Digital Commons Network^™

Optimizing Maintenance Routes For Highway Infrastructure Using Leader-Follower Autonomous Vehicles, Qing Tang, Chenxi Chen, Xianbiao Hu, Yuxin Ding, Tianjia Yang

Civil & Environmental Engineering Faculty Publications

The Autonomous Truck Mounted Attenuator (ATMA), a leader–follower style connected and automated vehicle system, enhances safety during transportation infrastructure maintenance in work zones. However, the significantly lower speed of ATMA, compared to regular vehicles, causes moving bottlenecks that reduce roadway capacity and prolong queuing, leading to further delays. Different ATMA routes lead to varying patterns of time-dependent capacity drop, affecting the user equilibrium traffic assignment and resulting in differing system costs. This study aims to optimize ATMA routing within a network to minimize the system cost associated with its slow-moving operation. To this end, a queuing-based traffic assignment approach is …

Short-Term Response Mechanisms Of Water Quantity And Quality Of Daihai Lake Under Temperature-Driven Changes, Hao Zhang, Xiaohong Shi, Xianhua Li, Junping Lu, Ruizhong Gao, Xixi Wang, Shuhao Zhang, Longmei Xie, Yu Liu

Civil & Environmental Engineering Faculty Publications

Temperature-driven mechanisms involving complex feedback and lag that affect the evolution of hydrological processes and ecological functions in cold- and arid-region lakes represent a core scientific issue in current hydrology and lake ecology research. In this study, based on month-scale temperature and environmental factor data from Daihai Lake in Inner Mongolia from January to December 2023, statistical methods (redundancy analysis, Tukey's test analysis, correlation analysis, structural equation modeling), time series analysis methods (dynamic time warping), and machine learning methods (random forest) were combined. A hierarchical and phased response framework was constructed that encompassed driver identification, path tracing, lag characterization, and …

Iuse: A Gamified Virtual Learning Platform For Connected Vehicle Applications To Enhance Undergraduate Transportation Education, Tianyu Shen, Di Yang, Kai Sun, Hong Yang, Kun Xie, Mansoureh Jeihani

Civil & Environmental Engineering Faculty Publications

Emerging transportation technologies are rapidly reshaping transportation systems and industry practice. However, most transportation undergraduate curricula still emphasize foundational topics such as geometric design, travel demand forecasting, pavements, and soil properties, typically delivered through lecture-centric instruction. While these subjects remain essential to the discipline, they do not fully reflect the pace of technological change or provide sufficient opportunities for experiential learning with modern tools and data. This gap limits students’ exposure to CV concepts and their ability to translate theory into practice.

Focusing on a key emerging technology, connected vehicles (CVs), this paper bridges the above gap by introducing a …

Modeling Hepa Filter Media Performance And Evaluation Of Filtration Leak Test Success, Daniel M. Abraham, Steven J. Eckels, Shahin N. Amiri

Civil & Environmental Engineering Faculty Publications

This research presents a cost-effective microscale modeling analysis of high-efficiency particulate air (HEPA) filter media. We analyzed leak-free and leaking media with known leak sizes. A three-dimensional fibrous model with non-homogeneous fiber diameters (0.2–2 µm) and a packing density of 7.5% was developed to replicate a HEPA filter sheet (H14) microstructure. The objective was to simulate and analyze the pressure drop and collection efficiency of nanofibrous media for aerosol particles with a diameter of d_p ≤ 0.5 µm. The results were compared to prior experimental and theoretical models, exhibiting a high level of agreement with pressure drop and efficiency …

Good Forestry In The Granite State - 3rd Edition, Cooperative Extension

UNH Cooperative Extension

No abstract provided.

