Open Access

STRIDE: a randomized trial of a lifestyle intervention to promote weight loss among individuals taking antipsychotic medications

  • Bobbi Jo H Yarborough1Email author,
  • Michael C Leo1,
  • Scott Stumbo1,
  • Nancy A Perrin1 and
  • Carla A Green1
BMC Psychiatry201313:238

https://doi.org/10.1186/1471-244X-13-238

Received: 11 September 2013

Accepted: 24 September 2013

Published: 28 September 2013

Abstract

Background

Individuals diagnosed with serious mental illnesses are at increased risk of obesity- and cardiovascular-related morbidity and early mortality. Lifestyle interventions aimed at weight loss, even those adapted to suit the needs of this particular subgroup, have rarely produced clinically meaningful reductions in weight.

Methods/design

The STRIDE study is a multi-site, parallel, two-arm randomized controlled translational trial. Participants were recruited from community mental health clinics and an integrated not-for-profit health system. Participants were randomized either to usual care or to a 12-month intervention that consisted of: 1) weekly group participation for six months covering topics on nutrition, physical activity and lifestyle changes; 2) monthly group participation for an additional six month maintenance period; and 3) individual monthly contacts from intervention group facilitators during the second six month phase. All participants are assessed at baseline, 6, 12, and 24 months post-enrollment. Process and implementation evaluations are included and the study design includes a cost-utility analysis. Participants include 200 individuals with serious mental illness with an average age of 47.1 years, a mean body-mass index of 38.3 kg/m2 and taking an average of 3.2 psychiatric medications at baseline. Baseline physiological measures included mean blood pressure (SBP/DBP) measurements of 119.2 (SD = 14.7)/79.4 (SD = 10.1); 35% reported a hypertension diagnosis and 11% took antihypertensive medications. Average lipid levels (mg/dL) were: a) triglycerides 188.0 (SD = 138.6), ranged from 43 to 1145; b) LDL 101.4 (SD = 32.9) and ranged from 17 to 185; c) HDL 45.8 (SD = 12.7) and ranged from 22 to 89; and d) total cholesterol 181.6 (SD = 39.7) and ranged from 50 to 324. Average fasting glucose levels were 108.9 (SD = 32.5) and ranged from 24 to 289. Average fasting insulin levels were 13.0 (SD=11.9) and ranged from 2 to 99.

Discussion

The STRIDE study is based on a modified version of the PREMIER comprehensive lifestyle intervention, DASH diet arm. STRIDE has successfully enrolled 200 individuals with serious mental illness in community-based settings. Baseline characteristics present a population at high risk for obesity-related negative health outcomes and demonstrate the need for evidence-based interventions to reduce these risks.

Trial registration

Clinical Trials.gov NCT00790517

Keywords

Antipsychotic medications Serious mental illness Obesity Lifestyle change Weight loss Physical activity Diabetes risk Blood pressure Lipids

Background

Individuals with serious mental illnesses are at greatly increased risk of a number of medical comorbidities including obesity [1], metabolic syndrome [24], diabetes mellitus [46], and subsequent early mortality [79], primarily from cardiovascular disease [2, 10, 11]. The causes of these elevated cardiometabolic risks can include factors such as poor access to medical care [12, 13], poor nutrition [14], sedentary lifestyle [11], and smoking [11, 15] but they can also be exacerbated by the antipsychotic agents prescribed to treat mental health conditions [1623]. Because many individuals diagnosed with serious mental illnesses rely on antipsychotic medications as a primary component of their treatment, complementary treatments that reduce weight gain and potentially reduce associated cardiometabolic risks have been suggested by clinicians and researchers [1]. Such treatments appear feasible [24, 25] and valued by potential recipients [25] but their effectiveness has been inconsistent and, when effective, weight losses have been modest [26]. Bartels and Desilets found that manualized lifestyle health promotion programs lasting more than 3 months—combining activities with education, emphasizing weight management through improved nutrition and physical exercise—promote success but nevertheless only result in clinically significant weight loss for a minority of individuals [26]. Community-based approaches which improve the magnitude of weight loss and physical fitness are needed as well as interventions that reduce cardiometabolic risks, e.g., blood pressure, insulin resistance, or lipid levels.

This protocol describes a randomized controlled trial assessing the effectiveness of translating a known efficacious and comprehensive lifestyle intervention, adapted for individuals with serious mental illnesses at high risk for obesity, diabetes, and cardiovascular disease, and receiving community-based treatment, including antipsychotic medications, for their mental health disorders.

Methods/design

The STRIDE study is a parallel-arm, multi-site, randomized controlled trial, translating the PREMIER comprehensive lifestyle intervention [2730] DASH diet arm [31] for individuals taking antipsychotic medications. The primary aims are to test the hypotheses that the intervention is more effective than usual care in: 1) reducing weight and body mass index (BMI); 2) reducing fasting insulin levels and increasing insulin sensitivity; and 3) reducing total cholesterol and LDL cholesterol at 6, 12, and 24 months. Secondary aims are to explore the effects of exercise motivation, dietary motivation, social support and weight loss expectations on primary outcomes, and to examine the moderating effects of ethnicity, gender, mental health diagnostic group, medication type, and metabolic syndrome. Process and implementation evaluations are designed to identify facilitators and barriers of behavior change among participants; a cost-utility analysis will provide economic information for program planners considering adoption.

Settings

To increase clinical relevance and the likelihood of future adoption, implementation, and sustainability of the intervention, we limited our exclusion criteria and worked with community partners to deliver the intervention in routine care settings. Our partners include two community mental health centers—Cascadia Behavioral Healthcare (Cascadia) and LifeWorks Northwest (LifeWorks)—and Kaiser Permanente Northwest (KPNW), a not-for-profit integrated health system. Cascadia serves approximately 12,000 low-income clients by providing outpatient mental health care and addiction treatment services across three Oregon counties. LifeWorks serves the mental health and addiction treatment needs of approximately 16,000 clients annually in 22 clinics throughout the greater Portland, Oregon metropolitan area. The majority of these clients come from impoverished and underserved segments of the population. KPNW provides comprehensive medical, mental health and addiction treatment to an insured population of about 480,000 members in northwest Oregon and southwest Washington. All study procedures were reviewed, approved, and are monitored by the Kaiser Permanente Northwest Institutional Review Board (other participating sites ceded authority to KPNW). All participants were informed of their rights and responsibilities as research participants and provided written informed consent prior to enrollment.

