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Original Investigation |

Patient Engagement Programs for Recognition and Initial Treatment of Depression in Primary Care:  A Randomized Trial FREE

Richard L. Kravitz, MD, MSPH1,2; Peter Franks, MD2,3; Mitchell D. Feldman, MD, MPhil4; Daniel J. Tancredi, PhD2,5; Christina A. Slee, MPH6; Ronald M. Epstein, MD7,8,9; Paul R. Duberstein, PhD7,8; Robert A. Bell, PhD2,10; Maga Jackson-Triche, MD, MSHS11; Debora A. Paterniti, PhD2,12; Camille Cipri, BS2; Ana-Maria Iosif, PhD13; Sarah Olson, BA4; Steven Kelly-Reif, MD14; Andrew Hudnut, MD15; Simon Dvorak, BA16; Charles Turner, PhD16; Anthony Jerant, MD2,3
[+] Author Affiliations
1Division of General Medicine, University of California at Davis, Sacramento
2Center for Healthcare Policy and Research, University of California at Davis, Sacramento
3Department of Family and Community Medicine, University of California at Davis, Sacramento
4Division of General Internal Medicine, University of California, San Francisco
5Department of Pediatrics, University of California at Davis, Sacramento
6University of California, Davis, Medical Center, Sacramento
7Department of Family Medicine, University of Rochester, Rochester, New York
8Department of Psychiatry, University of Rochester, Rochester, New York
9Department of Oncology, University of Rochester, Rochester, New York
10Department of Communication and Public Health Sciences, University of California at Davis, Davis
11VA Northern California Health Care System, University of California at Davis, Sacramento
12Department of Internal Medicine and Sociology, University of California at Davis, Sacramento
13Department of Public Health Sciences, University of California at Davis, Davis
14The Permanente Medical Group, Sacramento, California
15Sutter Medical Foundation, Sacramento, California
16Information and Educational Technology, Academic Technology Services, University of California at Davis, Davis
JAMA. 2013;310(17):1818-1828. doi:10.1001/jama.2013.280038.
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Published online

Importance  Encouraging primary care patients to address depression symptoms and care with clinicians could improve outcomes but may also result in unnecessary treatment.

Objective  To determine whether a depression engagement video (DEV) or a tailored interactive multimedia computer program (IMCP) improves initial depression care compared with a control without increasing unnecessary antidepressant prescribing.

Design, Setting, and Participants  Randomized clinical trial comparing DEV, IMCP, and control among 925 adult patients treated by 135 primary care clinicians (603 patients with depression and 322 patients without depression, defined by Patient Health Questionnaire–9 [PHQ-9] score) conducted from June 2010 through March 2012 at 7 primary care clinical sites in California.

Interventions  DEV targeted to sex and income, an IMCP tailored to individual patient characteristics, and a sleep hygiene video (control).

Main Outcomes and Measures  Among depressed patients, superiority assessment of the composite measure of patient-reported antidepressant drug recommendation, mental health referral, or both (primary outcome); depression at 12-week follow-up, measured by the PHQ-8 (secondary outcome). Among nondepressed patients, noninferiority assessment of clinician- and patient-reported antidepressant drug recommendation (primary outcomes) with a noninferiority margin of 3.5%. Analyses were cluster adjusted.

Results  Of the 925 eligible patients, 867 were included in the primary analysis (depressed, 559; nondepressed, 308). Among depressed patients, rates of achieving the primary outcome were 17.5% for DEV, 26% for IMCP, and 16.3% for control (DEV vs control, 1.1 [95% CI, −6.7 to 8.9], P = .79; IMCP vs control, 9.9 [95% CI, 1.6 to 18.2], P = .02). There were no effects on PHQ-8 measured depression score at the 12-week follow-up: DEV vs control, −0.2 (95% CI, −1.2 to 0.8); IMCP vs control, 0.9 (95% CI, −0.1 to 1.9). Among nondepressed patients, clinician-reported antidepressant prescribing in the DEV and IMCP groups was noninferior to control (mean percentage point difference [PPD]: DEV vs control, −2.2 [90% CI, −8.0 to 3.49], P = .0499 for noninferiority; IMCP vs control, −3.3 [90% CI, −9.1 to 2.4], P = .02 for noninferiority); patient-reported antidepressant recommendation did not achieve noninferiority (mean PPD: DEV vs control, 0.9 [90% CI, −4.9 to 6.7], P = .23 for noninferiority; IMCP vs control, 0.3 [90% CI, −5.1 to 5.7], P = .16 for noninferiority).

Conclusions and Relevance  A tailored IMCP increased clinician recommendations for antidepressant drugs, a mental health referral, or both among depressed patients but had no effect on mental health at the 12-week follow-up. The possibility that the IMCP and DEV increased patient-reported clinician recommendations for an antidepressant drug among nondepressed patients could not be excluded.

Trial Registration  clinicaltrials.gov Identifier: NCT01144104

Figures in this Article

Despite progress, depression in primary care remains underrecognized and undertreated.15 Barriers to improvement include system, clinician, and patient factors. System-level interventions are effective in increasing recognition and treatment of depression, but these interventions are difficult to disseminate.4,6 Clinician behavior is difficult to change.7 Patients are potentially attractive targets for intervention,8 but they may have difficulty articulating their distress and signaling openness to treatment for depression.911 Marketing strategies such as direct-to-consumer advertising encourage patients to report depression symptoms and accept depression treatment,12,13 but these interventions may also promote overprescribing.1317 More selective approaches are needed.

In shaping messages to influence health-related behavior, researchers have applied 2 approaches: targeting and tailoring. Targeting involves segmenting a general population into smaller, more homogeneous units based on observable factors (such as age, sex, or place of residence).18 Tailoring uses information elicited from the respondent, often through an interactive computerized interface, to craft messages specific to that person.19

We examined whether targeted and tailored communication strategies could enhance patient engagement and initial care for patients with depression. We also examined the extent to which each intervention promoted prescribing or recommendation of antidepressant medication, depression-related discussion, and antidepressant requests among patients who were not depressed. We developed 2 interventions for use in primary care: a depression engagement video (DEV) targeted to sex and income and an interactive multimedia computer program (IMCP) tailored to the characteristics, interests, and problems of the individual patient. Enrolled patients were categorized into 2 cohorts (depressed and nondepressed) according to whether they had significant depression symptoms. Within each of these 2 cohorts, we compared the effectiveness of each intervention with a control (sleep hygiene informational video). Among depressed patients, we hypothesized that each intervention would increase the delivery of depression treatments (primary outcome), encourage patients to ask questions about depression, and lead to improved mental health 12 weeks later compared with the control group. Among nondepressed patients, we hypothesized that each intervention would not increase antidepressant prescribing or recommendations (primary outcomes), depression-related discussion, patient requests for antidepressants, or clinician time and burden compared with the control group.

