OBJECTIVE: Study a quality improvement approach for implementing evidence-based employment services at specialty mental health clinics. DATA SOURCES/STUDY SETTING: Semistructured interviews with clinicians and administrators before, during, and after implementation. Qualitative field notes, structured baseline and follow-up interviews with patients, semistructured interviews with patients after implementation, and administrative data. STUDY DESIGN: Site-level controlled trial at four implementation and four control sites. Hybrid implementation-effectiveness study with mixed methods intervention evaluation design. DATA COLLECTION/EXTRACTION METHODS: Site visits, in-person and telephone interviews, patient surveys, patient self-assessment. A total of 801 patients completed baseline surveys and 53 clinicians and other clinical key stakeholders completed longitudinal qualitative interviews. PRINCIPAL FINDINGS: At baseline, sites varied in the availability, utilization, and quality of supported employment. Each site needed quality improvement for this service, though for differing reasons, with some needing development of the service itself and others needing increased service capacity. Improvements in knowledge, attitudes, beliefs, and referral behaviors were evident in mid- and postimplementation interviews, though some barriers persisted. Half of patients expressed an interest in working at baseline. Patients at implementation sites were 2.3 times more likely to receive employment services during the study year. Those who had a service visit were more likely to be employed at follow-up than those who did not. CONCLUSIONS: Studies of implementation and effectiveness require mixed methods to both enhance implementation in real time and provide context for interpretation of complex results. In this study, a quality improvement approach resulted in superior patient-level outcomes and improved clinician knowledge, attitudes, and behaviors, in the context of substantial variation among sites.

OBJECTIVE: Mobile technologies, such as smartphones, can improve health services by delivering assessments and interventions that reach people in their daily lives. There is, however, disagreement regarding whether people with serious mental illness make meaningful use of mobile technology and whether interventions that rely on mobile technology should be tailored for this population. METHODS: At two clinics, 249 people with serious mental illness were interviewed regarding mobile phone use, and their cognitive functioning was assessed. RESULTS: Mobile phones were used by 86% of participants, including 60% who used a smartphone. Phones were used for messaging by 81%, Internet by 52%, e-mail by 46%, and applications by 45%. Individuals who were older, had a persistent psychotic disorder rather than bipolar disorder, received disability income, or had worse neurocognitive functioning were less likely to own a smartphone (χ2=52.7, p<0.001). CONCLUSIONS: Most patients with serious mental illness owned a mobile phone; a majority owned a smartphone. Developers should consider tailoring mobile interventions for psychosis and cognitive deficits.

BACKGROUND: People with serious mental illness have high rates of obesity and related medical problems, and die years prematurely, most commonly from cardiovascular disease. Specialized, in-person weight management interventions result in weight loss in efficacy trials with highly motivated patients. In usual care, patient enrollment and retention are low with these interventions, and effectiveness has been inconsistent. OBJECTIVE: To determine whether computerized provision of weight management with peer coaching is feasible to deliver, is acceptable to patients, and is more effective than in-person delivery or usual care. DESIGN: Mixed-methods randomized controlled trial. PARTICIPANTS: Two hundred seventy-six overweight patients with serious mental illness receiving care at a Veterans Administration medical center. INTERVENTIONS: Patients were randomized to 1) computerized weight management with peer coaching (WebMOVE), 2) in-person clinician-led weight services, or 3) usual care. Both active interventions offered the same educational content. MAIN MEASURES: Body mass index; and feasibility and acceptability of the intervention. KEY RESULTS: At 6 months, in obese patients (n = 200), there was a significant condition by visit effect (F = 4.02, p = 0.02). The WebMOVE group had an average estimated BMI change from baseline to 6 months of 34.9 ± 0.4 to 34.1 ± 0.4. This corresponds to 2.8 kg (6.2 lbs) weight loss (t = 3.2, p = 0.001). No significant change in BMI was seen with either in-person services (t = 0.10, p = 0.92), or usual care (t = -0.25, p = 0.80). The average percentage of modules completed in the WebMOVE group was 49% and in the in-person group was 41% (t = 1.4, p = 0.17). When non-obese patients were included in the analyses, there was a trend towards a condition by visit effect (F = 2.8, p = 0.06). WebMOVE was well received, while the acceptability of in-person services was mixed. CONCLUSIONS: Computerized weight management with peer support results in lower weight, and can have greater effectiveness than clinician-led in-person services. This intervention is well received, and could be feasible to disseminate.

