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Diabetes Control With Reciprocal Peer Support Versus NurseCare ManagementA Randomized Trial

Michele Heisler, MD, MPA; Sandeep Vijan, MD, MS; Fatima Makki, MPH; and John D. Piette, PhD

Background: Resource barriers complicate diabetes care manage-ment. Support from peers may help patients manage their diabetes.

Objective: To compare a reciprocal peer-support (RPS) programwith nurse care management (NCM).

Design: Randomized, controlled trial. (ClinicalTrials.gov registrationnumber: NCT00320112)

Setting: 2 U.S. Department of Veterans Affairs health care facilities.

Patients: 244 men with hemoglobin A1c

(HbA1c

) levels greater than 7.5% during the previous 6 months.

Measurements: The primary outcome was 6-month change in

HbA1c level. Secondary outcomes were changes in insulin therapy;blood pressure; and patient reports of medication adherence,diabetes-related support, and emotional distress.

Intervention: Patients in the RPS group attended an initial groupsession to set diabetes-related behavioral goals, receive peer com-munication skills training, and be paired with another age-matchedpeer patient. Peers were encouraged to talk weekly using a tele-phone platform that recorded call occurrence and provided remind-ers to promote peer contact. These patients could also participate inoptional group sessions at 1, 3, and 6 months. Patients in the NCMgroup attended a 1.5-hour educational session and were assignedto a nurse care manager.

Results: Of the 244 patients enrolled, 216 (89%) completed theHbA

1c assessments and 231 (95%) completed the survey assess-

ments at 6 months. Mean HbA1c

level decreased from 8.02% to7.73% (change, 0.29%) in the RPS group and increased from7.93% to 8.22% (change, 0.29%) in the NCM group. The differ-ence in HbA

1c change between groups was 0.58% (P 0.004).

Among patients with a baseline HbA1c

level greater than 8.0%,those in the RPS group had a mean decrease of 0.88%, comparedwith a 0.07% decrease among those in the NCM group (between-group difference, 0.81%; P 0.001). Eight patients in the RPSgroup started insulin therapy, compared with 1 patient in theNCM group (P 0.020). Groups did not differ in blood pressure,self-reported medication adherence, or diabetes-specific distress, butthe RPS group reported improvement in diabetes social support.

Limitation: The study included only male veterans and lasted only6 months.

Conclusion: Reciprocal peer support holds promise as a method for diabetes care management.

Primary Funding Source: U.S. Department of Veterans AffairsHealth Services Research and Development Service.

Ann Intern Med. 2010;153:507-515. www.annals.org

For author affiliations, see end of text.

Many patients with diabetes would benefit from self-management assistance between clinic visits. In effi-cacy trials, nurse-led care management programs improvediabetes self-care and risk factor control (1–3). However,real-life practices face multiple barriers to delivering theseservices, especially in low-resource settings (4).

As recognized in such initiatives as the United King-dom’s Expert Patient Program (5), peer support among patients with the same chronic health problem is a prom-ising approach to increasing the quality and quantity of support (6). Peer support could allow patients to shareexperiences and receive reinforcement that is not availablefrom time-pressed clinicians, and it may especially benefitpatients who are tackling challenging medical tasks, such asinsulin management. Many patients with poor glycemiccontrol require either initiation or intensification of insulintherapy but resist these because of concerns about the ad-ditional self-management burdens (7, 8), which results inneuropathic or microvascular complications (9).

Although few peer-support models have been evalu-ated in randomized, controlled trials (RCTs), the effectivetested models combine peer support with a more struc-tured program of education and assistance (10, 11). Face-

to-face peer- and clinician-led group visits (12–14) andtraining sessions (15–17) improve some outcomes; how-ever, many patients assigned to face-to-face peer support orgroup sessions do not attend them (14, 15). It is thusimportant to identify novel delivery mechanisms to extendthe reach of evidence-based peer-support models (18).