Design Overview

Ethics approval for this trial was obtained from the institutional review boards at all performance sites. Study procedures and protocols have been detailed elsewhere.20 All patients provided written informed consent. The trial was designed as a randomized clinical trial comparing 3 interventions: a targeted DEV designed to encourage patient participation in depression-related discussion and care, a tailored IMCP, and a sleep hygiene informational video (control). We report separately on the results for depressed and nondepressed patient cohorts. We defined the 2 cohorts with a Patient Health Questionnaire–9 [PHQ-9] scoring system; a score of 5 or greater defined patients with depression and a score of less than 5 defined patients without depression.

Sampling

Patients and clinicians were recruited from 7 clinical sites affiliated with the University of California, San Francisco (UCSF); the San Francisco Veterans Affairs (VA) Medical Center; the University of California, Davis (UCD), Ambulatory Care Center; the UCD Primary Care Network; the Northern California (Sacramento) Veterans Affairs Health System; Kaiser Permanente, Sacramento; and Sutter Medical Group, Sacramento.

We recruited primary care clinicians through e-mail announcements and at in-person presentations. Clinicians were told that the study was a randomized trial of an intervention designed to improve communication about common physical and mental health symptoms in primary care. Although not blinded to patients’ participation in the study, clinicians were not alerted to patients’ group assignments. All clinicians agreed to enroll up to 12 patients.

Patient Enrollment

Eligible patients were aged 25 to 70 years, could read and understand English, use a computer, and were not currently taking antidepressant medication (with the exception of low-dose tricyclics for pain or sleep). We studied this age group because of the high social and economic burden imposed by depression upon adults in their working years.21 In all recruitment settings except UCSF urgent care, eligibility screening was conducted by telephoning patients who were scheduled for a routine primary care visit in the next 1 to 2 weeks. Patients were told that the study was designed to improve care for patients with common symptoms including sleep problems, depression, and chronic pain. Research staff made up to 3 attempts to reach each patient. Patients were randomly selected for telephoning from each clinic’s appointment lists until daily quotas were filled. Patients with significant depression symptoms based on the PHQ-822 (used in lieu of the PHQ-9 for telephone screening) were oversampled. Eligible patients who provided preliminary verbal consent were invited to a research appointment 1 hour prior to the index visit. At the UCSF urgent care clinic, patients were approached directly by research assistants in waiting rooms, without any prior telephone screening. Patients were offered an incentive of $20 to $35 for completing index visit procedures and an additional $10 for completing the 12-week follow-up telephone interview.

Interventions

The targeted DEVs and tailored IMCP were developed based on literature reviews and extensive formative research.23,24 The control intervention was a sleep hygiene informational video produced by HealthiNation.25 Screenshots of the DEV and IMCP are available from the authors on request.

The DEVs, produced in collaboration with a marketing firm, were designed to enhance depression recognition and care-seeking by educating patients about depression; emphasizing the importance of disclosing relevant symptoms; and suggesting ways to start a conversation with their primary care physician.9,10,23,26 The marketing firm produced 4 DEV variants targeted to sex and household income.24 By using terms and images likely to resonate with the intended audience, targeted messages are generally better attended to and more thoroughly processed than nontargeted messages.27

The IMCP was developed collaboratively by the study investigators, guided by standard software engineering principles. The IMCP provided patients with feedback tailored to their level of depression symptoms (eg, those with PHQ-9 scores <5 were told they were probably not depressed, whereas those with higher scores were told they might be depressed and were advised to talk with their clinician), visit agenda (intention to discuss depression, depression treatment, or both), depression causal attributions (biological, psychosocial, situational, existential),28,29 treatment preferences (medication, counseling, both, or neither),28,30,31 self-efficacy for communicating with health care professionals,32and depression stigma.9,33, The IMCP gave users control over knowledge acquisition (self-tailoring) by offering links to more detailed material.34 Tailored health messages are better remembered, more frequently read, and more often perceived as relevant compared with nontailored health messages. Tailored health messages are also superior to nontailored interventions in improving various health behaviors and outcomes across a broad array of patient populations and target conditions,35 including depression.36,37

Randomization and Patient Flow

A study research assistant met patients an hour prior to their primary care clinic appointment. Following written informed consent, patients were logged on to a tablet computer for randomization and intervention assignment.

The unit of randomization was the patient. As described previously,20 the computer randomization program stratified patients into categories defined by self-reported race/ethnicity (because of its association with socioeconomic position [a target of the DEV] and to enhance generalizability), sex, and site. Within each category, patients were randomly allocated in equal proportions to 1 of 3 study groups, in randomly permuted blocks of 9 patients (the size of the blocks was not disclosed to research staff during enrollment). After randomization, patients were again asked about current antidepressant use. Antidepressant users were excluded from participation.

After intervention assignment, patients answered additional questions to measure baseline depressive symptom burden (using the PHQ-9),38 and to assess baseline health status. Immediately thereafter, patients received their randomly assigned intervention: 1 of 4 targeted DEV variants, the tailored IMCP, or the control video. The DEVs and control video were each approximately 3 minutes long. Patients assigned to the IMCP spent a median of 5 minutes with the program (10th percentile, 2 minutes; 90th percentile, 15 minutes).

Following the office visit, patients completed a computer-based questionnaire, which included questions about the encounter (ie, whether they asked about or discussed depression, depression-related care, or both; whether the clinician recommended an antidepressant or made a mental health referral; and when the clinician arranged for primary care follow-up). Clinicians independently completed a brief questionnaire after the visit. Agreement between patient and clinician for antidepressant recommendation was 87% and for mental health referral 89%. Patients in the depressed cohort were telephoned 12 weeks later to assess depression severity (using the PHQ-8) and health status (using the SF-12).

Outcome Measures

Measures for this study were derived from the patient report immediately following the visit, the clinician report immediately following the visit, and the 12-week patient follow-up by telephone. Among patients categorized as depressed, we focused on patient reports because of the critical role of patient perceptions in driving health behaviors and assessing outcomes. Among patients categorized as nondepressed, we used both patient and clinician reports.

