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Web-based and Mobile Health Interventions (3297)

Published on in Vol 18, No 5 (2016): May

The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial

The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial

The Electronic CardioMetabolic Program (eCMP) for Patients With Cardiometabolic Risk: A Randomized Controlled Trial

Authors of this article:

Kristen MJ Azar1, 2 Author Orcid Image ;   Suneil Koliwad3 Author Orcid Image ;   Tak Poon4 Author Orcid Image ;   Lan Xiao2 Author Orcid Image ;   Nan Lv2 Author Orcid Image ;   Robert Griggs4 Author Orcid Image ;   Jun Ma2, 5, 6 Author Orcid Image
Article Authors Cited by (30) Tweetations (9) Metrics

    Original Paper

    1Sutter Health Research, Development and Dissemination, Walnut Creek, CA, United States

    2Palo Alto Medical Foundation Research Institute, Palo Alto, CA, United States

    3University of California San Francisco, San Francisco, CA, United States

    4Mills Peninsula Hospital, Burlingame, CA, United States

    5Stanford University School of Medicine, Palo Alto, CA, United States

    6University of Illinois, Chicago, IL, United States

    Corresponding Author:

    Kristen MJ Azar, MPH, MSN, RN

    Sutter Health Research, Development and Dissemination

    2121 N. California Blvd

    Walnut Creek, CA,

    United States

    Phone: 1 9252874052

    Email: azark@sutterhealth.org


    Background: Effective lifestyle interventions targeting high-risk adults that are both practical for use in ambulatory care settings and scalable at a population management level are needed.

    Objective: Our aim was to examine the potential effectiveness, feasibility, and acceptability of delivering an evidence-based Electronic Cardio-Metabolic Program (eCMP) for improving health-related quality of life, improving health behaviors, and reducing cardiometabolic risk factors in ambulatory care high-risk adults.

    Methods: We conducted a randomized, wait-list controlled trial with 74 adults aged ≥18 years recruited from a large multispecialty health care organization. Inclusion criteria were (1) BMI ≥35 kg/m2 and prediabetes, previous gestational diabetes and/or metabolic syndrome, or (2) BMI ≥30 kg/m2 and type 2 diabetes and/or cardiovascular disease. Participants had a mean age of 59.7 years (SD 11.2), BMI 37.1 kg/m2 (SD 5.4) and were 59.5% female, 82.4% white. Participants were randomized to participate in eCMP immediately (n=37) or 3 months later (n=37). eCMP is a 6-month program utilizing video conferencing, online tools, and pre-recorded didactic videos to deliver evidence-based curricula. Blinded outcome assessments were conducted at 3 and 6 months postbaseline. Data were collected and analyzed between 2014 and 2015. The primary outcome was health-related quality of life. Secondary outcomes included biometric cardiometabolic risk factors (eg, body weight), self-reported diet and physical activity, mental health status, retention, session attendance, and participant satisfaction.

    Results: Change in quality of life was not significant in both immediate and delayed participants. Both groups significantly lost weight and reduced waist circumference at 6 months, with some cardiometabolic factors trending accordingly. Significant reduction in self-reported anxiety and perceived stress was seen in the immediate intervention group at 6 months. Retention rate was 93% at 3 months and 86% at 6 months post-baseline. Overall eCMP attendance was high with 59.5-83.8% of immediate and delayed intervention participants attending 50% of the virtual stress management and behavioral lifestyle sessions and 37.8-62.2% attending at least 4 out of 7 in-person physical activity sessions. The intervention received high ratings for satisfaction.

    Conclusions: The technology-assisted eCMP is a feasible and well-accepted intervention and may significantly decrease cardiometabolic risk among high-risk individuals.

    Trial Registration: Clinicaltrials.gov NCT02246400; https://clinicaltrials.gov/ct2/show/NCT02246400 (Archived by WebCite at http://www.webcitation.org/6h6mWWokP)

    J Med Internet Res 2016;18(5):e134

    doi:10.2196/jmir.5143

    Keywords

    prevention; lifestyle; metabolic syndrome; cardiovascular disease; behavior change; health technology 


    The risk for cardiometabolic diseases remains high among US adults. Although the rates of obesity, a major contributor to this risk, are leveling, up to 35% of the population continue to be classified as obese (body mass index [BMI] ≥30 kg/m2) [1]. Addressing the prevention and treatment needs of the population remains a challenge. Effective and scalable health care systems are urgently needed to promote patient-centered population health management among patients who either have or are at risk for cardiometabolic diseases.

    Lifestyle intervention integrated into ambulatory care has consistently been emphasized as a crucial approach to cardiometabolic risk reduction. In the context of obesity, lifestyle modification has been shown to produce significant risk reduction even with modest weight loss (3-5%) [2]. While intensive lifestyle interventions to reduce cardiometabolic risk have been shown to be beneficial, much work is needed to translate efficacious interventions into practical and sustainable programs that can be offered by the existing health care infrastructure. Patient-centered population health management to prevent and control cardiometabolic disease requires scalable and sustainable lifestyle interventions.

    Technology-assisted approaches that are based in ambulatory care may increase the potential for widespread reach and adoption, resulting in improved long-term effectiveness and a shift towards a population based management model. Growing evidence [3-6] suggests that technology-assisted clinical tools and approaches can both increase access and decrease cost for clinic-based disease prevention and management programs that traditionally place a large burden on personnel and resources. While evidence suggests that technology-assisted lifestyle interventions for weight loss are effective, best practices remain unknown [7]. Shortcomings of existing interventions include low level of pragmatic methodology and use of technology that is not publicly available [7]. Further, there is growing concern that increased emphasis on automated online delivery modalities for lifestyle intervention can potentially fail because they sacrifice important face-to-face interactions between health care providers and patients, and among patients in group settings [8]. With this in mind, efforts to test the utility of technology-based interventions specifically for ambulatory care are underway, using widely available and low-cost tools to improve delivery mechanisms, enhance patient-provider communication, and preserve virtual face-to-face interactions while optimizing access and reach.

    The purpose of this study was to examine the potential effectiveness, feasibility, and acceptability of an evidence-based group lifestyle intervention via real-time videoconferencing with other technology-assisted tools to reduce obesity and cardiometabolic risk factors among high-risk individuals in an ambulatory care setting. We hypothesized that participants would report improvements in health-related quality of life at 3 months post-baseline, compared to the delayed control group. For secondary outcomes, we hypothesized that cardiometabolic risk reduction would result in the immediate intervention group at 3 months and that these findings would be replicated in the delayed group at 6 months, while the immediate group would show continued improvement.


