JMIR FORMATIVE RESEARCH

Horn et al

Original Paper

Characteristics and Weight Loss Practices From a Cohort of
20,000 Patients Using Direct-to-Consumer Telehealth:
Observational Cross-sectional Study
Deborah Bade Horn1, MPH, DO; Elizabeth Pash2, RD, LDN, MS, PhD; Megan S Zhou3, MS; Lauren Broffman3,
PhD; Damian Bialonczyk2, PharmD; Tzvi Doron3, DO; Elaine Chiquette2, PharmD
1

Center for Obesity Medicine and Metabolic Performance, University of Texas, Bellaire, TX, United States

2

Gelesis, Boston, MA, United States

3

Ro, New York, NY, United States

Corresponding Author:
Lauren Broffman, PhD
Ro
4th Floor
116 West 23rd Street
New York, NY, 10011
United States
Phone: 1 716 713 4178
Email: lauren.broffman@ro.co

Abstract
Background: Despite the increasing prevalence of obesity, the use of pharmacotherapy treatment remains low. Telehealthplatforms have the potential to facilitate access to pharmacotherapy interventions, but little is known about telehealth patients.
Objective: This study describes a large patient population taking Plenity, an oral superabsorbent hydrogel (OSH) used in thetreatment of excess weight or obesity (BMI 25-40 kg/m2). The analysis compared differences in weight loss practices and in-person
access to obesity care among telehealth patients with preobesity and obesity.
Methods: This was a cross-sectional assessment of a random sample of 20,000 telehealth patients who completed a structured,
web-based visit and received at least one prescription of OSH. Patients were eligible to receive care via telehealth if they were
adults, were not pregnant, and had a BMI ≥25 kg/m2. During the visit, patients provided baseline health information including
comorbidities, diet, and exercise habits. Their zip code of residence was used to determine their proximity to an obesity medicine
provider. Descriptive statistical analysis and tests of differences (chi-square and 2-tailed t tests) were used to compare patientswith preobesity (BMI 25-29.9 kg/m2) and obesity (BMI 30-40 kg/m2).
Results: Most (15,576/20,000, 77.88%) of the cohort were female, with a mean age of 44 (SD 11) years and a mean BMI of
32.4 (SD 4.1) kg/m2. Among the cohort, 32.13% (6426/20,000) had preobesity, and 40.18% (8036/20,000) of all patients had ≥1weight-related comorbidity. Almost all (19,732/20,000, 98.66%) patients attempted 1 weight loss method before OSH and half
(10,067/20,000, 50.34%) tried ≥4 different methods. Exercise and low-calorie diets were the most attempted weight loss methods,
and 28.76% (5752/20,000) of patients reported a prior prescription of weight loss medication. Patients with obesity were more
likely than patients with preobesity to have previously tried commercial weight loss plans (7294/13,574, 53.74% vs 2791/6426,
43.43%; P<.001), specialized diets (8493/13,574, 62.57% vs 3799/6426, 59.12%; P<.001), over-the-counter supplements(6807/13,574, 50.15% vs 2876/6426, 44.76%; P<.001), and prescription weight loss medications (4407/13,574, 32.47% vs1345/6426, 20.93%; P<.001). Females were more likely to seek treatment for preobesity (5332/15,576, 34.23% vs 1094/4424,
24.73% male; P<.001) and reported fewer comorbidities (5992/15,576, 38.47% vs 2044/4424, 46.2% male; P<.001), despite>90% of both sexes reporting the belief that excess weight negatively affected their health (14,247/15,576, 91.47% female
participants, 4116/4424, 93.04% male participants). Moreover, 29.25% (5850/20,000) of patients lived in the same zip code and85.15% (17,030/20,000) lived in the same county as an obesity medicine provider.
Conclusions: Data from this large patient cohort supports the potential for telehealth to provide prescriptive weight management
treatment to a population seeking care. Patients with preobesity are an undertreated population who actively seek new weighthttps://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 1
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

management options. Female participants sought weight management treatment earlier in the disease continuum than males,
despite reporting fewer comorbidities.
(JMIR Form Res 2023;7:e40062) doi: 10.2196/40062
KEYWORDS
obesity; preobesity; overweight; weight management; direct-to-consumer telehealth; oral superabsorbent hydrogel

Introduction
Epidemiology of Obesity
Excess weight, defined as a BMI >25 kg/m2, is associated withincreased morbidity and mortality and contributes to nearly 4
million deaths globally per year [1,2]. Obesity-related diseases
and comorbidities are associated with a loss of between 0.2 and11.7 years of life, depending on an individual’s sex, race, BMI
category, and age [3]. In the United States, approximately 70%
of adults have preobesity or obesity, and this number is expected
to rise to 80% by 2030 [4,5]. Despite the increasing prevalenceof obesity and difficulty in losing and maintaining lost weightin many patients, the use of guideline-supported treatments,
including pharmacotherapy, intensive behavioral counseling,
and bariatric surgery, remains low [6]. Only approximately 1%
to 2% of eligible patients with obesity are managed withpharmacotherapy [7-9]. Comparatively, 86% of eligible patientswith type 2 diabetes are prescribed pharmacotherapy [9].

Barriers to Effective Treatment
Several barriers exist for effective weight management inpatients with preobesity and patients with obesity [10].
Weight-related health stigma is one such barrier that
disproportionately affects people with obesity [11,12]. People
with obesity who experience weight stigma report more healthcare avoidance, increased perceived judgment from physicians,
lower frequency of routine checkups, less frequent listening andrespect from providers, and lower quality of health care [13].
Limited access to health care providers with training in obesity
medicine and interdisciplinary treatment teams (eg, behavioral
therapists, dietitians, and health coaches) poses yet anothersignificant barrier to effective care [14,15]. Among nonspecialist
providers, discussions about obesity are often not initiated withpatients primarily because of lack of time and higher priority
issues, despite more than half of health care providers
considering obesity at least as serious as most other healthconditions [10]. Geographic barriers, particularly in rural areas,
further reduce access to effective obesity care [14]. Close to
40% of excess weight related deaths and 36% ofdisability-adjusted life years can be attributed to preobesity(BMI 25-30 kg/m2), also known as overweight [1]. Despite this,weight management options for patients with preobesity are
generally limited to lifestyle modifications, as prescriptive
pharmacotherapy is only available for patients with obesity or
those with a BMI ≥27 kg/m2 with at least one obesity-relatedcomorbidity.