Participants

Our sample includes individuals age ≥18 who were taking antipsychotic agents at study enrollment and who were overweight or obese (defined as BMI ≥ 27). We asked prescribing clinicians to review records for potential participants to ensure that they did not have medical or psychiatric conditions that precluded participation in a lifestyle and weight loss program that included changes to dietary practices and promotion of moderate exercise. We excluded individuals with cognitive impairment sufficient to interfere with their ability to provide informed consent, complete study questionnaires, or participate in a group intervention. We excluded women who were pregnant, breastfeeding, or planning a pregnancy during the study because reduced calorie intake may be contraindicated. We also excluded people with a bariatric surgery history or a wasting illness (e.g., cancer) because they are at increased risk for weight loss as a result of their medical status. Finally, we excluded individuals currently enrolled in another weight reduction program as inclusion could confound the results of our intervention.

Recruitment, screening, and randomization

Using administrative and clinical databases at two sites (KPNW, Cascadia) and mental health clinician referral at the third site (LifeWorks), we identified potential study participants on the basis of their medication use, diagnoses, and current BMI. A personalized study invitation letter was prepared for each potential participant and sent to their primary care provider, psychiatrist, or psychiatric nurse practitioner, to review for suitability for the study and to co-sign if appropriate. Clinicians returned letters to study staff for mailing to potential participants. The letters provided details about the study, a toll-free study phone number, and a prepaid postcard to indicate interest in participating or to decline further contact. Letters were followed with phone calls by study recruitment staff who provided additional study details, answered questions, and conducted a brief screening. Those who met preliminary qualifications for the study were scheduled for the first screening visit.

At the first screening visit, potential participants attended a group orientation session where they received an overview of the study and had a chance to ask questions. Those who remained interested consented to having their height and weight measured. BMI was then calculated, inclusion and exclusion criteria reviewed, and eligibility established. Eligible participants completed a questionnaire and scheduled a second screening and randomization visit at which a baseline fasting blood specimen was drawn.

At the second screening visit, participants arrived having fasted for at least eight hours, had their blood drawn and their blood pressure and waist circumference measured. Individuals who had not been fasting were rescheduled. All individuals received reminder post-cards prior to the visit that included fasting and medication instructions (i.e., directions to postpone taking diabetes medications until after the fasting blood draw) and asking them to wear loose clothing to aid measurements. Participants also received reminder telephone calls the day before the visit. Following the blood draw, participants were provided with a snack and asked to take any medications at that time if they could not do so prior to the blood draw. They were then randomized to either intervention or usual care groups using a blocked, stratified procedure to ensure that group assignments remained balanced across gender and baseline BMI (27–34.9 and ≥ 35) within each site. Clinic staff involved in collecting data were blinded to participant assignment. Once group assignment was determined, non-clinic staff informed the participant and provided appropriate information about upcoming study activities. Intervention participants were notified when the group intervention was expected to begin; usual care participants were scheduled for the first follow up assessment, e.g. after 6 months. Usual care participants had no other obligation to the study and were free to initiate any weight loss effort on their own. We assessed all self-reported weight loss efforts at each follow-up for both the intervention and usual care participants.

Tailored lifestyle intervention

The STRIDE intervention was based on the NHLBI-funded PREMIER lifestyle intervention, DASH diet arm [30]. It was designed to promote weight loss and reduce obesity-related risks, including diabetes risk, through dietary changes, moderate calorie reduction, and increased energy expenditure. Intervention targets were based on clinical practice guidelines established for obesity treatment for individuals at increased risk for cardiovascular disease [32, 33]. The intervention built upon prior research [27], behavior change theories [34, 35], and motivational theory [3639] to enhance self-efficacy and promote long-term behavior change. Adaptations to content and implementation made specifically to suit this population have been described elsewhere [25]. The STRIDE intervention manual is available for download [40].

STRIDE began with a 6-month intensive group counseling phase followed by a less-intensive 6-month maintenance phase that included both group and individual contacts. Throughout both phases, implementation strategies included: frequent contacts, participant-centered group and individual facilitation approaches, and individual contacts that tailored the intervention to the participant’s preferences (as components of group meetings and separately, when needed). The program also targeted participants’ readiness to change, and encouraged group interactions that facilitated problem solving and social support, provided support for appropriate goal setting, and facilitated the acquisition of new information and skills for behavior change. Group leaders presented examples of behavioral options and used decisional balance approaches [41] to assist participants in moving toward action and setting new behavioral goals, paying careful attention to the cultural appropriateness of the program for minority participants. Sessions were designed to actively engage participants in small-group activities that fostered program ownership and allowed individual interaction with other participants and facilitators. Groups were co-led by two facilitators with complementary backgrounds; one with training as a mental health counselor, the other with training in nutritional interventions, though not a registered dietician. Having two interventionists provided flexibility during group sessions, however, and their differing backgrounds provided knowledge for managing a full range of problems and questions. All interventionists attended an initial two-day training meeting on the general intervention approach, and an additional two-day training meeting in the use of motivational interviewing techniques. In Year 1, an additional one-day training (webcast) addressed methods for presenting materials to individuals with mental illness, including those with cognitive and literacy limitations. Follow-up one-day trainings were held for all interventionists every six months. Trainers also provided ongoing supervision and individual training of all interventionists.

Intensive intervention (Phase 1)

The core of the intervention program was a series of weekly group meetings designed to achieve a weight loss of 4.5-6.8 kg (10–15 pounds) over a 6 month period. Groups were two hours in length and included 20 minutes of physical activity (walking). Participants received a STRIDE workbook with details on program content, self-assessments and goal-setting procedures, booklets for self-monitoring of food and activity, cooking and meal planning guides with recipes, a copy of the Calorie King book [42] to help them learn about the calorie content of typical foods, and a resistance band for strength training. Ancillary materials can be downloaded at the study website [43].

Using a manualized protocol, facilitators promoted the following specific strategies for achieving changes in behavior, activity level, and weight: 1) self-monitoring of diet and physical activity, 2) developing personalized diet and physical activity plans, 3) moderately reducing calories, 4) reducing portion sizes, substituting alternative foods, and modifying meals so that they are lower in calories and fat, 5) focusing on fruits and vegetables and increasing low-fat dairy products and fiber intake (DASH diet [44]), 6) increasing physical activity, 7) identifying situations that trigger poor diet or physical activity choices, and developing and rehearsing specific plans of action to deal with those situations, and 8) graphing behavioral progress and individual weight change.

