Investigation of the Montreal Cognitive Assessment (MoCA)as a cognitive screener in severe mental illness

Mandi W. Musso n, Alex S. Cohen, Tracey L. Auster, Jessica E. McGovernLouisiana State University, Department of Psychology, Baton Rouge, LA, USA

a r t i c l e i n f o

Article history:Received 12 June 2013Received in revised form28 July 2014Accepted 31 July 2014Available online 8 August 2014

Keywords:SchizophreniaDepressionBipolar disorderCognitive deficitsFunctional outcomesNeuropsychology

a b s t r a c t

This study examined the Montreal Cognitive Assessment (MoCA) as a neurocognitive screener and itsrelationship with functional outcomes in a sample of outpatients diagnosed with severe mental illness(SMI). The MoCA, Brief Assessment of Cognition in Schizophrenia (BACS), UCSD Performance-Based SkillsAssessment Test-2 (UPSA-2), and Global Assessment of Functioning (GAF) were administered to 28 SMIpatients and 18 non-psychiatric controls. Patients obtained significantly lower scores on the MoCA, BACS,UPSA-2, and GAF compared to non-patients. The cutoff score o26 of the MoCA resulted in favorablesensitivity (89%) but lower specificity (61%) in classification of SMI patients. The MoCA was significantlycorrelated with UPSA scores but not GAF scores, whereas the BACS was not significantly correlated withUPSA or GAF scores. When entered into hierarchical regression analyses, the MoCA accounted forsignificant variance in UPSA scores above variance accounted for by the BACS. Both the MoCA and theBACS contributed unique variance in GAF scores. Overall, the MoCA demonstrated high sensitivity as acognitive screener in SMI. Moreover, MoCA scores were related to performance-based measures offunctional capacity.

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1. Introduction

There is a large body of the literature documenting cognitivedeficits in patients across the severe mental illness (SMI) spectrum,including schizophrenia (Heinrichs and Zakzanis, 1998) and uni-polar (McDermott and Ebmeier, 2009) and bipolar mood disorders(Kurtz and Gerraty, 2009). In schizophrenia, for example, evidencesuggests that cognitive deficits reflect a core feature of illness(Goldberg and Green, 2002; Nuechterlein et al., 1994), with mod-erate to large effect sizes across neurocognitive domains (ds40.60,Heinrichs and Zakzanis, 1998). Performance on standard neuropsy-chological tests is reliably one of the most robust predictors offunctional abilities in patients with schizophrenia (Green, 1996).Accordingly, improvement of cognitive functioning has become atarget for clinical trials research (Green and Nuechterlein, 1999;Green et al., 2004). The National Institute of Mental Heath estab-lished the Measurement and Treatment Research to ImproveCognition in Schizophrenia (MATRICS) initiative in 2002 to establishstandards, including neuropsychological battery recommendations,for evaluating outcomes in the treatment of schizophrenia (Green

et al., 2004). Other neuropsychological batteries, for example, theBrief Assessment of Cognition in Schizophrenia (BACS; Keefe et al.,2004), have also been developed for this purpose. In the last decadeor so, a relatively large literature supporting the use of thesecognitive batteries has emerged.

While much of the research on neurocognition and severemental illness has focused on schizophrenia, cognitive impairmenthas been noted in other Axis I disorders. For example, a meta-analysis of 14 studies reported small but significant effect sizes fordeficits in episodic memory, executive function, and processingspeed in major depression (McDermott and Ebmeier, 2009).In addition, a recent meta-analysis found moderate to large effectsizes for associations between bipolar disorder and deficits inattention, working memory, verbal memory, nonverbal memory,language, psychomotor speed, and executive functioning (Kurtzand Gerraty, 2009). In this meta-analysis, the largest effect sizeswere for verbal learning (d¼0.81) and verbal and nonverbalmemory (d¼0.80–0.92). Two meta-analyses suggest cognitivedeficits persisted even during euthymic episodes (Robinsonet al., 2006; Kurtz and Gerraty, 2009). Similar to what is seen inschizophrenia, neurocognitive impairment has also been asso-ciated with poorer psychosocial functioning in bipolar disorder(Wingo et al., 2009).