Early Drowsiness Detection Via Second-Order Derivative Analysis Of Heart Rate Variability: A Non-Contact Ecg Approach With Machine Learning, Fabrice Vaussenat, Abhiroop Bhattacharya, Julie Payette, Alireza Saidi, Victor Bellemin, Geordi-Gabriel Renaud-Dumoulin, Sylvain G. Cloutier, Ghislain Gagnon

Études primaires

Drowsy driving contributes to roughly 20% of traffic fatalities, yet most detection systems rely on behavioral cues that appear only after impairment has set in. Here we ask whether first and second derivatives of heart rate variability (HRV) can detect pre-crash states earlier than conventional approaches. Twenty-five participants completed 49 driving simulator sessions while we recorded cardiac activity through capacitive ECG electrodes embedded in the seat backrest—a non-contact method that avoids the privacy concerns of camera-based monitoring. To prevent circular evaluation, ground truth labels were based solely on crash proximity rather than HRV-derived scores. The combined HRV feature set (conventional …

If Only Pickups And Vans Could Fly: Vehicle Maintenance And The Challenge Of Safely Lifting Large Vehicles On A Two-Post Lift, Damien Burlet-Vienney, Sylvie Beaugrand, Maud Gonella, Bertrand Galy, Élise Ledoux

Études primaires

The use of automotive lifts requires mechanics to work under or near a multi-ton load. Based on consultation with stakeholders, pickups and vans appear to pose problems when raised using two-post above-ground lifts (2PAG lifts), the most common type of automotive lift in garages. The aim of this study is therefore to highlight the work activity performed by mechanics when raising pickups and vans on 2PAG lifts and to describe the factors they take into account to ensure vehicle stability. Data collection took place under actual conditions in five garages. A total of 108 lifting situations were filmed and analyzed, …

On The Use Of Hearing Aid Algorithms In Industrial Noise: Balancing Speech Intelligibility And Hearing Protection, Solenn Ollivier, Fabien Bonnet, Alexis Pinsonnault-Skvarenina, Christian Giguère, Jérémie Voix, Hugues Nélisse

Études primaires

Hearing protection devices (HPDs) are widely used to mitigate the risks of noise-induced hearing loss in industrial environments. However, conventional HPDs can often hinder communication for workers with hearing loss. While those workers may rely on hearing aids in their daily lives, the potential benefits and associated risks of using such devices in noisy workplaces remain unexplored. This study evaluated the effects of different hearing aid signal processing algorithms on speech intelligibility and estimated noise exposure under industrial noise conditions to characterize the balance between communication and protection needed to develop solutions for workers with hearing loss. A digital hearing …

Computer Vision-Based Collision Prevention In Construction: Bridging Academic Advances And Industrial Requirements, Marwa Ziadia, François Gauthier, Damien Burlet-Vienney

Études primaires

Computer vision (CV) technologies have emerged as promising tools for real-time collision risk management. This paper examines CV-based collision-prevention systems from academic and industrial perspectives with a focus on identifying technical and implementation challenges affecting their deployment on construction sites. A scoping review of 158 publications was conducted, alongside an assessment of commercial CV-based collision-prevention systems currently used in construction. The analysis focused on five key functions: object detection, distance measurement and proximity estimation, tracking, worker re-identification (ReID), and deployment robustness. Academic studies demonstrate substantial progress in detection accuracy, real-time processing, trajectory tracking, and predictive risk assessment through deep learning …

Microbial Collagenase Activity Is Linked To Oral–Gut Translocation In Advanced Chronic Liver Disease, Shen Jin, Aurelie Cenier, Daniela Wetzel, Bethlehem Arefaine, Mar Moreno-Gonzalez, Marilena Stamouli, Merianne Mohamad, Mariia Lupatsii, Emilio Ríos, Sunjae Lee, Ane Zamalloa, Shilpa Chokshi, Adil Mardinoglu, Saeed Shoaie, Naiara Beraza, Vishal C. Patel, Melanie Schirmer

Peninsula Medical School

Microbiome perturbations are associated with advanced chronic liver disease (ACLD), but how microorganisms contribute to disease mechanisms is unclear. Here we analysed metagenomes of paired saliva and faecal samples from an ACLD cohort of 86 individuals, plus 2 control groups of 52 healthy individuals and 14 patients with sepsis. We identified highly similar oral and gut bacterial strains, including Veillonella and Streptococcus spp., which increased in absolute abundance in the gut of patients with ACLD compared with controls. These microbial translocators uniquely share a prtC gene encoding a collagenase-like proteinase, and its faecal abundance was a robust ACLD biomarker (area …

Designing A Mobile App For Post Traumatic Stress Disorder: Insights From Lived Experience And Clinical Practice, Rohit Shankar, Richard Laugharne, Michael Loizou, Marius Varga, Sivia Lin, Bojana Daw Srdanovic, Roger Farrell