Participants were encouraged to routinely monitor food intake, calories, and physical activity; to set reasonable short-term goals; formulate specific plans of action to achieve those goals; and to develop reinforcement and social support for carrying out each major element of the plan. Participants were also asked to keep records of servings of fruits and vegetables, servings of low-fat dairy products, and sodium and fat intake. These records were used by participants and interventionists to assess progress. Because this program placed a strong emphasis on increasing moderate-intensity physical activity, interventionists also helped participants determine how best to fit physical activity into their daily lives, taking into account each participant’s initial motivation, current activity patterns, and weekly progress. The goal was to get each participant to engage in 180 minutes per week, or about 25 minutes per day, of moderate physical activity—primarily walking. Participants were asked to record minutes of physical activity each day, as well as hours slept each night. Sleep hygiene was a module added to the original PREMIER intervention when modifications were made for this population. Other modifications included sessions focusing on stress management, advance planning for anticipated episodes of mental illness, empowering participants to engage in discourse with their prescribers regarding their medication and weight-related concerns, and tips on eating healthily on a restricted income.

Our experience has shown that willingness to make behavior changes shifts frequently during long-term weight loss intervention programs. Recognizing this reality, intervention techniques allowed group facilitators to help participants tailor weekly goals and action plans to their current stage of change. Although general guidelines for intervention components were provided by the interventionists (daily self-monitoring of caloric intake, five or more days per week of physical activity, etc.), participants were also encouraged to adjust their personal goals and action plans each week to their immediate situation, setting their own short-term goals each week in consultation with the group facilitator. Participants with no interest in physical activity were not forced to set an exercise goal, although those who declined to set a physical activity goal for several consecutive weeks were encouraged to schedule an individual brief session with the group facilitator who worked with them using motivational interviewing techniques [38]. The objective of this counseling session was to encourage reflection on long-term goals, increase motivation to engage in more physical activity, and address barriers to increased activity.

Maintenance intervention (Phase 2)

Where the focus of Phase 1 was on weight loss and acquisition of new information and behaviors, Phase 2 focused on maintaining weight loss through problem-solving and motivational enhancement. Because previous research has shown that greater frequency of contacts between participants and providers improves maintenance of weight loss [32], long-term obesity treatment was a priority for the high-risk participants in this study. At the same time, it is also important to recognize the difficulty of maintaining the type of high-intensity commitment required by Phase 1. Thus, Phase 2 consisted of one monthly group meeting to enhance group problem solving and social support, and one low-intensity individual session per month (by phone or e-mail) to support participants’ individualized goals without requiring time and travel to a group session. In these individual contacts, participants set the agenda and the facilitator and participants jointly reviewed the participants’ diet and activity efforts for the previous month, paying particular attention to barriers or difficulties.

Assessment data collection and measurement

Intervention and usual care groups were assessed at baseline, 6, 12, and 24 months. Laboratory, physiological and anthropometric measures were obtained at each assessment and included: 1) height in meters measured to the nearest 0.1 cm (baseline only), 2) weight in kilograms measured to the nearest 0.1 kg (body mass index was calculated as the Quetelet index [kg/m2]), 3) waist circumference, measured over participants’ underwear using inelastic tape at the narrowest part of torso and measured in a horizontal plane without compressing the skin, and 4) blood pressure, measured while participants were seated with legs uncrossed and without talking after a 5-minute rest period, and then again after an additional 30 second rest period. Blood was drawn and all lab panels were collected after an overnight (8–12 hour) fast. Tests included: 5) fasting insulin, 6) fasting plasma glucose, 7) fasting triglycerides, and 8) fasting cholesterol (total, HDL, LDL).

We followed the National Cholesterol Education Program (NCEP) guidelines [45] and defined metabolic syndrome as: Individuals with 3 or more of the following risk factors: a) waist circumference greater than 102 centimeters in men or 88 centimeters in women; b) triglycerides plasma level of 150 mg/dL or greater; c) HDL-cholesterol plasma level less than 40 mg/dL for men or less than 50 mg/dL for women (or taking cholesterol-lowering medications); d) blood pressure of 130/85 mm Hg or greater (or taking antihypertensive medications), and/or e) fasting glucose greater than or equal to 110 mg/dL (or taking glucose lowering medications).

To assess insulin resistance, we calculated the Homeostasis Model Assessment Index for Insulin Resistance (HOMA-IR) as follows: fasting glucose [mmol/L] × fasting insulin [μU/mL]/22.5. This index correlates very highly with the gold standard hyperinsulinemic euglycemic clamp [46].

We included several self-reported measures to describe participants and assess health and functional status and medication use. These measures are shown in Table 1.
Table 1

STRIDE measures and data collection schedule

 

BL

6 month

12 month

24 month

Physiologic measures

    

Height

x

   

Weight

x

x

x

x

Body mass index (BMI)

x

x

x

x

Waist circumference

x

x

x

x

Blood pressure

x

x

x

x

Laboratory measures

    

Fasting insulin

x

x

x

x

Fasting plasma glucose

x

x

x

x

Fasting triglycerides

x

x

x

x

Fasting cholesterol (total, HDL, LDL)

x

x

x

x

Self-reported measures

    

Age

x

   

Gender

x

   

Race

x

   

Ethnicity

x

   

Education level

x

x

x

x

Medicaid status

x

x

x

x

Medicare disability status

x

x

x

x

Marital status

x

x

x

x

Employment status

x

x

x

x

Annual and monthly income

x

x

x

x

Number of individuals supported by income

x

x

x

x

Income source

x

x

x

x

SF-36 general health subscale [4749]

x

x

x

x

Behavior and Symptom Identification Scale (BASIS-24) [50]

x

x

x

x

Colorado Symptom Index (CSI) [51, 52]

x

x

x

x

Self-reported medications1

x

x

x

x

Wisconsin Quality of Life Index (W-QLI) [5355]

x

x

x

x

Patient Activation Measure (PAM) [56]

x

x

x

x

Body Weight, Image and Self-Esteem Evaluation Questionnaire (B-WISE) [57]

x

x

x

x

Social support scales for eating and exercise behavior [58]

x

x

x

x

Exercise confidence questionnaire [59]

x

x

x

x

Eating habits confidence questionnaire [59]

x

x

x

x

Weight outcome expectancies- ideal and goal weight [60, 61]

x

x

x

x

7-day physical activity and sleep log [27]

x

x

x

x

7-day fruit and vegetable consumption [62]

x

x

x

x

Self-reported other efforts to lose weight

x

x

x

x

1Self-reported medications were also collected at 18 months.

