Skip to main content

Main menu

  • Home
  • Current Issue
  • Content
    • Current Issue
    • Early Access
    • Multimedia
    • Podcast
    • Collections
    • Past Issues
    • Articles by Subject
    • Articles by Type
    • Supplements
    • Plain Language Summaries
    • Calls for Papers
  • Info for
    • Authors
    • Reviewers
    • Job Seekers
    • Media
  • About
    • Annals of Family Medicine
    • Editorial Staff & Boards
    • Sponsoring Organizations
    • Copyrights & Permissions
    • Announcements
  • Engage
    • Engage
    • e-Letters (Comments)
    • Subscribe
    • Podcast
    • E-mail Alerts
    • Journal Club
    • RSS
    • Annals Forum (Archive)
  • Contact
    • Contact Us
  • Careers

User menu

  • My alerts

Search

  • Advanced search
Annals of Family Medicine
  • My alerts
Annals of Family Medicine

Advanced Search

  • Home
  • Current Issue
  • Content
    • Current Issue
    • Early Access
    • Multimedia
    • Podcast
    • Collections
    • Past Issues
    • Articles by Subject
    • Articles by Type
    • Supplements
    • Plain Language Summaries
    • Calls for Papers
  • Info for
    • Authors
    • Reviewers
    • Job Seekers
    • Media
  • About
    • Annals of Family Medicine
    • Editorial Staff & Boards
    • Sponsoring Organizations
    • Copyrights & Permissions
    • Announcements
  • Engage
    • Engage
    • e-Letters (Comments)
    • Subscribe
    • Podcast
    • E-mail Alerts
    • Journal Club
    • RSS
    • Annals Forum (Archive)
  • Contact
    • Contact Us
  • Careers
  • Follow annalsfm on Twitter
  • Visit annalsfm on Facebook
Research ArticleOriginal Research

Baseline Characteristics of PATHWEIGH: A Stepped-Wedge Cluster Randomized Study for Weight Management in Primary Care

Leigh Perreault, Krithika Suresh, Carlos Rodriguez, L. Miriam Dickinson, Emileigh Willems, Peter C. Smith, Johnny Williams, R. Mark Gritz and Jodi Summers Holtrop
The Annals of Family Medicine May 2023, 21 (3) 249-255; DOI: https://doi.org/10.1370/afm.2966
Leigh Perreault
1Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, Colorado
2Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: Leigh.perreault@CUAnschutz.edu
Krithika Suresh
3Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
4Adult and Child Center for Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, Colorado
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlos Rodriguez
3Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
L. Miriam Dickinson
3Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Emileigh Willems
5Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter C. Smith
3Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
MD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Johnny Williams Jr
3Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
R. Mark Gritz
4Adult and Child Center for Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, Colorado
6Department of Medicine, Division of Health Care Policy and Research, University of Colorado Anschutz Medical Campus, Aurora, Colorado
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jodi Summers Holtrop
3Department of Family Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
4Adult and Child Center for Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, Colorado
PhD, MCHES
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • eLetters
  • Info & Metrics
  • PDF
Loading

Abstract

PURPOSE To describe the characteristics of patients and practice of clinicians during standard-of-care for weight management in a large, multiclinic health system before the implementation of PATHWEIGH, a pragmatic weight management intervention.

METHODS We analyzed baseline characteristics of patients, clinicians, and clinics during standard-of-care for weight management before the implementation of PATHWEIGH, which will be evaluated for effectiveness and implementation in primary care using an effectiveness-implementation hybrid type-1 cluster randomized stepped-wedge clinical trial design. A total of 57 primary care clinics were enrolled and randomized to 3 sequences. Patients included in the analysis met the eligibility requirements of age ≥18 years and body mass index (BMI) ≥25 kg/m2 and had a weight-prioritized visit (defined a priori) during the period March 17, 2020 to March 16, 2021.

RESULTS A total of 12% of patients aged ≥18 years and with a BMI ≥25 kg/m2 seen in the 57 practices during the baseline period (n = 20,383) had a weight-prioritized visit. The 3 randomization sequences of 20, 18, and 19 sites were similar, with an overall mean patient age of 52 (SD 16) years, 58% women, 76% non-Hispanic White patients, 64% with commercial insurance, and with a mean BMI of 37 (SD 7) kg/m2. Documented referral for anything weight related was low (<6%), and 334 prescriptions of an antiobesity drug were noted.

CONCLUSIONS Of patients aged ≥18 years and with a BMI ≥25 kg/m2 in a large health system, 12% had a weight-prioritized visit during the baseline period. Despite most patients being commercially insured, referral to any weight-related service or prescription of antiobesity drug was uncommon. These results fortify the rationale for trying to improve weight management in primary care.