Pharmacological and Telehealth Solutions
Telemedicine, as a means of delivering weight loss treatment,
has been proposed to help address some of the existing barriers
https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

to care in people with preobesity and obesity [6,16,17]. Today,
multiple telehealth platforms provide patients seeking weight
management with increased access to physicians, advanced
practitioners,
andhealthcoaches.
Specifically,
direct-to-consumer (DTC) telehealth is an increasingly popularsource of health care uniquely suited to facilitate access totreatment for stigmatized conditions such as obesity [6].
Plenity, a nonsystemic, oral superabsorbent hydrogel (OSH),
is Food and Drug Administration cleared as a prescription
treatment indicated to aid weight management in adults withexcess weight or obesity, with a BMI of 25 to 40 kg/m², whenused in conjunction with diet and exercise [18]. OSH is available
by prescription from a nationwide DTC telehealth platform, Ro.
Using the telehealth platform, patients interested in obtaining
a prescription for OSH can request a web-based consultation
with a provider and, if eligible, receive therapy by mail.
Uniquely, OSH can be prescribed for patients with preobesity
irrespective of comorbidity status, differing from mostprescription options for weight management, which are generally
reserved for use in people with a higher BMI (≥30 or ≥27 kg/m2)
and obesity-related comorbidities.

Objective of the Study
The purpose of this analysis was to describe a population ofpatients prescribed OSH via a telehealth platform and to explore
the differences in weight loss practices between patients withpreobesity and patients with obesity in a large real-world cohort.
We also aimed to evaluate the extent to which users of the DTC
telehealth platform had geographic access to an obesity medicine
specialist.

Methods
Study Design
This is a retrospective cross-sectional study that examined theelectronic health records of 20,000 patients who were prescribedat least one dose of OSH. These 20,000 patients were randomly
selected from a sampling frame of patients being treated forweight management on a single DTC platform who wereprescribed Plenity, had received at least one prescription, andwhose records had complete baseline health information.

Telehealth Platform Overview and Participant
Eligibility
To formally become a patient on the DTC platform, potential
self-referred patients initiate a structured, dynamic intake form
in which they report their height, weight, health history,
comorbidity status, demographics, and other relevant
information. The information provided in the intake form
comprised the baseline information recorded in the electronic
health records. BMI was calculated from the patients’
JMIR Form Res 2023 | vol. 7 | e40062 | p. 2
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
self-reported height and weight. As a quality measure, patientswere also required to upload recent photographs of themselves.
Patients were considered eligible to receive weight management
care via telehealth if they were aged ≥18 years, were notpregnant, did not have any allergies to OSH or its ingredients,
did not report a history of eating disorders, and had a minimum
BMI of 25 kg/m2. Patients who did not meet these requirements
were automatically exited from the platform. Health informationcollected from eligible patients was reviewed by a health careprovider (Doctors of Medicine, Doctors of Osteopathic
Medicine, Nurse Practitioners, and other Advanced Practice
Providers) licensed to practice medicine in the state where thepatient resided, who then determined whether treatment wasappropriate. Patients in the sample were located in almost every
state, as well as Washington DC. If a provider deemed treatment
appropriate, they connected with patients via video call, phone
call, or asynchronously using a secure store-and-forward
messaging system to discuss a care plan and answer any
questions. Providers could also use these means ofcommunication to clarify any baseline information provided bypatients deemed necessary to make clinical decisions. If adecision was made to prescribe, providers sent prescriptions toa mail-order pharmacy and provided patients with educational
materials encouraging healthy changes to diet and regularexercise. Patients were able to contact their providers using asecure messaging system throughout the course of theirtreatment to ask questions or schedule additional synchronous
visits. To renew their initial prescription, the patients wererequired to complete renewal visits during regularly scheduledintervals.
Only patients who were prescribed Plenity, had received at least
one prescription, and whose records had complete baseline
health information were included in the study. Patients weregrouped as patients with preobesity (6426/20,000, 32.13%, BMI
25-29.9 kg/m2,) or patients with obesity (13,574/20,000, 67.87%,
BMI 30-40 kg/m2,) based on their self-reported height andweight at baseline. Patients were grouped in this manner toaddress the novelty of prescription weight loss treatment forthe patients with preobesity group, which warrants further
investigation. Patients were not further subdivided into classesof obesity because the clinical indication for OSH was the samefor all patients, regardless of BMI. In addition, the upper BMI
limit of the clinical indication of OSH would restrict the analyses
to Classes I and II.

Access to Care Outcomes
To determine rurality, the patients were geolocated to the zip
code of residence provided for medication shipment. These zip
codes were cross-referenced with 2019 rural-urban commuting
area codes. Zip codes categorized as 4–9 within the rural-urban
commuting area system were defined as rural. To determine
whether patients lived in a healthcare provider shortage area
(HPSA), publicly available data on the 2021 HPSA classification
were retrieved from the Health Resources and Services
Administration website. Patient zip codes were geolocatedwithin counties using the zip code to county crosswalk fromthe Housing and Urban Development website; patient zip codes
were converted to zip code tabulation areas. Zip code tabulation
https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

Horn et al
areas that spanned multiple counties were assigned to the county
where most residences were located. A list of counties
categorized specifically as primary care HPSA wascross-referenced with patient counties to determine whether
patients resided in primary care HPSA. The American Board
of Obesity Medicine (ABOM) provider practice locations wereextracted by matching the list of provider names and cities
available on the ABOM website with the National Provider
Index Registry.

Statistical Analysis
Chi-square and 2-tailed t tests were used as tests of significancewhen comparing BMI groups and sex. Bonferroni corrections
were included where appropriate to account for the bias
introduced by multiple tests. All data cleaning and analyses
were performed using R (version 4.1.2, R Foundation for
Statistical Computing).

Ethics Approval
This study was approved by the Biomedical Research Allianceof New York Institutional Review Board (number
21-12-274-599).