BACKGROUND: There are unmet primary care needs among people with serious mental illness that might be improved with integrated care and medical care management. Many healthcare organizations have attempted to address this problem, but few interventions have been rigorously studied and found to be effective. OBJECTIVE: Study the implementation and effectiveness of a novel, specialized primary care medical home designed to improve the healthcare of patients with serious mental illness. DESIGN, SETTING, AND PARTICIPANTS: Clustered controlled trial for a median of 401 days. One Veterans Health Administration medical center was assigned to intervention and two were assigned to usual care (control). Thirty-nine clinicians and managers were included in the study, as well as 331 patients who met eligibility criteria. INTERVENTION: A specialized medical home with systematic patient engagement, proactive nurse panel management, a collaborative care psychiatrist, and a primary care physician providing care that included psychiatric treatment. MAIN MEASURES: Quality of care, chronic illness care and care experience, symptoms, and quality of life. KEY RESULTS: Sixty-five intervention patients (40%) moved all psychiatric care to the primary care team. No adverse events were attributable to the intervention. Compared with control, intervention patients had greater improvement over time in appropriate screening for body mass index, lipids, and glucose (χ2 = 6.9, 14.3, and 3.9; Ps < .05); greater improvement in all domains of chronic illness care (activation, decision support, goal-setting, counseling, coordination) and care experience (doctor-patient interaction, shared decision-making, care coordination, access; F for each 10-24, Ps < .05); and greater improvement in mental health-related quality of life (F = 3.9, P = .05) and psychotic symptoms (F = 3.9, P = .05). CONCLUSION: A primary care medical home for serious mental illness can be feasible to implement, safe, and more effective than usual care. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01668355.

Objective

Psychostimulants are partially effective in reducing cognitive dysfunction associated with attention-deficit/hyperactivity disorder (ADHD). Cognitive effects of guanfacine, an alternative treatment, are poorly understood. Given its distinct action on α2A receptors, guanfacine may have different or complementary effects relative to stimulants. This study tested stimulant and guanfacine monotherapies relative to combined treatment on cognitive functions important in ADHD.

Method

Children with ADHD (n = 182; aged 7-14 years) completed an 8-week, double blind, randomized, controlled trial with 3 arms: d-methylphenidate (DMPH), guanfacine (GUAN), or combination treatment with DMPH and GUAN (COMB). A nonclinical comparison group (n = 93) had baseline testing, and a subset was retested 8 weeks later (n = 38). Analyses examined treatment effects in 4 cognitive domains (working memory, response inhibition, reaction time, and reaction time variability) constructed from 20 variables.

Results

The ADHD group showed impaired working memory relative to the nonclinical comparison group (effect size = -0.53 SD unit). The treatments differed in effects on working memory but not other cognitive domains. Combination treatment improved working memory more than GUAN but was not significantly better than DMPH alone. Treatment did not fully normalize the initial deficit in ADHD relative to the comparison group.

Conclusion

Combined treatment with DMPH and GUAN yielded greater improvements in working memory than placebo or GUAN alone, but the combined treatment was not superior to DMPH alone and did not extend to other cognitive domains. Although GUAN may be a useful add-on treatment to psychostimulants, additional strategies appear to be necessary to achieve normalization of cognitive function in ADHD.

Clinical trial registration information

Single Versus Combination Medication Treatment for Children With Attention Deficit Hyperactivity Disorder; http://clinicaltrials.gov/; NCT00429273.

Objective

Study a quality improvement approach for implementing evidence-based employment services at specialty mental health clinics.

