Sage Journals: Discover world-class research

Abstract

Objectives:

This study aimed to explore the relationship between multidimensional factors, such as environment, health status, behavior, social support, and the well-being of middle-aged and older adults.

Methods:

This study utilized data from 2 waves of the nationally representative Taiwan Longitudinal Study on Aging Survey Report (TLSA) conducted in 2015 and 2019. The TLSA assesses socioeconomic status, physical and health status, the 5-item World Health Organization Well-Being Index (WHO-5 index), and social support. Data regarding the degree of digital development were obtained from the 2020 Township Digital Development Report. We applied a generalized estimating equation (GEE) to analyze the influencing factors.

Results:

This study included 4796 participants. Residing in areas with a higher degree of digital development, having a higher socioeconomic status, and experiencing better physical and mental health were significantly associated with well-being. Furthermore, emotional and attentive support mediated the association between physical and mental status and well-being.

Conclusion:

People’s awareness of searching for and receiving social support and medical resources is important for enhancing their well-being. It is also crucial to pay attention to the living environment and maintain one’s health status to promote well-being.

Keywords

well-being social support cohort mediation effect

Introduction

According to the 2023 World Happiness Report, based on the Gallup World Poll, the subjective well-being scores are as follows: 6.6 in Taiwan, 6.7 in the United States, 6.7 in the United Kingdom, 6.2 in Japan, 7.7 in Finland (highest), and 1.3 in Afghanistan (lowest).¹ The low levels of well-being are closely linked with common mental disorders such as depression, anxiety, and stress-related disorders, contributing to the burden on mental health and the economy.² The pathway between the disruption of well-being activities and depressive symptoms with the mediators of social connectedness and rumination has been proposed.³ The rapid pace of social change, increased economic activity, expanded infrastructure, and a growing older population have influenced population well-being.^4,5 Economic prosperity and urbanization pose environmental threats, such as air pollution, and are associated with health risks, including high rates of obesity, hypertension, type 2 diabetes, and cardiovascular diseases, due to lifestyle and dietary habits.⁴ The combination of aging and health risks leads to a decrease in subjective well-being, as reported in a nationwide cohort study of U.S. adults aged 50 years and older.⁶ Previous studies have extensively explored the influence of economic support, social support, health status, and work satisfaction on the well-being using various scales. One commonly used scale is the 5-item World Health Organization Well-Being Index (WHO-5), which has clinical validity and serves as an adequate screening tool for depression.⁷ Previous studies have shown that low income, the absence of a partner, and social dysfunction are risk factors for unhappiness.^8,9 Living in an urban area, being more physically active, and in better health are significantly associated with better well-being.^10,11 Furthermore, a higher density of neighborhood health and financial facilities and social cohesion were associated with subjective well-being, as reported in a community-based study conducted through a questionnaire survey in Dongguan City, China.¹² The effect of social desirability on total subjective well-being was significantly positive among cognitively healthy community-dwelling older people recruited from Sardinia, Italy.¹³ In addition, several studies have included workers and retirees separately to explore satisfaction.^14,15 Individuals without jobs tend to have higher well-being than those who are dissatisfied with their work. Retirement can result in positive changes in well-being. Few studies have simultaneously explored the relationship between satisfaction and work/retirement status. In addition, a face-to-face survey conducted in Yulin County, Taiwan, where agriculture is the main industry, showed that social connections, economic support, and technology acceptance had significant positive predictive effects on the subjective well-being of the older individuals.¹⁶ However, no nationally representative study of older adults in Taiwan has explored the association between multidimensional factors and well-being, particularly in terms of follow-up duration. We explored the association between the development of residential areas, social support, work or retirement status, physical and mental status, and well-being through a nationally representative longitudinal survey of middle-aged and older adults.

Methods

Ethics

This study was approved by the Institutional Review Board of Biomedical Science Research, Academia Sinica (AS-IRB-BM-22053). Individually identified data were removed and anonymized throughout the study. This study was performed in accordance with the Declaration of Helsinki and followed an approved protocol.

Data Source and Study Population

We applied secondary data of both 2015 and 2019 Taiwan Longitudinal Study on Aging Survey Report (TLSA) from Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan (https://www.hpa.gov.tw/Pages/List.aspx?nodeid=108), and the data had to be analyzed in person at the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan. The TLSA is a nationally representative survey conducted every 3 to 4 years using a stratified 3-stage systematic random sampling method, with a completion rate exceeding 80% in each wave. Trained interviewers investigated the demographic characteristics, household structure, physical and mental status, medical care utilization, social support and welfare exchange, and economic status. The sample sizes for included participants aged 50 years and above in the 2015 and 2019 waves were 5304 and 4101, respectively (Figure 1). After excluding questionnaires answered by the proxy or both the respondents and proxies, the study included 8767 person-times. After removing missing values, we included 8059 person-times. A total of 3263 participants participated in both the 2015 and 2019 surveys; 1204 participants attended only the 2015 survey, and 329 participants only participated in the 2019 survey. If the participants had more than 1 record, we considered the earliest data as the baseline.

