Characteristics and Weight Loss Practices From a Cohort of 20,000 Patients Using Direct-to-Consumer Telehealth: Observational Cross-sectional Study
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».