About

Meg Salvia, PhD, RDN, is a Clinical Assistant Professor in the Department of Health Sciences Education at Boston University Sargent College of Health & Rehabilitation Sciences. She holds a BA in English from Boston College, an MS in Nutrition from Boston University, an MS in Biostatistics from Harvard University, and a PhD in Population Health Sciences from Harvard University. Her scholarly interests include modeling approaches to understand the impact of eating behaviors on health, nutrition knowledge and skills acquisition in vulnerable life stages such as adolescence, young adulthood, and pregnancy, and the use of technology and virtual platforms for disseminating and implementing nutrition interventions. She develops and assesses effective nutrition messaging and counseling strategies aimed at disease prevention and improving treatment outcomes, utilizing qualitative research methods to supplement quantitative analyses and better understand individuals’ experiences with nutrition interventions. Her specific research focuses include nutrition and mental health, eating disorders, binge eating, body image, type 2 diabetes, cardiovascular disease, and pregnancy and maternal nutrition. Dr. Salvia is actively involved in professional activities such as serving as a technical editor for the American Journal of Clinical Nutrition and participating in various professional organizations. Her work has contributed to advancing understanding in nutrition science, health communication, and public health policy.

Research topics

• Medicine
• Psychology
• Psychiatry
• Family medicine
• Linguistics
• Clinical psychology
• Psychotherapist
• Pathology
• Gerontology

Selected publications

• Artificial Intelligence and Machine Learning Resource Guide: The Academy of Nutrition and Dietetics and the American Society for Nutrition Joint Taskforce for Artificial Intelligence

  Journal of the Academy of Nutrition and Dietetics · 2026-04-01

  article
  PublisherDOI

• Artificial Intelligence and Machine Learning Resource Guide: The Academy of Nutrition and Dietetics and the American Society for Nutrition Joint Taskforce for Artificial Intelligence

  American Journal of Clinical Nutrition · 2026-04-02

  article
  PublisherDOI

• “People Who Get You”: A Qualitative Study of LGBTQ+-Affirming Eating Disorder Treatment

  The Counseling Psychologist · 2026-05-08

  article

  Lesbian, gay, bisexual, transgender, queer, and other sexual and gender diverse (LGBTQ+) individuals are under-represented in eating disorder (ED) research despite being an at-risk group. They also face additional barriers to accurate diagnosis, treatment access, and affirming care. This qualitative study used reflexive thematic analysis to explore the experiences of sixteen adults who attended a virtual ED treatment program designed specifically to treat LGBTQ+ individuals. Findings show how LGBTQ+-affirming ED care compares and contrasts with participants’ previous healthcare experiences. Analyses identified two major themes around (a) the important benefits of LGBTQ+ community within a treatment setting and (b) the effective ways the ED treatment program was structured to uniquely meet the needs of LGBTQ+ clients. This study provides important insights into the need for more LGBTQ+-affirming ED treatment settings and ways that providers can increase their cultural responsiveness to work with LGBTQ+ clients.

  PublisherDOI

• Adopting vegetarian and vegan eating patterns: Associations with disordered eating behaviors among young adult college students

  Eating Behaviors · 2025-03-17 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Abstract P287: Incorporating Behavioral and Social Determinants of Health Variables Into a Machine Learning Model Predicting Cardiometabolic Disease

