A Pilot Study of an eHealth-delivered Health Coaching Intervention
Conditions: Obesity, Technology, Videoconferencing, mHealth, Behavior, Health
Sponsor: Dartmouth-Hitchcock Medical Center

Study Title: 
Health Coaching & Technology in a Weight Loss Center 
NCT#:  
NCT03309787    
Date:  
 6 May 2019
Documents: Study protocol (includes statistical analysis plan) 

SPECIFIC AIMS 
The obesity epidemic affects 37% of adults1 and leads to major morbidity2, absenteeism3, mortality4 and 
high health costs5. Effective obesity therapy goals are to attain weight loss through healthy lifestyle changes6, 
reduce cardiac risk6, and prevent chronic illness. Integrating practical and sustainable approaches in obesity 
care can improve short- and long-term outcomes6. Nurse or physician-delivered Intensive Behavioral Therapy 
(IBT) using the 5 A’s (ask, advise, assess, assist, arrange)7,8, motivational interviewing9,10, and goal setting11 is 
the standard behavioral treatment for achieving a healthy weight and sustained lifestyle changes. In recent 
studies, trained health coaches have successfully used IBT to assist patients in improving health outcomes12,13.   
Despite the potential benefits of IBT10, access to trained providers is notably limited for rural adults14,15 
who face high obesity rates and related morbidity16. Barriers to accessing evidence-based obesity care include 
the need to travel distances for clinic visits, absence of local specialized obesity-related services, and the lack 
of trained staff in remote areas17,18. Telehealth offers practical and cost-effective solutions in overcoming such 
accessibility barriers19 to obesity treatment and health promotion approaches. Yet, even when patients achieve 
weight loss, maintaining their weight is difficult20. Wearable monitors using adaptable sensors offer health care 
providers a means to obtain real-time information over time. Providing biometric measures and real-time data 
for motivation and feedback can augment standard obesity strategies and be delivered by health coaches12,13.  
Our goal is to identify effective, accessible, and sustainable means for improving long-term lasting 
weight loss, using scalable health-promoting devices in rural obese adults. This proposal aims to conduct a 
study of remote health monitoring using Amulet21, a Dartmouth Computer Science developed mobile health 
(mHealth) device, incorporated into usual health coach visits and delivered in patients’ homes using video-
conferencing. Amulet provides an adaptable platform for behavioral change; unlike commercial devices, it uses 
an open source programming model capable of testing an array of other strategies and non-proprietary devices 
21. We previously described the challenges in delivering obesity care22,23 and promises of virtual modalities in 
overcoming geographic barriers to care24. However, use of technology in isolation, without human interactions, 
is unlikely to lead to success25. Hence, we propose an eHealth-delivered health coaching obesity intervention 
that delivers IBT by trained health coaches using telehealth and Amulet to enhance behavioral change. Our 
rationale is to pilot the intervention with 30 Dartmouth-Hitchcock (D-H) Weight & Wellness Center patients to 
determine its potential effectiveness on weight loss, physical function, self-reported health, and care pathways 
in a specialty obesity clinic. If effective, the program will enhance in-home care and be disseminated to remote, 
low resource areas to rural obese adults lacking access to care. We will address the following Specific Aims:  
 
Aim 1: Evaluate the feasibility and acceptability of an eHealth-delivered (remote monitoring and video-
conferencing) health coaching obesity intervention in a rural, academic, specialty obesity clinic. 
H1: An eHealth-delivered health coaching obesity intervention using video-conferencing and remote monitoring 
is: a) feasible (>80% of subjects will enroll; >80% of enrollees will complete the intervention); and b) acceptable 
in rural adults seeking treatment for obesity (using semi-structured interviews and Yip’s Telemedicine survey).  
 
Aim 2: Assess the potential effectiveness of an eHealth-delivered health coaching obesity intervention 
in improving weight, physical function and self-reported health in adults with obesity. 
H2: Measures of improved weight (>5% loss), physical function (6-minute walk) & self-reported health (patient-
reported outcome measurement system-PROMIS) will allow reliable estimates for designing an evaluative trial.   
 
Aim 3: Ascertain the extent to which the intervention works within an existing specialty obesity clinic 
on implementation outcomes such as workflow adoption, organizational change and compatibility.  
H3: Implementation will be adopted into the workflow (qualitative interviews and Haug’s measure), lead to 
organizational change (General Organizational Index) and be perceived as having value (Willingness to Pay).  
 
