ArticlePatient education and counseling2019
Assessing the impact of the public nutrition information environment: Adapting the cancer information overload scale to measure diet information overload.
Article in Patient education and counseling, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Confusion and nutritional backlash from news media exposure to contradictory information about carbohydrates and dietary fats.Public health nutrition · 2019Trial
- Article
- Cancer Information Overload Across Time: Evidence from Two Longitudinal Studies.Health communication · 2023Article
- Measuring information overload and message fatigue toward COVID-19 prevention messages in USA and China.Health promotion international · 2023Article
- How can environmental degradation and income disparities influence national health: an eye bird view on China's provinces.Frontiers in public health · 2023Article
- Article
- Unmet Nutritional and Psychological Needs of Cancer Patients: An Integrated Multi-Professional Model Approach.Diseases (Basel, Switzerland) · 2022Article
- Determinants of Social Commerce Usage and Online Impulse Purchase: Implications for Business and Digital Revolution.Frontiers in psychology · 2022Article
- Pathway linking health information behaviors to mental health condition during the COVID-19 infodemic: A moderated mediation analysis.Frontiers in public health · 2022Article
- Article
- Cancer-related Beliefs and Preventive Health Practices among Residents of Rural versus Urban Counties in Alabama.Cancer prevention research (Philadelphia, Pa.) · 2021Observational
- "You Are Not Alone"-Opportunities and Challenges for University Students' Collaborative Engagement When Dealing With Online Information About COVID-19.Frontiers in psychology · 2021Article
- Beyond fatalism: Information overload as a mechanism to understand health disparities.Social science & medicine (1982) · 2018Article
- Information overload regarding COVID-19: Adaptation and validation of the cancer information overload scale.Indian journal of psychiatryArticle
- ChatGPT and human dietitian responses to diet-related questions on an online Q&A platform: A comparative study.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectiveA growing body of research suggests that exposure to too much information - particularly contradictory information that characterizes much health-related information - can lead to feeling overwhelmed. This construct has been conflated with fatalistic beliefs that are negatively associated with preventive behaviors. The objective of this study was to adapt the 8-item Cancer Information Overload (CIO) scale to assess overload of healthy diet information.
methodsConfirmatory factor analyses with a community sample of rural California adults (n = 290; 75% female; 58% Latino; 46% ≤ H.S./G.E.D.).
resultsItems assessing Diet Information Overload loaded significantly on their relevant factor; factor loadings were acceptable (β >.40). The adapted original scale (CFI = 1.000, RSMEA = .000, SMSR = .022) and a shorter 5-item scale (CFI = .984, RMSEA = .051, SMSR = .026) fit well.
conclusionThe Cancer Information Overload scale was successfully adapted and shortened to measure perceptions - previously mischaracterized as fatalistic - pertaining to diet information. Improved measures distinguishing between fatalistic beliefs and outcomes of the information environment are critical. PRACTICE IMPLICATIONS: Understanding information overload is important for shaping prevention messages distinct from those needed to address fatalistic beliefs. Nutrition education efforts should consider the broader - cluttered - information environment in which nutrition education and communication occurs, and public health messages may drown.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.