Key words:
  • body mass index
  • primary care
  • weight management

INTRODUCTION

Obesity remains one of the greatest current public health challenges, contributing to 4,000,000 deaths and 120,000,000 disability-adjusted life-years globally in 2015.1 Despite the human and economic costs of obesity, its treatment is rarely prioritized in the health care setting. Reasons for lack of weight management prioritization are extensive and complex. Lack of clinician education on effective weight management and processes that systematically address weight loss and weight-loss maintenance long-term are commonly cited.2 The advent of better tools and access to them might be the key to reversing this trend. Intensive behavioral therapy for obesity is now a covered benefit under Medicare.3 Medications for weight loss are increasingly efficacious, and some have shown decreases in weight-related complications.4-7 Bariatric surgery can lead to substantial weight loss and reverse potentially life-threatening conditions such as heart disease and diabetes in both adolescents and adults.8-11 Integrating these new approaches into primary care practice represents both a substantial challenge and a significant opportunity.

To support primary care clinicians in using evidence-based treatments for obesity, our team, comprising physicians (primary care and endocrinology) and behavioral health professionals, developed a set of disease prioritization tools for weight management in primary care called PATHWEIGH. We built tools into Epic (the electronic medical record used by our institution) (Epic Systems Corp) that were designed to remove barriers for clinicians to provide, and patients to receive, care for weight. Specifically, placards are placed in the clinic alerting patients that they can request a weight-prioritized visit, the duration of which varies based on the clinician’s schedule. Seventy-two hours before a weight-prioritized visit, a questionnaire is sent to patients via the patient portal, which captures key historical information (ie, history of weight gain, current behaviors, barriers, and goals) and imports the patient-recorded information into the clinician’s note, which ultimately guides the conversation and treatment plan. Pilot work showed a 7.2% body weight decrease for patients with PATHWEIGH vs 2.1% with standard-of-care (SOC) over a period of 18 months.12 Early success garnered the endorsement of our regional health system leadership to implement PATHWEIGH in all 57 of its primary care clinics, with funding from the National Institutes of Health. The objective of the present analysis was to describe baseline characteristics of qualifying patients at the beginning of the study, to provide insight into the state of weight-management efforts before intervention.

METHODS

Design

The full protocol for this study has been published.13 In brief, we are using an effectiveness-implementation hybrid type-1 design with a stepped-wedge cluster randomized sequence to assess the effectiveness of PATHWEIGH on patient weight loss and weight-loss maintenance, as well as to determine patient, clinician, and clinic-level factors associated with its effectiveness and implementation.14,15 This article describes 12 months of data collection during the baseline period (ie, the year before intervention). Hence, these data are considered the initial control condition for weight management (ie, SOC). Clinics were subsequently randomized to 3 sequences using covariate constrained randomization to balance potential confounders in the stepped-wedge design. The intervention will be implemented sequentially in the 57 participating clinics in 3 steps over a 4-year period.

Outcomes

After collection of the baseline preintervention data (presented herein), the intervention was deployed and is currently ongoing. The eventual aims of this study are to (1) compare the effectiveness of PATHWEIGH vs SOC on patient weight loss and weight-loss maintenance, (2) identify patient, clinician, and clinic-level factors that are associated with weight loss and weight-loss maintenance, and (3) describe factors associated with practice adoption, implementation, and maintenance of PATHWEIGH. To achieve aims 1 and 2, prespecified data from eligible patients are extracted from the electronic medical record and deidentified with the support of the joint Health Data Compass Warehouse project (healthdatacompass.org) using a proprietary process. Raw data are delivered to research statisticians for cleaning, preparation, and data analysis. Here, we present data for patients’ first eligible weight-prioritized visit (defined below) during the 1-year baseline, control, preintervention SOC period.

Participants

Overall data handling during the baseline period (March 17, 2020 to March 16, 2021) is presented in the Consolidated Standards of Reporting Trials diagram (Figure 1), which shows how many patient visits occurred at the clinics, how many discrete patients were seen, how many patients were age ≥18 years with a body mass index (BMI) ≥25 kg/m2, and how many patients who were age ≥18 years with a BMI ≥25 kg/m2 had a weight-prioritized visit (either in person or via telehealth). The latter group are the target group of interest and are described herein. A weight-prioritized visit was defined by the research team for the purpose of establishing a control condition preintervention to which the intervention will eventually be compared. The patient may or may not have requested a specific visit to discuss their weight. Focusing on weight would have been at the discretion of the patient or clinician. Weight-prioritized visits are defined as a visit with a clinician with a National Provider Identifier and ≥1 of the following: (1) the chief complaint or reason for the visit being “overweight,” “obesity,” or “weight” (excluding “weight loss” that appeared unintentional), (2) weight-related International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for billing E66-E.66.9, Z68.25-45, or (3) use of a brief standardized obesity-focused history of present illness questionnaire administered during the rooming process (also known as the Obesity Brief History of Present Illness [HPI]). There were no systematic weight-management interventions occurring in any of the clinics for the data collection period. Information is also collected on the clinicians and clinics and will be examined as possible predictors of patient weight loss.