To build on the potential benefits of face-to-face peersupport while circumventing access barriers, we designedand piloted a novel intervention that supplemented op-tional periodic nurse-led group sessions with telephone-based peer support between paired diabetic patients (19).The model was intended to encourage both peers to receive

See also:

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Editors’ Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508

Editorial comment. . . . . . . . . . . . . . . . . . . . . . . . . . 544

Summary for Patients. . . . . . . . . . . . . . . . . . . . . . . I-54

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Conversion of graphics into slides

Annals of Internal Medicine Original Research

www.annals.org 19 October 2010 Annals of Internal Medicine Volume 153 • Number 8 507


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and provide support, with no designation of a “helper” or“helpee.” Although peer support may serve to activate pa-tients by having them help others (20), such reciprocalmodels have not been examined in chronic disease man-agement. Here, we compare our reciprocal peer-support(RPS) program with nurse care management (NCM) in anRCT in a real-life clinical setting. We hypothesized thathelping and receiving help from other diabetic patients ingroup sessions and one-on-one telephone conversations

would bolster patients’ autonomous motivation (21) andself-efficacy (22) to execute diabetes self-care tasks and im-

prove their glycemic control.

METHODSFrom April 2007 to April 2009, we identified veterans

with diabetes and poor glycemic control who were receiv-ing care at 2 midwestern U.S. Department of Veterans

Affairs (VA) facilities by using a validated algorithm (23) tosearch electronic medical records. Patients had to have a most recent recorded hemoglobin A

1c (HbA

1c) level greater

than 7.5% within the past 6 months. Exclusion criteria were an International Classification of Diseases, NinthEdition, diagnosis of posttraumatic stress disorder, bipolar

disorder, dementia, schizophrenia, or personality disorder.Telephone Screening

The VA Ann Arbor Healthcare System institutionalreview board approved our protocol. Lists of patients weregenerated every 4 to 6 weeks. Forty-nine providers allowedtheir patients to participate; they were sent lists of poten-tially eligible patients before contact by research staff andsigned the initial information letter sent to the patients. A research associate then telephoned the patients, describedthe study as a comparison of 2 diabetes self-managementsupport models (to avoid possible expectation bias), andadministered a screening questionnaire. We excluded pa-

tients who reported active substance abuse, severe depres-sion, hearing loss, terminal illnesses, or participation inother diabetes studies. Eligible and interested patients werescheduled for a face-to-face initial session in groups of 4 to18. We alternated recruitment between cohorts aged 45 to66 years and cohorts 65 years or older to facilitate group

cohesion in the sessions and help to pair patients with anage-matched peer partner.

Recruitment and Random Assignment

At the initial group session, participants completed written informed consent and a self-administered survey,had their blood pressure measured, had their HbA

1c level

measured with a Bayer DCA2000 Analyzer (Bayer,Keverkusen, Germany) (24), and were randomly assignedto either the RPS or NCM group. Patients received $20 forthe baseline and 6-month assessments.

Random sequence generation and treatment group as-signment were determined centrally just before the initial

session. The sequence was concealed until interventions were assigned. Patients, research staff, and care managers were blinded to randomization results until after the base-line surveys and physiologic measures were completed.Data assessors remained blinded to group assignmentthroughout the study. Because the RPS group needed tohave an even number of patients to pair all members, ran-domization algorithms ensured allocation of an even num-ber to that group. On the basis of evidence that peers closerin age have an increased likelihood of providing effectivepeer support (6), patients in the RPS group were paired by age, after random assignment, with a peer partner who

attended the same initial session.

InterventionInitial Care Manager Training

Because this was an effectiveness study, all care man-agers (9 at one site and 6 at the other) facilitated interven-tion group sessions as part of their assigned VA work duties

with no additional salary. The study was explained to caremanagers and other providers as a comparison of 2 diabetesself-management support models, with no mention of specific hypotheses. Care managers completed a 4-hourtraining course in motivational interviewing (25) andempowerment-based approaches (26). The managers weretrained to facilitate group discussion, encourage patients toidentify diabetes-related behavioral goals that were consis-tent with their goals and values, and develop a short-termaction plan of specific steps to meet these goals (27). Caremanagers also attended two 1-hour booster sessions. Be-cause VA clinical leaders wanted all care managers to par-ticipate in both programs, we actively encouraged the caremanagers to use the same behavioral approaches when in-teracting with patients in both groups so that we couldassess the additional value of peer-support interactions inthe RPS group. Patients in both groups were given thesame instructions about algorithms for self-adjusting insu-

Context

Many patients with diabetes have elevated blood glucoselevels despite being in standard treatment programs.