Figure 1.

Flowchart of selection process of study subjects.

To quantify the effect of urbanization on well-being, we used the 2020 Township Digital Development Report from the National Development Council’s open-source website (https://www.ndc.gov.tw/). This report classified 368 townships into 4 clusters based on their degree of digital development, which includes the structure of human resources, information application, education and culture, environment and information infrastructure, and social economy. These 4 clusters were ranked as very high (mature), high, medium, or low (starting) in the report.

Explanatory Factors

TLSA investigates marital status, socioeconomic level, economic satisfaction, work, retirement, social activity, house type, house ownership, number of child, number of people living together, living with child, satisfaction with cohabitant living together, health behavior such as number of days eating vegetable in a week, number of days eating fruit in a week, smoking, drinking, chewing betel nut, and exercise, health status, vision, hearing, comorbidity such as hypertension, diabetes, heart disease, stroke, cancer, kidney disease, hyperlipidemia, mental illness, dementia, and Parkinson, daily activity scale (DAS), Instrumental Activities of Daily Living scale (IADL), Activities of Daily Living (ADL), social support, WHO-5 index, loneliness scale, Center for Epidemiological Studies Depression Scale (CES-D Scale), and mental health and life satisfaction.

Socioeconomic levels were self-reported on a scale from 1 (worst) to 10 (highest), where values 1 to 4 were classified as low, 5 to 7 as medium, and 8 to 10 as high. The “working history” variable combined work and retirement conditions and was classified into 4 categories: work and retirement, work and no retirement, no work and retirement, and no work and no retirement.

We conducted a principal component analysis to extract the factors using 11 social support-related questions (Supplemental Material File). Based on a scree plot and oblique (promax) rotation, we categorized the questions into 3 factors: emotional support, supervisory support, and attentive support. Questions with factor loadings greater than 0.5 were considered significant and classified into the factor. The value of each factor was calculated by multiplying the score for each question by the factor loadings. We summarized the value of each factor to obtain the total well-being score. Lower total social support scores indicated more support.

The DAS, comprising 10 questions, focuses on assessing the degree of difficulty that individuals encounter when performing specific actions independently. A 4-point scale was used, with 4 answer options: “not difficult” (0 point), “some difficult”, “very difficult”, and “totally impossible” (4 points). IADL includes 9 items in 8 areas, including ability to use telephone, shopping, food preparation, housekeeping, laundry, traveling via car or public transportation, medication use, and ability to handle finances. Each item ranges from 0 (not difficult) to 3 (totally impossible). The ADL comprises 6 items: bathing, dressing, eating, toileting, transferring from bed to chair, and walking indoors. The total score ranged between 18 (maximum performance) and 0 (lack of performance). Mental health and life satisfaction were evaluated using 9 items to identify the degree of stress or trouble related to health, economics, work, and marital status. The total score ranged between 18 (indicating maximum stress or trouble) and 0 (indicating lack of stress or trouble). The loneliness scale consists of 7 items with scores ranging from 0 (indicating no loneliness) to 2 (indicating loneliness). The CES-D consists of 11 items and the total score ranges from 0 (no depression) to 33 (depression).

Outcome

In the WHO-5 questionnaire, which is our response variable, individuals were asked how they felt in the past 2 weeks in terms of their mood with 5-items on a 6-point scale, ranging from 0 (at no time) to 5 (all of the time).¹⁷ The Chinese version of the WHO-5 has been previously validated.¹⁸ The raw score was transformed into a scale ranging from 0 (absence of well-being) to 100 (maximum well-being) by multiplying by 4.⁷ Participants were classified into those with low and high well-being, based on the WHO-5 score. A WHO-5 cut-off score of ≤50 indicated low well-being, while a score of >50 indicated high well-being.

Statistical Analysis

We used the Wilcoxon rank-sum test for continuous variables and the chi-square test for categorical variables to test the differences between the low and high well-being groups at baseline. Residual analysis was conducted to examine which cells contribute significantly to the association when the chi-square test is significant. We presented adjusted residuals to show how far the expected values deviate from the observed values, and the cell or category is considered significant when its adjusted residual exceeds ±1.96. We applied a generalized estimating equation (GEE) and first-order autoregressive [AR(1)] correlation structure. We used mediation analyses to explore the mediating effect of social support on the associations between economic satisfaction, satisfaction with cohabitants living together, IADL, mental health and life satisfaction, and well-being. The time points of the variables used in the mediation analysis were identical. To quantify the magnitude of the mediation effect, the mediation proportion was computed by dividing the indirect effect by the total effect. The significance of the mediating effect was assessed using bootstrap resampling. Model validation was performed with 2000 bootstrap resampling iterations. All statistical analyses were conducted using RStudio,¹⁹ including the geepack package²⁰ and mediation package,²¹ and the level of significance was set at a 2-sided P-value of .05.