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

CONSORT diagram.

BMI = body mass index; CONSORT = Consolidated Standards of Reporting Trials.

Measures

Patient-level data included demographic information, health metrics, diagnoses, procedures, and treatments. Patient age was censored at 90 years to protect patient privacy (given the few and therefore potentially identifiable patients of this age).

Patient BMI was extracted from the electronic medical record or when unavailable was computed using height and weight; BMI values were excluded if they were identified to be likely erroneous (height <54 in, >90 in, weight >600 lbs). Vital signs were identified as those that occurred at the weight-prioritized visit. Restrictions were imposed to censor values suggesting entry error (diastolic blood pressure <40 or >140 mm Hg, heart rate <30 or >200 beats per minute, respiratory rate <6 or >50 breaths per minute). Laboratory tests, procedures, and screening tools (2-, 8-, and 9-item Patient Health Questionnaire [PHQ-2, -8, -9] and 7-item Generalized Anxiety Disorder scale [GAD-7])16,17 were restricted to the most recent measures that occurred within 2 weeks before or 6 months after the weight-prioritized visit. Medications were only selected if the patient acknowledged them as active during the baseline period and may have been prescribed by any health care clinician in the system (eg, beyond primary care). Medications associated with weight gain and weight loss are described in Supplemental Table 1. Referrals and receipt of bariatric surgery were selected if they occurred during the baseline period but after the weight-prioritized visit. Comorbidities were identified based on ICD-10-CM codes for billing and were captured if they occurred in the patient’s health record during the baseline period. Clinician-level data included sex and number of weight-prioritized visits conducted. Clinic-level data included type (academic, nonacademic, affiliate), location (urban, rural), and specialty (family medicine, general internal medicine, both).

Statistical Analysis

We used descriptive statistics (mean, median, frequency, proportion) to summarize the patient, clinician, and clinic characteristics overall and by randomization sequence. Given the large sample size, we report on clinical rather than statistical differences.

RESULTS

Patients

During the period March 17, 2020 to March 16, 2021, a total of 164,904 patients aged ≥18 years with a BMI ≥25 kg/m2 had a visit at 1 of 57 primary care clinics. Of these, 20,383 (12%) had a weight-prioritized visit. The average number of weight-prioritized visits per patient during the 12-month data collection period was 1.39 (range, 1-20) with a mean time of 94.2 days between visits among patients with >1 visit. There were a total of 32,306 weight-prioritized visits during the baseline period, for which approximately 98% were identified by ICD-10-CM codes used for billing, 4.4% were identified by a chief complaint or reason for visit as “weight,” “overweight,” or “obesity” (identification schemes were not mutually exclusive), and none were identified by use of the “obesity brief HPI” intake questionnaire, despite the latter being encouraged as SOC. Baseline patient demographic characteristics are presented in Table 1 and were similar between the 3 sequences. Patients were mostly commercially insured (64%), Non-Hispanic White (76%), women (58%), with an average age of 52 years. The number of patients insured by Medicaid was low overall (8.5%).

View this table:
  • View inline
  • View popup
Table 1.

Patient Demographic Characteristics at Baseline

Baseline health metrics were also not materially different between the 3 sequences (Table 2). Baseline means for BMI (~37 kg/m2), anthropometric data, vital signs, and laboratory values of interest (liver, kidney, and thyroid function tests, as well as lipids and hemoglobin A1c) were similar between sequences. Similar percentages of patients were using medications associated with weight gain (12%) as with weight loss (11%). Weight-related comorbidities were also not different between sequences (Table 3).

View this table:
  • View inline
  • View popup
Table 2.

Patient Health Metrics at Baseline

View this table:
  • View inline
  • View popup
Table 3.

Weight-Related Comorbidities Present in ≥3% of Patients

Across the sequences, the prevalence of patients currently using oxygen, continuous positive airway pressure, or bilevel positive airway pressure was 16% to 19% (Table 2). Most patients (84%) were screened for anxiety and depression using the PHQ-2 at the time of their visit in primary care. Of those who received additional screening (92.5% had a PHQ-8, 22% had a PHQ-9, and 17% had a GAD-7 performed; these were not mutually exclusive), the mean values for PHQ-8, PHQ-9 and GAD-7 were 1, 9, and 8, respectively, where cut points of 5 and 10 represent mild and moderate depression/anxiety for all 3 scales. The proportion of patients reporting as current smokers (8%) was less than the national average18 and similar across sequences. Documented referral for anything weight related (ie, to a dietician, endocrinology, bariatrics) was low (<6%), and only 334 unique prescriptions of an antiobesity drug were noted across all sequences (Table 4).