Results
Demographics
Tables 1 and 2 present the demographic characteristics of thestudy population. Patients had a mean BMI of 32.4 kg/m2 anda median age of 43 (SD 11; range 19-86) years; approximately
one-fourth (5407/20,000, 27.04%) of the sample population
was >50 years, and 7.91% (1582/20,000) of the patients were
≥60 years old (Figure 1). The population was mostly female(15,576/20,000, 77.88%), and approximately one-third(13,574/20,000, 67.87%) of the cohort had preobesity. A
question asking patients to report race or ethnicity data wasadded after initial baseline data were collected; subsequently,
these data were only collected for 397 patients (of these patients,
6, 1.5% declined to answer and were excluded from the table).
Of the patients who self-reported their race, approximately
one-fourth (98/397, 24.7%) reported a race or ethnicity otherthan non-Hispanic White or White. When comparing geographic
differences across patients, the data showed that 12.04%(2407/20,000) of all patients lived in a rural county.
We broke down the demographic characteristics across BMI(Table 1) and biological sex (Table 2) categories. Compared topatients with obesity, patients with preobesity were more likely
to be female. No significant differences in race or ethnicity weredetected between patients with preobesity and patients withobesity (χ25=4.2; P=.47), but patients with obesity were slightly
more likely to live in a rural area compared with patients withpreobesity (χ21=23.8; P<.001). No significant differences in age
were observed between the 2 categories.
Significant differences were observed across biological sex,
racial, and ethnic categories. Compared with males, females
were more likely to self-report their race and ethnicity as
non-Hispanic White (156/15,576, 78.4% female vs 143/4424,
72.2% male) and non-Hispanic Black (16/199, 8% female vs
JMIR Form Res 2023 | vol. 7 | e40062 | p. 3
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

7/198, 3.5% male), and were less likely to self-report as Hispanic
or Latino (8/199, 4% female vs 26/198, 13.1% male). Females
were also significantly more likely to live in rural areas(2020/15,576, 12.97% female vs 387/4424, 8.75% male;χ21=57.6; P<.001).

Table 1. Sample demographics by BMI category.
Characteristic

Total patients(N=20,000)

Patients with obesity Patients with pre(n=13,574)
obesity (n=6426)

Wilcoxon rank sum

Chi-square (df)

P value

BMI, mean (SD)

32.4 (4.1)

34.6 (3.0)

27.8 (1.4)

87,226,524

N/Aa

<.001

Age, mean years (SD)44 (11)44 (11)44 (11)

43,845,972

N/A

.54

N/A

142.3 (1)

<.001

N/A

4.2 (5).52

N/A

23.8 (1)<.001

Biological sex, n (%)
Female15,576 (77.88) 10,244 (75.47)

5332 (82.98)

Male4424 (22.12)1094 (17.02)

3330 (24.53)

Race or ethnicity, n (%)
Asian or Pacific Islander

19 (4.79)

12 (5.02)

7 (4.43)

Hispanic

34 (8.56)

24 (10.04)

10 (6.33)

Native American or American
Indian2 (0.50)

1 (0.42)1 (0.63)

Non-Hispanic Black23 (5.79)

17 (7.11)

6 (3.80)

Non-Hispanic White

299 (75.31)

174 (72.80)

125 (79.11)

Other

20 (5.04)

11 (4.60)

9 (5.70)

Geography, n (%)
Rurala2407 (12.04)

1739 (12.81)

668 (10.40)

N/A: not applicable.

Table 2. Sample demographics by biological sex.
Characteristic

Total patients(N=20,000)

Female (n=15,576) Male (n=4424)

Wilcoxon rank sum

Chi-square (df)

P value

BMI, mean (SD)

32.4 (4.1)

32.3 (4.2)

32.9 (3.8)

31,109,884

N/Aa

<.001

Age, mean years (SD)44 (11)44 (11)44 (11)

34,733,596

N/A

.41

N/A

N/A

.02

57.6 (1)<.001

Race or ethnicity, n (%)
Asian or Pacific Islander

19 (4.79)

8 (4.02)11 (5.56)

Hispanic

34 (8.56%)

8 (4.02)26 (13.13)

Native American or American Indian 2 (0.50)

1 (0.50)

1 (0.51)

Non-Hispanic Black23 (5.79)

16 (8.04)

7 (3.54)

Non-Hispanic White

299 (75.31)

156 (78.39)

143 (72.22)

Other

20 (5.04)

10 (5.03)

10 (5.05)

Geography, n (%)
Rurala2407 (12.04)

2020 (12.97)

387 (8.75)

N/A

N/A: not applicable.

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 4
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

Figure 1. Number of weight loss (WL) attempts by BMI category. PwPO: people with preobesity; PwO: people with obesity.

Comorbidities and Perceptions of Health
Overall, 40.18% (8036/20,000) of the patients reported being
diagnosed with at least one comorbidity (Tables 3 and 4).
Hypertension was the most commonly reported comorbidity(3535/20,000, 17.68%), followed by high cholesterol or
triglycerides (3378/20,000, 16.89%), and diabetes or prediabetes(2276/20,000, 11.38%). Over 90% (18,363/20,000, 91.82%) of
all patients reported that excess weight negatively affected theirhealth, regardless of gender.
The disease burden was greater overall for patients with obesity
compared with patients with preobesity. Approximately one-fifth(2793/13,754, 20.58%) of patients with obesity reported having
hypertension or high cholesterol or triglyceride levels(2453/13,574, 18.07%). Almost half (8036/20,000, 40.18%) of
the patients reported having one or more comorbidities, withhttps://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

significantly more patients with obesity compared with patientswith preobesity reporting 1 or more comorbidities (χ21=372.6;
P<.001).
Females were less likely to report having at least one
comorbidity (5992/15,576, 38.47% female vs 2044/4424,
46.20% male; χ21=85.7; P<.001), and were significantly lesslikely to present with comorbidities other than osteoarthritis
and gallbladder disease. Most of the comorbidities more likely
to be reported by males carry increased cardiometabolic risk,
including hypertension, high cholesterol, obstructive sleep
apnea, and cardiovascular disease. Females and males similarly
reported the belief that their weight negatively impacted theirhealth (14,247/15,576, 91.47% female vs 4116/4424, 93.04%
male; χ21=11.3; P<.001).

