Development and Pilot Testing of a Telehealth Weight Loss Program : Translational Journal of the American College of Sports Medicine

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Feasibility/Pilot Study Report

Development and Pilot Testing of a Telehealth Weight Loss Program

Hales, Sarah B.1; Smith, Caitlin E.1; Turner, Tonya F.1; Sword, David O.2; DuBose-Morris, Ragan3; Blackburn, David4; Malcolm, Robert1; O’Neil, Patrick M.1

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Translational Journal of the ACSM 8(2):e000226, Spring 2023. | DOI: 10.1249/TJX.0000000000000226
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Improvements in diet and physical activity (PA) behaviors are effective means of helping individuals with overweight and obesity lose weight and decrease risk of chronic disease (1). There has been much work in traditional face-to-face delivery of evidence-based behavioral weight loss interventions with successful outcomes for patients (2). Clinicians continue to leverage technology to maximize public accessibility to these types of interventions as technology use becomes more pervasive among adults in the United States (3–7). Internet usage and smartphone ownership among US adults continue to rise, with 90% of American adults reporting Internet use in 2019 (8). Recently, the need to use telehealth as a predominant mode of clinical care delivery has become paramount given the coronavirus pandemic (9–11).

There are many online and app-based weight loss programs currently available; however, researchers have identified several limitations with these programs. In particular, some do not appear to be based on input from health experts and do not use many of the evidence-based techniques necessary for weight loss, like the inclusion of personalized goal setting and individualized feedback from qualified health professionals (12,13). In an effort to address these gaps, researchers have developed their own weight loss programs using digital components (e.g., mobile apps, wearable activity monitors, wireless body weight scales). Although there are behavioral weight loss interventions that use some digital components (specifically TeleMOVE and SmartLoss (14–16)), there are few programs that use digital tools exclusively to remotely provide weight loss treatment with both individualized feedback on behavior change and lifestyle instruction. One such digital weight loss program is the Track Intervention, which targets adults with obesity, hypertension, diabetes, and hyperlipidemia in a community health care setting (17). In a randomized controlled trial, the Track Intervention resulted in significantly greater weight loss outcomes among subjects at 12 months as compared with subjects in the usual care control group (17). Another recent program found enhanced self-monitoring, intervention adherence, and greater weight loss among participants that received weekly video coaching from clinicians versus those in a self-guided group (18,19).

The overarching goal of this pilot project was to transform an existing maintenance-focused weight monitoring program into a remotely delivered, standalone, comprehensive, behavioral weight loss intervention. The aims of this study were as follows: 1) develop a completely virtual comprehensive behavioral weight loss program called Home Weight Loss (HWL) by a) creating instructional lifestyle change modules to be delivered remotely for participant weight loss and b) adapting components from an existing maintenance monitoring program to make them appropriate for patients beginning weight loss; and 2) pilot test the HWL program among a sample of adults (n = 30) over the course of 12 wk. Participant use of the digital components, program satisfaction, and weight change were primary outcomes. Secondary outcomes such as anthropometric measurements, PA measures, and eating behaviors are reported as well.



In an effort to increase the virtual program offerings for patients at an academic medical weight management center, the authors adapted an existing completely remotely delivered maintenance-oriented monitoring program into an asynchronous virtual weight loss intervention spanning 12 wk. The resulting HWL program included several aspects, including weekly instructional written-lesson modules, self-monitoring of daily behaviors, and feedback to patients from clinicians (a lifestyle change behavioral specialist, a registered dietitian nutritionist, and an exercise specialist) regarding monitored behaviors. Patients were instructed to track their behaviors (diet, PA, and weight) daily using either the commercially available HealthTrac platform and app (SetPointHealth, Newton Upper Falls, MA) or the Fitbit app (Fitbit, San Francisco, CA) based solely on their preference after trying both platforms. Clinicians were able to monitor patient data entered/synced in either app using the HealthTrac platform.