Data sources/study setting

Semistructured interviews with clinicians and administrators before, during, and after implementation. Qualitative field notes, structured baseline and follow-up interviews with patients, semistructured interviews with patients after implementation, and administrative data.

Study design

Site-level controlled trial at four implementation and four control sites. Hybrid implementation-effectiveness study with mixed methods intervention evaluation design.

Data collection/extraction methods

Site visits, in-person and telephone interviews, patient surveys, patient self-assessment. A total of 801 patients completed baseline surveys and 53 clinicians and other clinical key stakeholders completed longitudinal qualitative interviews.

Principal findings

At baseline, sites varied in the availability, utilization, and quality of supported employment. Each site needed quality improvement for this service, though for differing reasons, with some needing development of the service itself and others needing increased service capacity. Improvements in knowledge, attitudes, beliefs, and referral behaviors were evident in mid- and postimplementation interviews, though some barriers persisted. Half of patients expressed an interest in working at baseline. Patients at implementation sites were 2.3 times more likely to receive employment services during the study year. Those who had a service visit were more likely to be employed at follow-up than those who did not.

Conclusions

Studies of implementation and effectiveness require mixed methods to both enhance implementation in real time and provide context for interpretation of complex results. In this study, a quality improvement approach resulted in superior patient-level outcomes and improved clinician knowledge, attitudes, and behaviors, in the context of substantial variation among sites.

Background

People with serious mental illness have high rates of obesity and related medical problems, and die years prematurely, most commonly from cardiovascular disease. Specialized, in-person weight management interventions result in weight loss in efficacy trials with highly motivated patients. In usual care, patient enrollment and retention are low with these interventions, and effectiveness has been inconsistent.

Objective

To determine whether computerized provision of weight management with peer coaching is feasible to deliver, is acceptable to patients, and is more effective than in-person delivery or usual care.

Design

Mixed-methods randomized controlled trial.

Participants

Two hundred seventy-six overweight patients with serious mental illness receiving care at a Veterans Administration medical center.

Interventions

Patients were randomized to 1) computerized weight management with peer coaching (WebMOVE), 2) in-person clinician-led weight services, or 3) usual care. Both active interventions offered the same educational content.

Main measures

Body mass index; and feasibility and acceptability of the intervention.

Key results

At 6 months, in obese patients (n = 200), there was a significant condition by visit effect (F = 4.02, p = 0.02). The WebMOVE group had an average estimated BMI change from baseline to 6 months of 34.9 ± 0.4 to 34.1 ± 0.4. This corresponds to 2.8 kg (6.2 lbs) weight loss (t = 3.2, p = 0.001). No significant change in BMI was seen with either in-person services (t = 0.10, p = 0.92), or usual care (t = -0.25, p = 0.80). The average percentage of modules completed in the WebMOVE group was 49% and in the in-person group was 41% (t = 1.4, p = 0.17). When non-obese patients were included in the analyses, there was a trend towards a condition by visit effect (F = 2.8, p = 0.06). WebMOVE was well received, while the acceptability of in-person services was mixed.

Conclusions

Computerized weight management with peer support results in lower weight, and can have greater effectiveness than clinician-led in-person services. This intervention is well received, and could be feasible to disseminate.