Extensive neuropsychological testing provided by current bat-teries has many benefits; however, elaborate testing of all psychiatric

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Psychiatry Research

http://dx.doi.org/10.1016/j.psychres.2014.07.0780165-1781/& 2014 Elsevier Ireland Ltd. All rights reserved.

n Correspondence to : LSU Health-Baton Rouge, Department of EmergencyMedicine, 5246 Brittany Drive, Baton Rouge, LA 70803, USA. Tel.: þ1 225 757 4148.

E-mail address: mandimusso@gmail.com (M.W. Musso).

Psychiatry Research 220 (2014) 664–668

patients is not practical in some settings, such as medical settings,where sessions are time-limited, resource-limited, and neuropsy-chologists or trained psychometricians may be unavailable. TheMATRICS battery requires approximately 60–90 min for administra-tion. Even shorter batteries such as the BACS require over 30 min foradministration. Patients with severe mental illness may have diffi-culty withstanding the duration and frustration of such batteries.There is a need for brief screening measures that can be quickly andeasily administered and that provide important information aboutcognitive and functional status to inform health care providers aboutthe need for further neuropsychological assessment.

There is a paucity of research regarding the use of briefscreening measures in outpatients with severe mental illness.Importantly, some studies have reported that brief cognitivescreening measures may be useful in predicting functional out-comes in patients with major depression (Withall et al., 2009). TheMini-Mental State Examination (MMSE), widely considered themost oft-used measure of mental status, has been associated withpoorer ability to perform Instrumental Activities of Daily Living asmeasured by self-report on the Personal Self-MaintenanceScale (Lawton and Brody, 1969) in patients with severe depression(McCall and Dunn, 2003). However, a number of studies thatexamined the utility of the MMSE in community-dwellingpatients with schizophrenia noted poor sensitivity to subtlecognitive deficits, as few patients scored below the impaired range(Ganjuli et al., 1998; Moore et al., 2004; Manning et al., 2007). TheMontreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) isa more recently developed, 30-item, screener designed for use byhealth professionals that shows promise in SMI populations.Compared with the MMSE, it places greater emphasis on thedomains of attention and executive functioning. Two recentstudies from the University of Sarajevo examined the utility ofthe MoCA in schizophrenia and found the MoCA appears to bemore sensitive to mild cognitive impairment in schizophreniacompared to the MMSE (Fisekovic et al., 2012b, 2012a). Despitethis promise, the MoCA has largely gone unexamined in studies ofcognition in SMI.

This was an experimental study designed to examine the utilityand psychometric properties of the MoCA in a group of outpatientswith SMI. This study also compared the MoCA to the BACS, avalidated but lengthier and more extensive neuropsychologicalbattery commonly employed in SMI research. We tested thefollowing hypotheses: (1) SMI patients would obtain significantlylower scores on the MoCA compared to healthy controls, and theMoCA would offer acceptable sensitivity to subtle cognitive deficitsin the SMI group. (2) Poorer MoCA performance would be asso-ciated with functional deficits measured by both a performance-based measure of functional abilities (the UPSA-2) and clinicianratings on the Global Assessment of Functioning (GAF). (3) Inexploratory analyses, the MoCA would be significantly associatedwith functional deficits to a similar degree in comparison withthe BACS.

2. Methods

2.1. Participants

2.1.1. PatientsTwenty-eight participants with severe mental illness were recruited from

outpatient treatment programs located in Louisiana (see Table 1 for demographicinformation). At the time of testing, all participants were under the supervision of amulti-disciplinary team within a community mental health clinic. All participantsmet criteria for past or present psychotic or mood disorders. All patients wereprescribed psychotropic medications at the time of testing, and there wassubstantial variability in type and dosage of medication based on patients' needs.Diagnoses were made using information obtained from a structured clinicalinterview (SCID-CV; First et al., 1996) and medical records. Interviews were

conducted by a team of doctoral students under the supervision of a licensedclinical psychologist (Dr. Alex Cohen). Diagnostic decisions were made based onunanimous agreement at a case conference meeting led by Dr. Cohen. To be eligiblefor this study, participants had to be designated severely mentally ill by the state(i.e., psychiatric disorder with profound impairment). To be included in the currentstudy, participants also had to have completed the MoCA during neuropsycholo-gical testing. Exclusion criteria included the following: (a) significant visual,auditory, or other sensory deficit that may have affected task performance;(b) current or history of DSM-IV-TR or otherwise significant substance dependenceas indicated by physical symptoms; (c) neurological insult or head trauma thatrequired overnight hospitalization; (d) inhalant use or ingestion of volatile vapors(e.g., aerosols) with significant lifetime frequency (i.e., greater than six times);and (e) GAF (American Psychiatric Association, 2000) ratings below 30, whichcharacterized a disturbance caused by symptoms that would potentiallyhave interfered with study performance. Participants were compensated $40 forcompletion of the study. Informed consent was obtained for all participants.All procedures complied with the Declaration of Helsinki and were approved bythe university's Institutional Review Board.