A transdisciplinary team will execute a pilot study for conducting a Type 2 hybrid effectiveness-
implementation design26 of this T3/T4 intervention. Expected outcomes include its feasibility and acceptability, 
the value added to specialized obesity care by improving outcomes, and the potential for eHealth integration 
into practice. The pilot builds logically to an R01 grant addressing critical gaps in pragmatic obesity strategies 
to improve health and care delivery pathways to with limited access to specialty care for long-term weight loss.   
 
SIGNIFICANCE 
Standard obesity advice within primary care is often ineffective in the long-term27,28, which can amplify 
the morbidity observed in this population. The need to refer to specialty obesity care is needed6, yet access to 
care is lacking, especially in rural areas24. Most evidence-based practices require busy clinicians to add visits29 
for direct, in-person IBT. These pathways are impractical due to workforce shortages, time constraints, and 
caseloads30,31. Health coaches can assist in mitigating such challenges by effectively delivering IBT at higher 

value13, allowing clinicians to focus on other issues of high complexity. Our eHealth-delivered health coaching 
intervention can potentially surmount the problem of delivering obesity interventions in busy practices, while 
supporting remote activity monitoring. mHealth is a promising adjunct to conventional therapies that improve 
adherence using automated health behavior change32-39. It can overcome the limits of counseling which heavily 
rely on self-motivation40. Yet, devices alone insufficiently achieve sustained engagement in health behavior 
change in the absence of facilitation and support by coaches41. Emerging telehealth systems42,43 can fill a 
coverage gap44,45 by providing rural patients opportunities to access high quality specialty obesity care46-49. 
This modality can address the access and distance barriers faced by rural adults50. The project conceivably 
may alter care delivery by offering innovative and practical strategies to improve patient, system and practice-
based outcomes. Our strategy aligns with the NIH Strategic Obesity Plan, the Institute of Medicine’s call for 
Telehealth Research51, and Medicare’s population-health vision for improving access and delivering high-value 
care to an at-risk population52 with obesity in rural areas46,53. The results will inform a future effectiveness study 
designed to improve outcomes and health of an emerging demographic with obesity54, all which may reduce 
health costs55. Our approach may satisfy a significant service mismatch of improved health care quality44,45 
while redefining novel delivery processes for achieving efficiencies in an overburdened system.  
 
INNOVATION 
The proposed eHealth-delivered health coaching obesity intervention is innovative because it proposes to: 
 Conduct the first pragmatic pilot using rural obese adults assessing health coach delivered IBT augmented 
by a remote monitoring device in a home-based program, with potential for scalability to other systems; 
 Move beyond conventional methods of research-based obesity interventions using in-person clinician visits 
that integrates telehealth and mHealth into health coach based care to facilitate behavioral weight loss; 
 Advance health promotion science using home-based Telehealth in rural areas allowing its incorporation into 
a real-world setting to support long-term adherence to lifestyle change; 
 Use a unique mHealth approach with open-source application coding and a future basis for ecological 
momentary assessment extending beyond commercial devices that are at risk for becoming obsolete;  
 Incorporates emerging technology into a comprehensive care model in obesity care; 
 Create collaborations with mHealth technologists and telehealth experts allowing for translation of innovative 
laboratory-based technologies into practical applications for medically complex populations in rural settings;  
 Use mixed-methods to identify factors leading to behavioral change using technology in the study population; 
 Apply lessons learned from this study to other disadvantaged, low-income disparity populations.  
 
APPROACH 
Study Design: A single-arm, mixed-methods study that will adopt a pragmatic delivery approach to: a) assess 
the intervention’s feasibility/acceptability; b) investigate the potential effectiveness; and c) ascertain outcomes 
of implementation. We will recruit 30 obese patients from the D-H Weight & Wellness Center (DH-WWC) to 
participate in a 12-week pilot that includes monthly assessments and qualitative semi-structured interviews.  
 
Study Site: D-H, in Lebanon, NH, serves 1.5 million persons in New Hampshire and Vermont. Demographics 
mirror a typical rural New England town (95.4% Caucasian56). The DH-WWC is a specialty referral-based 
obesity clinic whose aim is to improve the health and wellness of patients with obesity. The clinic uses the Epic 
electronic health record (EHR), and has rooms for interviews, assessments, and video-conferencing.  
 