JMIR Form Res 2023 | vol. 7 | e40062 | p. 5
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

Table 3. Sample health history by BMI category.
Health history variable

Total patients(N=20,000), n (%)

Patients with obesity(n=13,574), n (%)

Patients with preobesity(n=6426), n (%)

Chi-square (df)

P value

Hypertension

3535 (17.68)

2793 (20.58)

742 (11.55)

244.4 (1)<.001High cholesterol

3378 (16.89)

2453 (18.07)

925 (14.39)

42.0 (1)<.001

Diabetes

2276 (11.38)

1796 (13.23)

480 (7.47)

143.6 (1)<.001

Gallbladder disease1007 (5.04)

775 (5.71)

232 (3.61)

40.2 (1)<.001

Obstructive sleep apnea

1248 (6.24)

1102 (8.12)

146 (2.27)

254.8 (1)<.001

Osteoarthritis

848 (4.24)

610 (4.49)

238 (3.70)

6.7 (1).01

Fatty Liver disease

517 (2.58)

439 (3.23)

78 (1.21)

70.7 (1)<.001

Cardiovascular disease

192 (0.96)

144 (1.06)

48 (0.75)

4.5 (1).03≥1 comorbidity

8036 (40.18)6079 (44.78)

1957 (30.45)

372.6 (1)<.001

Believe weight negatively affects health

18,363 (91.82)

12,732 (93.80)

5631 (87.63)

220.8 (1)<.001

Comorbidity type

Table 4. Sample health history by biological sex.
Health history variable

Total patients
Female (n=15,576), n (%) Male (n=4424), n (%) Chi-square (df)
(N=20,000), n (%)

P value

Hypertension

3535 (17.68)

2433 (15.62)

1102 (24.91)

204.3 (1)<.001High cholesterol

3378 (16.89)

2430 (15.60)

948 (21.43)

83.4 (1)<.001

Diabetes

2276 (11.38)

1736 (11.15)

540 (12.21)

3.8 (1).05

Gallbladder disease1007 (5.04)

922 (5.92)

85 (1.92)

115.2 (1)<.001

Obstructive sleep apnea

1248 (6.24)

649 (4.17)

599 (13.54)

517.4 (1)<.001

Osteoarthritis

848 (4.24)

759 (4.87)

89 (2.01)

69.5 (1)<.001

Fatty liver disease

517 (2.58)

356 (2.29)

161 (3.64)

25.1 (1)<.001

Cardiovascular disease

192 (0.96)

127 (0.82)

65 (1.47)

15.5 (1)<.001≥1 comorbidity

8036 (40.18)

5992 (38.47)

2044 (46.20)

85.7 (1)<.001

Believe weight negatively affects health

18,363 (91.82)

14,247 (91.47)

4116 (93.04)

11.3 (1)<.001

Comorbidity type

Behavioral Characteristics
Almost all (19,732/20,000, 98.66%) patients had attempted atleast one previous weight loss method, and half (10,067/20,000,
50.34%) of the patients had attempted 4 different weight loss
methods (Tables 5 and 6). Overall, 28.76% (5752/20,000) of
the patients reported previous use of prescription weight loss
medications. Notably, 1.24% (247/20,000) of the patients in thefull cohort reported prior bariatric surgery. This group had amean BMI of 33.2 (SD 4.0) kg/m2 at baseline.
When comparing past weight loss behaviors across BMI groups,
results showed that more patients with obesity reported having
tried >4 weight loss methods compared with patients withpreobesity (χ21=110.1; P<.001; Figure 1). Exercise and a
low-calorie diet were the most commonly attempted weight loss
methods by both patients with preobesity and patients withobesity (Figure 2). More patients with obesity compared withpatients with preobesity reported previous use of commercial

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

weight loss plans (χ21=185.2), specialized diets (omission of acategory of food, χ21=2 1.9), over-the-counter supplements(χ21=50.8), and prescription weight loss medications (χ21=283.3;
all P<.001). Approximately one-third (4761/13,574, 35.07%)
of patients with obesity reported engaging in aerobic exercise
3 or more days per week; 19.89% (2700/13,574) reported
strength training of 3 or more days per week. Patients withpreobesity were more likely to engage in aerobic (χ21=183.2;
P<.001) and strength training 3 or more times per week(χ21=161.3; P<.001).
Females were more likely to have engaged in any and all pastweight loss methods and were more likely to follow a
specialized diet at the start of treatment. However, men weremore likely to be engaged in strength training for 3 days per
week (3256/15,576, 20.90% females vs 1238/4424, 27.98%
males; χ21=99.1, P<.001).

JMIR Form Res 2023 | vol. 7 | e40062 | p. 6
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

Table 5. Sample weight loss and dieting behavioral characteristics by BMI category.
Characteristic

Total patients
Patients with obesity(N=20,000), n (%) (n=13,574), n (%)

Patients with preobesity(n=6426), n (%)

Chi-square (df)

P value≥1 WLa method

19,732 (98.66)

13,393 (98.67)

6339 (98.65)

0.0 (1).96≥2 WL methods

18,644 (93.22)

12,691 (93.49)

5953 (92.64)

5.1 (1).03≥3 WL methods

15,172 (75.86)

10,498 (77.34)

4674 (72.74)

50.5 (1)<.001≥4 WL methods

10,067 (50.33)

7179 (52.88)

2888 (44.94)

110.1 (1)<.001

≥5 WL methods

5434 (27.17)

4023 (29.64)

1411 (21.96)

130.0 (1)<.001

Exercise

16,613 (83.06)

11,113 (81.87)

5500 (85.59)

42.9 (1)<.001

Low-calorie diet

16,072 (80.36)

10,879 (80.15)

5193 (80.81)

1.2 (1).28

Specialized diet

12,292 (61.46)

8493 (62.57)

3799 (59.12)

21.9 (1)<.001

Commercial plan

10,085 (50.42)7294 (53.74)

2791 (43.43)

185.2 (1)<.001

Supplements or OTCb

9683 (48.42)

6807 (50.15)2876 (44.76)

50.8 (1)<.001

Rx WL medication

5752 (28.76)

4407 (32.47)

1345 (20.93)

283.3 (1)<.001

Bariatric surgery

247 (1.24)

189 (1.39)

58 (0.90)

8.6 (1).003

No method

268 (1.34)

181 (1.33)

87 (1.35)

0.0 (1).96

Not following any diet

12,439 (62.20)

8623 (63.53)

3816 (59.38)

31.8 (1)<.001

Aerobic exercise >3 days/week

7655 (38.27)

4761 (35.07)

2894 (45.04)

183.2 (1)<.001

Strength training >3 days/week

4494 (22.47)

2700 (19.89)

1794 (27.92)

161.3 (1)<.001

Number of methods tried

Types of methods tried

Habits at treatment initiationa

WL: weight loss.

b

OTC: over-the-counter.