Contribution

When researchers paired similar diabetic patients and

encouraged them to talk with each other weekly, thepatients’ HbA1c levels decreased 0.6% more than thoseof control patients in a nurse care management program.

Caution

All patients were male veterans, and the study lasted only6 months.

Implication

Pairing diabetic patients facing similar self-managementchallenges might improve patient outcomes.

—The Editors

Original Research Reciprocal Peer Support in Diabetes

508 19 October 2010 Annals of Internal Medicine Volume 153 • Number 8 www.annals.org


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lin as with usual care and were not instructed to self-adjustoral antihyperglycemic medications.

Nurse Care Management

At baseline and 6-month follow-up, all study patientshad their HbA

1c level and blood pressure checked and were

informed of the results and the most recent cholesterolvalues were in their medical record. Patients in the NCMgroup then attended a 1.5-hour session, led by a care man-ager, to review their laboratory and blood pressure results,ask questions, and receive information on VA care man-agement services. They were provided their assigned caremanager’s contact information and encouraged to schedulefollow-up telephone calls or face-to-face visits with thatcare manager. Each patient was also provided with diabetesself-management educational materials. Patients in theNCM group thus received enhanced usual care, becauseeven though they would all be eligible for nurse care man-

ager support at the study sites, many patients are not awareof and do not avail themselves of this service unless referredby their physician.

Reciprocal Peer Support

After the baseline assessment, patients in the RPSgroup attended a 3-hour group session facilitated by a caremanager and a research associate. In the first half of thesession, patients’ laboratory and blood pressure results werereviewed and action planning was introduced. In the sec-ond half, patients received brief training in basic peer com-munication skills and were paired with another age-

matched patient in their cohort. Peer partners wereencouraged to call each other at least once a week using aninteractive, voice-response–facilitated telephone platformthat recorded call initiation, frequency, and duration; en-abled partners to contact each other without exchanging telephone numbers and to set periods during which callscould be blocked; and generated automated reminders ev-ery 7 days if no peer calls were attempted. During a re-minder call, patients could be transferred automatically totheir peer partner’s number. The system also allowed pa-tients to leave voice messages for research staff or care man-agers. At the end of the initial session, patients were givena DVD that demonstrated peer communication skills and a diabetes self-management workbook that they could use tohelp guide their peer telephone calls.

Patients were also offered 3 optional 1.5-hour groupsessions at months 1, 3, and 6. These were completely patient-driven sessions at which patients were encouragedto share concerns, questions, strategies, and progress ontheir action plans. Sessions were facilitated by a care man-ager and a research associate. Research associates werepresent to help maintain intervention fidelity by encourag-ing nondirective facilitation of group discussions and tocomplete a checklist of key areas covered and communica-tion skills used in each session.

Outcomes and Measurements

The primary outcome was change between baselineand 6-month HbA

1c levels, as measured with a Bayer

DCA2000 Analyzer (24). This assay has a test coefficientof variation less than 5%, as required by the National Di-abetes Data Group (24). A subsample of HbA

1c results

were compared with those from the VA’s laboratory ser-vices, and no significant discrepancies were found. Bloodpressure was recorded according to American Heart Asso-ciation guidelines (28) with an Omron Intellisense BloodPressure Monitoring System (Omron Corporation, Kyoto,

Japan). We had intended to include change in point-of-service cholesterol levels, but we dropped this measure afterthe trial began because we determined (from comparisonsof a subsample of assays with results from VA laboratories)that the quality of measurement was poor. Secondary self-report outcomes measured by survey at baseline and 6months included validated measures of medication adher-ence (29), diabetes-related emotional distress (30), and

diabetes-specific social support (31). Finally, we reviewedmedical records to determine all primary care and diabetes-related subspecialty clinic visits, care manager contacts(telephone or face-to-face), and increases in dosage or numberof antihyperglycemic or blood pressure medications.