2.1.2. Non-patientsThe non-patient group consisted of 18 non-psychiatric control participants

(38.9% males, 61.1% females) who were recruited from the southern Louisianacommunity. Inclusion and exclusion criteria for this sample were the same as withthe severe mental illness sample with the exception they be free of current andpast psychotic disorders and mood disorders, based on structured clinical inter-views (SCID-I/NP First et al., 2002).

2.2. Measures

2.2.1. The Brief Assessment of Cognition in Schizophrenia (BACS)Cognition was evaluated using the BACS (Keefe et al., 2004), a comprehensive

battery assessing verbal fluency, verbal memory, problem solving, attention, andworking memory. A composite score, computed as a z-score of each of the subscalescores, was employed in this study.

2.2.2. Montreal Cognitive Assessment (MoCA)The MoCA (Nasreddine et al., 2005) is a brief 30-item screening tool that

examines cognitive domains including executive functioning, confrontation nam-ing, attention, sentence repetition, verbal fluency, delayed verbal recall, andorientation. Individuals also complete two verbal learning trials; however, verballearning is not scored. One additional point is allotted for individuals who have 12or fewer years of education. Scores o26 are suggestive of cognitive impairment.

2.2.3. UCSD Performance-Based Skills Assessment Test (UPSA-2)The UPSA-2 is the brief version of the UCSD Performance-Based Skills Assess-

ment Test and is a performance measure of a person's everyday living skills in thefollowing five selected domains of daily living: (1) communication, (2) organiza-tion/planning, (3) financial skills, (4) household management, and (5) transporta-tion. The UPSA-2 takes approximately 30 min to administer and has shown hightest–retest reliability and participant tolerance above and beyond other co-primarymeasures (Harvey et al., 2010). Each subsection requires role play tasks thatevaluate participants' performance in carrying out activities of daily living suchas being able to shop for food and correctly use a telephone and transportation.A composite score, computed as a z-score of the subscale scores, was used inthis study.

2.3. Statistical analyses

Analyses were conducted in four steps. First, demographic and descriptiveinformation was examined using independent samples t-tests. A series of analysesof covariance (ANCOVAs) were used to determine whether groups differed oncognitive or functional measures. For these ANCOVAs, group (patients vs. non-patients) was the independent variable, and the MoCA, BACS, and UPSA-2 weredependent variables. Gender and education were used as covariates. Second, weexamined the psychometric properties of the MoCA in the SMI sample. For theseanalyses, sensitivity and specificity were calculated for the MoCA cutoff score ofo26 for the patients and non-patients, respectively. We went one step further ininvestigating performance of the MoCA in patients with functional deficits byexamining MoCA scores in individuals who obtained UPSA-2 scores that were 1 S.D.below the mean to determine whether the MoCA would have identified thesepatients as impaired. Third, we examined the relationships between the cognitivetests (MoCA and BACS) and functional scores (e.g., GAF and UPSA-2) using Spear-man's correlations for the SMI sample. Finally, we compared the MoCA and BACS intheir prediction of variance in UPSA-2 and GAF scores using hierarchical regres-sions. For a first set of hierarchical regressions, the BACS z-score was entered intoStep 1 then the MoCA z-score was entered into Step 2. For the second set ofhierarchical regressions, entry of the MoCA and BACS scores was reversed.