Instructional modules developed by the clinical team covered diet, PA, and behavioral topics for weight loss. Participants received one lesson per week (12 total), each pertaining to a specific weight management topic. The lessons included the following: 1) behavioral topics such as expectations of weight loss, goal setting, slip and relapse prevention, and social support; 2) dietary topics such as dietary prescription, energy balance, meal planning, label reading, and portion control; and 3) exercise topics such as the benefits and types of exercise (including frequency, intensity, and time), and exercising for weight loss and weight loss maintenance. Individual diet and exercise recommendations were tailored for participants to achieve a calorie deficit sufficient to produce modest weight loss (1–2 lb·wk−1), based on recommendations from the American College of Cardiology/American Heart Association Task Force on Practice Guidelines/The Obesity Society Guidelines (20). The delivery schedule for the lesson modules was implemented via the HealthTrac platform. Enrollment in the HealthTrac platform took place at the in-person baseline and consent session. Lessons were set to be delivered to patients (via HealthTrac using in-app messages and e-mails) on the Monday following their enrollment in the study and for 11 additional consecutive Mondays.

Participants were instructed to self-monitor their diet (daily, using the HealthTrac or Fitbit mobile app), PA (daily, using Fitbit zip, provided), and weight (daily, using the BodyTrace scale, provided; BodyTrace, Inc., Palo Alto, CA). Numbers of steps per day and minutes of PA were automatically synced from study-provided Fitbit devices to HealthTrac. Participants were instructed to track calories from all food and beverages daily using either the Fitbit or HealthTrac app (dietary data entered into Fitbit automatically synced to HealthTrac).

Clinicians could view the self-monitored and uploaded data on the HealthTrac clinician dashboard to make personalized recommendations on these specific behaviors to aid weight loss. The clinicians produced one brief (10 min or less) video-recorded feedback file for each participant each week. Clinician feedback focused on participants’ self-monitored data for diet, PA, frequency of weighing, and weight trend each week. Feedback also included comments and suggestions on goals set by the participants, including water consumption, minutes of sleep, and overall adherence to the weight loss plan if these data were self-monitored. Participant feedback was recorded by clinicians using WebEx (Cisco, San Jose, CA) software, and links to the video files were sent via e-mail and messages in HealthTrac for participants to view at their convenience. For this study, patients could only view their recorded feedback from a laptop or desktop computer with Internet access.


Participants were adults age 18–70 yr, with body mass index (BMI) of 25.0–40.0 kg·m−2, who had Internet access, a valid e-mail address, and a smartphone. Exclusion criteria were as follows: currently dieting (having lost more than 10 lb in the past 3 months), inability to engage in PA, diagnosis of diabetes (other than treated by diet alone), unmanaged thyroid condition, use of prescription or over-the-counter weight loss medications, current/planned pregnancy, and implanted electronic devices (due to the smart scale used to measure weight in this study).

Recruitment occurred on a rolling basis beginning in February 2018 and ending in April 2018. Participants were recruited from the Charleston, South Carolina, area using several methods, including e-mail, worksite newsletter, social media (Facebook and Yammer), research recruitment sites such as, and word of mouth. Recruitment advertisements directed interested participants to contact the principal investigator (PI) or study coordinator via e-mail or phone to request additional information and to enroll in the study. Interested participants were contacted via e-mail and phone and were provided with a letter, including a description of the study, procedures to be completed as part of the study, and the major inclusion and exclusion criteria. Respondents who were interested and believed they fit the criteria contacted the PI or study coordinator to complete a phone screen. Patients who successfully completed the phone screen were invited to attend an in-person orientation followed by providing written consent and attending a baseline visit if they wished to participate.


The following data were collected in person at baseline: weight, height, waist circumference, hip circumference, and body composition (percent body fat). Weight was measured to the nearest 0.1 kg using a calibrated digital scale (Panther scale, professionally calibrated quarterly; Mettler Toledo, Greifensee, Switzerland) with participants in light clothing and no shoes. Height was collected for all participants only at baseline. Participant standing height was measured without shoes using a wall-mounted stadiometer (Novel Products, Inc., Rockton, IL). Body fat composition was measured using a handheld bioelectrical impedance device (HBF-306C body fat analyzer; Omron, Kyoto, Japan). Participants also completed the Eating Behavior Inventory, a 26-item instrument that measures the extent to which an individual engages in behaviors conducive to weight management, and the Power of Food Scale, a 15-item measure that assesses an individual’s responsiveness to and preoccupation with food in the absence of physical hunger (21,22). At posttest only, participants completed an assessment developed by the researchers to measure satisfaction with the intervention and usability of program components using a 5- to 6-point Likert scale measuring agreement with response options. Specifically, participants rated their overall experience with the program, satisfaction with the amount of weight lost, understanding of program parameters (i.e., food and exercise plans), ease of incorporating weekly feedback from clinicians into their routines, reactions to the smartphone-based apps (HealthTrac and Fitbit), and perceptions of the program’s strengths and areas for improvement (using free text response options for program strengths and weaknesses). Use of the tracking apps was assessed by exporting the number of entries by participant for diet, steps, weight, and exercise minutes using the HealthTrac clinician monitoring platform.

