Observational studyBMJ open2022
Machine-learning approaches to identify determining factors of happiness during the COVID-19 pandemic: retrospective cohort study.
Observational study in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Enhancing happiness and life satisfaction in university students: analysis with a machine learning approach.BMC psychology · 2025Article
- Article
- Profiles of subjective well-being among people living with HIV during the COVID-19 pandemic.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023Article
- Partner relationships, hopelessness, and health status strongly predict maternal well-being: an approach using light gradient boosting machine.Scientific reports · 2023Article
- Cultural values and changes in happiness in 78 countries during the COVID-19 pandemic: An analysis of data from the World Happiness Reports.Frontiers in psychology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveTo investigate determining factors of happiness during the COVID-19 pandemic.
designObservational study.
settingLarge online surveys in Japan before and during the COVID-19 pandemic.
participantsA random sample of 25 482 individuals who are representatives of the Japanese population.
main outcome measureSelf-reported happiness measured using a 10-point Likert scale, where higher scores indicated higher levels of happiness. We defined participants with ≥8 on the scale as having high levels of happiness.
resultsAmong the 25 482 respondents, the median score of self-reported happiness was 7 (IQR 6-8), with 11 418 (45%) reporting high levels of happiness during the pandemic. The multivariable logistic regression model showed that meaning in life, having a spouse, trust in neighbours and female gender were positively associated with happiness (eg, adjusted OR (aOR) for meaning in life 4.17; 95% CI 3.92 to 4.43; p<0.001). Conversely, self-reported poor health, anxiety about future household income, psychiatric diseases except depression and feeling isolated were negatively associated with happiness (eg, aOR for self-reported poor health 0.44; 95% CI 0.39 to 0.48; p<0.001). Using machine-learning methods, we found that meaning in life and social capital (eg, having a spouse and trust in communities) were the strongest positive determinants of happiness, whereas poor health, anxiety about future household income and feeling isolated were important negative determinants of happiness. Among 6965 subjects who responded to questionnaires both before and during the COVID-19 pandemic, there was no systemic difference in the patterns as to determinants of declined happiness during the pandemic.
conclusionUsing machine-learning methods on data from large online surveys in Japan, we found that interventions that have a positive impact on social capital as well as successful pandemic control and economic stimuli may effectively improve the population-level psychological well-being during the COVID-19 pandemic.
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