Statistical Analysis

We estimated the sample size to provide 80% power with a 2-sided level of 0.05 to detect a difference inHbA

1c level of 0.5% between groups. The unit of analysis

was the individual, but we allowed for a possible correla-tion of outcomes between members of the same pair (in-

traclass correlation or ) of 0.05 in the intervention groupby using the methods of Cohen (32). A standard deviationof 1.2 was estimated for HbA

1c level, on the basis of pre-

vious RCTs (33).In accordance with international guidelines for analy-

sis and reporting of clinical trials (34), baseline data forstudy end points and other potential prognostic indicators

were examined for clinically important differences betweengroups. The xtmixed command in STATA, version 11.0(StataCorp, College Station, Texas), which fits multilevelmixed-effects linear regression models, was used to assessthe primary end point of change in mean HbA

1c level,

with clustering by assigned pairs (35). All models that eval-uated changes between baseline and 6-month values in-cluded patient baseline values and group assignments asindependent variables. Although the groups did not signif-icantly differ in baseline characteristics (P 0.100), fur-ther analyses were adjusted for variables that could hypo-thetically influence the outcome (such as insulin use, age,or comorbid conditions). Because the results did not differ,

we report the unadjusted analyses.The intrapair for changes in HbA

1c level in our

sample was 0.092, and the results did not differ in ouranalyses that accounted for clustering (using the xtmixedcommand) or used simple linear multiple regression (using

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the regress command). To maintain the integrity of our a priori study design, in which pairing of peer partners was a key element, we conducted all analysis clustering by as-signed pairs in the RPS group and by sham pairs, createdby matching age-matched patients, in the NCM group(36). The 2 patients in the RPS group who requested re-assignment to another peer were analyzed according totheir initial pairing. All analyses were intention-to-treat.

We also conducted alternative analyses that adjusted forpotential clustering by cohort and by site, with no differ-ences in our results.

Six-month HbA 1c

data were missing for 28 randomly assigned patients (11%). We therefore conducted a secondanalysis that imputed missing data (37, 38). A third sensi-tivity analysis examined the worst-case scenario, in which

baseline HbA 1c

levels of patients who lacked 6-monthHbA

1c data remained unchanged at 6 months. The results

remained unchanged in both analyses.

Role of the Funding Source

The U.S. Department of Veterans Affairs Health Ser-vices Research and Development Service, the Michigan Di-

abetes Research and Training Center, the Robert Wood Johnson Foundation Clinical Scholars Program, and theMichigan Institute for Clinical and Health Research sup-ported this research. The funding sources had no role inthe study design; data collection; administration of the in-terventions; analysis, interpretation, or reporting of data; ordecision to submit the findings for publication.

RESULTSFigure 1 shows the flow of patients through the study.

We enrolled 244 (21%) of the 1171 patients who werecontacted and eligible; 126 were randomly assigned to the

RPS group and 119 to the NCM group. One patient inthe RPS group did not receive the intervention because of incorrectly completed written informed consent. Of the244 patients enrolled, 216 (89%) completed the HbA

1c

assessments and 231 (95%) completed the survey assess-ments at 6 months. Table 1 shows patient baseline char-acteristics and assessment results. The groups did not sig-nificantly differ in any measure (39).

Figure 2 shows the average duration and number of recorded calls in each month among the 90% of peer pairs

who had at least 1 conversation. Twenty pairs reportedthat they talked without using the system. Sixty-one per-

cent of patients in the RPS group attended the 1-monthgroup session, 59% attended the 3-month session, and63% attended the 6-month session. Forty percent attendedall 3 optional sessions, 26% attended 2, and 12% attended1. During the intervention, the groups did not significantly differ in number of additional care manager contacts(mean contacts, 1.24 [SD, 1.58] in the RPS group vs. 1.48[SD 1.50] in the NCM group) or physician visits (meanvisits, 1.70 [SD, 1.23] vs. 1.57 [SD, 0.99]). The number of care manager encounters ranged from 0 to 7 in the RPSgroup and 0 to 8 in the NCM group. No adverse events,including severe hypoglycemia, were reported in eithergroup.