    Study Design

    The Electronic CardioMetabolic Program (eCMP) pilot study was a randomized, wait-list controlled trial among patients who either had or were at high risk for developing type 2 diabetes and/or cardiovascular disease (cardiometabolic disease). The primary end point was originally intended to be 6 months post baseline. The original intent had been for the wait-list control group to not begin the intervention until the immediate group completed the 6-month intervention, allowing for between-group comparisons at 3 and 6 months. However, this was a pilot study and logistic challenges including limited duration and funding were encountered after the study began. In order to comply with logistical limitations, we phased in the wait-list control sooner and therefore were able to compare outcomes between groups (intervention vs control) at 3 months post baseline and compare changes in outcomes within the immediate intervention group at 6 months post baseline and for the delayed-intervention group at 3 months post intervention (the mid-way point). We looked at reproducibility of early/mid-intervention effects between the first 3 months of participation and saw similar patterns of change as well as a continuation of trends.

    Recruitment and Participants

    Participants were recruited from an outpatient multispecialty group practice organization in Northern California. High-risk adults (≥18 years old) in need of primary prevention of cardiovascular disease and/or diabetes (body mass index [BMI] ≥35 kg/m2 and pre-diabetes, previous gestational diabetes and/or metabolic syndrome) or secondary prevention (BMI ≥30 kg/m2 and type 2 diabetes and/or cardiovascular disease) were invited to participate. Participants were required to be proficient in written and spoken English and to have access to the Internet to allow for remote self-tracking, viewing of online materials and resources (eg, pre-recorded didactic videos) as well as participation in videoconference group visits. Exclusion criteria included type 1 diabetes or insulin dependence, pregnancy or active breastfeeding at the time of enrollment, current treatment for a serious medical condition (ie, cancer, except non-melanoma skin cancer), presence of any safety concerns related to significant physical or mental health issues, or life expectancy less than 12 months.

    Potential eligible participants were identified through electronic health records. Participants were first screened for eligibility via phone or online and were then invited to attend the baseline assessment visit where informed consent was obtained. During the baseline visit, participants were given more information regarding the study and baseline clinical measures were obtained and eligibility was confirmed prior to randomization. Participants were interviewed at each follow-up visit about possible adverse events during the past 3 months, and the study physician adjudicated the events per study safety protocol. Data were collected and analyzed between June 2014 and January 2015 in Burlingame, California.

    The study was approved by the Palo Alto Medical Foundation Institutional Review Board. Of the 294 patients who responded to recruitment letters after their primary care provider approved study contact, 164 patients declined participation and 56 were ineligible. This process yielded the target enrollment of 74 eligible and consenting participants (see Figure 1).

    ‎
    Figure 1. CONSORT diagram.
    View this figure

    Randomization and Allocation Concealment

    We applied our published dynamic block randomization method [9] to assure better than chance between-treatment balance across 5 prognostic factors (age, sex, race/ethnicity, BMI, and primary prevention). The method automatically ensures allocation concealment. Participants were randomized to receive the 6-month eCMP lifestyle intervention either immediately upon randomization (n=37) or after a 3-month wait period (n=37). A designated study staff person who did not have the ability to influence the allocation system’s execution performed randomization. While study group assignment was identifiable to participants and interventionists, blinding was otherwise maintained for data collection, outcome adjudication, and data analysis.

    Description of the eCMP Intervention

    The goal of the eCMP is to reduce cardiometabolic risk by improving diet, physical activity, and mental health behaviors through lifestyle change. eCMP is a 6-month comprehensive program relying on weekly face-to-face group meetings via video conferencing and the delivery of evidence-based curricula using online tools and pre-recorded didactic videos presented by physicians, nutritionists, exercise physiologists, and lifestyle coaches. Technology-assisted features include (1) portable and/or wearable mobile devices for data collection (eg, Fitbit and Withings Smart Scale WS-30 [wireless scale]), (2) a comprehensive online platform and participant portal for hosting program materials (eg, homework assignments, didactic videos, and calendars), and (3) virtual small groups via real-time, encrypted, Web-based videoconferencing (see Table 1). Wireless scales were provided to the immediate intervention groups only. Technology-assisted tools for self-tracking and participation were provided for use during the study period. All participants attended an in-person orientation session post baseline assessment and prior to their first group visit. At the orientation, they participated in a technology-training workshop and received all intervention tools and materials. There were a total of 24 virtual group sessions offered alternatively between the stress management and behavioral lifestyle component and 7 in-person sessions for group-based physical activity.

    Table 1. eCMP curriculum contents and delivery modalities in ambulatory care settings.
    ComponentFunctionFeatures
    Evidence- and theory-based curriculumaLifestyle modification and behavior change contentWeight management
    Healthy eating
    Physical activity
    Stress management
    Online platform and participant portalHosting program materials and participant-coach communicationDidactic videos
    Homework assignments
    Calendar/schedule
    Other educational resources
    Mobile monitoring devicesParticipant self-monitoring, bio-feedback, and remote data captureWireless body scaleb
    Pedometer
    Coach-led virtual small group sessionsCurriculum content delivery for weight management, healthy eating, and stress managementWeekly (24) sessions
    Real-time, encrypted, Web-based video conferencing
    Coach-led in-person sessionsExercise curriculum content deliveryPeriodic (7) sessions

    aCurriculum used includes Diabetes Prevention Program Group Lifestyle Balance (weight management and healthy eating), Active Living Every Day (physical activity), and Heart Matters (stress management).

    bImmediate intervention group only.

    Stress Management Component

    Interactive sessions for stress management consisted of biweekly virtual small group meetings of 7-10 participants. Each 1-hour session was facilitated by a trained health coach and was based on a proprietary, evidence-based [10-17] stress management curriculum called Heart Matters. Self-awareness is the foundation of the Heart Matters program, which includes these topics: belief systems, emotional responses to experiences, anger, hostility, time pressure, mindfulness, self-esteem, and forgiveness. Prior to each stress management session, participants were instructed to view an accompanying, supplemental video available via the online platform.

    Behavioral Lifestyle Component

    Interactive sessions for diet counseling consisted of biweekly virtual small group meetings of 7-10 participants. A registered dietitian facilitated each 1-hour session, using the core curriculum of the evidence-based Group Lifestyle Balance program developed by researchers at the University Of Pittsburgh [18,19]. Participants were asked to track their daily dietary intake via an Internet/mobile-based self-monitoring app.