M.W. Musso et al. / Psychiatry Research 220 (2014) 664–668 665

3. Results

3.1. Demographics and descriptive statistics

Within the SMI group, 10 participants were diagnosed withmood disorders and 18 were diagnosed with schizophrenia. BACSdata was missing for one patient, and two patients were missingdata for the UPSA-2 and GAF. There were no significant differencesbetween the schizophrenia and mood disorders group for age, t(26)¼0.97, p¼0.34, or education, t(26)¼0.93, p¼0.37. In addition,the groups did not differ in MoCA scores, t(26)¼0.56, p¼0.58,BACS z-scores, t(25)¼0.46, p¼0.65, UPSA-2 z-score, t(23)¼0.90,p¼0.38, GAF score, t(24)¼1.21, p¼0.24. Because there were nosignificant differences between groups, the mood disorder andschizophrenia groups were combined into a common severemental illness (patients) group for the remaining analyses.

When the 28 patients were compared with the 18 non-patients, there were no significant differences in age, t(44)¼0.583, p¼0.55. Non-patients obtained approximately three moreyears of education compared to patients, t(44)¼4.13, po0.001.Chi-square analyses indicated there were significant differences,χ2(1)¼6.00, po0.01, between percentages of males in the patients(75%) and non-patients (39%) groups. There were no significantdifferences in race between the two groups, χ2(3)¼6.53, p¼0.09.

3.2. Patients vs. non-patients

Separate one-way ANCOVAs with gender and education as covari-ates revealed significant main effects for each of the following scores:(1) MoCA, F (1, 42)¼15.29, po0.001, η2¼0.27; (2) BACS z-composite,F (1, 41)¼20.78, po0.001, η2¼0.23; (3) UPSA-2, F (1, 37)¼9.91,po0.01; η2¼0.21; and (4) GAF, F (1, 38)¼131.37, po0.001, η2¼0.78.Of note, patients scored significantly lower compared with non-patients in all cases.

3.3. Psychometric properties of the MoCA

Sensitivity and specificity of the MoCA cutoff score of o26were examined. The cutoff of o26 demonstrated 89% sensitivity(95% confidence interval (CI): 72–98) and 61% specificity (95% CI:36–83). Positive and Negative Predictive Powers were 78 (95% CI:60–91) and 79, (95% CI: 49–95), respectively. The odds ratio was13.1 (95% CI: 2.8–60.3). The positive likelihood ratio was 2.30 (95%CI: 1.27–4.15) and the negative likelihood ratio was 0.18 (95% CI:0.06–0.54). When we examined performance of the MoCA inindividuals with functional deficits, five individuals obtainedz-scores below 1 S.D. on the UPSA-2. All five individuals werefrom the patient group (four were diagnosed with schizophreniaand one with bipolar disorder). MoCA raw scores for theseindividuals ranged from 14 to 20, suggesting all of these indivi-duals would have screened positive for cognitive impairment.Overall, the MoCA demonstrated excellent sensitivity in patientswith SMI.

3.4. Relationship between the cognitive and functional measures

For SMI patients, the MoCA total score was significantlycorrelated with the UPSA-2, r(24)¼0.66, po0.001 but not withGAF scores, r(24)¼0.28, p¼0.24. The BACS was not significantlycorrelated with the UPSA-2, r(23)¼0.25, p¼0.19, or GAF scores,r(23)¼0.10, p¼0.63. Also, there was no significant correlationbetween the MoCA total raw score and the BACS z-score, r(25)¼0.34, p¼0.08. Neither the MoCA nor the BACS scores weresignificantly correlated with age or education level (Table 2).Interestingly, the GAF and UPSA-2 were not significantly correlatedin the current study, r(21)¼0.08, p¼0.72. Overall, the MoCA wasmore closely associated with functional measures than the BACS.

3.5. Predictive validity of the MoCA and BACS

The first two hierarchical regression analyses examined pre-diction variance in UPSA-2 scores for the MoCA and BACS asfollows: (1) after Step 1, with the BACS z-score in the equation, themodel was significant, R2 Δ¼0.28, FΔ (1, 39)¼15.32, po0.001.Addition of the MoCA z-score to the equation resulted in asignificant increment in R2, R2Δ¼0.19, FΔ (1, 38)¼13.93,po0.001. (2) The model with MoCA z-score in Step 1 wassignificant, R2Δ¼0.45, FΔ (1, 39)¼32.10, po0.001. Prediction ofthe UPSA-2 z-score was not improved when the BACS z-score wasadded to Step 2, R2Δ¼0.02, FΔ (1, 38)¼1.68, p¼0.20.