  Circulation · 2024-03-19

  article1st authorCorresponding

  Introduction: Machine learning models hold potential for improved cardiometabolic disease prediction and more efficacious prevention and intervention efforts. Current models are limited by use of cross-sectional datasets and variables (features) consisting of diagnostic criteria (such as blood glucose or hemoglobin A1c in type 2 diabetes models) and overlook health behaviors and social determinants of health (HB-SDH). Hypothesis: Machine learning models built with HB-SDH variables will accurately predict type 2 diabetes. Methods: We used data from 2,493 participants without diabetes in the Adolescent to Adult Health study (mean age = 15.5 years at baseline (1994), 40.9% male) who contributed blood samples and anthropometric measures in wave 4 (2008) or 5 (2016-2018). We used an 80/20 split to form separate training and test datasets; the training dataset was used to build random forest and XGBoost models, and performance with respect to classifying diabetes was evaluated in the test dataset. We first built a model using 15 demographic and biomarker features as a comparator for our second model, which included 34 HB-SDH features measured during adulthood but no biomarkers. Results: Five-hundred sixteen participants developed type 2 diabetes. In XGBoost, the comparator model was found to predict diabetes with 92.6% accuracy (95% CI: 89.8%, 94.8%) and area under the curve (AUC) = 0.8165. The HB-SDH model had 88.7% accuracy (95% CI: 85.4, 91.4%) and AUC = 0.7631. Using random forest, the comparator model had 93.0% accuracy (95% CI: 90.3, 95.2%) and AUC = 0.8376, while the HB-SDH model had 89.8% (95% CI: 88.6, 92.4%) accuracy and AUC = 0.7650. The top 10 variables in ascertaining classifications in the random forest model included self-reported general health, waist circumference, self-reported anxiety diagnosis, self-reported hypertension diagnosis, income, BMI, education level, change in weight, financial scarcity, and experienced stigma, while in the XGBoost model these were self-reported general health, experienced stigma, financial scarcity, self-reported anxiety diagnosis, experienced weight stigma, waist circumference, income, self-reported hypertension diagnosis, sugar-sweetened beverage intake, and BMI. Conclusions: Machine learning models built with behavioral and social determinants of health variables perform comparably to models with biomarkers and identify salient features for diabetes risk. The features that factored into classification performance were similar between random forest and XGBoost models. In this population, HB-SDH of relevance included socioeconomic indicators, sugary beverages, anxiety, and experiencing stigma, in addition to adiposity and hypertension. This finding needs to be replicated in diverse cohorts that may experience different HB-SDH.

  PublisherDOI

• Design for Dissemination: Leaders Suggest Local Strategies for Implementing a Health Communications Campaign

  American Journal of Health Promotion · 2024-12-02

  article1st authorCorresponding

  PurposeUsing evidence-based health communication campaigns (EBHCC) is critical for addressing disparities in tobacco-related health outcomes among LGBTQ+ populations; therefore, this study aimed to examine processes and supports for community-based organizations (CBOs) to integrate evidence-based solutions into practice using a design-for-dissemination framework.ApproachQualitative interviews were conducted.SettingCBOs serving LGBTQ+ populations.Participants22 staff from U.S.-based CBOs participated in video interviews.MethodWe used reflexive thematic analysis to generate themes.Results3 key themes included: (1) leaders perceived storytelling as a desirable and effective way to operationalize hyperlocal adaptation of content, particularly when in-group stories came from the community and included video content (which was perceived to maximize reach); (2) researcher participation in content production/delivery was seen as a compelling implementation strategy; and (3) these requested components were seen as adding value rather than as substitutions for text- and image-based EBHCC content.Adaptations envisioned by participants are associated with increased demand for limited resources (ie, requiring more time or financial resources), on the part of CBOs, the research team, or both. Researcher strategies were identified to support meeting CBOs' needs given these contextual constraints.ConclusionThis research explores supports and processes requested by CBOs serving LGBTQ+ populations as part of the adaptations envisioned in implementing EBHCCs and highlights possible avenues to better meet CBOs' needs in effectively utilizing interventions.