Selection Criteria: For the purposes of this pilot, we limited inclusion criteria to: community-dwelling, English-
speaking patients (age 18-65 years; BMI ≥30kg/m2) with home Wi-Fi high-speed Internet and clearance from 
their clinician. Patients with dementia (lacking consent), bariatric surgery, severe mental or life-threatening 
illness, substance use, or exercise contraindications57 are excluded as these criteria interfere with the possible 
efficacy of the intervention. Future pragmatic trials will relax the criteria to allow a pragmatic interpretation.   
 
Study Population/Recruitment: The DH-WWC evaluated 315 new patients in 2016 (current waitlist ~3 mo.). 
As research director, the PI has access to subjects. To meet a target of n=30, ~10% of subjects would need to 
be enrolled. Staff will receive an overview at a weekly meeting. A research assistant (RA) will screen booked 
subjects by EHR review and identify eligible subjects. At the visit, the RA will proceed with a 2-way discussion 
focusing on the study goals, answering queries, providing contact details, and obtaining consent and baseline 
measurements. A Callahan score58 ≥4 and no EHR diagnosis of dementia will assure competence for consent. 
 
Retention: To minimize dropouts, we will provide feedback (ie: weight), frequent contacts, study incentives for 
assessments, and assertive outreach with the primary care provider (to engage partnerships). We anticipate 
that the duration of the pilot and patient motivation will lead to favorable retention.  
 

Baseline Measures: We will review EHR records for: age, race, co-morbidity, education, medications, and 
habits. We will ask about social support59,60 at baseline.  
 
Intervention Description: The pilot comprises of five components listed below which provide access to 
specialty obesity care to rural obese adults, and enhance it with technology (telehealth and remote monitoring):  
a) Team-based care: As in other US-based specialty obesity centers, the DH-WWC is an interdisciplinary, 
team-based, specialty clinic overseeing each patient’s care: a physician or associate provider formulates a 
medical plan and counsels on obesity’s adverse effects; a nurse assists in triaging medical concerns and 
manages drug therapy; a psychologist evaluates the patient for barriers to weight-loss such as untreated or 
underdiagnosed mental health disorders; and a dietician counsels the patient on nutrition/energy needs for 
achieving weight loss through nutritional therapy. The health coach is supervised by the clinician.  
b) Health coaching: In the DH-WWC Healthy Lifestyle Program, a health-coach led program plays an integral 
role in assisting with patient tracking, data gathering, self-monitoring, facilitating educational sessions and 
connecting patients and families with local resources. Coaches are trained in avoiding weight bias and, 
undergo specific motivational interviewing training, use shared decision-making, and reframe obesity as a 
chronic disease. They complete select wellness coaching programs, pass certification exams and serve as 
the program anchor, facilitating care coordination, executing treatment plans and setting SMART goals 
(specific, measurable, agreed upon, realistic, time-based). Content is based on national guidelines6. 
c) Telehealth: Weekly coaching sessions will be conducted by video-conferencing in lieu of in-person visits. Dr. 
Pletcher’s telehealth team will configure an Android tablet (using the HIPAA compliant software, Vidyo61) to 
the subject’s home intranet remotely, and will provide training to both patients and staff. The interface has 
easy access using simple taps. The coach’s computer will have similar software. Added coaching support is 
offered ad-hoc, as in usual care, using on-demand connectivity. Subjects will be located in their homes.  
d) Messaging: Currently, coaches send messages via the Epic integrated EHR patient portal, used in >40% of 
US medical centers. ‘Push’ message notifications to the tablet’s face screen are received indicating a 
message is waiting for review. Within 48 hours, coaches are notified if messages are read, and if not, the 
coach can contact the patient by phone. Communication is confidential, documented, and tracked.  
e) Remote Monitoring: The Amulet is an mHealth wrist-worn device with remote sensing and self-monitoring 
capabilities, functioning independently of a smartphone (battery life lasts >9 days). A 9-axis gyroscope, 
accelerometer, magnetometer and sensor captures motion, activity type, exercise, and speed data. As part 
of Dr. Batsis’ K23, Amulet applications were developed to measure steps, strength, and activity (sedentary, 
walking, running). Two-way data transfer occurs via a micro-SD card, or low-energy Bluetooth 4.0 to a 
secure cloud-based repository that is accessible to the team. Predetermined one-line automated scripts (if 
goals are not met), prompts, and asynchronous messaging can be sent by staff. Programmable reminders 
(blinking lights, text, haptic buzzer) and audio-visual feedback allow the Amulet to celebrate goal attainment.  
 