Peninsula Medical School

Post-traumatic stress disorder (PTSD) is a disabling anxiety disorder precipitated by severe trauma. Many people with this condition have little access to clinical interventions, especially in low-middle-income countries. For the development of any digital health intervention, it is vital to know the opinions of PLE and clinicians to guide the developers. We conducted two focus groups, one with clinicians (n = 5) and one with people with lived experience (PLE) (n = 11), to gain knowledge of their views on the development of a PTSD App. Transcripts of both focus groups were coded and thematically analysed. The key themes that …

Pab-Pif: A Hybrid Architecture To Evaluate Mutable And Immutable Blockchains In Iot–Fog Networks, Marco Scarpa, Mohammad Sadeghzadeh, Saeed Javanmardi, Bahareh Pahlevanzadeh, Mauro Iacono

Research Outputs: 2025-Present

Blockchain provides secure and decentralized data storage. Normal blockchains permanently store data. Mutable blockchains allow users to change data, but this reduces tamper resistance. This paper tests both methods in PaB-PIF, a hybrid architecture for IoT-Fog networks. Our design uses an immutable mainchain in the cloud layer and mutable sidechains in the fog layer. We analyze throughput, latency, and tamper resistance using math models and simulations. Results show that blockchain greatly improves network security. Without blockchain, the network has zero tamper resistance. The mutable blockchain in the fog layer has a tamper resistance of 0.58. The immutable blockchain in the …

Browser-Based Phishing Detection System Using Modern Web Technologies, Muhammad Arshad, Beena Sherin Kuriakose, Choo Wou Onn, Farhan Ahmad Siddiqui, Mohammad Shahid Kamal

Research Outputs: 2025-Present

Phishing remains one of the most persistent cybersecurity threats, exploiting human trust to steal sensitive information through deceptive websites. Traditional detection methods, reliant on blacklists and reactive reporting, offer limited protection against rapidly evolving zero-day attacks. To address these challenges, this study proposes and evaluates a hybrid browser-resident phishing detection framework that combines three complementary detection mechanisms: a locally executed Random Forest model using URL lexical features, lightweight real-time DOM structure analysis, and VirusTotal’s multi-engine reputation service. Communication between the client and server is optimised using gRPC over the QUIC protocol, ensuring secure, high-performance data exchange with built-in retry mechanisms …

Educating The Next Generation Of Engineers: Spatial Thinking Is Key To Their Success, Sheryl Sorby, Gavin Duffy, Jack Parkinson, Gibin Raju, John Lynch, Muhammad Asghar

Research Outputs: 2025-Present

This paper attempts to answer questions regarding the link between engineering success and spatial skills through three themes. The previous research of the authors in examining the questions surrounding each theme is the focus of this paper; we acknowledge that others around the world may have conducted similar research, but the specific findings of the authors are the main focus of this paper. In the first theme, we examine data from several author-led studies where positive correlations have been found between spatial skill levels and proficiency in tasks that are a part of what it means to be an engineer. …

3d-Vit-Unet: 3d Vision Transformer Based Unet-Like Model For Volumetric Brain Tumor Segmentation, Sikandar Afridi, Atif Jan, Muhammad Abeer Irfan, Muhammad Irfan Khattak, Taimur Ahmed Khan

Research Outputs: 2025-Present

Accurate volumetric segmentation of 3D medical imaging modalities is critical for therapy planning and clinical diagnosis, particularly for brain tumor delineation. Traditional convolutional neural network (CNN)-based architectures face challenges while capturing global contextual information and modeling long-range dependencies in complex 3D volumetric data, limiting their segmentation performance. Transformer-based models have emerged as promising alternatives to CNNs for such tasks, addressing their limitations in capturing global spatial dependencies. We propose 3D-ViT-UNet, a novel U-shaped vision transformer (ViT)-based encoder-decoder architecture for end-to-end volumetric brain tumor segmentation. The model employs 3D Window Multi-Head Self-Attention (3D-W-MSA) to capture local features and a 3D Dilated-Window …