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 7
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

Table 6. Sample weight loss and dieting behavioral characteristics by biological sex.
Characteristic

Total patients(N=20,000), n (%)

Female(n=15,576), n (%)

Male (n=4424), n (%)

Chi-square (df)

P value≥1 WLa method

19,732 (98.66)

15,439 (99.12)4293 (97.04)

112.9 (1)<.001

≥2 WL method

18,644 (93.22)

14,755 (94.73)

3889 (87.91)

253.7 (1)<.001

≥3 WL method

15,172 (75.86)

12,436 (79.84)

2736 (61.84)

609.3 (1)<.001

≥4 WL method

10,067 (50.33)

8663 (55.62)

1404 (31.74)

786.0 (1)<.001

≥5 WL method

5434 (27.17)4884 (31.36)

550 (12.43)

623.5 (1)<.001

Exercise

16,613 (83.06)

12,880 (82.69)

3733 (84.38)

7.0 (1).008

Low-calorie diet

16,072 (80.36)

12,797 (82.16)

3275 (74.03)

144.3 (1)<.001

Specialized diet

12,292 (61.46)

9757 (62.64)

2535 (57.30)

41.5 (1)<.001

Commercial plan

10,085 (50.42)8943 (57.42)

1142 (25.81)

1376.4 (1)<.001

Supplements or OTCb

9683 (48.42)8030 (51.55)

1653 (37.36)

277.8 (1)<.001

Rx WL medication

5752 (28.76)

5216 (33.49)

536 (12.12)

768.1 (1)<.001

Bariatric surgery

247 (1.24)

209 (1.34)

38 (0.86)

6.6 (1).01

No method

268 (1.34)

137 (0.88)

131 (2.96)

112.9 (1)<.001

Not following any diet

12,439 (62.20)

9442 (60.62)

2997 (67.74)

74.4 (1)<.001

Aerobic exercise >3 days/week

7655 (38.27)

5939 (38.13)

1716 (38.79)

0.6 (1).44

Strength training >3 days/week

4494 (22.47)

3256 (20.90)1238 (27.98)

99.1 (1)<.001

Number of methods tried

Types of methods tried

Habits at treatment initiationa

WL: weight loss.

b

OTC: over-the-counter.

Figure 2. Types of weight loss (WL) attempts by BMI category. OTC: over-the-counter; PwPO: people with preobesity; PwO: people with obesity.

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 8
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

Access to Care
A total of 21.26% (4252/20,000) of the patients lived in a county
that was designated as a primary care HPSA in its entirety.
Approximately 29.25% (5850/20,000) of patients lived in thesame zip code as an ABOM specialist, and most lived in thesame county as an ABOM specialist (Tables 7 and 8).

Comparing across categories, patients with preobesity andfemales were slightly more likely to live in a whole-county
primary care HPSA and slightly less likely to live in the samezip code or county as an ABOM specialist compared withpatients with obesity.

Table 7. Sample access to care by BMI category.
Characteristic

Total patients(N=20,000), n (%)

Patients with obesity(n=13,574), n (%)

Patients with preobesity(n=6426), n (%)

HPSAa category, county of residence, n (%)

a

Chi-square (df)

P value

40.9 (1)<.001

None of county is shortage area

1634 (8.20)

1102 (8.15)

532 (8.32)

Part of county is shortage area

14,034 (70.45)9364 (69.23)

4670 (73.03)

Whole county is shortage area

4252 (21.35)

3059 (22.62)

1193 (18.66)

Has obesity specialist in zip code

5850 (29.25)

3825 (28.18)

2025 (31.52)

23.5 (1)<.001

Has obesity specialist in county

17,030 (85.15)

11,437 (84.26)

5593 (87.05)

26.9 (1)<.001

HPSA: health care provider shortage area.

Table 8. Sample access to care by biological sex.
Characteristic

Total patients
Female (n=15,576), n (%) Male (n=4424), n (%) Chi-square (df)
(N=20,000), n (%)

HPSAa category, county of residencea

P value25.7 (1)<.001

None of county is shortage area

1634 (8.20)

1289 (8.30)

345 (7.86)

Part of county is shortage area

14,034 (70.45)

10,810 (69.62)

3224 (73.41)

Whole county is shortage area

4252 (21.35)

3429 (22.08)

823 (18.74)

Has obesity specialist in zip code

5850 (29.25)

4374 (28.08)

1476 (33.36)

46.4 (1)<.001

Has obesity specialist in county

17,030 (85.15)

13,060 (83.85)

3970 (89.74)

94.4 (1)<.001

HPSA: health care area shortage area.

Discussion
Principal Findings
To the best of our knowledge, this report constitutes the largest
published cohort of DTC telehealth users seeking prescriptive
weight management. The size of this sample of patients (20,000)
who sought and obtained OSH on a web-based DTC platform
supports the notion that telehealth appears to be a viable meansof delivering prescription weight management interventions
nationwide. Approximately one-third of the patients in our
sample had preobesity (ie, BMI 25-30 kg/m2) when they firstinitiated treatment with OSH, and most patients in this cohort
reported no weight-related comorbidities. In addition to OSH,
recommended treatment options for patients in this BMI
category are limited to lifestyle modification and orlistat, an
over-the-counter weight loss treatment. Our data suggest thatpeople with preobesity do seek prescription weight management
care as it becomes available to them. Given the chronic,
progressive nature of obesity and the willingness of patientswith preobesity to seek care, additional tools such as OSH and
improved access through DTC telehealth are important
opportunities for early intervention.
https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