View this table:
  • View inline
  • View popup
Table 4.

Patient Referrals and Treatment

Clinicians

At the time of this analysis, a total of 514 primary care clinicians (physician, physician assistant, or nurse practitioner) saw a patient aged ≥18 years with a BMI ≥25 kg/m2 and conducted an initial weight-prioritized visit during the baseline period (March 17, 2020 to March 16, 2021). These clinicians were mostly female (60%) with an average of 62 (SD 92) weight-prioritized visits during the baseline period (Table 5).

View this table:
  • View inline
  • View popup
Table 5.

Baseline Clinician Demographic Characteristics and Treatment Patterns

Clinics

Descriptive statistics for the 57 clinics participating in the study have been described elsewhere.13 Most of the clinics were nonacademic, family medicine practices and were located in urban or suburban settings. Characteristics, including type of practice, specialty, location, average numbers of patients seen, and percent of patients insured by Medicaid, were balanced across the sequences during the covariate constrained randomization.13

DISCUSSION

During the past decade, numerous strategies have been proposed to curtail the global epidemic of obesity.19 To date, no program has been able to show widespread reach, effectiveness, adoption, implementation, and maintenance. PATHWEIGH aims to be the first pragmatic, scalable, and sustainable approach to weight management, with aspirations to disseminate nationally and internationally. We strive to shift the prevailing paradigm from treating weight-related complications to treating weight in primary care. However, it is well known that implementing sustainable change in primary care is notoriously difficult.20,21 To address our ultimate aim, it was essential to first capture information about the state of usual care, to which the intervention will eventually be compared. The present analysis describes the baseline (preintervention) characteristics of patients, clinicians, and clinics for 57 primary care sites in which PATHWEIGH will be deployed. Of patients aged ≥18 years with a BMI ≥25 kg/m2 seen in the 57 primary care clinics, 12% had a weight-prioritized visit during the baseline period. Despite most patients being commercially insured, documented referral to any weight-related service or prescription of antiobesity drug (in primary care or elsewhere) was uncommon. These results underscore the need for the work that will follow.

Obesity is increasingly recognized not only as a risk factor for disease, but a disease unto itself.22 Despite this fact, <1% of people with any degree of overweight or obesity are offered anything other than lifestyle advice,23 suggesting that the medical community at large has yet to embrace its designation as a disease state. The Centers for Medicare and Medicaid Services Healthcare Effectiveness Data and Information Set–mandated measurement of BMI might be the tipping point to turn this tide. Body mass index is currently measured for only 30% to 60% of patients.23-25 Measurement of BMI has been linked to increased diagnosis and treatment of obesity.26-28 Data collected during the baseline/control period for the present trial show 90% capture of BMI for patients seen during this time in primary care. Of these patients, 20,383 aged ≥18 years with a BMI ≥25 kg/m2 received enough care for their weight that the clinician used a weight-related ICD-10-CM code for billing. These results are encouraging and imply fertile ground for the introduction of a novel approach to weight management in primary care.

Baseline demographic data for the patients receiving a weight-prioritized visit in our health care system’s primary care clinics reflect that patients are mostly commercially insured, White women, aged in the mid-50s, which is highly consistent with other reports.24,25,29 Despite this demographic, early signs of weight-related complications were recognized. For example, the mean BMI of 37 kg/m2, together with the high plasma triglyceride and hemoglobin A1c levels observed, is consistent with high rates of metabolic syndrome in this group.30 In addition, the mean estimated glomerular filtration rate was in the range for stage 2 chronic kidney disease.31 Although these diagnoses might not be commonly managed in primary care, our findings should prompt health care clinicians to consider prioritizing weight management as a way to harmonize therapy.

Most of the primary care clinics in the present study are nonacademic, community-based clinics located in urban or suburban areas. The baseline characteristics of the clinics and clinicians might or might not resemble those domestically as well as abroad. Nevertheless, practice patterns for weight management adopted by the clinicians are highly consistent across primary care regardless of setting.32-35 In general, the literature reports that clinicians are reluctant to treat obesity as a chronic, progressive medical disease.2 Our data reveal that referral for anything weight related was low (<6%), and only 334 prescriptions of an antiobesity drug were noted for 20,383 patients. To address the low rate of treating obesity, our health system designed an intake questionnaire to facilitate weight management (ie, the Obesity brief HPI). During the time of this data collection, no clinician used that intake questionnaire, which raises the question of whether any SOC actually exists for weight management, and if so, how it might be captured in the electronic medical record. Clinical trials are required to offer SOC to their placebo-treated participants. Arguably, the 500-kcal per day caloric restriction and 150-minutes/week of moderate aerobic activity recommended in clinical trials testing antiobesity drugs36,37 might be more rigorous than what patients receive in a medical setting despite knowing that the former renders nominal weight loss. These observations were made in primary care but most certainly extend throughout all health care contexts and speak to the need for collaborative, long-term approaches to weight management.