  PublisherDOI

• What Constitutes Gender-affirming Care? Results from a Qualitative Study Highlighting Patient Perspectives and Insights

  Journal of the Academy of Nutrition and Dietetics · 2024-09-18

  articleOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Equity as a priority in EAT–Lancet-aligned food system transformations

  Nature Food · 2024-10-01 · 23 citations

  review
  PublisherDOI

• ALGORITHMS, ADDICTION, AND ADOLESCENT MENTAL HEALTH: An Interdisciplinary Study to Inform State-level Policy Action to Protect Youth from the Dangers of Social Media

  American Journal of Law & Medicine · 2023-07-01 · 30 citations

  articleOpen access

  Abstract A recent Wall Street Journal investigation revealed that TikTok floods child and adolescent users with videos of rapid weight loss methods, including tips on how to consume less than 300 calories a day and promoting a “corpse bride diet,” showing emaciated girls with protruding bones. The investigation involved the creation of a dozen automated accounts registered as 13-year-olds and revealed that TikTok algorithms fed adolescents tens of thousands of weight-loss videos within just a few weeks of joining the platform. Emerging research indicates that these practices extend well beyond TikTok to other social media platforms that engage millions of U.S. youth on a daily basis. Social media algorithms that push extreme content to vulnerable youth are linked to an increase in mental health problems for adolescents, including poor body image, eating disorders, and suicidality. Policy measures must be taken to curb this harmful practice. The Strategic Training Initiative for the Prevention of Eating Disorders (STRIPED), a research program based at the Harvard T.H. Chan School of Public Health and Boston Children’s Hospital, has assembled a diverse team of scholars, including experts in public health, neuroscience, health economics, and law with specialization in First Amendment law, to study the harmful effects of social media algorithms, identify the economic incentives that drive social media companies to use them, and develop strategies that can be pursued to regulate social media platforms’ use of algorithms. For our study, we have examined a critical mass of public health and neuroscience research demonstrating mental health harms to youth. We have conducted a groundbreaking economic study showing nearly $11 billion in advertising revenue is generated annually by social media platforms through advertisements targeted at users 0 to 17 years old, thus incentivizing platforms to continue their harmful practices. We have also examined legal strategies to address the regulation of social media platforms by conducting reviews of federal and state legal precedent and consulting with stakeholders in business regulation, technology, and federal and state government. While nationally the issue is being scrutinized by Congress and the Federal Trade Commission, quicker and more effective legal strategies that would survive constitutional scrutiny may be implemented by states, such as the Age Appropriate Design Code Act recently adopted in California, which sets standards that online services likely to be accessed by children must follow. Another avenue for regulation may be through states mandating that social media platforms submit to algorithm risk audits conducted by independent third parties and publicly disclose the results. Furthermore, Section 230 of the federal Communications Decency Act, which has long shielded social media platforms from liability for wrongful acts, may be circumvented if it is proven that social media companies share advertising revenues with content providers posting illegal or harmful content. Our research team’s public health and economic findings combined with our legal analysis and resulting recommendations, provide innovative and viable policy actions that state lawmakers and attorneys general can take to protect youth from the harms of dangerous social media algorithms.

  PublisherOA PDFDOI

• 36. Adolescent Mental Health and Big Tech: Investigating Policy Avenues to Regulate Harmful Social Media Algorithms

  Journal of Adolescent Health · 2023-02-17 · 5 citations

  articleOpen access
  PublisherOA PDFDOI

Frequent coauthors

• Arthur Chatton

  Université de Montréal

  8 shared

• Anna H. Grummon

  Stanford University

  8 shared

• Paula A. Quatromoni

  7 shared

• Marilyn D. Ritholz

  Joslin Diabetes Center

  6 shared

• Amanda Raffoul

  5 shared

• Eric B. Rimm

  Brigham and Women's Hospital

  4 shared

• Katherine L.E. Craigen

  Walden University

  4 shared

• Cathrine Axfors

  4 shared

Awards & honors

• Academy of Nutrition & Dietetics Diabetes Group Publications…
• Academy of Nutrition & Dietetics BHN Research Excellence Awa…
• T.H. Chan Dept. of Biostatistics Certificate of Distinction…
• Academy of Nutrition and Dietetics Maxine & Arlene Wilson sc…
• Academy for Eating Disorders RSH scholarship (2021)