Characteristics and Weight Loss Practices From a Cohort of 20,000 Patients Using Direct-to-Consumer Telehealth: Observational Cross-sectional Study
Bibliographic record
Abstract
Background Despite the increasing prevalence of obesity, the use of pharmacotherapy treatment remains low. Telehealth platforms 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 the treatment 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 patients with 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 ≥1 weight-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% vs 1345/6426, 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 and 85.15% (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 weight management options. Female participants sought weight management treatment earlier in the disease continuum than males, despite reporting fewer comorbidities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".