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Record W2762574041 · doi:10.2196/publichealth.7875

College Freshmen Students’ Perspectives on Weight Gain Prevention in the Digital Age: Web-Based Survey

2017· article· en· W2762574041 on OpenAlexvenueno aff
Courtney M. Monroe, Gabrielle Turner‐McGrievy, Chelsea Larsen, Karen Magradey, Heather M. Brandt, Sara Wilcox, Beth Sundstrom, Delia Smith West

Bibliographic record

VenueJMIR Public Health and Surveillance · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesArnold School of Public Health, University of South CarolinaUniversity of South Carolina
KeywordsWeb surveyPsychologyMedical educationComputer scienceGerontologyWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: College freshmen are highly vulnerable to experiencing weight gain, and this phenomenon is associated with an increased risk of chronic diseases and mortality in older adulthood. Technology offers an attractive and scalable way to deliver behavioral weight gain prevention interventions for this population. Weight gain prevention programs that harness the appeal and widespread reach of Web-based technologies (electronic health or eHealth) are increasingly being evaluated in college students. Yet, few of these interventions are informed by college students' perspectives on weight gain prevention and related lifestyle behaviors. OBJECTIVE: The objective of this study was to assess college freshmen students' concern about weight gain and associated topics, as well as their interest in and delivery medium preferences for eHealth programs focused on these topics. METHODS: Web-based surveys that addressed college freshmen students' (convenience sample of N=50) perspectives on weight gain prevention were administered at the beginning and end of the fall 2015 semester as part of a longitudinal investigation of health-related issues and experiences in first semester college freshmen. Data on weight gain prevention-related concerns and corresponding interest in eHealth programs targeting topics of potential concern, as well as preferred program delivery medium and current technology use were gathered and analyzed using descriptive statistics. RESULTS: A considerable proportion of the freshmen sample expressed concern about weight gain (74%, 37/50) and both traditional (healthy diet: 86%, 43/50; physical activity: 64%, 32/50) and less frequently addressed (stress: 82%, 41/50; sleep: 74%, 37/50; anxiety and depression: 60%, 30/50) associated topics within the context of behavioral weight gain prevention. The proportion of students who reported interest in eHealth promotion programs targeting these topics was also generally high (ranging from 52% [26/50] for stress management to 70% [35/50] for eating a healthy diet and staying physically active). Email was the most frequently used electronic platform, with 96% (48/50) of students reporting current use of it. Email was also the most frequently cited preferred eHealth delivery platform, with 86% (43/50) of students selecting it. Facebook was preferred by the second greatest proportion of students (40%, 20/50). CONCLUSIONS: Most college freshmen have concerns about an array of weight gain prevention topics and are generally open to the possibility of receiving eHealth interventions designed to address their concerns, preferably via email compared with popular social media platforms. These preliminary findings offer a foundation to build upon when it comes to future descriptive investigations focused on behavioral weight gain prevention among college freshmen in the digital age.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.073
GPT teacher head0.440
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2017
Admission routes1
Has abstractyes

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