Baseline measurements were collected for all participants after obtaining their consent. Participants returned to the academic weight management center for posttest measures after the 12-wk intervention period and received a monetary stipend for time and travel. This study was approved by the Medical University of South Carolina Institutional Review Board for research with human subjects (Pro00070941) and was registered on (NCT03439579).

Statistical Analysis

Demographics and satisfaction scores were computed using frequency distributions, and data regarding perceptions of the program’s strengths and areas for improvement were aggregated from two open-response questions on the final survey. For satisfaction scores, response options indicating agreement with the item were combined (e.g., strongly agree and agree were combined) to compute frequencies and report percentages. Demographics were also evaluated to determine whether there were significant differences at baseline between the final sample (n = 27) and the intention-to-treat sample (n = 30). The primary outcome was percent weight change pre- to postintervention (calculated as a percentage of baseline weight). Pre- to postintervention changes in weight and related measures (i.e., changes in anthropometric measures and eating behaviors) were assessed using paired samples t-tests. Because this was a pilot study, power calculations were not performed. Correlational analyses were used to assess associations between weight loss and measures of engagement, such as use of the tracking apps (i.e., self-monitoring). Statistical Package for the Social Sciences (version 25; IBM, Chicago, IL) was used to perform all statistical analyses.


Thirty participants (70% female, 80% White, mean age = 44 yr, mean BMI = 32.4 kg·m−2) were enrolled and 27 provided posttreatment data. There were no significant differences between the final sample and the intention-to-treat sample on any demographic variables. See Table 1 for participant characteristics for the total sample.

TABLE 1 - Baseline Characteristics.
Participant Characteristics Total Sample (n = 30)
 Male 9 (30%)
 Female 21 (70%)
 African American 4 (13.3%)
 White 24 (80.0%)
 Hispanic 1 (3.3%)
 Other 1 (3.3%)
Age 44 ± 13.7
Baseline body weight (kg) 92.9 ± 16.6
Baseline BMI (kg·m−2) 32.4 ± 4.3
Educational attainment
 High school or equivalent 1 (3.3%)
 Some college 11 (36.7%)
 Bachelor’s degree 10 (33.3%)
 Master’s degree 6 (16.7%)
 Professional degree 2 (6.7%)
One participant did not provide information on educational attainment. Data are presented as mean ± SD or n (%).

Mean percent weight change at 12 wk was −4.12% ± 4.22%, and median percent weight change was −3.61%. Significant pre- to postintervention changes were observed for kilograms lost (−4.0 ± 4.5 kg), BMI change (−1.5 ± 2.0 kg·m−2), waist (−4.0 ± 1.4 cm) and hip (−3.6 ± 0.4 cm) circumference change, and percent body fat loss (−1.5% ± 2.0%). Scores on the Eating Behavior Inventory and Power of Food Scale also showed significant improvement from pre- to posttest (15.8 ± 4.6 and −0.5 ± 0.2, respectively; Table 2).

TABLE 2 - Mean Pre- to Postintervention Changes in Secondary Outcomes (n = 27).
Variable Mean ± SD Change ± SD P
Baseline weight (kg) 93.4 ± 17.1 −4.0 ± 4.5 <0.001
Final weight (kg) 89.4 ± 16.3
Baseline BMI 32.7 ± 4.4 −1.5 ± 2.0 <0.001
Final BMI 31.3 ± 4.5
Baseline percent body fat 36.6 ± 5.9 −1.5 ± 2.0 0.001
Final percent body fat 35.1 ± 6.6
Baseline waist circumference (cm) 103.4 ± 13.3 −4.0 ± 1.4 0.003
Final waist circumference (cm) 99.4 ± 11.9
Baseline hip circumference (cm) 115.1 ± 8.2 −3.6 ± 0.4 <0.001
Final hip circumference (cm) 111.5 ± 7.8
Baseline Eating Behavior Inventory total score 70.4 ± 10.1 15.8 ± 4.6 <0.001
Final Eating Behavior Inventory total score 86.2 ± 14.7
Baseline Power of Food Scale total score 2.9 ± 0.9 −0.5 ± 0.2 <0.001
Final Power of Food Scale total score 2.4 ± 0.7
All 27 participants provided the baseline and posttreatment weight and eating behavior data needed to analyze posttreatment differences.