Data analysis

Effectiveness analyses of primary aims

We will use generalized estimating equations (GEE) [63] to test the effectiveness of the intervention. GEE estimates the population averaged model while accounting for the correlation among observations as a result of having multiple measures from the same person over time. The primary coefficient of interest will be the time by group (control vs. intervention) interaction, which will indicate the degree of differential change across time between the control and intervention groups. We will include age and study site and time-invariant covariates. Use of outcomes-related medications will be included as time-varying covariates (e.g., controlling for metformin usage when testing intervention efficacy on diabetes risk). The GEE models will be based on the normal distribution with identity link, and the working covariance matrix will be specified as exchangeable. An advantage of GEE is that it is able to estimate models when data are incomplete, assuming the data are missing at random and the working covariance matrix is correctly specified [63]. This will allow us to include all participants according to their initial group assignment in the analyses, which is consistent with the intent-to-treat principle.

For every outcome, we will conduct a series of sensitivity analyses by using transformations that improve the normality of the outcome, using a different family and link (e.g., negative binomial with log link) where appropriate, and using an unstructured working covariance matrix.

Finally, we will also examine between-group differences for various conceptualizations for the primary outcome of weight, including percentage weight change, the proportion of participants who are at or below his or her baseline weight, and the proportion of participants who lose at least 5% and 10% of their baseline weight. These will be computed at all follow-up timepoints. These are not intent-to-treat results, as these computations require that data are not missing in order to compute the necessary change scores for deriving these different measures of weight. We will test the difference in percentage weight change between the intervention and control groups using a one-way ANCOVA, and test whether the proportion of participants who are at or below their baseline weight at follow-up is different between the arms using multiple logistic regression. We will include age, site, and medications that are known to affect weight as covariates in all analyses.

Analyses of secondary aims

We plan on examining the effects of exercise motivation, dietary motivation, social support and outcomes expectancies on weight change. We will also explore whether ethnicity, gender, mental health diagnostic group, medication type, and metabolic syndrome moderates the treatment effect on weight change. Furthermore, we will examine whether there was an effect of the intervention on patient reported outcomes including body image (Body Weight, Image and Self-Esteem Evaluation Questionnaire - B-WISE), psychiatric symptoms (Colorado Symptom Index - CSI), quality of life (Wisconsin Quality of Life Index - W-QLI), health-related self-efficacy (Patient Activation Measure - PAM), health (SF-36 general health subscale), and functional status (Behavior and Symptom Identification Scale - BASIS-24). Finally, we will evaluate whether dose, defined as the number of intervention sessions and number of telephone and email contacts, is related to change in the outcomes across time among participants who received the intervention.

Power analysis of primary aims

We used data from PREMIER DASH diet arm results [30] to estimate power for repeated measures analysis of variance for change in weight. We used repeated measures ANOVA given the lack of well-established methods for estimating power using GEE. Data were available for change at 6 and 18 months. Because reported means and standard deviations were based on completers alone, we adjusted for attrition in our power analyses (although primary analyses will be intent-to-treat). PREMIER reported a 5.9% decrease in weight at 6 months for the DASH diet group and a 1.6% decrease for control (advice only). With an initial total sample size of 280, a 10% (worst case) attrition rate at six months, and alpha level of .05, we have power of .96 to detect a significant group-by-time interaction at 6 months. Using the 18-month values (−1.6% for advice only, -4.4% for DASH) as a conservative estimate for weight change at 12 months, we have power of .87 for the group-by-time interaction term for weight (assuming a 15% worst-case attrition rate). To estimate power at 24 months, we assumed the same rate of weight gain in the DASH group and weight loss from 6 to 18 months in the UC group, consistent with PREMIER. For a 3.6% decrease from baseline in the DASH group and 1.8% decrease for UC at 24 months, we have power of .80 to detect a significant group-by-time interaction at 24 months, assuming 20% attrition. Power for waist circumference, fasting insulin, and insulin sensitivity were based on data reported by Ard [31]. Power for total cholesterol, LDL, and HDL were based on data reported by Obarzanek [64]. Only baseline and 6-month means were available for these variables, so we have computed power to detect a significant group-by-time interaction at 6 months only. For all variables, power exceeds .95 to detect the group-by-time interaction.

Implementation and process evaluations

The implementation evaluation included observations of organizational and other facilitators and barriers to implementation; results of this evaluation are described elsewhere [25]. Fidelity assessments documented adherence to the intervention protocol and thus will allow examination of implementation differences across groups. Assessment of intervention fidelity includes both qualitative and quantitative measures.

The study design also included a process evaluation component, which sought to understand participants’ feelings toward, and responses to, the different elements of the intervention and to lifestyle change more generally. Semi-structured interviews lasting approximately 30 minutes were conducted with participants at 3- (n = 38), 9- (n = 35), and 18-months (n = 29) following randomization. Interviewers asked about barriers and facilitators of dietary and exercise changes. Of these, 76% (n = 78) were conducted with intervention participants and 24% (n = 24) with control participants. Interviews were transcribed verbatim and coded using Atlas.ti. Analyses will identify themes related to lifestyle improvements across participants; check coding will assess inter-coder reliability.

Cost-utility analysis

The study was designed to conduct a cost-utility analysis and includes estimates of the direct costs, per participant, of implementing the intervention. Relevant costs for these analyses will include the resources necessary to deliver the intervention and the change in care costs incurred by the organization that derive from the intervention. We have included the EQ-5D [65], a brief, self-administered, health-related quality of life measure that assesses mobility, self-care, usual activities, pain/discomfort, and anxiety/depression dimensions. Response patterns yield 243 unique health states, and are commonly used to generate a composite score or index reflecting the preference value (utility) associated with a given health state. We will calculate incremental cost-effectiveness ratios for each intermediate trial outcome, and will further report such a ratio for the improvement in mean EQ-5D scores between the intervention and control groups.