Table 2 shows changes in study measures from base-line to 6-month follow-up. Patients in the RPS group hada mean baseline HbA

1c level of 8.02%, which improved to

7.73% at 6 months (change, 0.29%). Mean HbA 1c

levelsincreased for patients in the NCM group, from 7.93% atbaseline to 8.22% at follow-up (change, 0.29 percentagepoints). The difference in change in HbA

1c between the

groups was 0.58% (P 0.004). In stratified analyses of patients with baseline HbA

1c levels greater than 8.0%, pa-

tients in the RPS group had a mean decrease of 0.88% at 6months, compared with 0.07% among those in the NCMgroup (between-group difference, 0.81%; P 0.001)

Figure 1. Study flow diagram.

Assessed for eligibility (n = 1699)

Random assignment (n = 244)

A n a l y s i s

6 - M o n t h F o l l o w - u p

A l l o c a t i o n

E n r o l l m e n t

Analyzed survey

outcomes (n = 117)

Excluded (did not

complete surveys): 8Analyzed survey and

HbA1c and blood pressure

(n = 113)

Excluded (did not

complete

assessment): 12

Analyzed survey

outcomes (n = 114)

Excluded (did not

complete surveys): 5Analyzed survey and

HbA1c and blood pressure

(n = 103)

Excluded (did not

complete

assessment): 16

Excluded (n = 1455)

Did not meet inclusioncriteria: 307

Declined participation:

927

Could not be

contacted: 221

Excluded (n = 8)

Lost to follow-up: 3

Discontinued

intervention (too

busy): 3

Died: 2

Excluded (n = 5)

Lost to follow-up: 4

Died: 1

Peer intervention

(n = 126)

Received intervention:

125

Did not receive

intervention (invalidinformed consent): 1

Nurse care management

intervention (n = 119)

Received intervention:

119

HbA 1c hemoglobin A

1c.




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(analyses not shown). Mean blood pressures at baseline were relatively good in both groups (mean systolic bloodpressure, 138.4 mm Hg [SD, 17.9]) (40) and decreasedslightly over 6 months, with no significant differences be-tween groups (Table 2).

More patients started insulin therapy in the RPSgroup than in the NCM group (8 vs. 1 patients; P 0.020), and more intensification events occurred on aver-age among patients in the RPS group who were receiving oral antihyperglycemic medications (increases, 1.41 vs.1.00; P 0.010) (Table 2). Patients in the RPS group alsoreported greater increases in diabetes-specific social supportat 6 months (change in score, 11.4 vs. 4.5; P 0.010).The groups did not differ at follow-up in the numberof other medication intensification events, self-reported

Figure 2. Distribution of interactive voice-response

telephone call data.

M e a n C a l l s , n

M e a nD ur a t i on of C a l l s ,mi n

Month

Number of calls

Call duration

0

2018

16

14

12

10

8

6

4

2

0

2018

16

14

12

10

8

6

4

2

1

2.4

15.6

2

1.7

12.6

3

1.4

8.7

4

1

6.6

5

0.9

11.4

6

0.8

6.8

Table 1. Baseline Characteristics of Participants*

Characteristic Reciprocal Peer-Support Group(n 125)

Nurse CareManagement Group (n 119)

Total (n 244) P Value for Between-GroupDifference

Mean age (SD), y 61.8 (6.1) 62.3 (6.6) 62.0 (6.3) 0.26

Men, n (%) 125 (100) 119 (100) 244 (100) –

Race or ethnicity, n (%) 0.54White, non-Hispanic 99 (80) 98 (84) 197 (82)

Hispanic 5 (4) 2 (2) 7 (3)Black 11 (9) 11 (9) 22 (9)Other 9 (7) 5 (4) 14 (6)

Education, n (%) 0.67Some high school, high school graduate, or GED 34 (27) 35 (30) 69 (28)

Some college or technical or vocational training 91 (73) 83 (70) 174 (72)

Annual income, n (%) 0.94

$30 000 78 (63) 71 (63) 149 (63)$30 000 46 (37) 41 (37) 87 (37)

Social support Lives with a spouse/partner, n (%) 82 (66) 84 (71) 166 (68) 0.40Lives alone, n (%) 29 (23) 22 (18) 51 (21) 0.37