Depressed Cohort

The prespecified primary outcome applied to the depressed cohort of patients was a composite measure of initial depression care, defined as receiving an antidepressant recommendation, a mental health referral, or both during the index visit. Secondary outcomes included patient-clinician communication self-efficacy questionnaire score using a scale modified from Maly and coauthors32 (the sum of 6 items, each scored from 1 [not at all confident] to 5 [very confident]; scale range, 6-30); whether the patient reported asking the clinician for information about depression during the visit; scores on the PHQ-8 at the 12-week follow-up (sum of 8 items, each scored from 0 [not at all] to 3 [nearly every day]; scale range, 0-24)22,3840; and the 12-Item Short Form Health Survey’s (SF-12, version 2.0) Mental Health Component Summary (MCS-12) scores and Physical Health Component Summary (PCS-12) scores at the 12-week follow-up (both scored from 0-100, with higher scores representing better health).41,42

Nondepressed Cohort

The prespecified primary outcome applied to nondepressed patients was whether the clinician recommended or prescribed an antidepressant. This was assessed by a clinician report of antidepressant prescribing and by a patient report of whether the clinician recommended a medication for depression. Secondary outcomes among nondepressed patients included (1) whether depression or depression treatment were discussed (each classified as yes, no, or uncertain), (2) whether the patient requested medication for depression during the study visit (yes, no, or uncertain), (3) clinician-reported face-to-face visit time (minutes), and (4) clinician-reported visit burden, computed as the sum of 3 items that rated the visit in terms of the amount of time required, amount of effort required, and the degree to which the clinician found the patient visit difficult, each on a 0 to 2 scale (0, less than average; 1, about average; and 2, greater than average; Cronbach α, .79).

Statistical Analysis

Details on power calculations, model assumptions, and variable selection have been reported.20 Briefly, we fitted clustered data regression models that would allow assessment of the pairwise (intervention vs control) contrasts of interest, and accounted for study design–effects arising from the stratified sampling and randomization scheme and for the clustering of patients within clinicians. No adjustments were made for multiple comparisons. The target sample size of 170 patients per group for the analyses involving depressed patients was established to provide 80% power for 2-sided testing (α = 5%) to detect standardized pairwise differences of 0.3 (eg, 15 percentage points for a binary outcome with an expected value of 50%). For analyses of nondepressed patients, the per-group target sample of 102 patients was established to provide 80% power to reject the inferiority null hypothesis that the rate of antidepressant prescribing in the DEV and IMCP groups would be 3.5 percentage points higher than in the control group, under the alternative hypothesis that the true probability was 1%.

Outcomes were analyzed using Stata (StataCorp), version 12.1.43 Binary outcomes were assessed using random-effects logistic regression models or, for low event counts, generalized estimating equations. Relative comparisons for binary outcomes were expressed as adjusted odds ratios (ORs) from models that adjusted for the study design (to minimize omitted covariate bias).44 Absolute comparisons were expressed as cluster-adjusted, mean percentage point differences (PPDs) on the original scale of measurement. Cluster-adjusted, mean percentages and differences were estimated via Stata’s post-estimation command margins, immediately after fitting simple clustered data logistic regression models. For mixed-effects models, margins were estimated with the random effect for each observation set to 0 (the mean value).

Continuous outcomes were assessed using mixed-effects linear regression models with adjustment for stratifiers. In the depressed cohort, all pairwise contrasts were estimated with 95% CIs and tested with 2-sided P values. In the nondepressed cohort, 2-sided, 90% CIs are reported, equivalent to 1-sided testing of the inferiority null hypothesis. The significance threshold was a P value less than .05 for all contrasts. For harms, we report P values for noninferiority for only the antidepressant prescribing and recommendation outcomes, using prespecified noninferiority margins of 3.5 percentage points. When the P value for noninferiority is less than .05, the contrast is statistically significant in favor of noninferiority at the specified tolerance margin.

Models adjusting for strata included the following terms: patient sex, race/ethnicity, practice setting [multispecialty group, faculty or resident practice, health maintenance organization, or VA clinic]), and (in analyses of depressed patients) baseline PHQ-9 category (5-9 vs ≥10). The postvisit patient-clinician communication self-efficacy outcome analysis also adjusted for self-efficacy prior to the visit. For 12-week outcomes (PHQ-8, MCS-12, and PCS-12 scores), 3-level mixed-effects models estimated adjusted within-group mean (over time) differences and between-group differences in mean differences. This approach uses all available data, including baseline data from patients who dropped out, to avoid biases that could occur in complete case analysis.45

Although not prespecified prior to patient enrollment, we hypothesized on clinical grounds prior to examination of the data that the interventions might be particularly effective among patients with more severe depressive symptomatology. This hypothesis was assessed by conducting analyses stratified by PHQ-9 score. Heterogeneity of treatment effects by baseline depression severity (5-9 vs ≥10) was assessed by fitting a model including the group depression category interaction term (tested with the Wald χ2 test [2 df]).

Patient Flow and Baseline Characteristics

Of 135 consenting clinicians, 124 enrolled at least 1 patient with a PHQ-9 score of 5 or greater, and 106 enrolled at least 1 with a PHQ-9 score of less than 5. The Figure depicts the flow of patients from screening through the 12-week follow-up. Of 6191 patients assessed for eligibility, 3650 patients were invited to participate, and 925 patients (603 in the depressed cohort and 322 in the nondepressed cohort) were randomized to the DEV, IMCP, or control group prior to a primary care office visit. Of the 925 randomized patients, 58 were excluded due to ineligibility or withdrawal after randomization, leaving 867 analyzable patients (559 categorized as depressed and 308 as nondepressed). Of the 559, approximately 85% completed the 12-week telephone follow-up survey (Figure). Patients were enrolled from June 16, 2010, through November 8, 2011; follow-up was complete by March 31, 2012.

Place holder to copy figure label and caption
Figure.
Flow of Patients Through Study

PHQ indicates Patient Health Questionnaire; DEV, depression engagement video; IMCP, interactive multimedia computer program.

aIn the depressed cohort, 559 patients were included in the primary analysis; nondepressed cohort, 308 patients.

Graphic Jump Location

Within both the depressed and nondepressed cohorts, patients assigned to the 3 experimental groups were similar in sex, age, race/ethnicity, family income, depression symptoms, and baseline self-efficacy for communicating with the clinician about mental health issues (Table 1). In the depressed cohort, the DEV group had a higher mean baseline MCS-12 score than the IMCP or control group (P = .01).

Table Graphic Jump LocationTable 1.  Baseline Characteristics of Patients (Depressed Cohort and Nondepressed Cohort)
Results in Depressed Patients
Intervention Effects on the Primary Composite Care Outcome

Rates of receipt of the composite care measure were 17.5% for the DEV group, 26% for the IMCP group, and 16.3% for the control group (cluster-adjusted, mean PPD: DEV vs control, 1.1 [95% CI, −6.7 to 8.9], P = .79; IMCP vs control, 9.9 [95% CI, 1.6 to 18.2], P = .02, Table 2). Mixed-effects models confirmed the superiority of the IMCP compared with control (adjusted OR, 1.81 [95% CI, 1.04 to 3.16], Table 2). The adjusted IMCP ORs were of similar magnitude (albeit not statistically significant) with respect to the 2 components of the primary outcome (for antidepressant prescribing, 1.85 [95% CI, 0.95 to 3.59], P = .07; for mental health referral, 1.76 [95% CI, 0.97 to 3.18], P = .06). In stratified analyses, the IMCP effect was significant in those with at least moderate symptoms (adjusted OR, 2.42 [95% CI, 1.11 to 5.30]) but not in those with mild symptoms (adjusted OR, 1.10 [95% CI, 0.44 to 2.75]) (Table 2). The IMCP depression severity interaction term was nonsignificant (P = .31).