There are several limitations to the present analysis. First, we aimed to establish baseline SOC; however, it does not appear that SOC for weight management truly exists. Therefore, PATHWEIGH will ultimately be compared with usual care, rather than SOC, in practice. Our findings are consistent with other reports that comprehensive weight management is uncommon in primary care. Second, our definitions of a weight-prioritized visit might be imprecise, whether by differing rigor of care resulting in a weight-related ICD-10-CM code or misclassification based on chief complaint or reason for visit. Third, the analysis is limited to patients aged ≥18 years with a BMI ≥25 kg/m2 who had a weight-prioritized visit in the 57 primary care clinics during the period March 17, 2020 to March 16, 2021. This population might not be representative of areas with greater racial and ethnic diversity, different socioeconomic conditions, or in underserved or uninsured populations or do not choose to have care at a large health system practice. Patients meeting the age and BMI criteria who did not have a weight-prioritized visit were excluded from this analysis, which might create selection bias in the data. Lastly, the baseline data collection occurred during the coronavirus disease 2019 pandemic, including the initial lockdown period, at which time in-person access to medical care was limited, and many people might have experienced weight gain.

In conclusion, >160,000 patients aged ≥18 years with a BMI ≥25 kg/m2 were seen (mostly in person) in 1 of the 57 health system primary care clinics during the period March 17, 2020 to March 16, 2021, approximately 12% of whom had a weight-prioritized visit. Thus, 88% of patients who were eligible for a weight-prioritized visit did not have one. Even for those patients who were seen for their weight, very little was done in terms of medical treatment or referral to someone specializing in weight management. These results unmask an enormous unmet need to develop pragmatic approaches to implementing weight management in primary care.

Acknowledgments

We would like to acknowledge the faculty, staff, and patients of University of Colorado Health and University of Colorado Health Medical Group.

Footnotes

  • Conflicts of interest: Leigh Perreault has received personal fees for consulting and/or speaking from Novo Nordisk, Sanofi, Elli Lilly, Boehringer Ingelheim, AstraZeneca, Medscape, WebMD, and UpToDate. All other authors report none.

  • Read or post commentaries in response to this article.

  • Funding support: This work was funded by the National Institute of Diabetes and Digestive and Kidney Diseases (1R18DK127003).

  • Previous presentation: Presented at Obesity Week, November 2022, San Diego, California, and the NAPCRG Annual Meeting, November 2022, Phoenix, AZ.

  • Trial registration: NCT04678752

  • Supplemental materials

  • Received for publication July 28, 2022.
  • Revision received January 17, 2023.
  • Accepted for publication January 19, 2023.
  • © 2023 Annals of Family Medicine, Inc.