Results

At baseline, among the 4796 participants, those who were younger, married, had a higher socioeconomic level, were employed and not retired, lived with more people, expressed satisfaction with the economy and cohabitation, attended more social activities, lived in a house or villa, owned their house or had their spouse own it, had significantly higher well-being (Table 1).

Table 1.

Characteristics of Participants at Baseline in Relation to Well-being.

Characteristics	WHO-5 ≦ 50 (n = 930)		Adjusted residuals	WHO-5 > 50 (n = 3866)		Adjusted residuals	P-value
Age: Mean (SD)	65.47	10.28		63.48	9.42		<.001
Sex: N (%)							.05
Male	420	45.16%	−1.99	1886	48.78%	1.99
Female	510	54.84%	1.99	1980	51.22%	−1.99
Marriage: N (%)							<.001
Married	585	62.90%	−8.56	2962	76.62%	8.56
Never married	38	4.09%	3.10	88	2.28%	−3.10
Widowed	200	21.51%	4.61	590	15.26%	−4.61
Divorce and (formal) separation	107	11.51%	6.10	226	5.85%	−6.10
Socioeconomic level: N (%)							<.001
Low	456	49.03%	12.36	1081	27.96%	−12.36
Medium	452	48.60%	−8.64	2474	63.99%	8.64
High	22	2.37%	−6.12	311	8.04%	6.12
Economic satisfaction: N (%)							<.001
Yes	192	20.65%	−17.99	2066	53.44%	17.99
Ordinary	411	44.19%	4.12	1426	36.89%	−4.12
No	327	35.16%	19.75	374	9.67%	−19.75
Work status: N (%)							<.001
Work and no retirement	335	36.02%	−6.24	1831	47.36%	6.24
No work and retirement	198	21.29%	−3.03	1009	26.10%	3.03
No work and no retirement	397	42.69%	9.68	1026	26.54%	−9.68
Social activity: N (%)							<.001
Yes	289	31.08%	−6.29	1637	42.34%	6.29
No	641	68.92%	6.29	2229	57.66%	−6.29
House type: N (%)							<.001
Bungalow	91	9.78%	2.14	296	7.66%	−2.14
Walk-up apartment	139	14.95%	0.96	531	13.74%	−0.96
Sanheyuan	29	3.12%	3.18	60	1.55%	−3.18
House or villa	504	54.19%	−3.16	2315	59.88%	3.16
Apartment with elevators	143	15.38%	−0.59	625	16.17%	0.59
Other	24	2.58%	3.78	39	1.01%	−3.78
House ownership: N (%)							<.001
Self or spouse	555	59.68%	−5.68	2683	69.40%	5.68
Children or parents or ancestral property	210	22.58%	2.55	730	18.88%	−2.55
Rent or government or employer	103	11.08%	4.47	261	6.75%	−4.47
Nursing home	10	1.08%	5.25	3	0.08%	−5.25
Other	52	5.59%	0.88	189	4.89%	−0.88
Degree of town digital development: N (%)							.2
Very high (mature)	461	49.57%	−2.14	2067	53.47%	2.14
High	281	30.22%	1.69	1061	27.44%	−1.69
Medium	128	13.76%	0.89	490	12.67%	−0.89
Low (starting)	60	6.45%	0.04	248	6.41%	−0.04
Number of child(ren): Mean (SD)	2.90	1.45		2.80	1.27		.1
Number of people living together: Mean (SD)	3.68	2.13		3.82	2.09		.03
Living with child: N (%)							.6
Yes	639	68.71%	−0.55	2692	69.63%	0.55
No	291	31.29%	0.55	1174	30.37%	−0.55
Satisfaction with cohabitant living together: N (%)							<.001
Very satisfied	156	16.77%	−8.69	1201	31.07%	8.69
Satisfied	518	55.70%	−1.89	2285	59.11%	1.89
Neutral	214	23.01%	12.02	345	8.92%	−12.02
Not so satisfied	35	3.76%	6.85	32	0.83%	−6.85
Very dissatisfied	7	0.75%	4.05	3	0.08%	−4.05
Number of days vegetables are eaten in a week: Mean (SD)	6.41	1.49		6.75	0.99		<.001
Number of days fruits are eaten in a week: Mean (SD)	4.41	2.65		5.46	2.23		<.001
Smoking: N (%)							.1
Yes	336	36.13%	1.60	1290	33.37%	−1.60
No	594	63.87%	−1.60	2576	66.63%	1.60
Drinking in the past year: N (%)							<.001
Yes	265	28.49%	−4.54	1407	36.39%	4.54
No	665	71.51%	4.54	2459	63.61%	−4.54
Chewing betel Nuts: N (%)							.006