Table Graphic Jump LocationTable 2.  Effects of DEV and IMCP vs Control on Receipt of Composite Care Measure (Antidepressant Prescription and/or Mental Health Referral) in Depressed Cohort
Intervention Effects on Patient Engagement

The percentage of patients requesting information about depression during the visit was 17.7% (95% CI, 11.4% to 23.9%) in the DEV group, 19.5% (95% CI, 13.3% to 25.6%) in the IMCP group, and 9.5% (95% CI, 4.9% to 14.1%) in the control group. Patients assigned to the DEV and IMCP groups were significantly more likely than control patients to request information about depression (cluster-adjusted, mean PPD: DEV vs control, 8.1 [95% CI, 0.9 to 15.4], P = .03; IMCP vs control, 9.9 [95% CI, 2.8 to 17.1], P < .001]; and adjusted OR: DEV vs control, 2.11 [95% CI, 1.12 to 3.98], P = .02; IMCP vs control, 2.19 [95% CI, 1.19 to 4.04], P = .01).

There were no significant intervention effects on self-efficacy for communicating with the clinician about mental health issues (adjusted mean difference on the modified Maly scale: DEV vs control, 0.22 [95% CI, −0.75 to 1.19], P = .66; IMCP vs control, 0.01 [95% CI, −0.88 to 0.90], P = .98).

Intervention Effects on 12-Week Outcomes

Table 3 shows scores on the PHQ-8 (depression), MCS-12 (mental health), and PCS-12 (physical health) by intervention group at baseline and at 12-week follow-up. All 3 outcomes improved significantly from baseline to follow-up regardless of group assignment (P values all ≤ .01). There were no significant differences between IMCP and control or between DEV and control at 12-week follow-up (P values all ≥.05, Table 3). Similar results were obtained when the sample was restricted to patients with baseline PHQ-9 scores of 10 or greater (eTable 1 in the Supplement).

Table Graphic Jump LocationTable 3.  PHQ-8, PCS-12, and MCS-12 Scores at Baseline (n = 559) and 12-Week Follow-up (n = 473)a
Results in Nondepressed Patients

Among nondepressed patients, rates of clinician-reported antidepressant prescribing were 4.8% in the DEV group, 3.6% in the IMCP group, and 6.7% in the control group (Table 4). Rates of patient-reported clinician recommendations for antidepressant medication were 5.6% in the DEV group, 4.4% in the IMCP group, and 4.6% in the control group (Table 4). For the clinician-reported outcome, these results were consistent with noninferiority (ie, equivalence) of the 2 interventions compared with the control group (P < .05 for noninferiority, Table 4). However, using the patient-reported measure, the upper confidence limit for the DEV vs control difference extended to 6.7 percentage points (P = .23 for noninferiority) and for the IMCP vs control difference to 5.7 percentage points (P = .16 for noninferiority). Therefore, the 2 interventions were not found to be equivalent to the control group for the outcome of patient-reported recommendation for antidepressant medication. For discussion of depression (in general), discussion of depression treatment (specifically), and patient requests for depression medication, cluster-adjusted mean differences between each of the active interventions and control were consistently less than 6 percentage points, with 90% CIs for differences invariably crossing zero (Table 4). Similar results were obtained in more fully adjusted models (eTable 2 in the Supplement). There were no prespecified inferiority margins for these outcomes. Neither of the 2 active interventions had a significant effect (vs control) on clinician-reported visit burden or clinician-reported visit time (P > .60 for each of the 4 comparisons).

Table Graphic Jump LocationTable 4.  Potential Harms in the Nondepressed Cohort of 308 Patients (PHQ-9 Score <5)

Among patients with clinically relevant depression symptoms (ie, the depressed patient cohort), a tailored IMCP, but not a targeted DEV, delivered before a primary care clinician appointment increased the primary composite outcome of antidepressant recommendation or mental health referral, as reported by the patient immediately after the visit. Both the DEV and the IMCP increased patient-reported requests for information about depression. However, there were no significant improvements in mental health at the 12-week follow-up in response to either intervention. Among nondepressed patients, we observed no evidence of harm from either intervention for the outcome of clinician-reported antidepressant prescribing, but we could not exclude harm (defined as a higher rate of antidepressant prescriptions for nondepressed patients associated with each intervention) based on patient-reported antidepressant recommendation. There were no statistically significant adverse intervention effects on other visit processes, although the patients in the DEV group made approximately 3-fold more requests for antidepressants than IMCP or control group patients.

Overall in the depressed cohort, assignment to the IMCP, but not the DEV, was associated with a statistically significant 10-percentage point increase in the likelihood of receiving the primary composite outcome of antidepressant recommendation, mental health referral, or both. The estimated intervention effect was statistically significant in the subgroup of patients with PHQ-9 scores of 10 or higher (for whom current guidelines endorse prompt provision of medication or psychotherapy),38,46 but not those with lower scores. Although clinically plausible, these subgroup analyses were not prespecified and should be viewed as exploratory, especially since there was no statistically significant interaction between intervention group and PHQ-9 score category.

In considering the mechanism by which the IMCP improved clinical processes of care, we speculate that individualized information about depression and its manifestations may have helped some depressed individuals to identify their personal symptoms and distress as depression and to communicate these insights to providers verbally or nonverbally. In turn, clinicians may have been less deterred by perceptions of depression-related stigma on the part of patients and consequently more disposed to offer treatment. In addition, individualized information about depression treatment may have increased some patients’ receptiveness to antidepressant medication or psychotherapy. These tentative explanations should be tested in future studies.

Among patients who were depressed, assignment to the DEV or IMCP was associated with a 2-fold increased likelihood of asking the treating clinician about depression. However, regardless of intervention group, most patients never broached the topic. The dearth of depression-related discussion could reflect more pressing clinical issues, competing demands,47 or reluctance to raise the issue of depression.

Among depressed patients who participated in the 12-week follow-up telephone interview, depression symptom scores and MCS-12 and PCS-12 scores improved from baseline in all 3 treatment groups. However, neither the DEV nor the IMCP was associated with improved mental or physical health outcomes compared with control. Thus, our interventions did not demonstrate benefit at the 12-week follow-up. Translating improvements in initial depression process of care into better clinical outcomes may require reinforcement, clinician support, or systems improvement and additional research examining the effect of combined interventions is warranted.