Next, two hierarchical regressions were used to predict GAFscores. In the first regression, the BACS z-score was entered intoStep 1, and the MoCA z-score was entered into Step 2. After Step 1,with the BACS z-score in the equation, the model was significant,R2Δ¼0.42, FΔ (1, 39)¼28.14, po0.001. Addition of the MoCA z-scoreto the equation resulted in a significant increment in R2, R2Δ¼0.07,FΔ (1, 38)¼5.17, po0.03. Entry of the MoCA and BACS scores wasreversed for Steps 1 and 2 for the second hierarchical regression. Themodel was significant after Step 1, R2Δ¼0.40, FΔ (1, 39)¼26.14,po0.001). Prediction of the GAF score was improved when the BACSz-score was added to Step 2, R2Δ¼0.09, FΔ (1, 38)¼6.49, p¼0.02.

Table 1Descriptive statistics for non-patients and patients.

Age Education Gender Ethnicity MoCA BACS UPSA-2 GAFMean (S.D.) Mean (S.D.) %Male %Caucasian Mean z (S.D.) Mean z (S.D.) Mean z (S.D.) Mean z (S.D.)

Non-patients (n¼18) 41.67 (12.32) 14.00 (2.50) 39 55 26.72 (2.63) 0.82 (0.64) 0.67 (0.67) 82.00 (8.19)Patients (n¼28) 39.68 (11.63) 11.13 (1.98) 75 46 20.57 (4.64) �0.55 (0.81) �0.43 (0.94) 44.12 (9.02)Schizophrenia (n¼18) 38.17 (12.34) 10.89 (2.03) 89 60 20.17 (4.19) �0.61 (0.71) �0.55 (1.0) 42.31 (7.7)Mood/affective (n¼10) 42.40 (10.27) 11.60 (1.90) 50 39 21.30 (5.39) �0.44 (0.98) �0.21 (0.85) 47.00 (10.6)

Table 2Spearman's rho correlations between variables for patients with severe mentalillness.

Age Education WRATRead-ing

UPSA-2z-score

GAF MoCArawscore

Age 1Education 0.16 1WRAT reading �0.25 0.01 1UPSA-2 z-score �0.10 �0.14 0.25 1GAF 0.16 0.19 0.11 0.08 1MoCA raw score �0.62 0.18 0.33 0.66nn 0.24 1BACS z-score �0.08 �0.07 0.04 0.27 0.10 0.34

nn po0.001.

M.W. Musso et al. / Psychiatry Research 220 (2014) 664–668666

Overall, the MoCA contributed unique variance beyond the BACS inpredicting UPSA scores, and both the MoCA and BACS contributedunique variance in predicting GAF scores.

4. Discussion

The purpose of the present study was to examine the utility of theMoCA, a brief cognitive screening instrument, in a sample of out-patients diagnosed with SMI. Overall, the results support the use of theMoCA for understanding neurocognitive deficits in patients with SMI.There were four important findings from this study. First, replicatingprior research, patients obtained significantly lower scores on thecognitive measures (MoCA and BACS) and functional measures (GAFand UPSA-2) compared to healthy controls. Perhaps more importantly,there were no significant differences in scores between patientsdiagnosed with mood/affective disorders or schizophrenia on anycognitive or functional measures. Second, the cutoff scores of theMoCA resulted in favorable sensitivity for predicting SMI groupmembership. Third, the MoCA was significantly correlated withUPSA-2 scores whereas the BACS was not significantly correlated witheither functional outcome measure. Finally, when entered in hierarch-ical regression analyses, the MoCA accounted for significant variancein UPSA-2 scores above variance accounted for by the BACS. In termsof GAF scores, both the MoCA and the BACS contributed uniquevariance in GAF scores.

Our results suggest that the MoCA is a promising measure foruse with SMI populations. In the present study, the MoCAdemonstrated reasonably good utility as a clinical predictor ofSMI group status. Of the patient sample, 89% scored below thecutoff of o26 on the MoCA, suggesting the MoCA is sensitive tosubtle cognitive impairment. It is noteworthy that seven of the 18healthy controls also scored below the cutoff for cognitive impair-ment, suggesting somewhat low specificity (61%). This finding isconsistent with Rossetti et al. (2011) who reported that a largenumber of individuals score below the recommended cutoff ofo26. As a screening measure, high levels of sensitivity are ofutmost concern and lower specificity is acceptable. For this reason,alternative cutoff scores were not examined, though this may be avaluable direction for future research (Larner, 2012).