Baseline characteristics

A total of 200 individuals with serious mental illnesses were randomized to one of the treatment arms. Two participants became ineligible after randomization, one due to pregnancy and one due to an unspecified but serious health problem that caused repeated vomiting and resulted in a significant weight loss. At baseline (Table 2), participant diagnoses were affective psychosis (38%), bipolar disorder (32%), schizophrenia spectrum disorders (29%) and PTSD (2%). Participants were taking an average of 3.2 psychiatric medications and 91% were taking an atypical antipsychotic medication. Study participants were an average age of 47.1 (SD = 10.7) years old. Thirty percent were currently working and 35% reported being disabled. About 31% of the sample had a high school diploma, GED or less, and 43% were married or living with a partner.
Table 2

Baseline characteristics of the study participants (N = 200)

Characteristic

n (%)1

Inclusion diagnosis

 

Affective psychosis

75 (38)

Bipolar disorder

63 (32)

Schizophrenia spectrum disorder

58 (29)

Post-traumatic stress disorder

4 (2)

Gender

 

Male

56 (28)

Female

144 (72)

Race2

 

White

171 (88)

Non-white

24 (12)

Hispanic ethnicity

4 (2)

Education

 

Less than high school

15 (8)

High school graduate/GED

46 (23)

Some college/technical school

87 (44)

College graduate

37 (19)

Post graduate

15 (8)

Marital status

 

Never married

57 (29)

Widowed/Divorced/Separated

57 (29)

Married

69 (34)

Living with partner

17 (9)

Annual household income

 

$0 - $9,999

54 (28)

$10,000 - $29,999

58 (30)

$30,000 – $49,999

34 (17)

$50,000 or higher

49 (25)

Number of people supported by income, mean (SD)

2.0 (1.3)

Employment status

 

Currently working

59 (30)

Disabled

71 (36)

Retired, unemployed, student, homemaker, temporarily laid off, or other

70 (35)

Atypical antipsychotic medication use

182 (91)

Medications taken that affect weight

 

≥1 that cause slight/moderate weight loss

77 (39)

≥1 that do not affect weight

168 (84)

≥1 that cause slight/moderate weight gain

21 (11)

≥1 that cause severe weight gain

128 (64)

Mood stabilizer medication use

97 (49)

Anti-depressant medication use

33 (17)

Smoked in the past year

65 (33)

Self-reported diabetes diagnosis

 

No

145 (73)

Yes

48 (24)

Don’t know

7 (4)

Self-reported hypertension diagnosis

 

No

114 (57)

Yes

70 (35)

Don’t know

15 (8)

 

Mean (SD)

Age—years

47.1 (10.7)

Weight—kg.3

107.7 (25.1)

Waist circumference—cm.3

113.9 (18.7)

Body mass index4

38.3 (8.3)

Blood pressure: systolic—mmHg

119.2 (14.7)

Blood pressure: diastolic—mmHg

79.4 (10.1)

Lipids

 

Triglycerides—mg/dL

188.0 (138.6)

LDL—mg/dL

101.4 (32.9)

HDL—mg/dL

45.8 (12.7)

Total cholesterol—mg/dL

181.6 (39.7)

Fasting glucose—mg/dL

108.9 (32.5)

Fasting insulinu—U/mL

13.0 (11.9)

Goal weight differential—kg.5

−31.7 (18.5)

Very likely to achieve goal weight, n (%)

33 (17)

Ideal weight differential—kg.6

−38.2 (21.5)

Very likely to achieve ideal weight, n (%)

21 (11)

Number of psychiatric medications

3.2 (1.5)

Psychiatric measures

 

CSI score7

19.3 (11.4)

BASIS-24 score8

1.37 (0.68)

SF36-GH score9

53.2 (21.3)

PAM13 (normed) score10

62.4 (16.0)

B-WISE score11

21.9 (3.6)

1Percentages reported use number of valid responses in the denominator. Percentages may not sum to 100% because of rounding.

2Race and ethnic groups were self-reported.

3To convert values for weight to pounds, multiply kilograms by 2.2. To convert values for centimeters to inches, multiply centimeters by 0.39.

4Body Mass Index is the weight in kilograms divided by the square of height in meters.

5The difference between self-reported goal weight and observed weight at baseline.

6The difference between self-reported ideal weight and observed weight at baseline.

7Colorado Symptom Index.

8Behavior and Symptom Identification Scale 24-item version.

9SF-36 General Health Subscale.

10Patient Activation Measure.

11Body Weight, Image and Self-Esteem Evaluation Questionnaire (B-WISE).

The sample had an average baseline weight of 107.7 kilograms (SD = 25.1), waist circumference of 113.9 centimeters (SD = 18.7), BMI of 38.3 (SD = 8.3) and 24% of the sample reported a diabetes diagnosis. Differences between observed weight and goal or ideal weights were 31.7 kilograms (SD = 18.5) and 38.2 kilograms (SD = 21.5) respectively. Baseline physiological measures included mean blood pressure (SBP/DBP) measurements of 119.2 (SD = 14.7)/79.4 (SD = 10.1); 35% reported a hypertension diagnosis with 11% of people taking antihypertensive medications. Average lipid levels (mg/dL) were: a) triglycerides 188.0 (SD = 138.6), ranged from 43 to 1145; b) LDL 101.4 (SD = 32.9) and ranged from 17 to 185; c) HDL 45.8 (SD = 12.7) and ranged from 22 to 89; and d) total cholesterol 181.6 (SD = 39.7) and ranged from 50 to 324. Average fasting glucose levels were 108.9 (SD = 32.5) and ranged from 24 to 289. Average fasting insulin levels were 13.0 (SD=11.9) and ranged from 2 to 99. Psychiatric measures at baseline include a mean CSI score of 19.3 (SD = 11.4, possible scores range from 14–70 with higher scores indicating more frequent symptoms), and a BASIS-24 mean score of 1.37 (SD = 0.68, possible scores range from 0 to 4, higher scores represent higher levels of difficulty in symptoms and functioning). Patient activation, measured by the PAM score averaged 62.4 (SD = 16.0; scores between 55.2-67.0 are associated with beginning to take action in managing one’s health). Body image, measured with the B-WISE, averaged 21.9 (SD = 3.6, potential scores range from 12–36 with higher scores indicating a more positive body image).

Discussion

Though lifestyle interventions aimed at reducing weight have been adapted for individuals with serious mental illnesses, their effectiveness has been limited. Even effective interventions have only resulted in modest weight loss for a minority of participants. There is a critical need for more evidenced-based programs shown to benefit a greater proportion of individuals, produce clinically meaningful weight loss, reduce obesity-related cardiovascular risks, and improve physical fitness. Moreover, it is important that such interventions can be implemented in community-based settings.

STRIDE is a lifestyle intervention modified for the unique needs of overweight individuals taking antipsychotic medications and delivered in community mental health and integrated care settings. Despite the challenges of implementing a rigorous weight reduction intervention in any population, especially a population with SMI, we are encouraged by our ability to recruit and retain participants in STRIDE. To succeed in retaining individuals in the study we employed several strategies for accommodating the life circumstances of this disadvantaged population. We met in several locations to minimize transportation, we provided reminders prior to group meetings and study visits. We also called individuals who missed group meetings and offered make-up sessions.