1-h travel time to appointments, n (%) 33 (26) 37 (31) 70 (29) 0.42Mean diabetes social support score (SD)† 55.1 (24.5) 53.3 (25.4) 54.2 (24.9) 0.72Mean diabetes distress score (SD)‡ 26.5 (16.4) 26.4 (19.8) 26.4 (18.1) 0.52

Antihyperglycemic medications, n (%) 0.93Only oral diabetes medication 55 (44) 53 (45) 108 (44)

Insulin, with or without oral medication 70 (56) 66 (55) 136 (56)

Medication adherence, n (%)

Missed 1 insulin dose/wk 52 (74) 48 (73) 100 (74) 0.84Missed 1 oral medication dose/wk 71 (72) 66 (66) 137 (69) 0.38

Self-rated fair or poor general health status, n (%) 59 (47) 56 (47) 115 (47) 0.98

Physiologic testsMean hemoglobin A1c level (SD), % 8.02 (1.32) 7.93 (1.40) 7.98 (1.40) 0.70Mean systolic blood pressure (SD), mm Hg 140.3 (18.6) 136.4 (16.9) 138.4 (17.9) 0.96Mean diastolic blood pressure (SD), mm Hg 77.1 (11.5) 75.8 (10.7) 76.5 (11.1) 0.81

* Results from surveys and blood samples were taken during the first group session. We used the t test for continuous measures of age, physiologic tests, diabetes distress, anddiabetes social support and used the chi-square test for all other categorical variables.† Assessed by using 6 questions from the Diabetes Support Scale (31). Each question had 6 answer choices, ranging from “strongly disagree” to “strongly agree.” The answers were scored from 0 to 5 points, with higher scores indicating higher levels of diabetes social support, and the total score was calculated as a percentage of possible points.‡ Assessed by using 14 questions from the Diabetes Distress Scale (30). Each question had 5 answer choices, ranging from “not a problem” to “serious problem.” The answers were scored from 0 to 4 points, with higher scores indicating higher levels of distress, and the total score was calculated as a percentage of possible points.

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medication adherence, or diabetes-specific quality of life(Table 2).

DISCUSSIONPatients who were randomly assigned to receive RPS

achieved HbA 1c

levels that were 0.58% lower on averagethan those of patients who received NCM. Patients in theRPS group with baseline HbA

1c levels greater than 8.0%

achieved a mean decrease of 0.88%, compared with a 0.07% decrease among those in the NCM group. Thesedifferences are both statistically and clinically significant.The UK Prospective Diabetes Study (41) found that a mean difference in HbA

1c level of 5% translates into an

absolute 2.8% risk reduction in diabetes events over 10years, or a number needed to treat of 36. More patients inthe RPS group started insulin therapy, and patients in thisgroup also reported higher diabetes-specific social supportat 6 months; however, the groups did not differ in bloodpressure, self-reported medication adherence, or diabetesdistress.

Peer-support interventions are less resource-intensivethan many diabetes management programs, because they

mobilize patients to help each other. When we assessed6-month outcomes, the 46% of patients in the RPS group

who attended the initial, 1-month, and 3-month groupsessions had 4.5 more hours of face-to-face meetings overthe 6 months than any patient in the NCM group, inaddition to the peer telephone calls. This is far less time-intensive than other diabetes self-management programsthat achieved similar or smaller improvements in glycemiccontrol (1, 42, 43). In addition, most oral medications leadto HbA

1c level decreases of 0.5% to 1.0% when introduced

as monotherapy, which is similar to the decreases achieved with this intervention (44).

Our findings reinforce evidence from observationaland nonrandomized studies that suggest health benefitsfrom both receiving and giving social support (20, 39,45, 46) and address the call by 2 recent Cochrane re-views (10, 11) for high-quality research on the clinicaleffectiveness of peer support in chronic disease manage-ment. To our knowledge, ours is the first RCT to dem-onstrate benefits from a reciprocal model of peer sup-port. In addition, because we conducted our trial as aneffectiveness trial with nurse care managers who pro-

Table 2. Changes in Outcome Measures From Baseline to 6 Months

Measures Reciprocal Peer-Support Group* Nurse Care Management Group† P Valuefor Between-GroupDifference

