Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study

Abstract

Background The national health promotion program in the twenty-first century Japan (HJ21) correlates life purpose with disease prevention, facilitating the adoption of healthy lifestyles. However, the influence of clustered healthy lifestyle practices on life purpose, within the context of this national health campaign remains uninvestigated. This study assessed the association between such practices and life purpose, in line with the HJ21. Methods We performed a nationwide cross-sectional survey on certified specialists in health management. Participants’ demographic information, lifestyle, and purpose in life were measured using a validated tool. The cohort was median-split into two groups based on their clustered health-related lifestyle score. The values for health-related lifestyle and purpose were compared between the two groups and the correlation between health-related lifestyle and purpose in life was measured. Results Data from 4820 participants were analyzed. The higher-scoring health-related lifestyle group showed a significantly higher life purpose than the lower group (35.3 vs 31.4; t = 23.6, p < 0.001). There was a significant association between the scores of clustered healthy lifestyle practices and life purpose (r = 0.401, p < 0.001). The higher-scoring health-related lifestyle group achieved a higher life purpose than the lower-scoring group. This association between healthy lifestyle practices and life purpose denotes a positive and linear relationship. Conclusions Our results suggest that individuals who have a better health-related lifestyle gain a higher sense of life purpose. In other words, a healthy lifestyle predicts a purpose in life. Our findings posit that examining the causal relationship between healthy lifestyle and purpose in life may be a more efficient approach toward health promotion.

Ikigai and subsequent health and wellbeing among Japanese older adults: Longitudinal outcome-wide analysis

“…Moreover, we could not assess some important outcomes that could be related to Ikigai (e.g., diet for the health behavior domain) as we lacked these information. 58 Fourth, the 3-year follow-up period may have been too short for some of the beneficial effects of Ikigai to manifest, which could explain the lack of conclusive evidence for an association with all-cause mortality and other null findings in this study. Our ad-hoc analysis indicated that changes from 2013 to 2016 were relatively small for some outcomes; for example, only 6% of the analytic sample (n=506) died during the three-year follow-up, and only 3% (n=162) experienced changes in the binary self-reported health outcome (Table S9).…”

Negative and Positive Psychosocial Factors in Relation to Cognitive Health in Older African Americans

“…There are several pathways by which purpose in life may be related to cognitive health. First, older individuals with greater purpose in life appear more likely to have better health behaviors; previous research has suggested that purpose in life is related to more use of preventive health services ( E. S. Kim et al, 2014 ), to healthy dietary habits and regular exercise ( Hirooka et al, 2021 ; E. S. Kim et al, 2020 ), which have been associated with cognition and dementia risk ( Kivipelto et al, 2018 ). Another possible pathway is that purpose is related to biological processes relevant to cognitive function ( Ryff et al, 2006 ).…”

What makes life purposeful? Identifying the antecedents of a sense of purpose in life using a lagged exposure-wide approach

“…These prior studies have made important contributions to the literature but remain somewhat limited. First, while an increasing number of studies are longitudinal, many older studies are cross-sectional and cannot assess directionality ( Hedberg et al, 2010 ; Hirooka et al, 2021 ; Stoddard & Pierce, 2015 ; Sumner, 2017 ). Second, many studies did not adequately adjust for key potential confounders (e.g., only adjusting for basic demographics).…”

“…Third, most studies only evaluated a limited number of predictors, so that we cannot directly compare effect sizes, which is helpful when trying to determine intervention targets that might produce the largest effects. Fourth, many studies use data from small samples or specific subpopulations (e.g., cancer survivors), limiting generalizability to broader populations ( George & Park, 2013 ; Hirooka et al, 2021 ). Fifth, some studies used suboptimal assessments of purpose (e.g., single-item measures).…”