor-embedded tablets of aripiprazole.

OBJECTIVE: Digital medicine system (DMS) is a novel drug-device combination that objectively measures and reports medication ingestion. The DMS consists of medication embedded with an ingestible sensor (digital medicine), a wearable sensor, and software applications. This study evaluated usability of the DMS in adults with schizophrenia rated by both patients and their health care providers (HCPs) during 8-week treatment with prescribed doses of digital aripiprazole. METHODS: Six US sites enrolled outpatients into this Phase IIa, open-label study (NCT02219009). The study comprised a screening phase, a training phase (three weekly site visits), and a 5-week independent phase. Patients and HCPs independently rated usability of and satisfaction with the DMS. RESULTS: Sixty-seven patients were enrolled, and 49 (73.1%) patients completed the study. The mean age (SD) of the patients was 46.6 years (9.7 years); the majority of them were male (74.6%), black (76.1%), and rated mildly ill on the Clinical Global Impression - Severity scale (70.1%). By the end of week 8 or early termination, 82.1% (55/67) of patients had replaced the wearable sensor independently or with minimal assistance, based on HCP rating. The patients used the wearable sensor for a mean (SD) of 70.7% (24.7%) and a median of 77.8% of their time in the trial. The patients contacted a call center most frequently at week 1. At the last visit, 78% (47/60) of patients were somewhat satisfied/satisfied/extremely satisfied with the DMS. CONCLUSION: A high proportion of patients with schizophrenia were able to use the DMS and reported satisfaction with the DMS. These data support the potential utility of the DMS in clinical practice.

Optimization of a Digital Medicine System in Psychiatry.

BACKGROUND: Nonadherence to medication compromises the effectiveness of psychiatric treatments in patients with serious mental illness (SMI). A newly developed digital medicine system (DMS) offers an opportunity to objectively assess and report patient medication adherence. DMS includes a wearable sensor that receives a data signal from a medication tablet with an embedded ingestible sensor after ingestion of the medication and transmits that data to the patient's mobile device to display health care information for the patient and treatment team. METHODS/RESULTS: Development of a DMS requires a program that investigates safety, tolerability, and usability of the system in patients with SMI. It necessitates rapid design adaptation of the individual components and the integrated system and human factors studies with the intended users. This article describes the program's methodology and shows results from 3 early studies, conducted in 2013 and 2014, to illustrate diversity of the programs' methodology. First, a standard 28-day study showed minimal skin irritation and demonstrated acceptable wearability of the wearable sensor. Second, a 16-week study provided usability feedback from patients with SMI and caregivers to improve the mobile application. Third, end-to-end bench-level integrated system testing led to multiple substudies of a master protocol (ClinicalTrials.gov identifier: NCT02091882) to investigate various aspects of the system (eg, ingestible sensor detection and latency). CONCLUSIONS: To develop a DMS in psychiatry, the system's multiple components must be considered simultaneously using various methodologies. A focus on usability, along with agile evaluation and feedback across studies, provides an optimal strategy for ensuring patient acceptance and successful regulatory review.

Developing a Digital Medicine System in Psychiatry: Ingestion Detection Rate and Latency Period.

BACKGROUND: A digital medicine system (DMS) has been developed to measure and report adherence to an atypical antipsychotic, aripiprazole, in psychiatric patients. The DMS consists of 3 components: ingestible sensor embedded in a medication tablet, wearable sensor, and secure mobile and cloud-based applications. An umbrella study protocol was designed to rapidly assess the technical performance and safety of the DMS in multiple substudies to guide the technology development. METHODS: Two sequential substudies enrolled 30 and 29 healthy volunteers between March-April 2014 and February-March 2015, respectively, to assess detection accuracy of the ingestible sensor by the DMS and the latency period between ingestion and detection of the ingestion by the wearable sensor or the cloud-based server. RESULTS: The first substudy identified areas for improvement using early versions of the wearable sensor and the mobile application. The second substudy tested updated versions of the components and showed an overall ingestion detection rate of 96.6%. Mean latency times for the signal transmission were 1.1-1.3 minutes (from ingestion to the wearable sensor detection) and 6.2-10.3 minutes (from the wearable sensor detection to the server detection). Half of transmissions were completed in < 2 minutes, and ~90% of ingestions were registered by the smartphone within 30 minutes of ingestion. No serious adverse events, discontinuations, or clinically significant laboratory/vital signs findings were reported. CONCLUSIONS: The DMS implementing modified versions of the smartphone application and the wearable sensor has the technical capability to detect and report tablet ingestion with high accuracy and acceptable latency time. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02091882.