Baseline 6-MonthFollow-up

Change Baseline 6-MonthFollow-up

Change

Physiologic Mean hemoglobin A1c level (SD), % 8.02 (1.32) 7.73 (1.32) 0.29 7.93 (1.40) 8.22 (1.74) 0.29 0.004Mean systolic blood pressure (SD), mm Hg 140.3 (18.6) 136.9 (16.8) 3.4‡ 136.4 (16.9) 135.0 (17.7) 1.4 0.91

Mean diastolic blood pressure (SD), mm Hg 77.1 (11.5) 76.8 (11.9) 0.3 75.8 (10.7) 76.1 (10.6) 0.3 0.100

Medication change‡

Started insulin therapy, n§ – – 8 – – 1 0.020Participants with insulin dosage increase, % – – 38.5 – – 43.3 0.56Participants with an increase in dosage or number

of oral antihyperglycemic medications, %– – 16.5 – – 17.2 0.90

Participants with an increase in dosage or number of blood pressure medications, %

– – 24.0 – – 24.4 0.95

Mean insulin dosage increases (SD), n – – 1.50 (0.78) – – 1.31 (0.54) 0.28Mean oral antihyperglycemic medication increases

(SD), n– – 1.41 (0.62) – – 1.00 (0.00) 0.010

Mean blood pressure medication increases (SD), n – – 1.40 (0.86) – – 1.41 (0.78) 0.95

Self-reportedMean diabetes social support score (SD)¶ 55.1 (24.5) 66.5 (19.5) 11.4** 53.3 (25.4) 57.8 (25.8) 4.5 0.010

Mean diabetes distress score (SD)†† 26.5 (16.4) 22.8 (17.2) 4.0** 26.4 (19.8) 20.8 (18.8) 5.6** 0.24Medication adherence, n (%)

Missed 1 insulin dose/wk 52 (74) 49 (69) 3 (5) 48 (73) 38 (63) 10 (10) 0.67

Missed 1 oral medication dose/wk 71 (72) 65 (70) 6 (2) 66 (66) 59 (63) 7 (3) 0.54

* Baseline values for all measures reflect the total number of 125 participants. For measures related to 6-mo follow-up, 117 participants completed a 6-mo follow-up patientsurvey and 113 provided 6-mo follow-up physiologic measures.† Baseline values for all measures reflect the total number of 119 participants. For measures related to 6-mo follow-up, 114 participants completed a 6-mo follow-up patientsurvey and 103 provided 6-mo follow-up physiologic measures.‡ From medical chart review.§ From both medical chart review and patient survey reports. From patient survey reports.¶ Assessed by using 6 questions from the Diabetes Support Scale (31). Each question had 6 answer choices, ranging from “strongly disagree” to “strongly agree.” The answers were scored from 0 to 5 points, with higher scores indicating higher levels of diabetes social support, and the total score was calculated as a percentage of possible points.** Statistically significant within-group difference at P 0.050.

†† Assessed by using 14 questions from the Diabetes Distress Scale (30). Each question had 5 answer choices, ranging from “not a problem” to “serious problem.” Theanswers were scored from 0 to 4 points, with higher scores indicating higher levels of distress, and the total score was calculated as a percentage of possible points.




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Box 130170, MS 152, Ann Arbor, MI 48113-0170; e-mail, [email protected].

Current author addresses and author contributions are available at www .annals.org.

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16585660]10. Dale J, Caramlau IO, Lindenmeyer A, Williams SM. Peer support tele-phone calls for improving health. Cochrane Database Syst Rev. 2008:CD006903. [PMID: 18843736]11. Doull M, O’Connor AM, Robinson V, Tugwell P, Wells GA. Peer supportstrategies for improving the health and well-being of individuals with chronicdiseases [Protocol]. Cochrane Database Syst Rev. 2007:CD005352.12. Clancy DE, Huang P, Okonofua E, Yeager D, Magruder KM. Group visits:promoting adherence to diabetes guidelines. J Gen Intern Med. 2007;22:620-4.[PMID: 17443369]13. Trento M, Basile M, Borgo E, Grassi G, Scuntero P, Trinetta A, et al. A randomised controlled clinical trial of nurse-, dietitian- and pedagogist-led GroupCare for the management of type 2 diabetes. J Endocrinol Invest. 2008;31:1038-42. [PMID: 19169063]14. Wagner EH, Grothaus LC, Sandhu N, Galvin MS, McGregor M, Artz K,