Among nondepressed patients (PHQ-9 score < 5), we found small differences (0-3 percentage points) in rates of both antidepressant prescribing (reported by clinicians) and antidepressant recommendations (reported by patients). Using the patient-reported measure, we could not exclude the possibility that the 2 interventions increased rates of antidepressant prescriptions by at least 3.5 percentage points among the nondepressed. There was, however, no substantive evidence of adverse intervention effects as measured by clinician-reported visit burden or duration. In judging the overall merits of the IMCP, physicians and care managers will have to weigh the benefits (improved process of care) against potential risks of overtreatment.

The brevity of both interventions makes them potentially suitable, when further validated, for widespread implementation in health care settings. Patients could complete depression screening questionnaires on touchscreen machines and, if warranted, receive prompts to select an appropriate multimedia program.

There were study limitations. Eligibility and classification into depressed and nondepressed categories was based on the PHQ-9 score, a valid measure of depression symptom burden but not a diagnostic instrument. Patients were volunteers recruited from 2 metropolitan regions in northern California; the generalizability of our findings to other settings and types of patients is unknown. Randomization by patient rather than by clinician or clinic had advantages, but may have diluted intervention effects. Although allocation concealment was achieved, full blinding was infeasible. The primary outcome among depressed patients was based on patient report—arguably the most appropriate choice for the goal of patient activation, but still subject to reporting bias. Incomplete follow-up could have skewed 12-week outcomes, even though the direction of this bias is unpredictable. Finally, this study examined the effectiveness of the interventions in office settings. Administration in a different context (eg, via the Internet) could produce different results.

Conclusions

Among depressed patients evaluated in a primary care setting, the use of a tailored IMCP immediately prior to a primary care visit resulted in the increased receipt of the primary composite outcome of antidepressant prescription recommendation, mental health referral, or both during the primary care visit compared with a control group. However, the tailored IMCP intervention had no effect on 12-week, clinically meaningful outcomes. Although there was no evidence of excess antidepressant prescribing among patients with minimal symptoms of depression as determined by the clinician-reported outcome, potential overtreatment cannot be excluded based on the patient-reported outcome. Further research is needed to determine effects on clinical outcomes and whether the benefits outweigh possible harms.

Corresponding Author: Richard L. Kravitz, MD, MSPH, Division of General Medicine, University of California, Davis, 4150 V St, Ste 2400 PSSB, Sacramento, CA 95817 (rlkravitz@ucdavis.edu).

Author Contributions: Dr Kravitz had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Kravitz, Franks, Feldman, Tancredi, Epstein, Duberstein, Bell, Paterniti, Kelly-Reif, Dvorak, Turner, Jerant.

Acquisition of data: Kravitz, Feldman, Slee, Jackson-Triche, Cipri, Olson, Kelly-Reif, Hudnut, Jerant.

Analysis and interpretation of data: Kravitz, Franks, Tancredi, Slee, Epstein, Duberstein, Jackson-Triche, Iosif, Jerant.

Drafting of the manuscript: Kravitz, Franks, Slee, Jackson-Triche, Cipri, Iosif, Jerant.

Critical revision of the manuscript for important intellectual content: Franks, Feldman, Tancredi, Epstein, Duberstein, Bell, Jackson-Triche, Paterniti, Iosif, Olson, Kelly-Reif, Hudnut, Dvorak, Turner, Jerant.

Statistical analysis: Franks, Tancredi, Iosif.

Obtained funding: Kravitz, Feldman, Epstein, Bell, Jerant.

Administrative, technical, or material support: Kravitz, Jackson-Triche, Cipri, Olson, Kelly-Reif, Hudnut, Dvorak, Turner.

Study supervision: Slee, Jackson-Triche.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr Franks reports receiving grant funding and the provision of writing assistance, medicines, equipment, or administrative support from the University of California, Davis (UCD). Dr Tancredi reports receiving grant funding from the National Institutes of Health and consulting for the International Scientific Association of Probiotics and Prebiotics. Dr Duberstein reports receiving travel accommodations from the Substance Abuse and Mental Health Services Administration. Dr Hudnut reports receiving grant funding from the University of California, Davis, subcontract to Sutter Institute for Medical Research. No other disclosures were reported.

Funding/Support: This work was supported by grants 1R01MH079387 (Kravitz), K24MH072756 (Kravitz), and K24 K24MH072712 (Duberstein) from the National Institute of Mental Health.

Role of the Sponsor: The National Institute of Mental Health had no role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Additional Contributions: We thank Julia Huerta, MPH, and Dustin Gottfeld, BS (UCD), Ana Fernandez-Lamothe, Jeff Kohlwes, MD, and Seth Berkowitz, MD (University of California, San Francisco), for coordinating and facilitating recruitment and participation of patients in the study. All except Dr Kohlwes were compensated from grant funds for their time. We also thank Robert Burnett, MA (UCD Information and Educational Technologies), for developing and programming the animations used in the tailored interactive multimedia computer program. Mr Burnett was paid from grant funds for his time. Finally, we thank all of the clinicians, offices, and patients who participated.