This study was designed to test the effectiveness of this intervention to reduce weight, fasting insulin levels, total cholesterol and LDL cholesterol and increase insulin. The study was also designed to explore potential mediators and moderators of the intervention’s effects, to examine barriers and facilitators of behavior change, to assess issues with implementing the intervention in this population and to provide cost-effectiveness data in order to inform future research and provide decision makers with information needed for future program adoption decisions. The results of this study will contribute to a better understanding of how to assist individuals with mental illnesses to manage their weight and improve their overall health.

Abbreviations

BMI: 

Body-mass index

GEE: 

Generalized estimating equation

HOMA: 

Homeostasis Model Assessment

KPNW: 

Kaiser Permanente Northwest

PTSD: 

Post-traumatic stress disorder

GED: 

General equivalency degree

WQLI: 

Wisconsin Quality of Life Index

CSI: 

Colorado symptoms index

BASIS: 

Behavior and Symptom Identification Scale

SMI: 

Serious mental illness

PAM: 

Patient Activation Measure

B-WISE: 

Body Weight, Image and Self-Esteem Evaluation

LDL: 

Low-density lipoprotein

HDL: 

High-density lipoprotein.

Declarations

Acknowledgements

Funding for this study is provided by the National Institute of Diabetes and Digestive and Kidney Diseases, Grant R18DK076775, Reducing Weight and Diabetes Risk in an Underserved Population.