Sustainable Development And Faith, Tony Kealy

Research Outputs: 2025-Present

The contribution of faith-based input to the broader idea of sustainable development has not been widely acknowledged in the traditional sense; however, recent global events suggest it may warrant further exploration. This qualitative study examines how faith-based activities, such as missionary work, have historically and can continue to contribute significantly to the environmental, economic, and human/social aspects of organizational decision-making. The focus is on the Catholic faith, specifically the role played by the Irish-based Medical Missionaries of Mary religious congregation in the mid-to-late twentieth century. An initial literature review was conducted to gather information from various sources to define sustainable …

A Taxonomy And Empirical Evaluation Of Webassembly Runtimes For Performance And Energy Across Machine Learning And Arithmetic Workloads, Sallar Khan, Tania Malik, Khalid Hasanov

Research Outputs: 2025-Present

WebAssembly (Wasm) has emerged as a lightweight, portable, and secure execution environment beyond the browser, with growing adoption in data-intensive, machine learning, and performance-sensitive workloads. However, the runtime ecosystem remains fragmented, and a systems-level understanding of how runtime design affects performance and energy efficiency is still lacking. This paper makes two contributions. First, it presents a taxonomy of modern WebAssembly runtimes that classifies the runtime landscape by execution model, compilation strategy, integration style, and systemslevel capabilities. Second, it provides an empirical evaluation of 13WASI-compatible runtimes and a native baseline across eight benchmarks, namely, four machine learning workloads (Logistic Regression, K-Means, …

Ai For Prognosis Among People Living With Hiv: Protocol For A Systematic Review And Meta-Analysis, Degninou Yehadji, Markus Hofmann, Petros Isaakidis, Smaila Alidou, Olushina Ayo Junior Ale, Kofivi Mawouko Yena, Kodjo Guidigan, Geraldine Gray, Nadjime Pindra, Gayo Diallo

Research Outputs: 2025-Present

Background: Advanced HIV disease remains a major global health concern, with nearly 40.8 million people living with HIV as of 2024. Antiretroviral therapy has improved outcomes, but its success depends on timely intervention, adherence, and retention in care. AI, including machine learning and deep learning, offers promising tools for prognostic modeling that could support clinical decision-making and personalized treatment. Existing syntheses, however, have been largely narrative and have not systematically evaluated the performance, risk of bias, reporting quality, or clinical readiness of AI-based prognostic models, leaving a critical gap in understanding their validity and applicability. Objective: This study aims to …

Editorial: Harnessing Of Soil Microbiome For Sustainable Agriculture: Harnessing Of Soil Microbiome For Sustainable Agriculture, Debasis Mitra, Marika Pellegrini, Leonard Koolman

Research Outputs: 2025-Present

No abstract provided.

Cognitive Causal Modeling Of Blockchain Adoption In Agri-Food Supply Chains: A Fuzzy-Dematel Approach, Ali Ala, Behnam Malmir, Hamed Baziyad, Vladimir Simic

Research Outputs: 2025-Present

Background / Introduction: Blockchain can improve agri-food supply-chain transparency, but adoption decisions are cognitively complex because managers judge interdependent criteria under uncertainty. This study frames adoption as cognitive causal decision modeling, aligning with cognitive computation through expert judgment, linguistic uncertainty, causal reasoning, and explainable decision support. Methods: We applied Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DEMATEL) to judgments from 20 experts. Eight technology-adoption criteria were modeled using triangular fuzzy numbers, and an impact relationship map was used to classify criteria by outgoing influence (R), incoming influence (C), prominence (R + C), and net causality (R − C). Results: Smart Contracts …

High-Sensitivity Fiber Optic Temperature Sensor Based On Vernier Effect In A Polymethyl Methacrylate Capillary And Barium Titanate Microsphere, Zhuochen Wang, Ginu Rajan, Zhe Wang, Anuradha Rout, Rayhan Habib Jibon, Anand Vadakkedathu Raveendran, Yuliya Semenova

Research Outputs: 2025-Present

This paper proposes and investigates a novel fiber Fabry–Perot interferometer (FPI) temperature sensor that leverages the Vernier effect for enhanced sensitivity. The sensor comprises a cascade of two FPIs formed by a short polymethyl methacrylate capillary, a barium titanate microsphere, and a cleaved single-mode fiber. The Vernier effect, induced by the dual Fabry–Perot cavities, amplifies the temperature sensitivity. The fabrication process involves the precise alignment and gluing of components, resulting in a simple, cost-effective sensor. The experimental results indicate a temperature sensitivity of 1.90 nm °C⁻¹. The sensor also exhibits excellent repeatability and stability, making it suitable for …