Berardi  D, Menchetti  M, Cevenini  N, Scaini  S, Versari  M, De Ronchi  D.  Increased recognition of depression in primary care: comparison between primary-care physician and ICD-10 diagnosis of depression. Psychother Psychosom. 2005;74(4):225-230.
PubMed   |  Link to Article
Lotfi  L, Flyckt  L, Krakau  I, Mårtensson  B, Nilsson  GH.  Undetected depression in primary healthcare: occurrence, severity and comorbidity in a 2-stage procedure of opportunistic screening. Nord J Psychiatry. 2010;64(6):421-427.
PubMed   |  Link to Article
Simon  GE, Fleck  M, Lucas  R, Bushnell  DM; LIDO Group.  Prevalence and predictors of depression treatment in an international primary care study. Am J Psychiatry. 2004;161(9):1626-1634.
PubMed   |  Link to Article
Unützer  J, Katon  W, Callahan  CM,  et al; IMPACT Investigators: Improving Mood-Promoting Access to Collaborative Treatment.  Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA. 2002;288(22):2836-2845.
PubMed   |  Link to Article
Wells  KB.  Caring for depression in primary care: defining and illustrating the policy context. J Clin Psychiatry. 1997;58(suppl 1):24-27.
PubMed
Katon  W, Unützer  J, Wells  K, Jones  L.  Collaborative depression care: history, evolution, and ways to enhance dissemination and sustainability. Gen Hosp Psychiatry. 2010;32(5):456-464.
PubMed   |  Link to Article
Cabana  MD, Rand  CS, Powe  NR,  et al.  Why don’t physicians follow clinical practice guidelines? a framework for improvement. JAMA. 1999;282(15):1458-1465.
PubMed   |  Link to Article
Kravitz  RL.  Beyond gatekeeping: enlisting patients as agents for quality and cost-containment. J Gen Intern Med. 2008;23(10):1722-1723.
PubMed   |  Link to Article
Bell  RA, Franks  P, Duberstein  PR,  et al.  Suffering in silence: reasons for not disclosing depression in primary care. Ann Fam Med. 2011;9(5):439-446.
PubMed   |  Link to Article
Epstein  RM, Duberstein  PR, Feldman  MD,  et al.  “I didn’t know what was wrong”: how people with undiagnosed depression recognize, name, and explain their distress. J Gen Intern Med. 2010;25(9):954-961.
PubMed   |  Link to Article
Kanter  JW, Rusch  LC, Brondino  MJ.  Depression self-stigma: a new measure and preliminary findings. J Nerv Ment Dis. 2008;196(9):663-670.
PubMed   |  Link to Article
Donohue  JM, Cevasco  M, Rosenthal  MB.  A decade of direct-to-consumer advertising of prescription drugs. N Engl J Med. 2007;357(7):673-681.
PubMed   |  Link to Article
Block  AE.  Costs and benefits of direct-to-consumer advertising: the case of depression. Pharmacoeconomics. 2007;25(6):511-521.
PubMed   |  Link to Article
Mintzes  B, Barer  ML, Kravitz  RL,  et al.  How does direct-to-consumer advertising (DTCA) affect prescribing? a survey in primary care environments with and without legal DTCA. CMAJ. 2003;169(5):405-412.
PubMed
Kravitz  RL, Epstein  RM, Feldman  MD,  et al.  Influence of patients’ requests for direct-to-consumer advertised antidepressants: a randomized controlled trial. JAMA. 2005;293(16):1995-2002.
PubMed   |  Link to Article
Niederdeppe  J, Byrne  S, Avery  RJ, Cantor  J.  Direct-to-consumer television advertising exposure, diagnosis with high cholesterol, and statin use. J Gen Intern Med. 2013;28(7):886-893.
PubMed   |  Link to Article
Avery  RJ, Eisenberg  MD, Simon  KI.  The impact of direct-to-consumer television and magazine advertising on antidepressant use. J Health Econ. 2012;31(5):705-718.
PubMed   |  Link to Article
Schmid  KL, Rivers  SE, Latimer  AE, Salovey  P.  Targeting or tailoring? Mark Health Serv. 2008;28(1):32-37.
PubMed
Jerant  A, Sohler  N, Fiscella  K, Franks  B, Franks  P.  Tailored interactive multimedia computer programs to reduce health disparities: opportunities and challenges. Patient Educ Couns. 2011;85(2):323-330.
PubMed   |  Link to Article
Tancredi  DJ, Slee  CK, Jerant  A,  et al.  Targeted versus tailored multimedia patient engagement to enhance depression recognition and treatment in primary care: randomized controlled trial protocol for the AMEP2 study. BMC Health Serv Res. 2013;13(1):141.
PubMed   |  Link to Article
Stewart  WF, Ricci  JA, Chee  E, Hahn  SR, Morganstein  D.  Cost of lost productive work time among US workers with depression. JAMA. 2003;289(23):3135-3144.
PubMed   |  Link to Article
Kroenke  K, Strine  TW, Spitzer  RL, Williams  JB, Berry  JT, Mokdad  AH.  The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1-3):163-173.
PubMed   |  Link to Article
Bell  RA, Paterniti  DA, Azari  R,  et al.  Encouraging patients with depressive symptoms to seek care: a mixed methods approach to message development. Patient Educ Couns. 2010;78(2):198-205.
PubMed   |  Link to Article
Kravitz  RL, Epstein  RM, Bell  RA,  et al.  An academic-marketing collaborative to promote depression care: a tale of two cultures. Patient Educ Couns. 2013;90(3):411-419.
PubMed   |  Link to Article
Epstein  A. Common Sleeping Problems [video]. HealthiNation; 2011.
Kravitz  RL, Paterniti  DA, Epstein  RM,  et al.  Relational barriers to depression help-seeking in primary care. Patient Educ Couns. 2011;82(2):207-213.
PubMed   |  Link to Article
Petty  R, Cacioppo  J. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York, NY: Springer; 1986.
Dwight-Johnson  M, Sherbourne  CD, Liao  D, Wells  KB.  Treatment preferences among depressed primary care patients. J Gen Intern Med. 2000;15(8):527-534.
PubMed   |  Link to Article
Karasz  A, Sacajiu  G, Garcia  N.  Conceptual models of psychological distress among low-income patients in an inner-city primary care clinic. J Gen Intern Med. 2003;18(6):475-477.
PubMed   |  Link to Article
Cooper-Patrick  L, Powe  NR, Jenckes  MW, Gonzales  JJ, Levine  DM, Ford  DE.  Identification of patient attitudes and preferences regarding treatment of depression. J Gen Intern Med. 1997;12(7):431-438.
PubMed   |  Link to Article
Dwight-Johnson  M, Unutzer  J, Sherbourne  C, Tang  L, Wells  KB.  Can quality improvement programs for depression in primary care address patient preferences for treatment? Med Care. 2001;39(9):934-944.
PubMed   |  Link to Article
Maly  RC, Frank  JC, Marshall  GN, DiMatteo  MR, Reuben  DB.  Perceived efficacy in patient-physician interactions (PEPPI): validation of an instrument in older persons. J Am Geriatr Soc. 1998;46(7):889-894.
PubMed
Cooper  AE, Corrigan  PW, Watson  AC.  Mental illness stigma and care seeking. J Nerv Ment Dis. 2003;191(5):339-341.
PubMed
Deci  EL, Ryan  RM.  The support of autonomy and the control of behavior. J Pers Soc Psychol. 1987;53(6):1024-1037.
PubMed   |  Link to Article
Kreuter  M, Farrell  D, Olevitch  L, Brennan  L. Tailoring Health Messages: Customizing Communication With Computer Technology. Mahwah, NJ: Erlbaum; 2000.
Duffy  SA, Ronis  DL, Valenstein  M,  et al A tailored smoking, alcohol, and depression intervention for head and neck cancer patients. Cancer Epidemiol Biomarkers Prev.2006;15(11):2203-2208.
PubMed   |  Link to Article
Ryan  GL, Skinner  CS, Farrell  D, Champion  VL.  Examining the boundaries of tailoring: the utility of tailoring vs targeting mammography interventions for 2 distinct populations. Health Educ Res. 2001;16(5):555-566.
PubMed   |  Link to Article
Kroenke  K, Spitzer  RL, Williams  JB.  The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613.
PubMed   |  Link to Article
Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann. 2002;32(9):1-7. http://www.lphi.org/LPHIadmin/uploads/.PHQ-9-Review-Kroenke-63754.PDF. Accessed October 14, 2013.
Wells  TS, Horton  JL, LeardMann  CA, Jacobson  IG, Boyko  EJ.  A comparison of the PRIME-MD PHQ-9 and PHQ-8 in a large military prospective study, the Millennium Cohort Study. J Affect Disord. 2013;148(1):77-83.
PubMed   |  Link to Article
Fleishman  JA, Selim  AJ, Kazis  LE.  Deriving SF-12v2 physical and mental health summary scores: a comparison of different scoring algorithms. Qual Life Res.2010;19(2):231-241.
PubMed   |  Link to Article
Ware  J  Jr, Kosinski  M, Keller  SD.  A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233.
PubMed   |  Link to Article
Stata Statistical Software [computer program]. Version 12.1. College Station, TX: StataCorp LP; 2011.
Hauck  WW, Anderson  S, Marcus  SM.  Should we adjust for covariates in nonlinear regression analyses of randomized trials? Control Clin Trials. 1998;19(3):249-256.
PubMed   |  Link to Article
DeSouza  CM, Legedza  AT, Sankoh  AJ.  An overview of practical approaches for handling missing data in clinical trials. J Biopharm Stat. 2009;19(6):1055-1073.
PubMed   |  Link to Article
MacArthur Foundation. Initiative on depression and primary care. http://www.macfound.org/networks/initiative-on-depression-primary-care/. Accessed September 27, 2013.
Nutting  PA, Rost  K, Smith  J, Werner  JJ, Elliot  C.  Competing demands from physical problems: effect on initiating and completing depression care over 6 months. Arch Fam Med. 2000;9(10):1059-1064.
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure.
Flow of Patients Through Study