References

  1. 1.↵
    1. Afshin A,
    2. Forouzanfar MH,
    3. Reitsma MB, et al; GBD 2015 Obesity Collaborators
    . Health effects of overweight and obesity in 195 countries over 25 years. N Engl J Med. 2017; 377(1): 13-27. doi:10.1056/NEJMoa1614362
    OpenUrlCrossRefPubMed
  2. 2.↵
    1. Swinburn BA,
    2. Sacks G,
    3. Hall KD, et al.
    The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011; 378(9793): 804-814. doi:10.1016/S0140-6736(11)60813-1
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Kaplan LM,
    2. Golden A,
    3. Jinnett K, et al.
    Perceptions of barriers to effective obesity care: results from the national ACTION study. Obesity (Silver Spring). 2018; 26(1): 61-69. doi:10.1002/oby.22054
    OpenUrlCrossRef
  4. 4.↵
    1. Bohula EA,
    2. Scirica BM,
    3. Inzucchi SE, et al; CAMELLIA-TIMI 61 Steering Committee Investigators
    . Effect of lorcaserin on prevention and remission of type 2 diabetes in overweight and obese patients (CAMELLIA-TIMI 61): a randomised, placebo-controlled trial. Lancet. 2018; 392(10161): 2269-2279. doi:10.1016/S0140-6736(18)32328-6
    OpenUrlCrossRef
  5. 5.
    1. Bohula EA,
    2. Wiviott SD,
    3. McGuire DK, et al; CAMELLIA–TIMI 61 Steering Committee and Investigators
    . Cardiovascular safety of lorcaserin in overweight or obese patients. N Engl J Med. 2018; 379(12): 1107-1117. doi:10.1056/NEJMoa1808721
    OpenUrlCrossRefPubMed
  6. 6.
    1. Garber AJ.
    Anti-obesity pharmacotherapy and the potential for preventing progression from prediabetes to type 2 diabetes. Endocr Pract. 2015; 21(6): 634-644. doi:10.4158/EP14460.RA
    OpenUrlCrossRef
  7. 7.↵
    1. Svanström H,
    2. Ueda P,
    3. Melbye M, et al.
    Use of liraglutide and risk of major cardiovascular events: a register-based cohort study in Denmark and Sweden. Lancet Diabetes Endocrinol. 2019; 7(2): 106-114. doi:10.1016/S2213-8587(18)30320-6
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Carlsson LMS,
    2. Sjöholm K,
    3. Karlsson C, et al.
    Long-term incidence of microvascular disease after bariatric surgery or usual care in patients with obesity, stratified by baseline glycaemic status: a post-hoc analysis of participants from the Swedish Obese Subjects study. Lancet Diabetes Endocrinol. 2017; 5(4): 271-279. doi:10.1016/S2213-8587(17)30061-X
    OpenUrlCrossRef
  9. 9.
    1. Fisher DP,
    2. Johnson E,
    3. Haneuse S, et al.
    Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity. JAMA. 2018; 320(15): 1570-1582. doi:10.1001/jama.2018.14619
    OpenUrlCrossRefPubMed
  10. 10.
    1. Inge TH,
    2. Courcoulas AP,
    3. Jenkins TM, et al; Teen-LABS Consortium
    . Weight loss and health status 3 years after bariatric surgery in adolescents. N Engl J Med. 2016; 374(2): 113-123. doi:10.1056/NEJMoa1506699
    OpenUrlCrossRef
  11. 11.↵
    1. Sjöström L,
    2. Narbro K,
    3. Sjöström CD, et al; Swedish Obese Subjects Study
    . Effects of bariatric surgery on mortality in Swedish obese subjects. N Engl J Med. 2007; 357(8): 741-752. doi:10.1056/NEJMoa066254
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Perreault L,
    2. Hockett CW,
    3. Holmstrom H,
    4. Tolle L,
    5. Kramer ES,
    6. Holtrop JS.
    PATHWEIGH tool for chronic weight management built into EPIC electronic medical record: methods, pilot results and future directions. Journal of Obesity and Chronic Diseases. 2020; 4(1): 42-48. doi:10.17756/jocd.2020-036
    OpenUrlCrossRef
  13. 13.↵
    1. Suresh K,
    2. Holtrop JS,
    3. Dickinson LM, et al.
    PATHWEIGH, pragmatic weight management in adult patients in primary care in Colorado, USA: study protocol for a stepped wedge cluster randomized trial. Trials. 2022; 23(1): 26. doi:10.1186/s13063-021-05954-7
    OpenUrlCrossRef
  14. 14.↵
    1. Curran GM,
    2. Bauer M,
    3. Mittman B,
    4. Pyne JM,
    5. Stetler C.
    Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care. 2012; 50(3): 217-226. doi:10.1097/MLR.0b013e3182408812
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Hussey MA,
    2. Hughes JP.
    Design and analysis of stepped wedge cluster randomized trials. Contemp Clin Trials. 2007; 28(2): 182-191. doi:10.1016/j.cct.2006.05.007
    OpenUrlCrossRefPubMed
  16. 16.↵
    1. Gilbody S,
    2. Richards D,
    3. Brealey S,
    4. Hewitt C.
    Screening for depression in medical settings with the Patient Health Questionnaire (PHQ): a diagnostic meta-analysis. J Gen Intern Med. 2007; 22(11): 1596-1602. doi:10.1007/s11606-007-0333-y
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Spitzer RL,
    2. Kroenke K,
    3. Williams JBW,
    4. Löwe B.
    A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006; 166(10): 1092-1097. doi:10.1001/archinte.166.10.1092
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Centers for Disease Control and Prevention
    . Current cigarette smoking among adults in the United States. Updated Mar 17, 2022. Accessed May 18, 2022. https://www.cdc.gov/tobacco/data_statistics/fact_sheets/adult_data/cig_smoking/index.htm#references
  19. 19.↵
    1. Hawkes C,
    2. Smith TG,
    3. Jewell J, et al.
    Smart food policies for obesity prevention. Lancet. 2015; 385(9985): 2410-2421. doi:10.1016/S0140-6736(14)61745-1