Authors’ Affiliations

(1)
Kaiser Permanente Center for Health Research

References

  1. Allison DB, Newcomer JW, Dunn AL, Blumenthal JA, Fabricatore AN, Daumit GL, et al: Obesity among those with mental disorders: a national institute of mental health meeting report. Am J Prev Med. 2009, 36 (4): 341-350. 10.1016/j.amepre.2008.11.020.View ArticlePubMedGoogle Scholar
  2. Newcomer JW: Metabolic syndrome and mental illness. Am J Manag Care. 2007, 13 (7 Suppl): S170-S177.PubMedGoogle Scholar
  3. McEvoy JP, Meyer JM, Goff DC, Nasrallah HA, Davis SM, Sullivan L, et al: Prevalence of the metabolic syndrome in patients with schizophrenia: baseline results from the clinical antipsychotic trials of intervention effectiveness (CATIE) schizophrenia trial and comparison with national estimates from NHANES III. Schizophr Res. 2005, 80 (1): 19-32. 10.1016/j.schres.2005.07.014.View ArticlePubMedGoogle Scholar
  4. Cohn T, Prud'homme D, Streiner D, Kameh H, Remington G: Characterizing coronary heart disease risk in chronic schizophrenia: high prevalence of the metabolic syndrome. Can J Psychiatry. 2004, 49 (11): 753-760.PubMedGoogle Scholar
  5. Mukherjee S, Decina P, Bocola V, Saraceni F, Scapicchio PL: Diabetes mellitus in schizophrenic patients. Compr Psychiatry. 1996, 37 (1): 68-73. 10.1016/S0010-440X(96)90054-1.View ArticlePubMedGoogle Scholar
  6. Kohen D: Diabetes mellitus and schizophrenia: historical perspective. Br J Psychiatry Suppl. 2004, 47: S64-S66.View ArticlePubMedGoogle Scholar
  7. Kilbourne AM, Ignacio RV, Kim HM, Blow FC: Are VA patients with serious mental illness dying younger?. Psychiatr Serv. 2009, 60 (5): 589-10.1176/appi.ps.60.5.589.View ArticlePubMedGoogle Scholar
  8. Lawrence D, Hancock KJ, Kisely S: The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers. BMJ. 2013, 346: f2539-10.1136/bmj.f2539.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Saha S, Chant D, McGrath J: A systematic review of mortality in schizophrenia: Is the differential mortality gap worsening over time?. Arch Gen Psychiatry. 2007, 64 (10): 1123-1131. 10.1001/archpsyc.64.10.1123.View ArticlePubMedGoogle Scholar
  10. Colton CW, Manderscheid RW: Congruencies in increased mortality rates, years of potential life lost, and causes of death among public mental health clients in eight states. Prev Chronic Dis. 2006, 3 (2): A42-PubMedPubMed CentralGoogle Scholar
  11. Kilbourne AM, Morden NE, Austin K, Ilgen M, McCarthy JF, Dalack G, et al: Excess heart-disease-related mortality in a national study of patients with mental disorders: Identifying modifiable risk factors. Gen Hosp Psychiatry. 2009, 31 (6): 555-563. 10.1016/j.genhosppsych.2009.07.008.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Osborn DP, Baio G, Walters K, Petersen I, Limburg H, Raine R, et al: Inequalities in the provision of cardiovascular screening to people with severe mental illnesses in primary care: cohort study in the United Kingdom THIN primary care database 2000–2007. Schizophr Res. 2011, 129 (2–3): 104-110.View ArticlePubMedGoogle Scholar
  13. Kilbourne AM, McCarthy JF, Post EP, Welsh D, Pincus HA, Bauer MS, et al: Access to and satisfaction with care comparing patients with and without serious mental illness. Int J Psychiatry Med. 2006, 36 (4): 383-399. 10.2190/04XR-3107-4004-4670.View ArticlePubMedGoogle Scholar
  14. Casagrande SS, Anderson CA, Dalcin A, Appel LJ, Jerome GJ, Dickerson FB, et al: Dietary intake of adults with serious mental illness. Psychiatr Rehabil J. 2011, 35 (2): 137-140.View ArticlePubMedGoogle Scholar
  15. Compton MT, Daumit GL, Druss BG: Cigarette smoking and overweight/obesity among individuals with serious mental illnesses: a preventive perspective. Harv Rev Psychiatry. 2006, 14 (4): 212-222. 10.1080/10673220600889256.View ArticlePubMedGoogle Scholar
  16. Daumit GL, Goff DC, Meyer JM, Davis VG, Nasrallah HA, McEvoy JP, et al: Antipsychotic effects on estimated 10-year coronary heart disease risk in the CATIE schizophrenia study. Schizophr Res. 2008, 105 (1–3): 175-187.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Chaggar PS, Shaw SM, Williams SG: Effect of antipsychotic medications on glucose and lipid levels. J Clin Pharmacol. 2011, 51 (5): 631-638. 10.1177/0091270010368678.View ArticlePubMedGoogle Scholar
  18. Haupt DW, Newcomer JW: Hyperglycemia and antipsychotic medications. J Clin Psychiatry. 2001, 62 (Suppl 27): 15-26. Discussion 40–1PubMedGoogle Scholar
  19. Newcomer JW, Haupt DW, Fucetola R, Melson AK, Schweiger JA, Cooper BP, et al: Abnormalities in glucose regulation during antipsychotic treatment of schizophrenia. Arch Gen Psychiatry. 2002, 59 (4): 337-345. 10.1001/archpsyc.59.4.337.View ArticlePubMedGoogle Scholar
  20. Newcomer JW: Metabolic considerations in the use of antipsychotic medications: a review of recent evidence. J Clin Psychiatry. 2007, 68 (Suppl 1): 20-27.PubMedGoogle Scholar
  21. Newcomer JW: Antipsychotic medications: metabolic and cardiovascular risk. J Clin Psychiatry. 2007, 68 (Suppl 4): 8-13.PubMedGoogle Scholar
  22. Newcomer JW: Second-generation (atypical) antipsychotics and metabolic effects: a comprehensive literature review. CNS Drugs. 2005, 19 (Suppl 1): 1-93.PubMedGoogle Scholar
  23. American Diabetes Association, American Psychiatric Association, American Association of Clinical Endocrinologists, North American Association for the Study of Obesity: Consensus development conference on antipsychotic drugs and obesity and diabetes: consensus statement. Diabetes Care. 2004, 27 (2): 596-601.View ArticleGoogle Scholar
  24. Daumit GL, Dickerson FB, Wang NY, Dalcin A, Jerome GJ, Anderson CA, et al: A behavioral weight-loss intervention in persons with serious mental illness. N Engl J Med. 2013, 17: 1594-1602.View ArticleGoogle Scholar
  25. Yarborough BJ, Janoff SL, Stevens VJ, Kohler D, Green CA: Delivering a lifestyle and weight loss intervention to individuals in real-world mental health settings: lessons and opportunities. Transl Behav Med. 2011, 1 (3): 406-415. 10.1007/s13142-011-0056-9.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Bartels S, Desilets R: Health Promotion Programs for People with Serious Mental Illness (Prepared by the Dartmouth Health Promotion Research Team). 2012, Washington, D.C.: SAMHSA-HRSA Center for Integrated Health SolutionsGoogle Scholar
  27. Appel LJ, Champagne CM, Harsha DW, Cooper LS, Obarzanek E, Elmer PJ, et al: Effects of comprehensive lifestyle modification on blood pressure control: main results of the PREMIER clinical trial. JAMA. 2003, 289 (16): 2083-2093.PubMedGoogle Scholar
  28. Funk KL, Elmer PJ, Stevens VJ, Harsha DW, Craddick SR, Lin PH, et al: PREMIER–a trial of lifestyle interventions for blood pressure control: intervention design and rationale. Health Promot Pract. 2006, 9 (3): 271-280. 10.1177/1524839906289035.View ArticlePubMedGoogle Scholar
  29. McGuire HL, Svetkey LP, Harsha DW, Elmer PJ, Appel LJ, Ard JD: Comprehensive lifestyle modification and blood pressure control: a review of the PREMIER trial. J Clin Hypertens (Greenwich). 2004, 6 (7): 383-390. 10.1111/j.1524-6175.2004.03147.x.View ArticleGoogle Scholar
  30. Svetkey LP, Harsha DW, Vollmer WM, Stevens VJ, Obarzanek E, Elmer PJ, et al: Premier: a clinical trial of comprehensive lifestyle modification for blood pressure control: rationale, design and baseline characteristics. Ann Epidemiol. 2003, 13 (6): 462-471. 10.1016/S1047-2797(03)00006-1.View ArticlePubMedGoogle Scholar
  31. Ard JD, Grambow SC, Liu D, Slentz CA, Kraus WE, Svetkey LP: The effect of the PREMIER interventions on insulin sensitivity. Diabetes Care. 2004, 27 (2): 340-347. 10.2337/diacare.27.2.340.View ArticlePubMedGoogle Scholar
