Observational studyJournal of global health2024
Key lifestyles and health outcomes across 16 prevalent chronic diseases: A network analysis of an international observational study.
Observational study in Journal of global health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A study of the prosocial behavior of middle-aged and elderly sports club members: a network analysis.BMC public health · 2025Article
- Article
- Individuals' positive gains from the COVID-19 pandemic: a qualitative study across 30 countries.Journal of global health · 2025Article
- Country-specific key lifestyle factors and health outcomes for resource allocation in the general population: a network analysis across 29 countries.Journal of global health · 2025Article
- Understanding facilitators and barriers to adherence in home-based pulmonary rehabilitation for chronic obstructive pulmonary disease patients using the Health Belief Model: a qualitative study.Frontiers in public health · 2025Article
- Global spatio-temporal evolution and health inequalities in high BMI-associated kidney cancer burden from 1990 to 2021 and burden prediction to 2040.Frontiers in oncology · 2025Article
- Psychological Status and its Influencing Factors of Staff in a District of Shenzhen: A Retrospective Study.Actas espanolas de psiquiatria · 2025Article
- Network analysis of chronic disease among middle-aged and older adults in China: a nationwide survey.Frontiers in public health · 2025Article
- Longitudinal associations between Big Five personality and insomnia: evidence based on a 4-year perspective cohort study among community residents.Frontiers in psychology · 2025Article
- Burden of disease attributable to high body mass index: an analysis of data from the Global Burden of Disease Study 2021.EClinicalMedicine · 2024Article
- The Prevalence, Characteristics, and Factors Associated with Chinese Herbal Medicine Use Among Chinese Middle-Aged and Older Adults with Chronic Lung Disease: A Cross-Sectional Study.Journal of multidisciplinary healthcare · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
68 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Central and bridge nodes can drive significant overall improvements within their respective networks. We aimed to identify them in 16 prevalent chronic diseases during the coronavirus disease 2019 (COVID-19) pandemic to guide effective intervention strategies and appropriate resource allocation for most significant holistic lifestyle and health improvements. Methods: We surveyed 16 512 adults from July 2020 to August 2021 in 30 territories. Participants self-reported their medical histories and the perceived impact of COVID-19 on 18 lifestyle factors and 13 health outcomes. For each disease subgroup, we generated lifestyle, health outcome, and bridge networks. Variables with the highest centrality indices in each were identified central or bridge. We validated these networks using nonparametric and case-dropping subset bootstrapping and confirmed central and bridge variables' significantly higher indices through a centrality difference test. Findings: Among the 48 networks, 44 were validated (all correlation-stability coefficients >0.25). Six central lifestyle factors were identified: less consumption of snacks (for the chronic disease: anxiety), less sugary drinks (cancer, gastric ulcer, hypertension, insomnia, and pre-diabetes), less smoking tobacco (chronic obstructive pulmonary disease), frequency of exercise (depression and fatty liver disease), duration of exercise (irritable bowel syndrome), and overall amount of exercise (autoimmune disease, diabetes, eczema, heart attack, and high cholesterol). Two central health outcomes emerged: less emotional distress (chronic obstructive pulmonary disease, eczema, fatty liver disease, gastric ulcer, heart attack, high cholesterol, hypertension, insomnia, and pre-diabetes) and quality of life (anxiety, autoimmune disease, cancer, depression, diabetes, and irritable bowel syndrome). Four bridge lifestyles were identified: consumption of fruits and vegetables (diabetes, high cholesterol, hypertension, and insomnia), less duration of sitting (eczema, fatty liver disease, and heart attack), frequency of exercise (autoimmune disease, depression, and heart attack), and overall amount of exercise (anxiety, gastric ulcer, and insomnia). The centrality difference test showed the central and bridge variables had significantly higher centrality indices than others in their networks (P < 0.05). Conclusion: To effectively manage chronic diseases during the COVID-19 pandemic, enhanced interventions and optimised resource allocation toward central lifestyle factors, health outcomes, and bridge lifestyles are paramount. The key variables shared across chronic diseases emphasise the importance of coordinated intervention strategies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.