    Physical Activity Component

    Participants were provided with instructional videos available through the online platform that encourage participants to identify opportunities throughout their day for increasing physical activity. The video content and exercise modalities were adapted from the Active Living Every Day program designed to increase physical activity among sedentary individuals [20,21]. In addition to the videos, participants were encouraged to attend 7 drop-in exercise sessions and to track their daily steps with a Fitbit pedometer.

    Outcome Measures

    All outcome assessors were trained to perform the measurements and interviews per standardized protocols and procedures at baseline and at 3 and 6 months post baseline. The primary outcome, overall health-related quality of life, was measured by the Short Form-8 Health Survey (SF-8), an 8-item version of the SF-36 [22]. Secondary outcomes included biometric cardiometabolic risk factors (eg, body weight), self-reported diet and physical activity, and mental health status, retention, session attendance, and participant satisfaction. Published protocols were used to obtain height (baseline only), weight, waist circumference, and blood pressure measurements [23,24]. Participants also completed fasting blood draws at the onsite clinical laboratory for assays of plasma lipid profile. Dietary intake was assessed using multiple-pass 24-hour diet recall [25] of one typical day over the phone with participants on the Windows-based Nutrition Data System for Research (NDSR; Nutrition Coordinating Center, University of Minnesota). Diet quality was assessed using a composite Dietary Approaches to Stop Hypertension (DASH) concordance index (range 0-9) calculated by summing 9 nutrients, including total fat, saturated fat, protein, cholesterol, fiber, magnesium, calcium, sodium, and potassium [26]. Physical activity was assessed using the Stanford 7-Day Physical Activity Recall interview [27]. Stress and mental health measures included the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), and Perceived Stress Scale. The PHQ-9 is a 9-item depression symptom assessment with scores ranging from 0-27. PHQ-9 scores of 5-9, 10-14, 15-19, and 20-27 represent mild, moderate, moderately severe, and severe depression, respectively [28,29]. The GAD-7 is a valid and reliable 7-question scale for screening generalized anxiety disorder and strongly associated with multiple domains of functional impairment [30]. The Perceived Stress Scale, a 14-item instrument, is a global measure of perceived stress that asks participants to report how often their lives seem to be uncontrollable or overloaded during the last year [31].

    Attendance was tracked for all participants for the entire 6-month study period. Anonymous feedback and satisfaction ratings with the eCMP intervention components were obtained from participants online through survey questions ranked on a 5-point Likert scale with 1 indicating “not at all satisfied” and 5 indicating “extremely satisfied.” The survey included questions pertaining to session-related components (ie, technology, coach, and group dynamics) and home activities and materials (eg, self-monitoring activities, video/DVD, handouts, and the “virtual package”). All eCMP participants (both arms) were encouraged by the coach to complete the survey after each virtual group and/or in-person session (31 sessions total).

    Statistical Analysis

    Frequencies and percentages were calculated for describing categorical variables, and means and standard deviations were used for continuous variables. Intention-to-treat analyses of between-group and within-group differences in primary and secondary outcomes were tested in a repeated-measures mixed-effects linear model with adjustment of the baseline value of the outcome of interest. Between-group differences (immediate intervention vs delayed intervention) were examined at 3 months post baseline. Within-group differences for both immediate intervention and the delayed-intervention group were assessed at 6 months post baseline. All analyses were conducted using SAS, version 9.3.


    Retention and Baseline Characteristics

    Patients were recruited from March 21, 2014, through May 13, 2014. Follow-up was completed on January 2, 2015. Of the 74 randomized participants, 89% (33/37) of intervention and 97% (36/37) of delayed intervention participants were assessed at 3 months and 86% (32/37) and 86% (32/37) at 6 months (Figure 1). There was no discernable pattern to the attrition. Participants were 59.7 (11.2) years old, mostly female, non-Hispanic white, and severely obese (see Table 2). At baseline, 27% (20/74) of participants were enrolled for primary prevention and 73% (54/74) for secondary prevention.

    Table 2. Baseline characteristics.

    		All (N=74)Immediate (n=37)Delayed (n=37)
    Age in years, mean (SD)59.7 (11.2)59.6 (11.9)59.8 (10.5)
    Body mass index, kg/m2, mean (SD)37.1 (5.4)37.0 (5.7)37.3 (5.2)
    Female, %59.559.559.5
    Race, %



    		Hispanic5.45.45.4

    		Non-Hispanic white82.483.881.1

    		Non-Hispanic black4.108.1

    		Asian/Pacific Islander2.72.72.7

    		Other5.48.12.7
    Primary preventiona, %27.027.027.0
    Secondary preventionb, %73.073.073.0

    aPrimary prevention is defined as BMI ≥35 kg/m2and prediabetes and/or metabolic syndrome.

    bSecondary prevention is defined as BMI ≥30 kg/m2 and type 2 diabetes and/or cardiovascular disease.

    Quality of Life, Weight, and Cardiometabolic Risk Factors

    Change in health-related quality of life was not significant in both immediate and delayed participants. The mean weight loss was -2.3 kg (-2.0% of baseline) in immediate participants versus -0.2 kg (-0.01%) in delayed controls at 3 months. The between-group mean difference in change was -2.1 kg (95% CI -4.3 to 0.1) (see Table 3). At 6 months post baseline, the immediate group achieved significant weight loss of -3.1 kg (95% CI -4.7 to -1.5), achieving a 2.8% weight reduction. Equivalently, immediate intervention participants had greater reductions in BMI than delayed controls at 3 months and the net reduction was -0.7 kg/m2 (95% CI -1.5 to -0.02). Delayed controls achieved comparable BMI reductions at 6 months. At 6 months, the immediate intervention group achieved an overall mean reduction of -1.0 kg/m2 (95% CI -1.5 to -0.5).

    Differences in waist circumference were not significant between the immediate and delayed groups at 3 months, with a decrease of -0.6 cm (95% CI -3.8 to 2.6). The immediate intervention group showed a significant reduction in waist circumference at 6 months post baseline. Participants had normal mean blood pressure, and fasting plasma lipids at baseline, with non-significant but consistent tendency of improvements in the immediate relative to the delayed group at 3 months and inconsistent changes due to insufficient sample size (n=19) for the immediate group at 6 months (see Tables 3 and 4).

    Table 3. Changes in quality of life, anthropometric, blood pressure, diet, physical activity, and stress and mental health at 3 and 6 months.
     