OBJECTIVE: Major obstacles to data harmonization in neuropsychology include lack of consensus about what constructs and tests are most important and invariant across healthy and clinical populations. This study addressed these challenges using data from the National Neuropsychology Network (NNN). METHOD: Data were obtained from 5,000 NNN participants and Pearson standardization samples. Analyses included variables from four instruments: Wechsler Adult Intelligence Scale, 4th Edition (WAIS-IV); Wechsler Memory Scale, 4th Edition (WMS-IV); California Verbal Learning Test, 3rd Edition (CVLT3); and Delis-Kaplan Executive Function System (D-KEFS). We used confirmatory factor analysis to evaluate models suggested by prior work and examined fit statistics and measurement invariance across samples. We examined relations of factor scores to demographic and clinical characteristics. RESULTS: For each instrument, we identified four first-order and one second-order factor. Optimal models in patients generally paralleled the best-fitting models in the standardization samples, including task-specific factors. Analysis of the NNN data prompted specification of a Recognition-Familiarity factor on the WMS-IV and an Inhibition-Switching factor on the D-KEFS. Analyses showed strong to strict factorial invariance across samples with expected differences in factor means and variances. The Recognition-Familiarity factor correlated with age more strongly in NNN than in the standardization sample. CONCLUSIONS: Factor models derived from healthy groups generally fit well in patients. NNN data helped identify novel Recognition-Familiarity and Inhibition-Switching factors that were also invariant across samples and may be clinically useful. The findings support efforts to identify evidence-based and optimally efficient measurements of neuropsychological constructs that are valid across groups. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The delivery of primary care to homeless individuals with mental health conditions presents unique challenges. To inform healthcare improvement, we studied predictors of favorable primary care experience among homeless persons with mental health conditions treated at sites that varied in degree of homeless-specific service tailoring. This was a multi-site, survey-based comparison of primary care experiences at three mainstream primary care clinics of the Veterans Administration (VA), one homeless-tailored VA clinic, and one tailored non-VA healthcare program. Persons who accessed primary care service two or more times from July 2008 through June 2010 (N = 366) were randomly sampled. Predictor variables included patient and organization characteristics suggested by the patient perception model developed by Sofaer and Firminger (2005), with an emphasis on mental health. The primary care experience was assessed with the Primary Care Quality-Homeless (PCQ-H) questionnaire, a validated survey instrument. Multiple regression identified predictors of positive experiences (i.e. higher PCQ-H total score). Significant predictors of a positive experience included a site offering tailored service design, perceived choice among providers, and currently domiciled status. There was an interaction effect between site and severe psychiatric symptoms. For persons with severe psychiatric symptoms, a homeless-tailored service design was significantly associated with a more favorable primary care experience. For persons without severe psychiatric symptoms, this difference was not significant. This study supports the importance of tailored healthcare delivery designed for homeless persons needs, with such services potentially holding special relevance for persons with mental health conditions. To improve patient experience among the homeless, organizations may want to deliver services that are tailored to homelessness and offer a choice of providers.

Background

Beginning in 2010, Los Angeles County Departments of Health Services and Mental Health collaborated to increase access to effective mental health care. The Mental Health Integration Program (MHIP) embedded behavioral health specialists in primary care clinics to deliver brief, problem-focused treatments, and psychiatric consultation support for primary care-prescribed psychotropic medications.

Objective

The aim was to compare primary care visits associated with psychiatric diagnoses before and after MHIP implementation.

Methods

This retrospective cohort study (2009-2014) examined 62,945 patients from 8 safety-net clinics that implemented MHIP in a staggered manner in Los Angeles. Patients' primary care visits (n=695,354) were either associated or not with a previously identified or "new" (defined as having no diagnosis within the prior year) psychiatric diagnosis. Multilevel regression models used MHIP implementation to predict odds of visits being associated with psychiatric diagnoses, controlling for time, clinic, and patient characteristics.

Results

9.4% of visits were associated with psychiatric diagnoses (6.4% depression, 3.1% anxiety, <1% alcohol, and substance use disorders). Odds of visits being associated with psychiatric diagnoses were 9% higher [95% confidence interval (CI)=1.05-1.13; P<0.0001], and 10% higher for diagnoses that were new (CI=1.04-1.16; P=0.002), after MHIP implementation than before. This appeared to be fueled by increased visits for depression post-MHIP (odds ratio=1.11; CI=1.06-1.15; P<0.0001).

Conclusions

MHIP implementation was associated with more psychiatric diagnoses coded in safety-net primary care visits. Scaling up this effort will require greater attention to the notable differences across patient populations and languages, as well as the markedly low coding of alcohol and substance use services in primary care.