Multi-Agent-Based Simulation of a Complex Ecosystem of Mental Health Care.

This paper discusses the creation of an Agent-Based Simulation that modeled the introduction of care coordination capabilities into a complex system of care for patients with Serious and Persistent Mental Illness. The model describes the engagement between patients and the medical, social and criminal justice services they interact with in a complex ecosystem of care. We outline the challenges involved in developing the model, including process mapping and the collection and synthesis of data to support parametric estimates, and describe the controls built into the model to support analysis of potential changes to the system. We also describe the approach taken to calibrate the model to an observable level of system performance. Preliminary results from application of the simulation are provided to demonstrate how it can provide insights into potential improvements deriving from introduction of care coordination technology.

Racial differences in antipsychotic use: Claims database analysis of Medicaid-insured patients with schizophrenia.

BACKGROUND: Database analyses have indicated that medical treatment for schizophrenia varies among racial groups. This study assessed antipsychotic use and healthcare utilization across races in Medicaid-insured patients with schizophrenia. METHODS: A Medicaid database of inpatient/outpatient medical claims and outpatient prescription claims for more than 28 million enrollees in 11 geographically diverse states was analyzed. The primary outcome, racial differences in antipsychotic use in 2012, was examined in 5 multivariable logistic regression models: (1) any antipsychotic, (2) first-generation (FG) long-acting injectables (LAIs), (3) FG oral antipsychotics, (4) second-generation (SG) LAIs, and (5) SG oral antipsychotics. RESULTS: Odds ratios and adjusted predicted probabilities were comparable for any antipsychotic use between black and white patients. Black patients were less likely to receive SG oral antipsychotics (P < .001) and more likely to receive SG or FG LAIs (P = .001 and P < .001, respectively) and FG oral antipsychotics (P = .003) vs white patients. Further, black patients had a higher mean number of emergency room visits (P < .001) and a lower mean number of hospitalizations (P < .05) vs white patients; the mean number of physician visits was comparable. CONCLUSIONS: Disparities in antipsychotic use and healthcare utilization across races in patients with schizophrenia warrant further investigation and elimination of these disparities should be a national goal.

Reduction in inpatient resource utilization and costs associated with long-acting injectable antipsychotics across different age groups of Medicaid-insured schizophrenia patients.

OBJECTIVE: Evaluate utilization of inpatient healthcare resources and associated costs after 12 months of treatment using long-acting injectable (LAI) antipsychotic medications among a large sample of Medicaid-insured patients categorized by different age groups. METHOD: Adult patients with schizophrenia were identified from the Thomson Reuters MarketScan Research database (1/1/2006-12/31/2010) before initiation of treatment using LAI antipsychotic agents. Utilization of inpatient healthcare resources and associated direct medical costs were compared for 12-month baseline and 12-month follow-up periods. RESULTS: Among 3,094 Medicaid-insured patients with schizophrenia initiating treatment with LAIs, the mean number of all-cause hospitalizations and hospitalization days were reduced by 24% and 31% (p<0.0001) compared with baseline, respectively, with similar significant reductions among all age groups (18-30, 31-40, 41-50, and 51-60 years). During 12-month follow-up with LAIs, mean reductions in all-cause costs were $4,369 (18-30 years, p<0.0001), $3,681 (31-40 years, p<0.0001), $2,051 (41-50 years, p=0.1332), and $4,492 (51-60 years, p=0.0107). Subanalyses separating first-generation and second-generation medication groups resulted in mean reduction in all-cause costs of $3,561 and $3,645, respectively. CONCLUSIONS: Results from this large cohort study provide naturalistic real-world evidence of the utility of LAIs in patients with schizophrenia and suggest that these agents may help to reduce the risk of relapse across all age groups (especially among younger patients). Given that relapse prevention is the ultimate goal of antipsychotic treatment, results from this large Medicaid patient population establish the value of LAIs for the management of schizophrenia.

Integrated multisystem analysis in a mental health and criminal justice ecosystem.

Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. These gaps in care may lead to increased mental health disease burden and relapse, as well as repeated incarcerations. Further, the complex health, social service, and criminal justice ecosystem within which the patient may be embedded makes it difficult to examine the role of modifiable risk factors and delivered services on patient outcomes, particularly given that agencies often maintain isolated sets of relevant data. Here we describe an approach to creating a multisystem analysis that derives insights from an integrated data set including patient access to case management services, medical services, and interactions with the criminal justice system. We combined data from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system. We applied Cox models to test the associations between delivery of services and re-incarceration. Using this approach, we found an association between arrests and crisis stabilization services in this population. We also found that delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Additionally, we used machine learning to train and validate a predictive model linking non-modifiable and modifiable risk factors and outcomes. A predictive model, constructed using elastic net regularized logistic regression, and considering age, past arrests, mental health diagnosis, as well as use of a jail diversion program, outpatient, medical and case management services predicted the probability of re-arrests with fair accuracy (AUC=.67). By modeling the complex interactions between risk factors, service delivery and outcomes, we may better enable systems of care to meet patient needs and improve outcomes.

Mental health needs of returning veterans.

As more service members return from war zones around the world, clinicians must be prepared to treat them. Veterans with PTSD and other mental disorders must overcome the stigma associated with seeking treatment, and clinicians must equip these patients and their family members to deal with challenging symptoms. Clinicians should learn about military culture and jargon to better understand these patients and should become familiar with veteran resources to direct veterans and their families to the appropriate services. Clinicians may also need to communicate with their patients' employers to help both parties deal with illnesses such as PTSD. A coordinated effort is needed to meet the needs of veterans and their families, and clinicians play an integral role in recognizing and meeting those needs.

Nitrite anion stimulates ischemic arteriogenesis involving NO metabolism.

Nitric oxide (NO) is a potential regulator of ischemic vascular remodeling, and as such therapies augmenting its bioavailability may be useful for the treatment of ischemic tissue diseases. Here we examine the effect of administering the NO prodrug sodium nitrite on arteriogenesis activity during established tissue ischemia. Chronic hindlimb ischemia was induced by permanent unilateral femoral artery and vein ligation. Five days postligation; animals were randomized to control PBS or sodium nitrite (165 µg/kg) therapy twice daily. In situ vascular remodeling was measured longitudinally using SPY angiography and Microfil vascular casting. Delayed sodium nitrite therapy rapidly increased ischemic limb arterial vessel diameter and branching in a NO-dependent manner. SPY imaging angiography over time showed that nitrite therapy enhanced ischemic gracillis collateral vessel formation from the profunda femoris to the saphenous artery. Immunofluorescent staining of smooth muscle cell actin also confirmed that sodium nitrite therapy increased arteriogenesis in a NO-dependent manner. The NO prodrug sodium nitrite significantly increases arteriogenesis and reperfusion of established severe chronic tissue ischemia.

The impact of the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) on prescribing practices: an analysis of data from a large midwestern state.

BACKGROUND: The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) was a series of effectiveness trials. The results of these trials began publication in September 2005. Among other findings, these studies were interpreted to suggest that (1) second-generation antipsychotics might have fewer advantages over first-generation antipsychotics than had been generally thought; (2) among the agents assessed, olanzapine had the best efficacy outcome; and (3) after treatment failure with a second-generation antipsychotic, the most efficacious second-line medication is clozapine. To examine the actual impact on practice of these publications, we looked at change in physician prescribing behavior based on these 3 conclusions before and after publication of CATIE. METHOD: Rates of antipsychotic medication prescriptions to 51,459 patients with an ICD-9 code of 295 for schizophrenia were extracted from a Missouri Medicaid claims database. χ² Tests were used to compare the rates of prescribing antipsychotic medications before and after each of 3 key CATIE publications (time 1 was September 2005, time 2 was December 2006, and time 3 was April 2006). RESULTS: At all time points, we demonstrated a decrease in prescriptions by all prescribers for olanzapine (P < .0001). One year after time 1, we found an increase in prescriptions by all prescribers for aripiprazole (P < .0001). No statistically significant increases in clozapine prescribing were observed. Also, a small but statistically significant increase was seen in prescriptions of perphenazine (P < .02 at time 3). However, this increase occurred only for prescriptions written by psychiatrists and not other prescribers. CONCLUSIONS: We found some evidence in our sample that the publication of the results from CATIE had a small but statistically significant effect on prescribing habits of psychiatrists but not other physicians in our sample population. However, larger changes occurred in prescribing behavior that were largely unrelated to the CATIE trial. We propose a hypothesis to explain the direction of observed changes.