PHQ indicates Patient Health Questionnaire; DEV, depression engagement video; IMCP, interactive multimedia computer program.

aIn the depressed cohort, 559 patients were included in the primary analysis; nondepressed cohort, 308 patients.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1.  Baseline Characteristics of Patients (Depressed Cohort and Nondepressed Cohort)
Table Graphic Jump LocationTable 2.  Effects of DEV and IMCP vs Control on Receipt of Composite Care Measure (Antidepressant Prescription and/or Mental Health Referral) in Depressed Cohort
Table Graphic Jump LocationTable 3.  PHQ-8, PCS-12, and MCS-12 Scores at Baseline (n = 559) and 12-Week Follow-up (n = 473)a
Table Graphic Jump LocationTable 4.  Potential Harms in the Nondepressed Cohort of 308 Patients (PHQ-9 Score <5)

References

Berardi  D, Menchetti  M, Cevenini  N, Scaini  S, Versari  M, De Ronchi  D.  Increased recognition of depression in primary care: comparison between primary-care physician and ICD-10 diagnosis of depression. Psychother Psychosom. 2005;74(4):225-230.
PubMed   |  Link to Article
Lotfi  L, Flyckt  L, Krakau  I, Mårtensson  B, Nilsson  GH.  Undetected depression in primary healthcare: occurrence, severity and comorbidity in a 2-stage procedure of opportunistic screening. Nord J Psychiatry. 2010;64(6):421-427.
PubMed   |  Link to Article
Simon  GE, Fleck  M, Lucas  R, Bushnell  DM; LIDO Group.  Prevalence and predictors of depression treatment in an international primary care study. Am J Psychiatry. 2004;161(9):1626-1634.
PubMed   |  Link to Article
Unützer  J, Katon  W, Callahan  CM,  et al; IMPACT Investigators: Improving Mood-Promoting Access to Collaborative Treatment.  Collaborative care management of late-life depression in the primary care setting: a randomized controlled trial. JAMA. 2002;288(22):2836-2845.
PubMed   |  Link to Article
Wells  KB.  Caring for depression in primary care: defining and illustrating the policy context. J Clin Psychiatry. 1997;58(suppl 1):24-27.
PubMed
Katon  W, Unützer  J, Wells  K, Jones  L.  Collaborative depression care: history, evolution, and ways to enhance dissemination and sustainability. Gen Hosp Psychiatry. 2010;32(5):456-464.
PubMed   |  Link to Article
Cabana  MD, Rand  CS, Powe  NR,  et al.  Why don’t physicians follow clinical practice guidelines? a framework for improvement. JAMA. 1999;282(15):1458-1465.
PubMed   |  Link to Article
Kravitz  RL.  Beyond gatekeeping: enlisting patients as agents for quality and cost-containment. J Gen Intern Med. 2008;23(10):1722-1723.
PubMed   |  Link to Article
Bell  RA, Franks  P, Duberstein  PR,  et al.  Suffering in silence: reasons for not disclosing depression in primary care. Ann Fam Med. 2011;9(5):439-446.
PubMed   |  Link to Article
Epstein  RM, Duberstein  PR, Feldman  MD,  et al.  “I didn’t know what was wrong”: how people with undiagnosed depression recognize, name, and explain their distress. J Gen Intern Med. 2010;25(9):954-961.
PubMed   |  Link to Article
Kanter  JW, Rusch  LC, Brondino  MJ.  Depression self-stigma: a new measure and preliminary findings. J Nerv Ment Dis. 2008;196(9):663-670.
PubMed   |  Link to Article
Donohue  JM, Cevasco  M, Rosenthal  MB.  A decade of direct-to-consumer advertising of prescription drugs. N Engl J Med. 2007;357(7):673-681.
PubMed   |  Link to Article
Block  AE.  Costs and benefits of direct-to-consumer advertising: the case of depression. Pharmacoeconomics. 2007;25(6):511-521.
PubMed   |  Link to Article
Mintzes  B, Barer  ML, Kravitz  RL,  et al.  How does direct-to-consumer advertising (DTCA) affect prescribing? a survey in primary care environments with and without legal DTCA. CMAJ. 2003;169(5):405-412.
PubMed
Kravitz  RL, Epstein  RM, Feldman  MD,  et al.  Influence of patients’ requests for direct-to-consumer advertised antidepressants: a randomized controlled trial. JAMA. 2005;293(16):1995-2002.
PubMed   |  Link to Article
Niederdeppe  J, Byrne  S, Avery  RJ, Cantor  J.  Direct-to-consumer television advertising exposure, diagnosis with high cholesterol, and statin use. J Gen Intern Med. 2013;28(7):886-893.
PubMed   |  Link to Article
Avery  RJ, Eisenberg  MD, Simon  KI.  The impact of direct-to-consumer television and magazine advertising on antidepressant use. J Health Econ. 2012;31(5):705-718.
PubMed   |  Link to Article
Schmid  KL, Rivers  SE, Latimer  AE, Salovey  P.  Targeting or tailoring? Mark Health Serv. 2008;28(1):32-37.
PubMed
Jerant  A, Sohler  N, Fiscella  K, Franks  B, Franks  P.  Tailored interactive multimedia computer programs to reduce health disparities: opportunities and challenges. Patient Educ Couns. 2011;85(2):323-330.
PubMed   |  Link to Article
Tancredi  DJ, Slee  CK, Jerant  A,  et al.  Targeted versus tailored multimedia patient engagement to enhance depression recognition and treatment in primary care: randomized controlled trial protocol for the AMEP2 study. BMC Health Serv Res. 2013;13(1):141.
PubMed   |  Link to Article
Stewart  WF, Ricci  JA, Chee  E, Hahn  SR, Morganstein  D.  Cost of lost productive work time among US workers with depression. JAMA. 2003;289(23):3135-3144.
PubMed   |  Link to Article