         		Baseline, mean (SD)Change from baseline to 3 months,
    mean (95% CI)    		Change from 3 to 6 months,
    mean (95% CI)    		Change from baseline to 6 months,
    mean (95% CI)
    All (N=74)Immediate (intensive phase) (n=33)Delayed (no intervention) (n=36)Between-group difference, difference in changeImmediate (maintenance phase) (n=32)Delayed (intensive phase) (n=32)Immediate (intensive+ maintenance) (n=32)
    Quality of life

    		SF-8 mental component49.2 (8.1)-1.3 (-4.0 to 1.5)1.5 (-1.1 to 4.1)-2.8 (-6.6 to 1.0)3.0 (-0.7 to 6.6)1.3 (-2.3 to 4.8)1.7 (-1.3 to 4.6)

    		SF-8 physical component45.1 (7.2)2.3 (-0.4 to 5.1)0.9 (-1.7 to 3.6)1.4 (-2.4 to 5.2)-2.6 (-5.8 to 0.6)1.7 (-1.4 to 4.8)-0.2 (-3.1 to 2.7)
    Anthropometric and blood pressure

    		BMI, kg/m237.1 (5.4)-0.7 (-1.3 to -0.2)0 (-0.5 to 0.5)-0.7 (-1.5 to -0.02)a-0.3 (-0.7 to 0.2)-0.6 (-1.0 to -0.1)b-1.0 (-1.5 to -0.5)b

    		Weight, kg107.4 (18.8)-2.3 (-3.9 to -0.7)-0.2 (-1.7 to 1.3)-2.1 (-4.3 to 0.1)-0.8 (-2.1 to 0.4)-1.6 (-2.9 to -0.4)b-3.1 (-4.7 to -1.5)b

    		Weight change, %0-2.0 (-3.5 to -0.6)0 (-1.4 to 1.4)-2.0 (-4.0 to -0.1)a-0.8 (-1.9 to 0.4)-1.5 (-2.6 to -0.4)b-2.8 (-4.2 to -1.4)b

    		Waist circumference, cm119.6 (13.9)-1.9 (-4.2 to 0.4)-1.3 (-3.5 to 0.9)-0.6 (-3.8 to 2.6)-2.2 (-4.2 to -0.2)b-1.1 (-3.0 to 0.8)-4.1 (-6.5 to -1.7)b

    		Systolic blood pressure124.0 (10.1)0.7 (-2.2 to 3.6)1.1 (-1.7 to 3.9)-0.4 (-4.4 to 3.6)-0.3 (-3.8 to 3.1)-2.7 (-6.0 to 0.6)0.3 (-2.7 to 3.4)

    		Diastolic blood pressure72.6 (10.7)2.9 (0.6-5.3)2.6 (0.4-4.8)0.3 (-2.9 to 3.6)2.0 (-1.0 to 5.0)2.8 (-0.5 to 6.1)5.0 (2.5 to 7.4)b
    Diet and physical activity 

    		DASH scorec2.4 (1.4)0.4 (-0.1 to 0.8)0.1 (-0.4 to 0.6)0.3 (-0.4 to 0.9)-0.3 (-0.9 to 0.4)0.3 (-0.4 to 0.9)0.1 (-0.4 to 0.6)

    		Stanford 7-day Physical Activity Recall (metabolic equivalents)680.5 (634.5)652.8 (236.8-1068.7)103.6 (-294.8 to 501.9)549.2 (-26.8 to 1125.2)-708.5 (-1224.8 to -192.2)b-419.2 (-926.1 to 87.7)-55.7 (-477.7 to 366.3)
    Stress management

    		PHQ-95.2 (4.1)-1.1 (-2.2 to 0)-0.8 (-1.9 to 0.2)-0.3 (-1.8 to 1.2)0.3 (-0.9 to 1.6)-0.8 (-1.9 to 0.4)-0.8 (-1.9 to 0.4)

    		GAD-73.4 (3.1)0.2 (-0.7 to 1.2)-0.5 (-1.5 to 0.4)0.8 (-0.6 to 2.1)-1.2 (-2.4 to 0)0.4 (-0.8 to 1.5)-1.0 (-2.0 to 0)

    		Perceived Stress Scale13.1 (6.1)0.3 (-1.3 to 2.0)-1.2 (-2.7 to 0.4)1.5 (-0.8 to 3.8)-1.7 (-3.9 to 0.5)0.4 (-1.7 to 2.6)-1.4 (-3.2 to 0.4)

    aP<.05 between group difference.

    bP<.05 within group difference.

    cDASH scores were calculated based on combining nine nutrient targets (ie, total fat, saturated fat, protein, cholesterol, fiber, magnesium, calcium, sodium, and potassium). The intermediate target of each nutrient was halfway between the DASH target and population mean (based on the National Health and Nutrition Examination Surveys 2007-2008, latest data available at the inception of this study). For a nutrient, participants reaching the DASH target were assigned one point, those reaching the intermediate target were assigned a half-point, and those not meeting the intermediate target were given 0 point. The DASH score was the sum of points for all 9 nutrients.

    Table 4. The changes in fasting plasma lipids by groups at 3 and 6 months.
     
         		Baseline, mean (SD)Change from baseline to 3 months,
    mean (95% CI)    		Change from 3 to 6 months,
    mean (95% CI)    		Change from baseline to 6 months,
    mean (95% CI)
    All (N=63)Immediate (intensive phase) (n=22)Delayed (no intervention) (n=32)Between-group difference,
    difference in change    		Immediate (maintenance phase) (n=13)Delayed (intensive phase) (n=24)Immediate (intensive+ maintenance) (n=19)
    TC175.1 (40.6)-5.0 (-19.3 to 9.2)5.4 (-5.8 to 16.7)-10.5 (-28.7 to 7.7)16.1 (3.1-29.1)-0.8 (-10.4 to 8.7)11.1 (-2.2 to 24.3)
    HDL-C49.7 (16.3)5.0 (1.8-8.2)2.5 (0.1- 4.9)2.5 (-1.5 to 6.5)2.1 (-1.3 to 5.5)0.4 (-2.2 to 3.0)7.2 (4.3-10.1)a
    LDL-C98.5 (36)-10.7 (-23 to 1.5)0.6 (-9.0 to 10.2)-11.3 (-26.9 to 4.3)16.0 (4.2-27.9)1.8 (-7.0 to 10.6)5.3 (-6.1 to 16.7)
    TC:HDL ratio3.8 (1.1)-0.4 (-0.8 to 0.1)-0.1 (-0.4 to 0.3)-0.3 (-0.8 to 0.3)0.2 (-0.1 to 0.4)0 (-0.2 to 0.2)-0.2 (-0.6 to 0.2)
    Tri-glyceride134.5 (61.5)-0.5 (-23.8 to 22.8)-7.1 (-28.9 to 14.6)-12.6 (-42.5 to 17.3)-6.7 (-28.1 to 14.8)-14.8 (-30.5 to 0.9)-7.1 (-28.9 to 14.6)

    aP<.05 within-group difference.