 
Outcomes Measures: These were chosen based on their validity, brevity and presence within the HER:  
 
Table 1: Study Variables and Objective Outcome Measures  
Aim 1: Feasibility and acceptability of an eHealth-delivered health coaching obesity intervention  
Timepoint 
Domain 
Outcome/Co-Variate 
Instrument 
Adm Length 
Source 
0 1 2 3 
Feasibility 
Recruitment/Retention 
Eligibility, enrollment, dropout data  RA 
--- 
Study Log     
 
Engagement/Assessments >80% attend + complete sessions  
RA 
--- 
Study Log     
Acceptability 
Satisfaction of Intervention Semi-structured interview  
RA 
30 min 
Participant  
 

 
Patient Satisfaction 
Yip Telemedicine questionnaire62 
Self 
15 item 
Participant  
 

Aim 2: Potential effectiveness in improving weight, physical function and self-reported health
Timepoint 
Domain 
Outcome/Co-Variate 
Instrument 
Adm Length 
Source 
0 1 2 3 
Participant 
Level 
Outcomes 
Anthropometry  
Weight/body mass index 
CS 
--- 
Participant     
Physical function 
6 minute walk test (6MWT)63 
CS 
6 min 
Participant   
  
Diet/Exercise Measures 
REAP-S64/IPAQ65 
Self 
16/7 items Participant  
 
Subjective Health 
PROMIS general health 1066 
Self 
10items 
Participant   
  
Readiness to Change 
URICA67 
Self 
12 item 
Participant     
Aim 3a: Ascertain the extent to which the intervention works on implementation outcomes  
Timepoint 
Domain 
Outcome/Co-Variate 
Instrument 
Adm Length 
Source 
0 1 2 3 
Adoption 
Workflow adoption  
Qualitative Interviews 
RA 
30 min 
Staff 
 
 

Staff adoption 
Evidence-based practice adoption68 RA 
12 item 
Staff 
 
 

Implementation  Organizational Change 
General Organizational Inventory69 
RA 
12 item  
Staff 
 
 
 
Compatibility 
Patient Perception of Value Willingness to Pay70  
Self 
2 item 
Participant 
ADM- Administration; CS – clinical staff; IPAQ – International Physical Activity Questionnaire; PROMIS: Patient Reported Outcome Measures Information Systems; RA: 
research assistant; REAP-S: Rapid Eating Assessment for Participants – Shortened Version; 6MWT: 6-minute walk test; URICA: University of Rhode Island Change 
Assessment: Short Form for Physical Health Behavior State. 

Aim 1: Evaluate the feasibility and acceptability of an eHealth-delivered (remote monitoring and video-
conferencing) health coaching obesity intervention in a rural, academic, specialty obesity clinic. 
 
Feasibility: The following rates will be computed: screened for inclusion (# screened ÷ # potentially eligible); 
eligibility [# screen positive ÷ # screened for inclusion]; enrollment [# enrolled ÷ (# screen positive & eligible)]; 
completion (# completing sessions ÷ # enrolled); assessment (# completing all assessments ÷ # enrolled). We 
will assess entry criteria (reasons for dropout/non-adherence) and time to collect data. Enrollment success will 
be defined as 30 patients. Adequate retention is defined as a dropout rate of <20%. Completion of >80% of 
post-study measures will be defined as adequate. Attending >80% of sessions will be considered acceptable. 
Staff will track equipment, software or technical issues (malfunctions, data loss, hardware) in a journal. 
 
Acceptability: Qualitative interviews will ensure the appropriateness of the pilot and its strengths/weaknesses. 
The RA will conduct, record, and transcribe each 30min interview. Field notes, questions, and themes outlining 
the content/context allows for codebook development, tailoring it to the subject’s needs in the future. Topics 
include: feasibility, acceptability; missing features; telehealth’s impact (including sound/video quality); Amulet’s 
usability and perceived barriers to use; complexity and flaws; strategies for improving compliance; feedback. 
Quantitative data will be measured using the 15-item, 5-point scale (Telemedicine satisfaction questionnaire62).  
 