Almost all patients (19,732/20,000, 98.66%) in our large cohort
reported previously attempting at least one weight loss method
before OSH. Surprisingly, almost one-third of the patients(5752/20,000, 28.76%) had previously used prescription weightloss medication. This is much higher than the ~2% of eligible
patients who are normally prescribed weight loss medication,suggesting that people in our cohort had a greater predisposition
to this type of treatment and were therefore more aware or
willing to try additional prescriptive therapy [7-9]. About halfof our cohort (including 2876/6426, 44.76% of patients withpreobesity) also reported previous use of weight loss
supplements and over-the-counter treatments. Dietary weightloss supplements are generally not recommended given the lackof evidence supporting their safety and efficacy. The fact that
half of our cohort (9683/20,000, 48.42%) had previously used
these unproven therapies further emphasizes the need foraccessible and evidence-based treatment options.
In addition, we examined age, sex, and geographic trends in ourcohort. In our sample, approximately 10% of patients were aged
≥60 years, with the oldest being 86 years, supporting the ideathat DTC telehealth is a viable treatment option among older
patients. This is consistent with recent studies that demonstrated
that older patients commonly utilized telehealth for ambulatory
JMIR Form Res 2023 | vol. 7 | e40062 | p. 9
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
visits during the COVID-19 pandemic [19]. These findings arecontrary to the concerns that older adults lack the awareness,experience, and confidence to successfully navigate technology[20,21].
In our cohort, women were more likely to initiate OSH and had
a lower BMI than men. Previous studies have similarly
demonstrated that men are less likely to engage in weight loss
therapies than are women [22]. Interestingly, both males andfemales in our cohort similarly believed that weight negatively
affected their health (>90% of each group). This observation
contrasts with that of a large survey, which suggested that only
about half of people with obesity are concerned with the impact
of excess weight on health [23]. This may be because of thedifferences in the 2 groups: our cohort-initiated treatment, which
demonstrates a level of self-awareness and readiness to takeaction, versus the survey respondents comprised of the general
population.
Finally, we found that most (17,030/20,000, 85.15%) patientswere located in the same county as an ABOM-certified
diplomate provider. This is consistent with a recent study thatshowed that the median travel time for a patient to reach an
obesity medicine provider was approximately 10 minutes.
Despite this, access to an obesity specialist is still hindered, inpart because of the shortage of obesity providers relative to
people with obesity (1 provider for every 20,000 patients). There
may also be other reasons that a patient who lives near an
obesity medicine provider elected to use the telehealth weight
management platform instead, including the convenience of
DTC telehealth, previous experience of weight stigma and bias,
extensive waitlists for new patient appointments, lack of patientawareness of nearby obesity specialist providers, and lesscomplex care requirements.

Practice Implications
Given the prevalence of preobesity and obesity, technology thatcan provide evidence-based weight management at scale is avaluable tool. Obesity is a disease that is particularly
well-positioned for management via DTC telehealth. First,
two-thirds of people with obesity reported experiencing weight
stigma from their providers. This results in greater health care
avoidance, lower frequency of routine checkups, and lower
quality of health care [13]. Most people with obesity (>80%)
actually believe that weight management is their sole
responsibility, whereas three-quarters of health care providers
think it is their responsibility as the provider to manage weight[10]. Ironically, health care providers rarely discuss weight loss
during routine appointments, with the most common reasons
being insufficient time, more important issues to discuss, andlack of belief that the patient is motivated to lose weight; thelatter reason is in and of itself an example of bias [10]. Finally,treatment options exist for weight management with easily
understood mechanisms of action and patient-centric routes ofadministration that are well suited for prescribing via
telemedicine [6].
Early intervention at scale on web-based platforms may also
play an important role in shifting the weight management
paradigm toward the prevention of obesity, rather than just thetreatment of obesity. People with preobesity represent an
https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

Horn et alimportant group for targeted treatment to prevent the progression
to obesity and its related comorbidities [24]. On an average,
people gain approximately 1 pound per year as they age [25].
Without interventional treatment, adults with preobesity continue
to gain weight, moving steadily into obesity and sometimes
severe obesity categories [26]. Therefore, early intervention inweight management (ie, in patients with preobesity) is
recommended to prevent disease progression to obesity [24].
However, weight loss interventions, including pharmacotherapy,
are inadequately used today for obesity prevention and mostare not approved for use until a patient has a higher preobesity
BMI range (≥27 kg/m2) with at least one weight-relatedcomorbidity. A recent large-scale review of electronic healthrecord data determined that less than 0.4% of eligible people
with preobesity received pharmacotherapy for weight loss [8].
However, most Food and Drug Administration–approved weightloss medications are indicated for use in people with preobesity
who have at least one weight-related comorbidity (eg, diabetes).
As demonstrated by the high proportion of people withpreobesity in our dataset, DTC telehealth platforms and earlyinterventional treatments such as OSH may reduce some of theexisting barriers to care for these patients, helping shift the focus
of management from treatment to prevention.
The acceptability and availability of telehealth has changed in
response to the pandemic. Demonstrated telehealth benefits
include connectivity, cost efficiency, improved access,medication adherence, and decreased hospitalizations [27,28].
In light of the findings of our study, DTC telehealth can help
address issues such as provider shortages and the need for
expanded geographical areas. Treating preobesity and obesity
via DTC telehealth could support patients with improved cost
efficiencies such as reduced travel to appointments, decreased
work absenteeism, and improved workday efficiency. All ofthese can translate to lower health care costs for patients,
employers, and insurers, and most importantly, improved
provision of care.