    Diet and Physical Activity Behaviors

    DASH score and assessment of leisure-time physical activity of at least moderate intensity did not show statistically significant improvement for either group at 3 and 6 months (see Table 3).

    Stress and Mental Health Measures

    None of the stress and mental health measures (ie, PHQ-9, GAD-7, and perceived stress scale) had significant improvement in the immediate relative to the delayed group at 3 months (see Table 3). The mean changes in PHQ-9, GAD-7, and perceived stress scale showed non-significant but consistent tendency of improvements at 6 months for the immediate intervention group.

    Intervention Attendance

    Attendance at virtual and in-person group sessions varied by component. For the stress management component, attendance was higher, with 65% (24/37) attendance and 78% (29/37) attendance of immediate and delayed intervention participants, respectively, attending at least half of the 12 total sessions offered. For the behavioral lifestyle component, a majority (60%, 22/37) of immediate intervention participants attended at least half of the sessions offered and 22% (8/37) attended at least 80% of the 12 total sessions offered over the 6-month intervention. Similarly, a large majority (84%, 31/37) of delayed intervention participants attended at least half of the sessions offered, and nearly half (43%, 16/37) of them attended at least 80% of sessions offered. Further, 19% (7/37) of immediate intervention participants compared to 49% (18/37) of delayed intervention participants attended at least 80% of total sessions for stress management offered. For the physical activity component, 38% (14/37) and 62% (23/37) of immediate and delayed intervention participants, respectively, attended at least 4 of the 7 offered sessions. Adherence to 80% of sessions offered was 16% (6/37) for immediate intervention and 11% (4/37) for delayed intervention group.

    Participant Satisfaction and Feedback

    Among all participants (both arms) who attended sessions, 39.10% (461/1179) completed the participant satisfaction surveys. Results between the immediate and delayed intervention group were similar (see Table 5). Overall satisfaction was high with scores ranging from mean 4.1 (SD 0.9) to 4.4 (SD 0.7), with health coaches and facilitators rated as the highest satisfying component compared to other components. More than half of participants indicated that they were satisfied (42.4%, 189/446) or extremely satisfied (38.1%, 170/446) with the technology used in group sessions. Most participants were either satisfied or extremely satisfied with the coach facilitators (92.8%, 415/447) and the general group dynamics (78.7%, 350/445). Among those components, fewer than 5% of scores were rated below 3 on the Likert scale. A majority of participants indicated that they were satisfied or extremely satisfied with self-monitoring activities (78.7%, 352/447), the video or DVD resources (88.5%, 386/436), handouts (85.4%, 345/404), and the “virtual package” as a whole (77.9%, 346/444). Further, among these components, fewer than 5% of ratings fell below 3 on the Likert scale.

    Table 5. Participant satisfaction resultsa.

    		Satisfied with the sessionSatisfied with home activities and materials
    Technology (n=446)Coach (n=447)Group dynamics (n=445)Self-monitoring activities (n=447)Video or DVD (n=436)Handouts (n=404)Virtual package (n=444)
    Mean (SD)4.1 (0.9)4.4 (0.7)4.1 (0.8)4.1 (0.9)4.2 (0.7)4.2 (0.7)4.1 (0.9)
    Rating (Likert scale), %







    		Not at all satisfied 12.00.20.71.10.70.20.5

    		24.50.44.34.91.82.56.1

    		313.06.516.415.28.911.915.5

    		442.441.045.838.549.85143.7

    		Extremely satisfied 538.152.032.840.338.834.434.2

    aParticipant satisfaction was rated on a voluntary basis by participants after each virtual group or in-person session.

    Adverse Events

    Two hospitalizations occurred during the 6-month trial and both of them were determined by the study physician to be not related to the study. There were no deaths.


    Principal Findings

    The purpose of this pilot study was to examine the potential clinical benefit, feasibility, and acceptability of a novel, evidence- and theory-based, technology-assisted behavioral lifestyle intervention for improving health-related quality of life and reducing cardiometabolic risk in ambulatory care. Our main findings suggest that although the eCMP intervention failed to improve health-related quality of life, it showed potential for decreasing cardiometabolic risk among high-risk individuals. Further, the tools and technology-assisted approaches utilized in the intervention demonstrated good feasibility and acceptability among participants.

    Comprehensive lifestyle intervention has become a crucial approach to prevention and treatment of obesity [2], metabolic syndrome [32], diabetes [33,34], and cardiovascular disease [35,36]. Previous paradigms for comprehensive lifestyle intervention, which often involve at least weekly in-person one-on-one or group meetings over months to a year, are insufficient to meet the growing population health management needs of the nation. Rising prevalence and suboptimal management of cardiometabolic conditions present a major challenge to the US health care system.

    These needs have risen to the national health care agenda, as the Center for Medicare and Medicaid Innovation, through legislation provided within the Affordable Care Act, has specifically called for the use of technology to improve the capacity to provide health services for patients with chronic conditions [37]. The Task Force on Community Preventive Services recommends technology-assisted, multicomponent weight-loss interventions [38]. Several recent studies have demonstrated the potential effectiveness of using digital health tools to promote behavior change and reduce cardiometabolic risk in adults [7,39-50]. Technology may provide the means by which efficacious lifestyle interventions can be translated into real-world, clinic-based settings while retaining effectiveness and increasing access and affordability. In their review, Khaylis et al identified five key components to efficacious technology-based weight loss interventions: use of a structured program, self-monitoring, feedback and communication, social support, and individual tailoring [51]. The eCMP program incorporates all of these elements. The eCMP intervention additionally incorporates several novel elements including semiremote intervention delivery, the use of virtual small groups, and the emphasis on stress management as a distinct but complementary component to diet, physical activity, and behavioral strategies.