Post: Pruning Oriented Security For Inversion Attack In Edge-Based Internet Of Things, Sunder Ali Khowaja, Abi Waqas, Mohammad Tabrez Quasim, Kapal Dev

Research Outputs: 2025-Present

With the recent emergence of artificial intelligence (AI), edge users in industries and manufacturing have been extensively using AI-based services, which pose privacy and security risks to data. Distributed learning approaches deployed in the manufacturing industries help reduce data risks. However, traditional approaches cannot handle deep models as well as the scalability of edge-based internet of things (E-IoT) devices, especially in the manufacturing sector. Studies have proposed SplitFed learning (SFL) by combining split learning and the federated learning paradigm, but they fail to achieve an optimal trade-off between communication and computational limitations and are vulnerable to inversion attacks. We present …

Influence Of Beamwidth, Gain Stability, And Antenna Element Spacing On Aoa Accuracy In Ieee 802.15.4z Uwb Systems, Hossein Raghib Hokmabadi, Wasi Ur Rehman Khan, Patrick Mcevoy, Max James Ammann

Research Outputs: 2025-Present

Precision is a crucial requirement in indoor localization, and the industrial sector increasingly demands technologies that provide both high accuracy and low cost. This work presents a hardware-based evaluation of dual-antenna arrays using an IEEE 802.15.4z-compliant UWB angle of arrival (AoA) development kit to assess antenna performance in terms of phase difference of arrival (PDoA)/AoA linearity. Four antenna types: 1) printed monopole; 2) CPW-fed slot; 3) stacked dielectric resonator antenna (DRA); and 4) dual-layer microstrip patch are evaluated on channels 5 and 9, with measurements performed in a calibrated anechoic chamber under controlled conditions. The results show that wide beamwidth …

Design And Optical Analysis Of A Non-Ideal Parabolic Trough Concentrator For Uniform Flux Distribution In Photovoltaic Applications, Muhammad Tayyab, Qamar Abbas, Muhammad Rizwan Shad, Philippe Lemarchand, Abida Shaheen, Sarah J. Mccormack, Hassan Bin Shahid, Jawad Sarwar

Research Outputs: 2025-Present

Concentrating photovoltaic (CPV) systems utilize optical elements to focus sunlight onto small-area solar cells. Among different concentrators, Parabolic trough concentrators (PTCs) have simple design and offer medium concentration ratios (typically 30–100 × ). But PTCs are line focus and their spatial non-uniform flux distribution limit their use in CPV applications. This study presents a geometric redesign of conventional PTCs, transforming their line focus into a spatially uniform focus and referred as a segmented non-ideal parabolic trough concentrator (NI-PTC). A numerical model is developed and validated. The validated model is used to investigate performance of NI-PTC by varying design parameters such …

Preclinical Ischemic Stroke Multicenter Trials (Prism) Collective Statement: Opportunities, Challenges, And Recommendations For A New Era, Cenk Ayata, Philip M Bath, Anna M Planas, Stuart M Allan, Johannes Boltze, Ryan P Cabeen, Claire L Gibson, Marilyn J Cipolla, Marcio A Diniz, Stefano Fumagalli, Fahmeed Hyder, Raymond C Koehler, Arthur Liesz, Sarah K Mccann, Tim Magnus, Louise D Mccullough, Emily S Sena, Simone Beretta, Jaroslaw Aronowski, Francesca Bosetti, Clinton B Wright, Patrick D Lyden, Lauren H Sansing

Faculty, Staff and Student Publications

Preclinical stroke research faces a critical translational gap, with animal studies failing to reliably predict clinical efficacy. To address this, the field is moving toward rigorous, multicenter preclinical randomized controlled trials (mpRCTs) that mimic phase 3 clinical trials in several key components. This collective statement, derived from experts involved in mpRCTs, outlines considerations for designing and executing such trials. mpRCTs offer advantages such as increased sample sizes, robust statistical design, incorporation of heterogeneity, and standardized protocols, but they face challenges in finding the right balance between standardization and heterogeneity, appropriate stroke model selection, and outcome measures, as well as the …