    OpenUrlCrossRefPubMed
  20. 20.↵
    1. Crabtree BF,
    2. Nutting PA,
    3. Miller WL, et al.
    Primary care practice transformation is hard work: insights from a 15-year developmental program of research. Med Care. 2011;49(Suppl)(Suppl):S28-S35. doi:10.1097/MLR.0b013e3181cad65c
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Lau R,
    2. Stevenson F,
    3. Ong BN, et al.
    Achieving change in primary care—causes of the evidence to practice gap: systematic reviews of reviews. Implement Sci. 2016; 11: 40. doi:10.1186/s13012-016-0396-4
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Garvey WT,
    2. Mechanick JI,
    3. Brett EM, et al; Reviewers of the AACE/ACE Obesity Clinical Practice Guidelines
    . American Association of Clinical Endocrinologists and American College of Endocrinology Comprehensive Clinical Practice Guidelines for Medical Care of Patients with Obesity. Endocr Pract. 2016; 22(Suppl 3): 1-203. doi:10.4158/EP161365.GL
    OpenUrlCrossRefPubMed
  23. 23.↵
    1. Nicholson BD,
    2. Aveyard P,
    3. Bankhead CR,
    4. Hamilton W,
    5. Hobbs FDR,
    6. Lay-Flurrie S.
    Determinants and extent of weight recording in UK primary care: an analysis of 5 million adults’ electronic health records from 2000 to 2017. BMC Med. 2019; 17(1): 222. doi:10.1186/s12916-019-1446-y
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Arterburn DE,
    2. Alexander GL,
    3. Calvi J, et al.
    Body mass index measurement and obesity prevalence in ten U.S. health plans. Clin Med Res. 2010; 8(3-4): 126-130. doi:10.3121/cmr.2010.880
    OpenUrlAbstract/FREE Full Text
  25. 25.↵
    1. Rose SA,
    2. Turchin A,
    3. Grant RW,
    4. Meigs JB.
    Documentation of body mass index and control of associated risk factors in a large primary care network. BMC Health Serv Res. 2009; 9: 236. doi:10.1186/1472-6963-9-236
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Bordowitz R,
    2. Morland K,
    3. Reich D.
    The use of an electronic medical record to improve documentation and treatment of obesity. Fam Med. 2007; 39(4): 274-279.
    OpenUrlPubMed
  27. 27.
    1. Schriefer SP,
    2. Landis SE,
    3. Turbow DJ,
    4. Patch SC.
    Effect of a computerized body mass index prompt on diagnosis and treatment of adult obesity. Fam Med. 2009; 41(7): 502-507.
    OpenUrlPubMed
  28. 28.↵
    1. Tang JW,
    2. Kushner RF,
    3. Cameron KA,
    4. Hicks B,
    5. Cooper AJ,
    6. Baker DW.
    Electronic tools to assist with identification and counseling for overweight patients: a randomized controlled trial. J Gen Intern Med. 2012; 27(8): 933-939. doi:10.1007/s11606-012-2022-8
    OpenUrlCrossRefPubMed
  29. 29.↵
    1. Conroy MB,
    2. Bryce CL,
    3. McTigue KM, et al.
    Promoting weight maintenance with electronic health record tools in a primary care setting: baseline results from the MAINTAIN-pc trial. Contemp Clin Trials. 2017; 54: 60-67. doi:10.1016/j.cct.2017.01.001
    OpenUrlCrossRef
  30. 30.↵
    1. Grundy SM,
    2. Brewer HB Jr.,
    3. Cleeman JI,
    4. Smith SC Jr.,
    5. Lenfant C; American Heart Association; National Heart, Lung, and Blood Institute
    . Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation. 2004; 109(3): 433-438. doi:10.1161/01.CIR.0000111245.75752.C6
    OpenUrlFREE Full Text
  31. 31.↵
    1. Levin A,
    2. Stevens PE.
    Summary of KDIGO 2012 CKD Guideline: behind the scenes, need for guidance, and a framework for moving forward. Kidney Int. 2014; 85(1): 49-61. doi:10.1038/ki.2013.444
    OpenUrlCrossRefPubMed
  32. 32.↵
    1. Booth HP,
    2. Prevost TA,
    3. Wright AJ,
    4. Gulliford MC.
    Effectiveness of behavioural weight loss interventions delivered in a primary care setting: a systematic review and meta-analysis. Fam Pract. 2014; 31(6): 643-653. doi:10.1093/fampra/cmu064
    OpenUrlCrossRefPubMed
  33. 33.
    1. Noël PH,
    2. Copeland LA,
    3. Pugh MJ, et al.
    Obesity diagnosis and care practices in the Veterans Health Administration. J Gen Intern Med. 2010; 25(6): 510-516. doi:10.1007/s11606-010-1279-z
    OpenUrlCrossRefPubMed
  34. 34.
    1. Suissa K,
    2. Schneeweiss S,
    3. Kim DW,
    4. Patorno E.
    Prescribing trends and clinical characteristics of patients starting antiobesity drugs in the United States. Diabetes Obes Metab. 2021; 23(7): 1542-1551. doi:10.1111/dom.14367
    OpenUrlCrossRef
  35. 35.↵
    1. Wadden TA,
    2. Butryn ML,
    3. Hong PS,
    4. Tsai AG.
    Behavioral treatment of obesity in patients encountered in primary care settings: a systematic review. JAMA. 2014; 312(17): 1779-1791. doi:10.1001/jama.2014.14173
    OpenUrlCrossRefPubMed
  36. 36.↵
    1. Pi-Sunyer X,
    2. Astrup A,
    3. Fujioka K, et al; SCALE Obesity and Prediabetes NN8022-1839 Study Group
    . A randomized, controlled trial of 3.0 mg of liraglutide in weight management. N Engl J Med. 2015; 373(1): 11-22. doi:10.1056/NEJMoa1411892
    OpenUrlCrossRefPubMed
  37. 37.↵
    1. Wilding JPH,
    2. Batterham RL,
    3. Calanna S, et al; STEP 1 Study Group
    . Once-weekly semaglutide in adults with overweight or obesity. N Engl J Med. 2021; 384(11): 989-1002. doi:10.1056/NEJMoa2032183
    OpenUrlCrossRefPubMed
PreviousNext
Back to top