Data Analysis: The analysis of Aim 1 is primarily descriptive. Data will be analyzed as follows: 
 Qualitative Data: Thematic analysis71-73 will be conducted by two researchers who will independently read 
transcripts, and conduct open coding74. This enhances research rigor by allowing for different viewpoints75.  
Through data immersion, focused coding using themes identified during open coding permits defined 
analyses74. Codes will be derived a priori from interviews, and inductively derived from qualitative data, after 
which, the query tool will retrieve text by code, reviewed for content, relevance, and prevalence of themes.  
 Statistical Analyses: Descriptive statistics (means, medians, proportions, 95% confidence intervals [CI]) will 
be computed for feasibility/acceptability. Telemedicine satisfaction will be assessed only at 12 week follow-up  
 Sample Size Considerations: For the pilot, we estimation the proportion of patients attending and completing 
10 of 12 scheduled sessions. If this proportion is 80%, then the half width of the expected 95%CI based on 
30 enrolled patients is 14.3%, based on a normal approximation for binomial data.  
 
Aim 2: Assess the potential effectiveness of an eHealth-delivered health coaching obesity intervention 
in improving weight, physical function and self-reported health in adults with obesity. 
 
Objective & Subjective Participant-Level Outcomes: 
 Weight & BMI: Weight will be measured using a Seca 770 analyzer (in clinic) and by the Omron HBF-514 (at 
home remotely). Target weight loss is defined by >5% of weight. BMI is calculated by Quetelet’s formula76. 
 Physical function: 6-Minute Walk (6MWT)63 is a cardiovascular fitness surrogate measuring distance (normal 
400-700m)77,78 related to function. A clinically important difference is 50-55m79.  
 Diet/Exercise: The Rapid Eating Assessment for Participants (REAPS)64 is a 16 item survey (score 13-39) of 
diet habits. The 7-item international physical activity questionnaire (IPAQ) measures health-related activity65. 
 Subjective Health: The 10-question, non-proprietary Patient Reported Outcomes Measurement Information 
Systems General Health-10 (PROMIS)66 captures physical, mental and social aspects of quality of life.  
 Readiness to Change: The University of Rhode Island Change Assessment67 is a 12 item questionnaire 
assessing one’s stages of change based on contemplation, action, maintenance and pre-contemplation.  
 
Data Analysis: Aim 2 will evaluate potential effectiveness of the intervention on patient outcomes:  
 Statistical Analyses: We will assess % weight loss and BMI change (primary outcomes), and change in the 
6MWT, REAP-S, IPAQ, PROMIS and URICA (secondary outcomes). Linear/non-linear mixed effects models 
will evaluate weight, BMI and URICA change over time. To account for repeated measures and variation in 
trajectory over time, models will include random individual-level intercepts and time trends. A significant 
coefficient for the mean trend over time will indicate changes in outcomes. All data will be included as the 
models are robust to data missing at random under the assumptions of a linear mixed model. Paired t-tests 
will assess the change between baseline and follow-up in 6MWT, REAP-S, IPAQ and PROMIS.  
 Sample Size Considerations: This pilot study intends to help develop future studies to evaluate recruitment, 
feasibility, acceptability and outcomes. Given the longitudinal nature of the data, a key parameter driving the 
future design is the intraclass correlation coefficient (ICC), a measure of dependence between observations 
taken on the same person. With 30 participants and a mean of 10 observations/person, we should be able to 
distinguish between a fairly low ICC of 0.15 versus an alternative of 0.34, based on a one-sample hypothesis 
test with a significance level of 0.05 and a power of 90%80. For the primary outcome of % weight loss, using 

a SD of 10.1%, the 95% CI would have half width of 3.6% based on our sample of 30, assuming a normal 
distribution.  
 
Aim 3: Ascertain the extent to which the intervention works within an existing specialty obesity clinic 
on implementation outcomes such as workflow adoption, organizational change and compatibility.  
 