    Satisfaction with eCMP and the technology-assisted tools was overall high, suggesting good acceptability among participants. The eCMP intervention utilized virtual groups as the primary delivery modality for coach-led face-to-face interactions and curriculum delivery for the stress management and behavioral lifestyle components of the intervention. Technology-assisted interventions for weight loss that incorporate remote intervention delivery and support have been shown to produce outcomes comparable to an in-person intervention [3,40,52,53]. Attendance was generally higher in components utilizing virtual groups compared with the physical activity component that required in-person group visits. This in part may have been due to the timing of the sessions, where virtual groups were offered on weeknights and physical activity in-person sessions were offered on weekends. Technology-assisted approaches to promote behavior change, such as virtual groups, have the potential to improve adherence by making lifestyle interventions more convenient and aiding individuals in overcoming some of the barriers they may encounter in attending frequent, clinic-based, in-person sessions [53,54].

    Retention rate was high in our study, compared to intervention randomized controlled trials in primary care settings [55-58]. Overall eCMP attendance was also high, relative to other studies and interventions [59,60] and especially considering the intensity of the intervention. For the 6-month eCMP intervention, 50% attendance correlates to attendance at approximately 15 sessions in 6 months. The novel use of virtual group visits was recently shown to be effective in the delivery of weight management interventions [53,54]. Virtual group visits are a promising approach to increase accessibility of lifestyle interventions to interested individuals [54]. Participants expressed high levels of satisfaction with the technology used in group sessions, self-monitoring activities, and the “virtual package” as a whole. Participants rated group dynamics very highly, suggesting that virtual group format did not hinder the experience of being in an in-person group setting. These findings suggest that it is possible to use technology to increase the scale of an intervention without losing socially important aspects of group-based behavioral lifestyle modification that have been a crucial part of more traditional face-to-face, in-person programs. Further, the participants in this study were older, with varying levels of comfort and skill with technology use. While most participants found the training useful and were able to participate without major difficulties, it may be useful in future interventions to tailor technology training using a pre-assessment of group participants according to baseline skill level, experience, and comfort.

    Measurement of health-related quality of life remained unchanged, close to the average score in the general US population [22]. Measures of clinical effectiveness and benefit were overall modest. While weight loss was modest (≤3%) among participants, reductions in waist circumference were significant in the immediate group at 6 months. Also, a majority of participants were enrolled for secondary prevention and were being medically managed for diabetes and/or cardiovascular disease (eg, hypertension and/or dyslipidemia). At baseline, blood pressure and lipid levels were well managed and near goal, making clinical effects of the intervention difficult to discern.

    Limitations

    Our study has a number of potential limitations. First, logistical challenges resulted in a change to the study design and primary endpoint. While this change limited our ability to analyze differences between groups after completing the intervention, we were able to assess patterns in trajectory between the two groups at similar timepoints during the course of treatment. Other limitations included a possibility of selective response to the feedback surveys among participants. Participants who responded might have been those who were more committed and more positive toward the intervention. To mitigate this possibility, all participants were highly encouraged by their coaches to complete the survey after each session and all participant evaluations were anonymous. Methodological limitations (eg, small sample size and short follow-up duration) are reflective of a pilot study. Despite these limitations, this study shows that the eCMP intervention is feasible and acceptable in a health care setting and has potential for decreasing cardiometabolic risk among high-risk patients.

    Conclusion

    The eCMP intervention showed potential for decreasing cardiometabolic risk among high-risk individuals and also emphasized stress management as a key component. The tools and technology-assisted approaches utilized in the intervention demonstrated good feasibility and acceptability among participants. Future interventions should continue to explore the use of technology to facilitate remote delivery of ambulatory care‒based interventions in order to optimize the partnership of patients and their health care providers in improving lifestyle behaviors.

    Acknowledgments

    This was an investigator-initiated study funded by the Palo Alto Medical Foundation. No sponsor or funding source had a role in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

    The authors would like to thank Laura Nolting, BS, for her help in the administrative support of this study. We are indebted to the following individuals for their instrumental contributions to the conduct of the study: Veronica Luna, BS, Elizabeth Jameiro, MD, and Nancy Wittels, MS, and Terry Reed, RN, MS, HN-BC, Debby Hagenmaier, RN, MS, Pauline Beggs, RN, Beth Kassler, BS, Leah Groppo, RD, CDE, Dania Saarony, RD, Mishaffner Jones, RN, MS, ACSM-CES, Beryl Onedera, BS, and Tony Ferrari, BS.

    Conflicts of Interest

    None declared.

