The Impact of a Digital Digestive Health Program and Telehealth Visits in Socially Vulnerable Populations: Cohort Evaluation
Bibliographic record
Abstract
BACKGROUND: Socially vulnerable populations have less access to quality gastrointestinal (GI) care. Digital telehealth services provided by GI-focused registered dietitian nutritionists (RDNs) and digestive health coaches (HCs) may improve digestive health outcomes by facilitating access to GI care and thereby reduce health care disparities among vulnerable populations. OBJECTIVE: The objectives of this study were to (1) evaluate the impact of a digital digestive health program on reducing GI symptoms among socially vulnerable populations and (2) assess whether telehealth visits with digital app use provide additional benefits in symptom reduction compared to digital app use alone among socially vulnerable populations. METHODS: A comprehensive digital digestive care program with optional telehealth visits with RDNs and HCs was provided to US employees of participating companies via their employee benefits. We enrolled participants in the program between 2022 and 2023 who tracked digestive symptoms multiple times at baseline and then over 90 days. We measured changes in GI symptoms from baseline to up to 3 months, comparing those who opted for telehealth visits with those who used the app only. We stratified participants by the median Social Vulnerability Index (SVI) to evaluate symptom improvement across socially vulnerable populations. Multivariable regressions adjusted for age, gender, race, BMI, and preexisting GI conditions. RESULTS: A total of 1656 participants met the inclusion criteria, of which 1362 (82%) scheduled at least one telehealth visit and 294 (18%) used only app-based resources. The majority (n=1417 86%) of participants saw GI symptom improvement, with an average reduction of 60% in symptom burden (P<.001). Participants who used telehealth services and the app had a reduction in symptoms 16% greater than that of app-only users (P=.01). High-SVI participants (ie, those with an SVI score above the median of 0.4, indicating greater social vulnerability) had a 22% greater reduction in GI symptom severity score than app-only high-SVI participants (P=.04). CONCLUSIONS: Digital health solutions may be an important resource in advancing equitable access to quality GI care and addressing disparities among populations with high social vulnerability. Virtual telehealth visits with RDNs and HCs appear to be particularly beneficial in improving digestive symptoms in such populations.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".