Limitations
Although our analysis provides valuable insights into thecharacteristics of people using telehealth for weight
management, there are several limitations that should be noted.
First, all data were self-reported by the patients, and errors inpatient recall or reporting could have skewed our results. For
example, self-reported weight tends to underestimate actual
weight; in this cohort, this may have skewed our population
toward a slightly higher proportion of patients with preobesity[29]. Similarly, the rate of hypertension reported in our patient
population was lower than that reported in the general
population, possibly because of underreporting or errors inpatient recall [30]. Incorporating objective methods of datacollection via remote patient monitoring opportunities (eg, awireless weight scale) would help reduce the self-reporting bias
in the future. Second, our cohort consisted only of patients who
were eligible to receive a prescription for OSH; therefore, people
with BMI <25 kg/m2 or >40 kg/m2 were excluded, which could
affect the generalizability of our observations. This may be
important in populations where preobesity and obesity are
diagnosed at a lower BMI, secondary to an associated increased
JMIR Form Res 2023 | vol. 7 | e40062 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
cardiometabolic risk. Our telehealth patient population was
gender-imbalanced, and while weight loss interventions are
typically weighted toward female patients, this could also affectgeneralizability [31]. Questions about race and ethnicity were
not initially included in the baseline telehealth questionnaire;
therefore, not all the patients in our cohort reported thisinformation. Future analyses could be stratified by baseline
demographics to ensure adequate and balanced representation.
In addition, while patient proximity to ABOM-certified
providers was used as a surrogate measure of access to obesity
medicine specialist care, it is important to note that
non-specialist providers can prescribe obesity treatment. Finally,

Horn et alsome of the practice site locations for ABOM-certified providers
were obtained using a manual web search, introducing thepossibility that an incorrect or secondary practice site was used
incorrectly for some providers.

Conclusions
Our analysis supports the use of telehealth as a means ofproviding prescriptive weight management treatment topopulations seeking care. Patients with preobesity are
undertreated and actively seeking new weight management
options. Future cross-sectional studies are needed to determine
whether these patient characteristic trends continue once DTC
telehealth is more broadly used for weight management.

Acknowledgments
The authors would like to acknowledge Dr Rekha Kumar for her contribution to the interpretation of the results of this analysis.
Financial support was provided by Gelesis, Inc, and Ro.

Data Availability
Owing to the nature of this research and its use of proprietary information, supporting data are not readily available for sharing.
However, requests from qualified researchers who engage in rigorous, independent scientific research and are willing to enter
into a formal data use agreement may be considered on a case-by-case basis. For more information, please contact research@ro.co.

Conflicts of Interest
DBH has received professional fees unrelated to this manuscript from Gelesis, Lilly Novo Nordisk, and Orexigen. EP, DB, andEC are or were employed by Gelesis, Inc and hold shares or stock options in Gelesis, Inc. MZ, LB, and TD are employed by Ro
and hold stock options in Ro.

References
1.
2.

3.

4.
5.
6.
7.

8.

9.

10.

11.

The GBD 2015 Obesity Collaborators. Health effects of overweight and obesity in 195 countries over 25 years. N Engl J
Med 2017 Jul 06;377(1):13-27. [doi: 10.1056/nejmoa1614362]
Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specificmortality: a population-based cohort study of 3·6 million adults in the UK. Lancet Diabet Endocrinol 2018 Dec;6(12):944-953.
[doi: 10.1016/s2213-8587(18)30288-2]
Chang K, Wang J, Saxon A, Matthews AG, Woody G, Hser Y. Causes of death and expected years of life lost among
treated opioid-dependent individuals in the United States and Taiwan. Int J Drug Policy 2017 May;43:1-6 [FREE Full text][doi: 10.1016/j.drugpo.2016.12.003] [Medline: 28160734]
Ward ZJ, Bleich SN, Cradock AL, Barrett JL, Giles CM, Flax C, et al. Projected U.S. State-level prevalence of adult obesityand severe obesity. N Engl J Med 2019 Dec 19;381(25):2440-2450. [doi: 10.1056/nejmsa1909301]
Global Health Observatory data repository web site. World Health Organization. URL: https://apps.who.int/gho/data/view.
main.BMI25CREGv?lang=en [accessed 2019-09-01]
Kahan S, Look M, Fitch A. The benefit of telemedicine in obesity care. Obesity (Silver Spring) 2022 Mar 23;30(3):577-586.
[doi: 10.1002/oby.23382] [Medline: 35195367]
Xia Y, Kelton CM, Guo JJ, Bian B, Heaton PC. Treatment of obesity: pharmacotherapy trends in the United States from1999 to 2010. Obesity (Silver Spring) 2015 Aug 20;23(8):1721-1728 [FREE Full text] [doi: 10.1002/oby.21136] [Medline:
26193062]
Saxon DR, Iwamoto SJ, Mettenbrink CJ, McCormick E, Arterburn D, Daley MF, et al. Antiobesity medication use in 2.2
million adults across eight large health care organizations: 2009-2015. Obesity (Silver Spring) 2019 Dec 11;27(12):1975-1981[FREE Full text] [doi: 10.1002/oby.22581] [Medline: 31603630]
Thomas CE, Mauer EA, Shukla AP, Rathi S, Aronne LJ. Low adoption of weight loss medications: a comparison ofprescribing patterns of antiobesity pharmacotherapies and SGLT2s. Obesity (Silver Spring) 2016 Sep 29;24(9):1955-1961[FREE Full text] [doi: 10.1002/oby.21533] [Medline: 27569120]
Kaplan LM, Golden A, Jinnett K, Kolotkin RL, Kyle TK, Look M, et al. Perceptions of barriers to effective obesity care:
results from the national ACTION study. Obesity (Silver Spring) 2018 Jan 31;26(1):61-69. [doi: 10.1002/oby.22054][Medline: 29086529]
Brewis A, SturtzSreetharan C, Wutich A. Obesity stigma as a globalizing health challenge. Global Health 2018 Feb
13;14(1):20 [FREE Full text] [doi: 10.1186/s12992-018-0337-x] [Medline: 29439728]

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
12.
13.

14.
15.
16.

17.

18.
19.

20.
21.

22.
23.

24.
25.
26.

27.
28.

29.

30.
31.