Objective and Subjective Adoption, Implementation and Compatibility-Level Outcomes: 
 Adoption Domain: Workflow Adoption: Questions using semi-structured interviews (methods per Aim 1) from 
the staff’s experience with the intervention (whether it enhances/interferes with workflow; sustainability; 
technical or other difficulties in delivering care) will be explored. Staff Adoption: Haug’s 12-item Measure of 
Evidence-Based Practice Adoption68 assesses stage of change, experience, attitudes, organization barriers 
and strategies to support evidence-based practices (1-5 point scale - strongly disagree to agree). 
 Implementation Level: Organizational Change:  General Organizational Index (GOI)69 is an interview of 11 
domains with a 5-point rating of: program philosophy, commitment, client eligibility and identification, health 
promotion plan and its treatment, training, process and outcome monitoring, assessment, quality assurance, 
choice supervision, & penetration. It has reliable inter-rater agreement and internal consistency.  
 Compatibility: Patient Perception of Value: Two questions will assess Willingness to Pay (WTP)70 – whether 
they would pay for telemedicine delivery of the intervention in lieu of in-person travel time or cost. 
 
Data Analysis: We will use mixed-methods to analyze data from Aim 3 as follows: 
 Qualitative Data: Interview data will be managed and analyzed using Dedoose as described in Aim 1. 
 Statistical Analyses: Descriptive statistics (means, medians, 95% CI) for adoption, organizational change will 
be assessed at follow-up only. Changes in WTP will be estimated using a paired 2-sample t-test.  
 Sample Size Considerations: For changes from baseline on WTP, there will be 80% power to detect large 
differences in change from 0.32 to 0.28 times a SD assuming a range of within-person correlations (0.2-0.4).  
 
Data Management: Our team is experienced in pilot delivery, data management/acquisition, and validation 
procedures including: a) health coach training; b) continuous feedback; and c) final data editing. The PI and co-
I’s will meet staff regularly, supervise and identify quality control issues early to address them promptly. Non-
HER entered measures will be collected by the RA using REDCap81, a secure web/tablet based data collection 
tool. The Analytics Institute manages a DH-WWC patient registry who will facilitate data extraction. Interviews 
will be recorded in duplicate using a digital tape recorder. Records will be kept in a locked cabinet. Identifiers 
will be assigned at enrollment and kept behind the D-H firewall, with a password protected linked file. Data will 
be exported to Dedoose and R for analysis with complete labeling intact (variables, labels, missing values)  
 
Potential Problems/Alternative Strategies: We anticipate that: a) adults may lack technology skills – our 
team can provide ongoing user support to participants; b) attrition will occur – yet, due to the recruitment of 
motivated persons and ongoing support82,83 this will be low; c) minorities will be under-represented consistent 
with rural New England’s demographics - our region’s socioeconomic diversity can create a strategy that can 
be adopted in other regions to enrich the sample with minorities; d) our criteria maximize external validity and 
limit internal validity used in randomized trials – consistent with pragmatic trials84; e) technology advances 
quickly raising the potential of obsolescence - Amulet was purposefully designed for capabilities of advanced 
capacity; and f)adverse outcomes exist - exercise contraindications and medical clearance are considered with 
symptoms prompting further evaluation. We do not anticipate an increase in risk with video-conferencing and 
eHealth. All other elements are components of standard care and hence would not alter the treatment plan. 
 
Study Timeline/Benchmarks to Success: The schedule for the anticipated funding is presented below:  
MONTH 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Staff Training/Preparation of Site 
X 
 
 
 
 
 
 
 
 
 
 
 
Recruitment & Enrollment 
 
X 
X 
X 
X 
X 
X 
X 
X 
 
 
 
Intervention/Data Management/Analysis 
 
 
X 
X 
X 
X 
X 
X 
X 
X 
X 
X 
Manual Development/R01/Manuscript 
 
 
 
 
 
 
 
 
 
 
X 
X 
 
PROPOSED R01 PROJECT GRANT 
This knowledge will generate promising data to support the design and submission of an adequately 
powered randomized trial to the National Institute of Diabetes, Digestive and Kidney (PA16-17-021) evaluating 
the effectiveness of the eHealth-delivered health coaching obesity intervention. The results will permit sample 
size calculations to detect a range of effect sizes for an eHealth arm (intervention) vs. usual clinic-based care. 
These methods will allow diverse recruitment across the regional D-H system, and be applied to other sites (ie: 
primary care) and populations. If effective, we can adapt the pilot to focus on sustainability of weight loss.  

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