The mean total number of app entries (i.e., count of app entries in all domains, including weight, steps, exercise minutes, and calories) over the course of the 12-wk intervention was 483.5 ± 332 and was significantly correlated with percent weight change, such that the more a participant self-monitored their weight loss–related behaviors, the more weight they lost (r = −0.502, P = 0.008). In examining each of the app entry domains further, the mean number of entries for weight (56.7 ± 24.2, P = 0.02), steps (63.0 ± 27.5, P = 0.02), and exercise minutes (47.3 ± 26.1, P = 0.03) were significantly correlated with percent weight change. Only app entries for calories (397.5 ± 255.39, P = 0.15) were not significantly correlated (see Table 3). Baseline characteristics were examined for differences among participants falling above or below the median percent weight loss, with no significant differences emerging between the two groups.

TABLE 3 - Mean ± SD and Correlations between Number of App Entries and Percent Weight Loss (n = 27).
Number of App Entries Mean ± SD r P
Total number of app entries 483.50 ± 332.00 −0.50 0.008
Weight entries 56.67 ± 24.18 −0.45 0.02
Exercise minutes entries 47.33 ± 26.14 −0.45 0.03
Step entries 63.00 ± 27.54 −0.44 0.02
Calorie entries 397.45 ± 255.39 −0.32 0.15

Most participants reported being satisfied with their overall experience in the HWL program (93%). Despite most participants achieving significant weight loss (Fig. 1), only 56% reported being satisfied with the amount of weight they lost in the program.

Figure 1:
Percent weight change by participant (n = 27).

The majority of participants reported understanding the food (63%) and exercise (82%) plans and ease of incorporating weekly feedback from clinicians into their routines regarding diet (93%), PA (96%), and behaviors (96%). Fifty-nine percent of participants also cited individualized feedback as a benefit of the program. Although participants expressed some dissatisfaction with the HealthTrac app, 63% found the HealthTrac app easy to use. Participants mentioned receiving tailored feedback on behaviors, use of tracking devices, and increased accountability during the intervention as the best aspects of the program. Areas for improvement included usability of tracking apps (e.g., difficulty entering data into the apps, including experiencing glitches/errors), cumbersome nature of self-monitoring calories consumed, and desire to receive feedback more frequently.


The goal of the present single-arm pilot study was to develop and pilot test a virtual comprehensive behavioral weight loss program distinguished by individualized feedback among a sample of adults (n = 30) over the course of a 12-wk intervention period. Results showed a statistically and clinically significant percent weight loss with participants losing an average of 4.12% ± 4.22% of their body weight at 12-wk follow-up. Secondary measures, including kilograms lost, BMI change, waist and hip circumference changes, percent body fat loss, Eating Behavior Inventory scores, and Power of Food Scale scores also showed significant improvements from baseline to follow-up. In an effort to characterize which aspects of the program were most effective, we examined correlations between the amount of app entries recorded in all domains, including weight, steps, exercise minutes, and calories, with percent weight loss. The more that participants recorded their weight, steps, and exercise minutes, the more weight they lost, showing the importance of tracking these parameters. Results from the pilot test of the HWL program also demonstrated feasibility and acceptability among the majority of enrolled participants.

