ArticleHeliyon2023
Effect of online and offline social network group clusters on life satisfaction across age groups.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Life satisfaction and risk of dementia in older adults: findings from 15 European countries and meta-analysis.Age and ageing · 2025Pooled it
- Community social capital and depressive symptoms among older adults relocated for poverty alleviation in Shanxi, China: a cross-sectional study.BMC public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Positive social relationships are critical for better subjective well-being across ages. Future research will benefit from examining how to improve life satisfaction by utilizing social groups in new, ever-changing social and technological contexts. This study aimed to evaluate the effect of online and offline social network group clusters on life satisfaction across different age groups. Methods: Data were derived from the Chinese Social Survey (CSS) (2019), which is a nationally representative survey. We adopted a K-mode cluster analysis algorithm to categorize participants into four clusters according to their online and offline social network groups. ANOVA and chi-square analysis were used to understand the associations among age groups, social network group clusters, and life satisfaction. Multiple linear regression was applied to identify the association between social network group clusters and life satisfaction across age groups. Results: Younger and older adults had higher life satisfaction than middle-aged adults. Individuals who joined diverse social network groups had the highest life satisfaction, followed by those who joined personal and working social groups, while those who joined restricted social groups had the lowest life satisfaction (F = 81.19, p < 0.001). According to the results of multiple linear regression, individuals who belonged to diverse social groups had higher life satisfaction than those who belonged to restricted social groups among adults aged 18-59 years, except students (p < 0.05). Individuals who joined personal and working social groups had higher life satisfaction than those who joined restricted social groups among adults aged 18-29 and 45-59 years (β = 2.15, p < 0.01; β = 1.45, p < 0.01). Conclusions: Interventions to promote participation in diverse social network groups among adults aged 18-59 years, except for students, are highly recommended to improve life satisfaction. Health practitioners could provide interventions to encourage young and middle-aged adults to join both personal and working social groups.
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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.