Kroenke  K, Strine  TW, Spitzer  RL, Williams  JB, Berry  JT, Mokdad  AH.  The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1-3):163-173.
PubMed   |  Link to Article
Bell  RA, Paterniti  DA, Azari  R,  et al.  Encouraging patients with depressive symptoms to seek care: a mixed methods approach to message development. Patient Educ Couns. 2010;78(2):198-205.
PubMed   |  Link to Article
Kravitz  RL, Epstein  RM, Bell  RA,  et al.  An academic-marketing collaborative to promote depression care: a tale of two cultures. Patient Educ Couns. 2013;90(3):411-419.
PubMed   |  Link to Article
Epstein  A. Common Sleeping Problems [video]. HealthiNation; 2011.
Kravitz  RL, Paterniti  DA, Epstein  RM,  et al.  Relational barriers to depression help-seeking in primary care. Patient Educ Couns. 2011;82(2):207-213.
PubMed   |  Link to Article
Petty  R, Cacioppo  J. Communication and Persuasion: Central and Peripheral Routes to Attitude Change. New York, NY: Springer; 1986.
Dwight-Johnson  M, Sherbourne  CD, Liao  D, Wells  KB.  Treatment preferences among depressed primary care patients. J Gen Intern Med. 2000;15(8):527-534.
PubMed   |  Link to Article
Karasz  A, Sacajiu  G, Garcia  N.  Conceptual models of psychological distress among low-income patients in an inner-city primary care clinic. J Gen Intern Med. 2003;18(6):475-477.
PubMed   |  Link to Article
Cooper-Patrick  L, Powe  NR, Jenckes  MW, Gonzales  JJ, Levine  DM, Ford  DE.  Identification of patient attitudes and preferences regarding treatment of depression. J Gen Intern Med. 1997;12(7):431-438.
PubMed   |  Link to Article
Dwight-Johnson  M, Unutzer  J, Sherbourne  C, Tang  L, Wells  KB.  Can quality improvement programs for depression in primary care address patient preferences for treatment? Med Care. 2001;39(9):934-944.
PubMed   |  Link to Article
Maly  RC, Frank  JC, Marshall  GN, DiMatteo  MR, Reuben  DB.  Perceived efficacy in patient-physician interactions (PEPPI): validation of an instrument in older persons. J Am Geriatr Soc. 1998;46(7):889-894.
PubMed
Cooper  AE, Corrigan  PW, Watson  AC.  Mental illness stigma and care seeking. J Nerv Ment Dis. 2003;191(5):339-341.
PubMed
Deci  EL, Ryan  RM.  The support of autonomy and the control of behavior. J Pers Soc Psychol. 1987;53(6):1024-1037.
PubMed   |  Link to Article
Kreuter  M, Farrell  D, Olevitch  L, Brennan  L. Tailoring Health Messages: Customizing Communication With Computer Technology. Mahwah, NJ: Erlbaum; 2000.
Duffy  SA, Ronis  DL, Valenstein  M,  et al A tailored smoking, alcohol, and depression intervention for head and neck cancer patients. Cancer Epidemiol Biomarkers Prev.2006;15(11):2203-2208.
PubMed   |  Link to Article
Ryan  GL, Skinner  CS, Farrell  D, Champion  VL.  Examining the boundaries of tailoring: the utility of tailoring vs targeting mammography interventions for 2 distinct populations. Health Educ Res. 2001;16(5):555-566.
PubMed   |  Link to Article
Kroenke  K, Spitzer  RL, Williams  JB.  The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606-613.
PubMed   |  Link to Article
Kroenke K, Spitzer RL. The PHQ-9: a new depression diagnostic and severity measure. Psychiatr Ann. 2002;32(9):1-7. http://www.lphi.org/LPHIadmin/uploads/.PHQ-9-Review-Kroenke-63754.PDF. Accessed October 14, 2013.
Wells  TS, Horton  JL, LeardMann  CA, Jacobson  IG, Boyko  EJ.  A comparison of the PRIME-MD PHQ-9 and PHQ-8 in a large military prospective study, the Millennium Cohort Study. J Affect Disord. 2013;148(1):77-83.
PubMed   |  Link to Article
Fleishman  JA, Selim  AJ, Kazis  LE.  Deriving SF-12v2 physical and mental health summary scores: a comparison of different scoring algorithms. Qual Life Res.2010;19(2):231-241.
PubMed   |  Link to Article
Ware  J  Jr, Kosinski  M, Keller  SD.  A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233.
PubMed   |  Link to Article
Stata Statistical Software [computer program]. Version 12.1. College Station, TX: StataCorp LP; 2011.
Hauck  WW, Anderson  S, Marcus  SM.  Should we adjust for covariates in nonlinear regression analyses of randomized trials? Control Clin Trials. 1998;19(3):249-256.
PubMed   |  Link to Article
DeSouza  CM, Legedza  AT, Sankoh  AJ.  An overview of practical approaches for handling missing data in clinical trials. J Biopharm Stat. 2009;19(6):1055-1073.
PubMed   |  Link to Article
MacArthur Foundation. Initiative on depression and primary care. http://www.macfound.org/networks/initiative-on-depression-primary-care/. Accessed September 27, 2013.
Nutting  PA, Rost  K, Smith  J, Werner  JJ, Elliot  C.  Competing demands from physical problems: effect on initiating and completing depression care over 6 months. Arch Fam Med. 2000;9(10):1059-1064.
PubMed   |  Link to Article
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Multimedia

Supplement.

eTable 1. PHQ-8, PCS-12, and MCS-12 Scores at Baseline (Patients With PHQ-9 > 10; n=268) and 12-Week Follow-up (n=227)

eTable 2. Event Frequencies and Adjusted Odds Ratios of DEV and IMCP (vs Control) on Depression Process of Care in the Nondepressed Sample of 308 (PHQ-9 Score < 5)

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