In this issue

The Annals of Family Medicine: 21 (3)
The Annals of Family Medicine: 21 (3)
Vol. 21, Issue 3
May/June 2023
  • Table of Contents
  • Index by author
  • Front Matter (PDF)
  • Plain-Language Summaries
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on Annals of Family Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Baseline Characteristics of PATHWEIGH: A Stepped-Wedge Cluster Randomized Study for Weight Management in Primary Care
(Your Name) has sent you a message from Annals of Family Medicine
(Your Name) thought you would like to see the Annals of Family Medicine web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
7 + 1 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
Baseline Characteristics of PATHWEIGH: A Stepped-Wedge Cluster Randomized Study for Weight Management in Primary Care
Leigh Perreault, Krithika Suresh, Carlos Rodriguez, L. Miriam Dickinson, Emileigh Willems, Peter C. Smith, Johnny Williams, R. Mark Gritz, Jodi Summers Holtrop
The Annals of Family Medicine May 2023, 21 (3) 249-255; DOI: 10.1370/afm.2966

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Get Permissions
Share
Baseline Characteristics of PATHWEIGH: A Stepped-Wedge Cluster Randomized Study for Weight Management in Primary Care
Leigh Perreault, Krithika Suresh, Carlos Rodriguez, L. Miriam Dickinson, Emileigh Willems, Peter C. Smith, Johnny Williams, R. Mark Gritz, Jodi Summers Holtrop
The Annals of Family Medicine May 2023, 21 (3) 249-255; DOI: 10.1370/afm.2966
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • INTRODUCTION
    • METHODS
    • RESULTS
    • DISCUSSION
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
  • eLetters
  • Info & Metrics
  • PDF

Related Articles

  • PubMed
  • Google Scholar

Cited By...

  • New Insights and Future Directions: The Importance of Considering Poverty in Studies of Obesity and Diabetes
  • Google Scholar

More in this TOC Section

  • Shared Decision Making Among Racially and/or Ethnically Diverse Populations in Primary Care: A Scoping Review of Barriers and Facilitators
  • Convenience or Continuity: When Are Patients Willing to Wait to See Their Own Doctor?
  • Feasibility and Acceptability of the “About Me” Care Card as a Tool for Engaging Older Adults in Conversations About Cognitive Impairment
Show more Original Research

Similar Articles

Subjects

  • Domains of illness & health:
    • Chronic illness
  • Person groups:
    • Community / population health
  • Methods:
    • Quantitative methods
  • Other research types:
    • Health services
    • Professional practice
  • Other topics:
    • Communication / decision making

Keywords

  • body mass index
  • primary care
  • weight management

Content

  • Current Issue
  • Past Issues
  • Early Access
  • Plain-Language Summaries
  • Multimedia
  • Podcast
  • Articles by Type
  • Articles by Subject
  • Supplements
  • Calls for Papers

Info for

  • Authors
  • Reviewers
  • Job Seekers
  • Media

Engage

  • E-mail Alerts
  • e-Letters (Comments)
  • RSS
  • Journal Club
  • Submit a Manuscript
  • Subscribe
  • Family Medicine Careers

About

  • About Us
  • Editorial Board & Staff
  • Sponsoring Organizations
  • Copyrights & Permissions
  • Contact Us
  • eLetter/Comments Policy

© 2025 Annals of Family Medicine