Horn et al

Puhl RM, Andreyeva T, Brownell KD. Perceptions of weight discrimination: prevalence and comparison to race and gender
discrimination in America. Int J Obes (Lond) 2008 Jun 4;32(6):992-1000. [doi: 10.1038/ijo.2008.22] [Medline: 18317471]
Puhl RM, Lessard LM, Himmelstein MS, Foster GD. The roles of experienced and internalized weight stigma in healthcare
experiences: perspectives of adults engaged in weight management across six countries. PLoS One 2021 Jun 1;16(6):e0251566[FREE Full text] [doi: 10.1371/journal.pone.0251566] [Medline: 34061867]
Batsis JA, Pletcher SN, Stahl JE. Telemedicine and primary care obesity management in rural areas - innovative approachfor older adults? BMC Geriatr 2017 Jan 05;17(1):6 [FREE Full text] [doi: 10.1186/s12877-016-0396-x] [Medline: 28056832]
Mauro M, Taylor V, Wharton S, Sharma AM. Barriers to obesity treatment. Eur J Intern Med 2008 May;19(3):173-180.
[doi: 10.1016/j.ejim.2007.09.011] [Medline: 18395160]
Beleigoli AM, Andrade AQ, Cançado AG, Paulo MN, Diniz MD, Ribeiro AL. Web-based digital health interventions forweight loss and lifestyle habit changes in overweight and obese adults: systematic review and meta-analysis. J Med Internet
Res 2019 Jan 08;21(1):e298 [FREE Full text] [doi: 10.2196/jmir.9609] [Medline: 30622090]
Liu N, Huang R, Baldacchino T, Sud A, Sud K, Khadra M, et al. Telehealth for noncritical patients with chronic diseases
during the COVID-19 pandemic. J Med Internet Res 2020 Aug 07;22(8):e19493 [FREE Full text] [doi: 10.2196/19493][Medline: 32721925]
Plenity homepage. Plenity. URL: https://www.myplenity.com/healthcare-professionals [accessed 2022-11-20]
Stevens JP, Mechanic O, Markson L, O'Donoghue A, Kimball AB. Telehealth use by age and race at a single academic
medical center during the COVID-19 pandemic: retrospective cohort study. J Med Internet Res 2021 May 20;23(5):e23905[FREE Full text] [doi: 10.2196/23905] [Medline: 33974549]
Berkowsky R, Sharit J, Czaja S. Factors predicting decisions about technology adoption among older adults. Innov Aging2018 Jan;2(1):igy002 [FREE Full text] [doi: 10.1093/geroni/igy002] [Medline: 30480129]
Fischer SH, Ray KN, Mehrotra A, Bloom EL, Uscher-Pines L. Prevalence and characteristics of telehealth utilization in
the United States. JAMA Netw Open 2020 Oct 01;3(10):e2022302 [FREE Full text] [doi:10.1001/jamanetworkopen.2020.22302] [Medline: 33104208]
Elliott M, Gillison F, Barnett J. Exploring the influences on men's engagement with weight loss services: a qualitative
study. BMC Public Health 2020 Feb 25;20(1):249 [FREE Full text] [doi: 10.1186/s12889-020-8252-5] [Medline: 32093707]
Caterson ID, Alfadda AA, Auerbach P, Coutinho W, Cuevas A, Dicker D, et al. Gaps to bridge: misalignment between
perception, reality and actions in obesity. Diabetes Obes Metab 2019 Aug;21(8):1914-1924 [FREE Full text] [doi:10.1111/dom.13752] [Medline: 31032548]
Kushner R, Primack C. Overweight: the overlooked risk factor. J Fam Pract 2020 Sep;69(7 Suppl):S51-S56. [Medline:
33104108]
Mozaffarian D, Hao T, Rimm EB, Willett WC, Hu FB. Changes in diet and lifestyle and long-term weight gain in womenand men. N Engl J Med 2011 Jun 23;364(25):2392-2404. [doi: 10.1056/nejmoa1014296]
Fryar CD, Carroll MD, Afful J. Prevalence of overweight, obesity, and severe obesity among adults aged 20 and over:
United States, 1960–1962 through 2017–2018. NCHS Health E-Stats. 2020. URL: http://www.cdc.gov/nchs/data/databriefs/
db360-h.pdf [accessed 2022-12-09]
Telehealth: defining 21st century care. American Telemedicine Association. URL: https://www.americantelemed.org/
resource/why-telemedicine/ [accessed 2022-09-01]
Wosik J, Fudim M, Cameron B, Gellad ZF, Cho A, Phinney D, et al. Telehealth transformation: COVID-19 and the riseof virtual care. J Am Med Inform Assoc 2020 Jun 01;27(6):957-962 [FREE Full text] [doi: 10.1093/jamia/ocaa067] [Medline:
32311034]
Wilson OW, Bopp CM, Papalia Z, Bopp M. Objective vs self-report assessment of height, weight and body mass index:
relationships with adiposity, aerobic fitness and physical activity. Clin Obes 2019 Oct 10;9(5):e12331. [doi:10.1111/cob.12331] [Medline: 31293064]
Facts about hypertension. Centers for Disease Control and Prevention. 2019. URL: https://www.cdc.gov/bloodpressure/facts.htm [accessed 2022-02-01]
Ahern AL, Aveyard P, Boyland EJ, Halford JC, Jebb SA. Inequalities in the uptake of weight management interventions
in a pragmatic trial: an observational study in primary care. Br J Gen Pract 2016 Feb 23;66(645):e258-e263. [doi:10.3399/bjgp16x684337]

Abbreviations
ABOM: American Board of Obesity Medicine
DTC: direct-to-consumer
HPSA: healthcare provider shortage area
OSH: oral superabsorbent hydrogel

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 12(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Horn et al

Edited by A Mavragani; submitted 03.06.22; peer-reviewed by D Saxon, J Chen; comments to author 06.09.22; revised version received
18.10.22; accepted 04.11.22; published 05.01.23
Please cite as:Horn DB, Pash E, Zhou MS, Broffman L, Bialonczyk D, Doron T, Chiquette E
Characteristics and Weight Loss Practices From a Cohort of 20,000 Patients Using Direct-to-Consumer Telehealth: Observational
Cross-sectional StudyJMIR Form Res 2023;7:e40062
URL: https://formative.jmir.org/2023/1/e40062
doi: 10.2196/40062
PMID: 36602855©Deborah Bade Horn, Elizabeth Pash, Megan S Zhou, Lauren Broffman, Damian Bialonczyk, Tzvi Doron, Elaine Chiquette.
Originally published in JMIR Formative Research (https://formative.jmir.org), 05.01.2023. This is an open-access article distributed
under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative
Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org,
as well as this copyright and license information must be included.

https://formative.jmir.org/2023/1/e40062

XSL• FO
RenderX

JMIR Form Res 2023 | vol. 7 | e40062 | p. 13(page number not for citation purposes)