In the present study, the BACS was not significantly correlatedwith the UPSA-2 or GAF scores. These results are not consistentwith Keefe et al. (2006), who found the BACS was correlatedr¼0.65 with the UPSA-2. One possible explanation for the dis-crepancy in findings is differences in populations between the twostudies. Keefe et al. (2006) utilized inpatients undergoing beha-vioral therapy while the current study assessed outpatients at acommunity mental health clinic. Not only does inpatient hospita-lization suggest more severe functional deficits, but their lower z-scores relative to those reported in the current study suggest moresignificant cognitive impairment in their sample as well. This mayaccount for more significant associations between the two mea-sures than those found in the current study.

This study employed two measures of functional ability: aperformance-based measure (UPSA-2 scores) and clinicians' rat-ings of functioning (GAF scores). We found that clinician-ratedassessments of global functioning were not significantly correlatedwith performance-based measures of functional ability, suggestingthe two measures index different constructs. Versterager et al.(2012) suggested performance-based measures of functional capa-city may be more similar to neurocognitive measures than real-world function, measured by GAF scores. With this caveat in mind,we examined whether the BACS and MoCA contributed significantunique variance to the UPSA-2 and GAF scores. First, we found theMoCA, not the BACS, contributed significant variance to the UPSA-2. Second, MoCA and the BACS contributed variance to GAF scores

in the current study. Of note, Keefe et al. (2006) reported thatUPSA-2 scores did not contribute unique variance in predictingreal-world functioning, measured by patients' self-report, beyondvariance accounted for by the BACS. These findings further supportthe hypothesis that performance-based measures and other sub-jective measures of real-world functioning do not measure aunitary construct of functional abilities. However, the MoCAcontributed unique variance to both clinician-rated and objectivemeasures of functioning, further supporting its utility in cognitivescreening for schizophrenia.

Limitations of the current study include a relatively modestsample size. Despite small sample sizes, statistical significance wasobserved at medium to large effect sizes. Another limitation is adisproportionate number of males in the patient sample. In addi-tion, individuals in the healthy control sample had significantlymore years of education. Gender differences and years of educationwere controlled for in statistical analyses, though results did notchange significantly when these covariates were used. Further,scores on the MoCA, BACS, UPSA-2, and GAF were not correlatedwith demographic variables such as age or education. Also, theMoCA was not designed to assess specific cognitive deficits ofschizophrenia, as schizophrenia has also been associated withimpaired verbal learning and processing speed deficits. It is possiblethat minor adjustments could be made to the current administra-tion and scoring that would incorporate additional domains with-out extending time of testing. For example, two verbal learningtrials are administered but are not scored. In addition, there are noprocessing speed measures in the MoCA; however, several execu-tive functioning tasks could potentially be timed. Introduction ofscoring criteria for these domains may prove useful in increasingsensitivity to cognitive deficits in individuals with severe mentalillness. While examination of alternative scoring of the MoCA isbeyond the scope of the current study, future research may wish toconsider examination of these domains.

The MoCA has demonstrated utility in the assessment of avariety of cognitive disorders and has been growing in popularitysince its introduction in 2005. The findings of the current studyprovide evidence for the validity of the MoCA as a screening toolfor measuring cognitive impairment in outpatients with severemental illness. While preliminary, these findings also suggest theMoCA is significantly associated with performance-based mea-sures of real-world functioning, the UPSA-2, and may provideadditional information regarding functional abilities to patients'inter-disciplinary teams. Future studies should continue to exam-ine the reliability and validity of the brief screening measures,including the MoCA for use in screening for cognitive impairmentin severe mental illness. Test–retest reliability of the MoCA as wellas its ability to monitor cognitive changes over time and acrossepisodes also needs to be established in this population.

Acknowledgments

The authors acknowledge the efforts of Gina Najolia, Kyle Minor,Laura Brown, and Rebecca MacAulay for their help with data collec-tion. They also thank the subjects for their participation and MMOBehavioral Health Systems for the assistance in subject outreach.

This study received no specific grant from any funding agency,commercial or not-for-profit sectors. There are no conflicts ofinterest.

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