    1. Ogden CL, Carroll MD, Flegal KM. Prevalence of obesity in the United States. JAMA 2014 Jul;312(2):189-190. [CrossRef] [Medline]
    2. Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, American College of Cardiology/American Heart Association Task Force on Practice Guidelines, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity Society. J Am Coll Cardiol 2014 Jul 1;63(25 Pt B):2985-3023 [FREE Full text] [CrossRef] [Medline]
    3. Spring B, Duncan JM, Janke EA, Kozak AT, McFadden HG, DeMott A, et al. Integrating technology into standard weight loss treatment: a randomized controlled trial. JAMA Intern Med 2013 Jan 28;173(2):105-111 [FREE Full text] [CrossRef] [Medline]
    4. Cole-Lewis H, Kershaw T. Text messaging as a tool for behavior change in disease prevention and management. Epidemiol Rev 2010;32:56-69 [FREE Full text] [CrossRef] [Medline]
    5. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006 May 16;144(10):742-752. [Medline]
    6. Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med 2013 Jan;10(1):e1001363 [FREE Full text] [CrossRef] [Medline]
    7. Levine DM, Savarimuthu S, Squires A, Nicholson J, Jay M. Technology-assisted weight loss interventions in primary care: a systematic review. J Gen Intern Med 2015 Jan;30(1):107-117. [CrossRef] [Medline]
    8. Lyden JR, Zickmund SL, Bhargava TD, Bryce CL, Conroy MB, Fischer GS, et al. Implementing health information technology in a patient-centered manner: patient experiences with an online evidence-based lifestyle intervention. J Healthc Qual 2013;35(5):47-57. [CrossRef] [Medline]
    9. Xiao L, Lavori PW, Wilson SR, Ma J. Comparison of dynamic block randomization and minimization in randomized trials: a simulation study. Clin Trials 2011 Feb;8(1):59-69 [FREE Full text] [CrossRef] [Medline]
    10. Blumenthal JA, Sherwood A, Babyak MA, Watkins LL, Waugh R, Georgiades A, et al. Effects of exercise and stress management training on markers of cardiovascular risk in patients with ischemic heart disease: a randomized controlled trial. JAMA 2005 Apr 6;293(13):1626-1634. [CrossRef] [Medline]
    11. Blumenthal JA, Wang JT, Babyak M, Watkins L, Kraus W, Miller P, et al. Enhancing standard cardiac rehabilitation with stress management training: background, methods, and design for the enhanced study. J Cardiopulm Rehabil Prev 2010;30(2):77-84 [FREE Full text] [CrossRef] [Medline]
    12. Cohen BE, Panguluri P, Na B, Whooley MA. Psychological risk factors and the metabolic syndrome in patients with coronary heart disease: findings from the Heart and Soul Study. Psychiatry Res 2010 Jan 30;175(1-2):133-137 [FREE Full text] [CrossRef] [Medline]
    13. Quartana PJ, Burns JW. Emotion suppression affects cardiovascular responses to initial and subsequent laboratory stressors. Br J Health Psychol 2010 Sep;15(Pt 3):511-528. [CrossRef] [Medline]
    14. Rozanski A, Blumenthal JA, Davidson KW, Saab PG, Kubzansky L. The epidemiology, pathophysiology, and management of psychosocial risk factors in cardiac practice: the emerging field of behavioral cardiology. J Am Coll Cardiol 2005 Mar 1;45(5):637-651 [FREE Full text] [CrossRef] [Medline]
    15. Jaremka LM, Lindgren ME, Kiecolt-Glaser JK. Synergistic relationships among stress, depression, and troubled relationships: insights from psychoneuroimmunology. Depress Anxiety 2013 Apr;30(4):288-296 [FREE Full text] [CrossRef] [Medline]
    16. Roohafza H, Sadeghi M, Naghnaeian M, Shokouh P, Ahmadi A, Sarrafzadegan N. Relationship between Metabolic Syndrome and Its Components with Psychological Distress. Int J Endocrinol 2014;2014:203463 [FREE Full text] [CrossRef] [Medline]
    17. Bergmann N, Gyntelberg F, Faber J. The appraisal of chronic stress and the development of the metabolic syndrome: a systematic review of prospective cohort studies. Endocr Connect 2014 Jun;3(2):R55-R80 [FREE Full text] [CrossRef] [Medline]
    18. Kramer MK, Kriska AM, Venditti EM, Miller RG, Brooks MM, Burke LE, et al. Translating the Diabetes Prevention Program: a comprehensive model for prevention training and program delivery. Am J Prev Med 2009 Dec;37(6):505-511. [CrossRef] [Medline]
    19. Diabetes Prevention Support Center. Group Lifestyle Balance Manual of Operations. Pittsburgh, PA: University of Pittsburgh; 2011.   URL: http:/​/www.​diabetesprevention.pitt.edu/​wps/​wp-content/​uploads/​2015/​01/​GroupLifestyleBalanceManualOperations.​pdf [accessed 2016-01-19] [WebCite Cache]
    20. Wilcox S, Dowda M, Griffin SF, Rheaume C, Ory MG, Leviton L, et al. Results of the first year of active for life: translation of 2 evidence-based physical activity programs for older adults into community settings. Am J Public Health 2006 Jul;96(7):1201-1209. [CrossRef] [Medline]
    21. Human Kinetics. 2015. Active Living Partners   URL: http://www.activeliving.info/ [accessed 2016-01-19] [WebCite Cache]
    22. Ware J, Kosinski M, Dewey J, Gandek B. How to Score and Interpret Single-Item Health Status Measures: A Manual for Users of the SF-8 Health Survey. Lincoln, RI: QualityMetric Incorporated; 2001.
    23. PhenX (consensus measures of Phenotypes and eXposures). 2014 Feb 04. Measures from the PhenX Toolkit version February 4 2011 Ver 4.2   URL: https://www.phenxtoolkit.org/ [accessed 2016-04-28] [WebCite Cache]
    24. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Hypertension 2005 Jan;45(1):142-161 [FREE Full text] [CrossRef] [Medline]
    25. Conway JM, Ingwersen LA, Vinyard BT, Moshfegh AJ. Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and nonobese women. Am J Clin Nutr 2003 May;77(5):1171-1178 [FREE Full text] [Medline]
    26. Mellen PB, Gao SK, Vitolins MZ, Goff DC. Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004. Arch Intern Med 2008 Feb 11;168(3):308-314. [CrossRef] [Medline]
    27. Blair SN, Haskell WL, Ho P, Paffenbarger RS, Vranizan KM, Farquhar JW, et al. Assessment of habitual physical activity by a seven-day recall in a community survey and controlled experiments. Am J Epidemiol 1985 Nov;122(5):794-804. [Medline]
    28. Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary Care Evaluation of Mental Disorders. Patient Health Questionnaire. JAMA 1999 Nov 10;282(18):1737-1744. [Medline]
    29. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [FREE Full text] [Medline]
    30. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med 2006 May 22;166(10):1092-1097. [CrossRef] [Medline]
    31. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983 Dec;24(4):385-396. [Medline]
    32. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, National Heart‚ Lung‚ and Blood Institute. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005 Oct 25;112(17):2735-2752 [FREE Full text] [CrossRef] [Medline]
    33. Centers for Disease Control and Prevention. Diabetes Prevention Recognition Program, Standards and Operating Procedures. Atlanta, GA: CDC; 2015.   URL: http://www.cdc.gov/diabetes/prevention/pdf/dprp-standards.pdf [accessed 2016-04-28] [WebCite Cache]