et al. Chronic care clinics for diabetes in primary care: a system-wide randomizedtrial. Diabetes Care. 2001;24:695-700. [PMID: 11315833]15. Lorig KR, Sobel DS, Ritter PL, Laurent D, Hobbs M. Effect of a self-management program on patients with chronic disease. Eff Clin Pract. 2001;4:256-62. [PMID: 11769298]16. Lorig K, Ritter PL, Villa FJ, Armas J. Community-based peer-led diabetesself-management: a randomized trial. Diabetes Educ. 2009;35:641-51. [PMID:19407333]17. Griffiths C, Foster G, Ramsay J, Eldridge S, Taylor S. How effective areexpert patient (lay led) education programmes for chronic disease? BMJ. 2007;334:1254-6. [PMID: 17569933]18. Glasgow RE, Nelson CC, Strycker LA, King DK. Using RE-AIM metrics toevaluate diabetes self-management support interventions. Am J Prev Med. 2006;30:67-73. [PMID: 16414426]19. Heisler M, Piette JD. “I help you, and you help me”: facilitated telephonepeer support among patients with diabetes. Diabetes Educ. 2005;31:869-79.

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32. Cohen J. Statistical Power Analysis for the Behavorial Sciences. 2nd ed.Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.33. Piette JD, Weinberger M, Kraemer FB, McPhee SJ. Impact of automatedcalls with nurse follow-up on diabetes treatment outcomes in a Department of Veterans Affairs Health Care System: a randomized controlled trial. DiabetesCare. 2001;24:202-8. [PMID: 11213866]34. Campbell MK, Elbourne DR, Altman DG; CONSORT group. CONSORTstatement: extension to cluster randomised trials. BMJ. 2004;328:702-8. [PMID:15031246]35. Murray DM. Design and Analysis of Group Randomized Trials. New York:Oxford Univ Pr; 1998.36. Imai K, King G, Nall C. The essential role of pair matching in cluster-randomized experiments, with application to the Mexican universal health insur-ance evaluation. Statistical Science. 2009;24:29-53.37. Lavori PW, Dawson R, Shera D. A multiple imputation strategy for clinicaltrials with truncation of patient data. Stat Med. 1995;14:1913-25. [PMID:8532984]38. Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: J Wiley; 1987.39. Gallant MP. The influence of social support on chronic illness self-management:a review and directions for research. Health Educ Behav. 2003;30:170-95.[PMID: 12693522]40. Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL

Jr, et al; Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure; National Heart, Lung, and Blood Institute;National High Blood Pressure Education Program Coordinating Committee.Seventh report of the Joint National Committee on Prevention, Detection, Eval-uation, and Treatment of High Blood Pressure. Hypertension. 2003;42:1206-52.[PMID: 14656957]41. UK Prospective Diabetes Study (UKPDS) Group. Intensive blood-glucosecontrol with sulphonylureas or insulin compared with conventional treatment




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Current Author Addresses: Drs. Heisler, Vijan, and Piette and Ms.

Makki: University of Michigan Medical School/Veterans Affairs Ann Arbor Health System, Box 130170, MS 152, Ann Arbor, MI 48113-

0170.

Author Contributions: Conception and design: M. Heisler, S. Vijan,

J.D. Piette.

Analysis and interpretation of the data: M. Heisler, S. Vijan, F. Makki, J.D. Piette.

Drafting of the article: M. Heisler, J.D. Piette.

Critical revision of the article for important intellectual content: M.

Heisler, S. Vijan, J.D. Piette.

Final approval of the article: M. Heisler, S. Vijan, J.D. Piette.

Provision of study materials or patients: F. Makki.

Statistical expertise: S. Vijan, J.D. Piette.

Obtaining of funding: M. Heisler, J.D. Piette.

Administrative, technical, or logistic support: M. Heisler, F. Makki.Collection and assembly of data: M. Heisler, F. Makki, J.D. Piette.

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