  32. NHLBI Obesity Education Initiative Expert Panel: Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. 1998, Bethesda, MD: National Heart Lung and Blood Institute, 1-228.Google Scholar
  33. NHLBI Obesity Education Initiative Expert Panel: Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The executive summary. 1998, Bethesda, MD: National Heart, Lung, Blood Institute, vii-xxvi.Google Scholar
  34. Watson DL, Tharp RG: Self-directed behavior: Self-modification for personal adjustment. 2002, Belmont, CA: Wadsworth/Thomson Learning, 8Google Scholar
  35. Prochaska JO, Velicer WF: The transtheoretical model of health behavior change. Am J Health Promot. 1997, 12 (1): 38-48. 10.4278/0890-1171-12.1.38.View ArticlePubMedGoogle Scholar
  36. Burke BL, Arkowitz H, Dunn C, et al: The efficacy of motivational interviewing and its adaptations: what we know so far. Motivational Interviewing: Preparing People to Change Addictive Behavior. Edited by: Miller WR, Rollnick S. 2002, New York, NY: The Guilford Press, 217-250.Google Scholar
  37. Rollnick S, Allison J, Ballasiotes S, Barth T, Butler CC, Rose GS, et al: Variations on a theme: motivational interviewing and its adaptation. Motivational Interviewing: Preparing People to Change Addictive Behavior. Edited by: Miller WR, Rollnick S. 2002, New York, NY: The Guilford Press, 270-283. 2Google Scholar
  38. Rollnick S, Mason P, Butler C: Health behavior change: A guide for practitioners. 1999, London: Churchill LivingstonGoogle Scholar
  39. Rollnick S, Miller WR: What is motivational interviewing?. Behav Cogn Psychother. 1995, 23: 325-334. 10.1017/S135246580001643X.View ArticleGoogle Scholar
  40. Yarborough BJH, Yarborough MT, Tehrani K, Funk KL, Stevens VJ, Green CA: Facilitator Guide for the STRIDE Program: A 30-Session Weight Loss and Weight Maintenance Program for People who live with Mental Illness. 2013, Portland (OR): Kaiser Permanente Center for Health Research, Available at: http://www.kpchr.org/stridepublic/ Google Scholar
  41. Di Noia J, Prochaska JO: Dietary stages of change and decisional balance: a meta-analytic review. Am J Health Behav. 2010, 34 (5): 618-632.View ArticlePubMedGoogle Scholar
  42. Borushek A: The Calorie King, Calorie, Fat, & Carbohydrate Counter 2013. 2012, Costa Mesa: Family Health PublicationsGoogle Scholar
  43. Green CA: STRIDE Study Website. 2013, http://www.kpchr.org/stridepublic/,Google Scholar
  44. National Health Lung and Blood Institute: Your guide to lowering your blood pressure with DASH, NIH Publication No. 06–4082. 2013, National Institutes of Health, U.S. Department of Health and Human Services, http://www.nhlbi.nih.gov/health/public/heart/hbp/dash/new_dash.pdf NIH Publication No. 06–4082. 2006Google Scholar
  45. Expert Panel On Detection: EVALUATION, AND TREATMENT OF HIGH BLOOD CHOLESTEROL IN ADULTS. Executive summary of the third report of The National Cholesterol Education Program (NCEP) Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). Jama. 2001, 285 (19): 2486-2497. 10.1001/jama.285.19.2486.View ArticleGoogle Scholar
  46. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985, 28 (7): 412-419. 10.1007/BF00280883.View ArticlePubMedGoogle Scholar
  47. Ware JE, Sherbourne D: The MOS 36-Item short-form health survey (SF-36): 1. Conceptual framework and item selection. Med Care. 1992, 30: 473-483. 10.1097/00005650-199206000-00002.View ArticlePubMedGoogle Scholar
  48. Ware JE, Kosinski M: SF-36 physical & mental health summary scales: A manual for users of version 1. 2001, QualityMetric, Inc: Lincoln, RI, SecondGoogle Scholar
  49. Tunis SL, Croghan TW, Heilman DK, Johnstone BM, Obenchain RL: Reliability, validity, and application of the medical outcomes study 36- item short-form health survey (SF-36) in schizophrenic patients treated with olanzapine versus haloperidol. Med Care. 1999, 37 (7): 678-691. 10.1097/00005650-199907000-00008.View ArticlePubMedGoogle Scholar
  50. Eisen SV, Normand SL, Belanger AJ, Spiro A, Esch D: The revised behavior and symptom identification scale (BASIS-R): reliability and validity. Med Care. 2004, 42 (12): 1230-1241. 10.1097/00005650-200412000-00010.View ArticlePubMedGoogle Scholar
  51. Shern DL, Wilson NZ, Coen AS, Patrick DC, Foster M, Bartsch DA, et al: Client outcomes II: longitudinal client data from the Colorado treatment outcome study. Milbank Q. 1994, 72 (1): 123-148. 10.2307/3350341.View ArticlePubMedGoogle Scholar
  52. Conrad KJ, Yagelka JR, Matters MD, Rich AR, Williams V, Buchanan M: Reliability and validity of a modified Colorado symptom index in a national homeless sample. Ment Health Serv Res. 2001, 3 (3): 141-153. 10.1023/A:1011571531303.View ArticlePubMedGoogle Scholar
  53. Becker M: A US experience: consumer responsive quality of life measurement. Can J Commun Ment Health. 1998, Winter (3 Suppl): 41-58.View ArticleGoogle Scholar
  54. Becker M, Diamond R, Sainfort F: A new patient focused index for measuring quality of life in persons with severe and persistent mental illness. Qual Life Res. 1993, 2 (4): 239-251. 10.1007/BF00434796.View ArticlePubMedGoogle Scholar
  55. Diamond R, Becker M: The Wisconsin quality of life index: a multidimensional model for measuring quality of life. J Clin Psychiatry. 1999, 60 (Suppl 3): 29-31.PubMedGoogle Scholar
  56. Hibbard JH, Stockard J, Mahoney ER, Tusler M: Development of the patient activation measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004, 39 (4 Pt 1): 1005-1026.View ArticlePubMedPubMed CentralGoogle Scholar
  57. Awad AG, Voruganti LN: Body weight, image and self-esteem evaluation questionnaire: development and validation of a new scale. Schizophr Res. 2004, 70 (1): 63-67. 10.1016/j.schres.2003.12.004.View ArticlePubMedGoogle Scholar
  58. Sallis JF, Grossman RM, Pinski RB, Patterson TL, Nader PR: The development of scales to measure social support for diet and exercise behaviors. Prev Med. 1987, 16 (6): 825-836. 10.1016/0091-7435(87)90022-3.View ArticlePubMedGoogle Scholar
  59. Sallis JF, Pinski RB, Grossman RM, Patterson TL, Nader PR: The development of self-efficacy scales for healthrelated diet and exercise behaviors. Health Educ Res. 1988, 3 (3): 283-292. 10.1093/her/3.3.283.View ArticleGoogle Scholar
  60. Foster GD, Wadden TA, Vogt RA, Brewer G: What is a reasonable weight loss? Patients’ expectations and evaluations of obesity treatment outcomes. J Community Psychol. 1997, 65 (1): 79-85.Google Scholar
  61. Linde JA, Jeffery RW, Finch EA, Ng DM, Rothman AJ: Are unrealistic weight loss goals associated with outcomes for overweight women?. Obes Res. 2004, 12 (3): 569-576. 10.1038/oby.2004.65.View ArticlePubMedGoogle Scholar
  62. Thompson FE, Kipnis V, Subar AF, Krebs-Smith SM, Kahle LL, Midthune D, et al: Evaluation of 2 brief instruments and a food-frequency questionnaire to estimate daily number of servings of fruit and vegetables. Am J Clin Nurt. 2000, 71 (6): 1503-1510.Google Scholar
  63. Liang KY, Zeger SL: Longitudinal data analysis using generalized linear models. Biometrika. 1986, 73 (1): 13-22. 10.1093/biomet/73.1.13.View ArticleGoogle Scholar
  64. Obarzanek E, Sacks FM, Vollmer WM, Bray GA, Miller ER, Lin PH, et al: Effects on blood lipids of a blood pressure-lowering diet: the dietary approaches to stop hypertension (DASH) trial. Am J Clin Nurt. 2001, 74 (1): 80-89.Google Scholar
  65. Luo N, Johnson JA, Shaw JW, Feeny D, Coons SJ: Self-reported health status of the general adult U.S. Population as assessed by the EQ-5D and health utilities index. Med Care. 2005, 43 (11): 1078-1086. 10.1097/01.mlr.0000182493.57090.c1.View ArticlePubMedGoogle Scholar
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    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/13/238/prepub

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