    34. Centers for Disease Control and Prevention (CDC). 2015. National Diabetes Prevention Program   URL: http://www.cdc.gov/diabetes/prevention/lifestyle-program/index.html [accessed 2016-01-19] [WebCite Cache]
    35. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van HL, American Heart Association Strategic Planning Task ForceStatistics Committee. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation 2010 Feb 2;121(4):586-613 [FREE Full text] [CrossRef] [Medline]
    36. Ebrahim S, Taylor F, Ward K, Beswick A, Burke M, Davey SG. Multiple risk factor interventions for primary prevention of coronary heart disease. Cochrane Database Syst Rev 2011(1):CD001561. [CrossRef] [Medline]
    37. American Telemedicine Association. Telemedicine in the Patient Protection and Affordable Care Act 2010. 2016.   URL: http:/​/www.​americantelemed.org/​docs/​default-source/​policy/​telehealth-provisions-within-the-patient-protection-and-affordable-care-act.​pdf?sfvrsn=14 [accessed 2016-01-19] [WebCite Cache]
    38. Ostroff C, Kinicki A, Tamkins M. Organizational culture and climate. In: Borman WC, Ilgen DR, Kilmoski RJ, editors. Handbook of Psychology. New York: Wiley; 2003.
    39. Mohr DC, Siddique J, Ho J, Duffecy J, Jin L, Fokuo JK. Interest in behavioral and psychological treatments delivered face-to-face, by telephone, and by internet. Ann Behav Med 2010 Aug;40(1):89-98 [FREE Full text] [CrossRef] [Medline]
    40. Ma J, Yank V, Xiao L, Lavori PW, Wilson SR, Rosas LG, et al. Translating the Diabetes Prevention Program lifestyle intervention for weight loss into primary care: a randomized trial. JAMA Intern Med 2013 Jan 28;173(2):113-121 [FREE Full text] [CrossRef] [Medline]
    41. Sevick MA, Zickmund S, Korytkowski M, Piraino B, Sereika S, Mihalko S, et al. Design, feasibility, and acceptability of an intervention using personal digital assistant-based self-monitoring in managing type 2 diabetes. Contemp Clin Trials 2008 May;29(3):396-409 [FREE Full text] [CrossRef] [Medline]
    42. Burke LE, Conroy MB, Sereika SM, Elci OU, Styn MA, Acharya SD, et al. The effect of electronic self-monitoring on weight loss and dietary intake: a randomized behavioral weight loss trial. Obesity (Silver Spring) 2011 Feb;19(2):338-344 [FREE Full text] [CrossRef] [Medline]
    43. Neuhausera L, Kreps G. eHealth communication and behavior change: promise and performance. Soc Semiot 2010;20(1):9-27.
    44. Krukowski RA, West DS, Harvey-Berino J. Recent advances in internet-delivered, evidence-based weight control programs for adults. J Diabetes Sci Technol 2009 Jan;3(1):184-189 [FREE Full text] [Medline]
    45. Krebs P, Prochaska JO, Rossi JS. A meta-analysis of computer-tailored interventions for health behavior change. Prev Med 2010;51(3-4):214-221 [FREE Full text] [CrossRef] [Medline]
    46. Neve M, Morgan PJ, Jones PR, Collins CE. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev 2010 Apr;11(4):306-321. [CrossRef] [Medline]
    47. Arem H, Irwin M. A review of web-based weight loss interventions in adults. Obes Rev 2011;12(20804523):236-243.
    48. Manzoni GM, Pagnini F, Corti S, Molinari E, Castelnuovo G. Internet-based behavioral interventions for obesity: an updated systematic review. Clin Pract Epidemiol Ment Health 2011 Mar;7:19-28 [FREE Full text] [CrossRef] [Medline]
    49. Spring B, Duncan JM, Janke EA, Kozak AT, McFadden HG, DeMott A, et al. Integrating technology into standard weight loss treatment: a randomized controlled trial. JAMA Intern Med 2013 Jan 28;173(2):105-111 [FREE Full text] [CrossRef] [Medline]
    50. Allen JK, Stephens J, Patel A. Technology-assisted weight management interventions: systematic review of clinical trials. Telemed J E Health 2014 Dec;20(12):1103-1120 [FREE Full text] [CrossRef] [Medline]
    51. Khaylis A, Yiaslas T, Bergstrom J, Gore-Felton C. A review of efficacious technology-based weight-loss interventions: five key components. Telemed J E Health 2010 Nov;16(9):931-938 [FREE Full text] [CrossRef] [Medline]
    52. Appel LJ, Clark JM, Yeh H, Wang N, Coughlin JW, Daumit G, et al. Comparative effectiveness of weight-loss interventions in clinical practice. N Engl J Med 2011 Nov 24;365(21):1959-1968 [FREE Full text] [CrossRef] [Medline]
    53. Azar KMJ, Aurora M, Wang EJ, Muzaffar A, Pressman A, Palaniappan LP. Virtual small groups for weight management: an innovative delivery mechanism for evidence-based lifestyle interventions among obese men. Transl Behav Med 2015 Mar;5(1):37-44 [FREE Full text] [CrossRef] [Medline]
    54. Dunn C, Whetstone LM, Kolasa KM, Jayaratne KSU, Thomas C, Aggarwal S, et al. Using synchronous distance-education technology to deliver a weight management intervention. J Nutr Educ Behav 2014;46(6):602-609. [CrossRef] [Medline]
    55. Brown MJ, Sinclair M, Liddle D, Hill AJ, Madden E, Stockdale J. A systematic review investigating healthy lifestyle interventions incorporating goal setting strategies for preventing excess gestational weight gain. PLoS One 2012;7(7):e39503 [FREE Full text] [CrossRef] [Medline]
    56. Cabassa LJ, Ezell JM, Lewis-Fernández R. Lifestyle interventions for adults with serious mental illness: a systematic literature review. Psychiatr Serv 2010 Aug;61(8):774-782 [FREE Full text] [CrossRef] [Medline]
    57. Fleming P, Godwin M. Lifestyle interventions in primary care: systematic review of randomized controlled trials. Can Fam Physician 2008 Dec;54(12):1706-1713 [FREE Full text] [Medline]
    58. Lombard CB, Deeks AA, Teede HJ. A systematic review of interventions aimed at the prevention of weight gain in adults. Public Health Nutr 2009 Nov;12(11):2236-2246. [CrossRef] [Medline]
    59. Goodpaster BH, Delany JP, Otto AD, Kuller L, Vockley J, South-Paul JE, et al. Effects of diet and physical activity interventions on weight loss and cardiometabolic risk factors in severely obese adults: a randomized trial. JAMA 2010 Oct 27;304(16):1795-1802 [FREE Full text] [CrossRef] [Medline]
    60. Vetter ML, Wadden TA, Chittams J, Diewald LK, Panigrahi E, Volger S, et al. Effect of lifestyle intervention on cardiometabolic risk factors: results of the POWER-UP trial. Int J Obes (Lond) 2013 Aug;37 Suppl 1:S19-S24 [FREE Full text] [CrossRef] [Medline]

    ‎
    DASH: Dietary Approaches to Stop Hypertension
    eCMP: Electronic CardioMetabolic Program
    GAD-7: Generalized Anxiety Disorder Scale
    NDSR: Nutrition Data System for Research
    PHQ-9: Patient Health Questionnaire

    Edited by G Eysenbach; submitted 22.09.15; peer-reviewed by I Pires, S Mummah, S Capra, N Kaufman; comments to author 22.11.15; revised version received 22.01.16; accepted 21.02.16; published 27.05.16

    Copyright

    ©Kristen MJ Azar, Suneil Koliwad, Tak Poon, Lan Xiao, Nan Lv, Robert Griggs, Jun Ma. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.05.2016.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.