Host Modulation Therapy In Periodontitis: From Established Therapies To Emerging Technologies, Aonjittra Phanrungsuwan, Jiaqi Huang, Neeraja Dharmaraj, Alejandra Cobos Perez, Omid Veiseh, Simon Young, Chun-Teh Lee

Faculty, Staff and Student Publications

Periodontitis is a biofilm-induced chronic inflammatory disease, characterized by gingival inflammation and alveolar bone loss. According to a national survey, approximately half of the U.S. adults are affected by periodontal disease. To effectively prevent and treat periodontitis, it is essential to address its underlying causes. The primary etiological factors include polymicrobial synergy and dysbiosis of the oral microbiota, and a dysregulated immune response. The standard therapeutic approach, mechanical removal of biofilm through debridement, sometimes demonstrates limited efficacy, particularly in cases of severe periodontitis, which may require adjunctive or additional therapy. Emerging evidence indicates that periodontal tissue destruction is initiated by …

Streamlining Eligibility Assessment For Alzheimer’S Disease-Modifying Therapies: Prediction Of Mmse Scores Using The Digital Clock And Recall, Ali Jannati, Claudio Toro-Serey, Marissa Ciesla, Emma Chen, John Showalter, David Bates, Alvaro Pascual-Leone, Sean Tobyne

Faculty, Staff and Students Publications

           Introduction: The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks of perpetuating health disparities, given the test's known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR™)-a rapid, FDA-listed digital cognitive assessment-to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage.

Methods: We conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 (BH) study ( …

Serum Protein Signatures For Breast Cancer Detection In Treatment-Naïve African American Women Using Integrated Proteomics And Pattern Analysis, Padma Tadi Uppala, Elmer Rivera, Hyun J. Kwon, Sharon S. Lum

Faculty Publications

Breast cancer is the leading cause of cancer-related mortality in African American (AA) women. In this study we evaluated the serum proteomic profile of AA women with breast cancer using an integrated proteomic framework with multivariate pattern analysis. Using 2D-DIGE, thousands of serum protein spots were detected across 33 gels; 46 spots met criteria for presence, statistical significance, and differential expression. Proteins from the spots were identified by MALDI-TOF/TOF and matched in curated databases, highlighting serum biomarkers including ceruloplasmin, alpha-2-macroglobulin, complement component C3 and C6, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein and haptoglobin-related protein. LC-MS/MS analysis revealed 163 differentiating peptides after imputing and …

Applications And Challenges Of Utilizing Digital Pathology And Ai-Enabled Workflows In Clinical Trials, Manu Sebastian, Harsh Batra, Monika Lamba Saini, Staci Kearney, Lorcan Sherry, Serge Alexanian, Michael Cohen, William Weber, Joe Lennerz, Anil V Parwani

Faculty, Staff and Student Publications

This is a comprehensive review on current utilization and challenges of digital pathology adoption in clinical trials and aims to provide a broad view on its impact on pathology review processes in clinical trials. It provides an overview of current pathology review practices in clinical trials and unique advantages digital pathology adoption can offer. The key areas including existing workflows, use case scenarios in different disease areas in clinical trials, including but not limited to patient identification and pre-screening, and regulatory aspects have been described with relevance. In addition, the review delves into the integration of genomics, AI, image analysis, …

Digital Communication Strategies For Culinary Medicine In Cancer Prevention And Survivorship: Information-Seeking Habits And Content Preferences Of Online Cooking Media Among Adults Participating In Four Cancer Prevention And Control Cohort Studies, Margaret Raber, Sara Fares, Maria Vazquez, Larkin Strong, Carrie Daniel-Macdougall, Lorna Mcneill, Karen Basen-Engquist

Faculty, Staff and Student Publications

Introduction

Poor diet and excess weight have been linked to increased risk for at least 13 types of cancer. Culinary medicine utilizes experiential cooking skill development to improve individuals’ capacity for healthy eating. Digital communication strategies offer pathways for scalable culinary medicine interventions, but little research has explored how online cooking tools could be leveraged for cancer prevention messaging. We conducted a cross-sectional survey study exploring online cooking information-seeking habits and content preferences among participants in four cancer prevention and control cohorts to inform future digital culinary medicine interventions.

Methods

A cross-sectional survey study was conducted with a convenience sample …