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.

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.

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.

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.

Self-injurious behavior in the nursing home setting.

BACKGROUND: Self-injurious behavior (SIB) in older adults is defined as harm inflicted on oneself without conscious suicidal intent. SIB as a separate entity distinct from suicidal intent is poorly understood. However, it is of great concern to the patients' families and caregivers and it poses serious clinical challenges for clinicians. METHODS: We searched the database of PubMed, Ovid Medline, and ScienceDirect for reports published between 1970 and 2009 using combination of the following keywords: "self-injurious behavior", "self-destructive behavior", "self-mutilating behavior", "older adults", "geriatric population", and "nursing homes". The term "self-harm behavior" which also appears in the literature is broader in scope than "self-injurious behavior". It encompasses high suicide intent and failed suicide attempts; therefore, we excluded this term in order to focus purely on "self-injurious behavior". Our search yielded 10 publications concerning SIB in older adults, four of which included studies investigating SIB in nursing homes. RESULTS: Clinical studies of SIB in older adult nursing home residents are sparse. This limited literature suggests that SIB is a prevalent phenomenon and is reported to be as high as 14% in one study of nursing home subjects aged 65 and older. It is reported to be strongly associated with dementia and a risk of accidental death. It has been suggested that SIB among demented patients occurs in the context of poor impulse control and physical isolation. CONCLUSION: SIB is likely a common phenomenon in older adult nursing home residents. There is little evidence-based treatment guidance for SIB in older population.

Strategies for addressing adherence problems in patients with serious and persistent mental illness: recommendations from the expert consensus guidelines.

Poor adherence to medication can have devastating consequences for patients with serious mental illness. The literature review and recommendations in this article are reprinted from The Expert Consensus Guideline Series: Adherence Problems in Patients with Serious and Persistent Mental Illness, published in 2009. The expert consensus survey (39 questions, 521 options) on adherence problems in schizophrenia and bipolar disorder was completed by 41 experts in 2008. This article first reviews the literature on interventions aimed at improving adherence. It then presents the experts' recommendations for targeting factors that can contribute to nonadherence and relates them to the literature. The following psychosocial/programmatic and pharmacologic interventions were rated first line for specific problems that can lead to nonadherence: ongoing symptom/ side-effect monitoring for persistent symptoms or side effects; services targeting logistic problems; medication monitoring/environmental supports (e.g., Cognitive Adaptation Training, assertive community treatment) for lack of routines or cognitive deficits; and adjusting the dose or switching to a different oral antipsychotic for persistent side effects (also high second-line for persistent symptoms). Among pharmacologic interventions, the experts gave high second-line ratings to switching to a long-acting antipsychotic when lack of insight, substance use, persistent symptoms, logistic problems, lack of routines, or lack of family/ social support interfere with adherence and to simplifying the treatment regimen when logistic problems, lack of routines, cognitive deficits, or lack of family/social support interfere with adherence. Psychosocial/programmatic interventions that received high second-line ratings in a number of situations included medication monitoring/environmental supports, patient psychoeducation, more frequent and/or longer visits if possible, cognitive behavioral therapy (CBT), family-focused therapy, and services targeting logistic problems. It is important to identify specific factors that may be contributing to a patient's adherence problems in order to customize interventions and to consider using a multifaceted approach since multiple problems may be involved.

A hypothesized role for dendritic remodeling in the etiology of mood and anxiety disorders.

An elegant theory that links hippocampal neurogenesis to mood and anxiety disorders and to the mechanism of action of antidepressant drugs has gained widespread attention. However, depression and anxiety disorders involve multiple areas of the brain, such as the amygdala and prefrontal cortex, where neurogenesis does not appear to occur in the adult mammalian brain. A complementary theory is proposed here in which neurogenesis is seen as an epiphenomenon of a more widespread alteration in dendritic length and spine number. According to this theory, exposure to chronic stress and stressful life events increases excitotoxic glutamatergic neurotransmission in multiple brain areas. To protect neurons from consequent apoptosis, dendrites retract and spine number decreases, thus limiting the number of exposed glutamate receptors. Drugs that reduce glutamatergic neurotransmission under these circumstances, many of which have already been shown helpful in treating mood and anxiety disorders, may prevent this dendritic retraction and thus protect synaptic connections throughout the brain.

Assessment of clinician awareness of nonadherence using a new structured rating scale.

INTRODUCTION: Lack of adherence to prescribed antipsychotic medication is recognized as a leading reason for poor outcomes and symptomatic relapse among patients with schizophrenia. There is evidence, however, that treating clinicians are often either unaware that their patients are not taking their medication or overestimate their adherence. METHODS: A structured instrument, the Medication Adherence Assessment Tool (MAAT), was developed by an expert group of clinicians convened and sponsored by Ortho-McNeil Janssen Scientific Affairs, LLC. Clinicians were asked to use the MAAT to rate the degree of adherence among a group of their patients who were prescribed antipsychotic medications. We compared the results of the MAAT evaluation with a validated, indirect measure of treatment adherence derived from pharmacy data, the medication possession ratio (MPR). RESULTS: Although the MAAT has good internal reliability, we found that MAAT scores were not significantly correlated with MPR. Conclusion. These findings suggest that, even when using a structured instrument, clinicians are unable to accurately assess the degree of treatment adherence among patients prescribed antipsychotic medications. Informing clinicians as to measures of medication possession, such as the MPR, appears to be a low-cost, minimally intrusive, and effective way to improve clinician assessment of patient adherence and thereby overall clinical outcomes.

Assessment of adherence problems in patients with serious and persistent mental illness: recommendations from the Expert Consensus Guidelines.

Poor adherence to medication treatment can have devastating consequences for patients with serious mental illness. The literature review and recommendations in this article concerning assessment of adherence are reprinted from The Expert Consensus Guideline Series: Adherence Problems in Patients with Serious and Persistent Mental Illness, published in 2009. The expert consensus survey contained 39 questions (521 options) that asked about defining nonadherence, extent of adherence problems in schizophrenia and bipolar disorder, risk factors for nonadherence, assessment methods, and interventions for specific types of adherence problems. The survey was completed by 41 (85%) of the 48 experts to whom it was sent. When evaluating adherence, the experts considered it important to assess both behavior and attitude, although they considered actual behavior most important. They also noted the importance of distinguishing patients who are not willing to take medication from those who are willing but not able to take their medication as prescribed due to forgetfulness, misunderstanding of instructions, or financial or environmental problems, since this will affect the type of intervention needed. Although self- and physician report are most commonly used to clinically assess adherence, they are often inaccurate and may underestimate nonadherence. The experts believe that more accurate information will be obtained by asking about any problems patients are having or anticipate having taking medication rather than if they have been taking their medication; They also recommended speaking with family or caregivers, if the patient gives permission, as well as using more objective measures (e.g., pill counts, pharmacy records, smart pill containers if available, and, when appropriate, medication plasma levels). Use of a validated self-report scale may also help improve accuracy. For patients who appear adherent to medication, the experts recommended monthly assessments for adherence, with additional assessments if there is a noticeable symptomatic change. If there is concern about adherence, they recommended more frequent (e.g., weekly) assessments. The article concludes with suggestions for clinical interview techniques for assessing adherence.