Results from the pilot HWL program produced similar weight loss as two other well-established programs: TeleMOVE and the Track Intervention (14,17). Participants in TeleMOVE, a telehealth behavioral weight loss program for veterans, lost an average of 3.9 kg at 90-d follow-up (14). Both TeleMOVE and our HWL program included the use of digital feedback, which appears to be influential in helping participants achieve clinically significant weight loss in telehealth-based interventions. Similarly, participants in the Track Intervention, a digital weight loss program for adults with obesity, hypertension, diabetes and hyperlipidemia, lost an average of 4.1 kg of their body weight at 6-month follow-up (17). Both the Track Intervention and our HWL program found completion of self-monitoring to be indicative of greater weight loss. Another recent program found that weekly telehealth video coaching led to enhanced self-monitoring, intervention adherence, and greater weight loss at 12-wk follow-up (18,19). That program paired digital feedback with individualized sessions delivered weekly via videoconferencing with a dietitian (18,19). Our pilot HWL program used many of the fundamental elements (e.g., digital feedback, self-monitoring, individualized feedback) that have been shown to produce weight loss in telehealth settings.

It is important to acknowledge that the weight loss achieved in this pilot study is somewhat less than similar 12-wk interventions. Our pilot HWL program was most similar to the telehealth-based weight loss program that incorporated both digital and individualized feedback (18,19), yet ours achieved lower percent weight loss. This discrepancy may be explained by the delivery and duration of the individualized video feedback sessions. The telehealth-based weight loss program health coaching sessions were performed live and weekly for 30 min (18,19), whereas our HWL program individualized sessions were delivered remotely via prerecorded videos for 10 min. Live sessions conducted weekly for longer intervals may directly contribute to greater weight loss success. However, depending on the target population, live sessions may not be feasible. Although participants in our study achieved lower weight loss compared with other similar 12-wk interventions, the amount of weight loss achieved was still clinically meaningful. Our goal was to design a program that could reach a broad audience, and our findings provide preliminary support that this HWL program may be a scalable and effective weight loss intervention for diverse and/or underserved groups with obesity.

At the same time, the HWL program was associated with improvements in eating- and weight-related behavioral factors. Improvements on the Eating Behavior Inventory and the Power of Food Scale indicated increased the use of effective weight control behaviors and a reduction in hedonic hunger or responsiveness to food cues. It is noteworthy that the magnitude of improvements on these measures was comparable with that seen with more intensive, in-person weight loss interventions (21,23,24).

There are several limitations to this pilot study worth noting. First, our sample was comprised mostly of White participants and our study did not have a control group, which limits our ability to generalize across groups of participants and establish overall intervention effectiveness (25). Future studies examining the long-term effectiveness of this HWL program should employ the use of a control group to address these limitations. Second, the duration of our intervention was short (12 wk) and therefore only captured short-term weight loss. Third, our individualized feedback sessions were prerecorded and delivered asynchronously, which may have contributed to lower percent weight loss compared with other similar weight loss interventions that incorporated live sessions. Finally, there were some technological issues with certain aspects of our HWL program (e.g., our prerecorded sessions were delivered via WebEx, and at the time of the study it was not possible to watch the sessions on cellular devices or track whether participants watched the sessions). There have been a number of technological advancements that have improved the quality of telehealth interventions broadly since the time of our pilot HWL study. Further, recent shifts in healthcare delivery due to the coronavirus pandemic have led to both providers and patients becoming more comfortable with telehealth-based clinical care. As we continue to evolve our HWL program and test its effectiveness, we plan to use technological advances as they become available.

In conclusion, the HWL program produced modest but clinically meaningful weight loss and demonstrated feasibility, acceptability, and the ability to be deployed on a completely remote basis. The novel aspects of this pilot study were the inclusion of not only self-monitoring and educational components but also individualized feedback from a multidisciplinary team on behaviors that are central to weight loss, including diet, exercise, and behavioral modification. Although this pilot study has several noted limitations, the results will allow future studies to expand and build upon our findings and provide a foundation for future scalability (e.g., extending the reach to rural communities via primary care settings to increase access to care). Overall, our findings provide important preliminary data on the effectiveness of this HWL intervention using digital and individualized feedback administered by a multidisciplinary team on achieving clinically relevant weight loss in a telehealth setting.

D.B. is the cofounder and CEO of SetPoint Health. P.M.O. receives research support from NovoNordisk, Eli Lilly, Epitomee Medical, and WW International, Inc. P.M.O. also receives honoraria from NovoNordisk and has been a consultant for Pfizer and Gedeon Richter. This pilot study was funded by a grant from the South Carolina Telehealth Alliance.

The results of this study do not constitute endorsement by the American College of Sports Medicine.


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