Yes	27	2.90%	−1.52	153	3.96%	1.52
Quit	84	9.03%	2.89	246	6.36%	−2.89
No	819	88.06%	−1.43	3467	89.68%	1.43
Exercise: N (%)							<.001
Yes	426	45.81%	−11.34	2548	65.91%	11.34
No	504	54.19%	11.34	1318	34.09%	−11.34
Health status: N (%)							<.001
Good	146	15.70%	−19.98	2010	51.99%	19.98
Ordinary	400	43.01%	3.19	1444	37.35%	−3.19
Not good	310	33.33%	18.21	384	9.93%	−18.21
Very poor	74	7.96%	13.73	28	0.72%	−13.73
Vision: N (%)							<.001
Clear	768	82.58%	−13.00	3673	95.01%	13.00
Unclear	162	17.42%	13.00	193	4.99%	−13.00
Hearing: N (%)							<.001
Clear	875	94.09%	−5.50	3772	97.57%	5.50
Unclear	55	5.91%	5.50	94	2.43%	−5.50
Hypertension: N (%)							<.001
Yes	450	48.39%	6.27	1438	37.20%	−6.27
No	480	51.61%	−6.27	2428	62.80%	6.27
Diabetes: N (%)							<.001
Yes	245	26.34%	7.54	611	15.80%	−7.54
No	685	73.66%	−7.54	3255	84.20%	7.54
Heart disease: N (%)							<.001
Yes	233	25.05%	10.86	437	11.30%	−10.86
No	697	74.95%	−10.86	3429	88.70%	10.86
Stroke: N (%)							<.001
Yes	77	8.28%	7.36	116	3.00%	−7.36
No	853	91.72%	−7.36	3750	97.00%	7.36
Cancer: N (%)							<.001
Yes	80	8.60%	5.10	172	4.45%	−5.10
No	850	91.40%	−5.10	3694	95.55%	5.10
Kidney disease: N (%)							<.001
Yes	79	8.49%	7.84	112	2.90%	−7.84
No	851	91.51%	−7.84	3754	97.10%	7.84
Hyperlipidemia: N (%)							<.001
Yes	249	26.77%	5.90	703	18.18%	−5.90
No	681	73.23%	−5.90	3163	81.82%	5.90
Mental illness: N (%)							<2e−16
Yes	87	9.35%	9.39	103	2.66%	−9.39
No	843	90.65%	−9.39	3763	97.34%	9.39
Dementia: N (%)							.005
Yes	13	1.40%	3.05	19	0.49%	−3.05
No	917	98.60%	−3.05	3847	99.51%	3.05
Parkinson: N (%)							<.001
Yes	18	1.94%	5.29	14	0.36%	−5.29
No	912	98.06%	−5.29	3852	99.64%	5.29
Daily Activity Scale (DAS): Mean (SD)	5.34	7.14		1.70	3.85		<.001
Instrumental Activities of Daily Living scale (IADL): Mean (SD)	2.80	5.90		0.61	2.58		<.001
Activities of daily living (ADL): Mean (SD)	0.83	2.82		0.11	1.01		<.001
Emotional support: Mean (SD)	6.78	2.36		5.36	1.72		<.001
Supervisory support: Mean (SD)	0.32	0.94		0.24	0.79		.6
Attentive support: Mean (SD)	3.04	1.29		2.24	0.97		<.001
Loneliness scale: Mean (SD)	5.25	2.33		3.19	1.76		<.001
CES-D scale: Mean (SD)	9.95	6.75		2.55	3.31		<.001
Satisfaction for mental health and life: Mean (SD)	5.69	4.86		2.65	3.29		<.001

Abbreviation: SD, Standard deviation; CES-D Scale, Center for Epidemiological Studies Depression Scale.

Bold indicates adjusted residuals that exceed ± 1.96.

However, participants who ate fewer vegetables or fruits per week tended to have lower levels of well-being. Those with lower levels of well-being were more likely to not drink, not chew or have quit betel nuts, and not engage in regular exercise. They also tended to have poor health status and physical performance (DAS, IADL, and ADL), receive less emotional and attentive support, experience depression and loneliness, and be less satisfied with their mental health and life.

Participants residing in areas with a higher degree of digital development, possessing a better health status, lacking hyperlipidemia, having a clear vision, experiencing higher economic satisfaction, belonging to a higher socioeconomic level, engaging in regular exercise, and reporting lower levels of depression and loneliness tended to have higher well-being (Table 2). Participants who had never been married tended to have lower well-being than those who were divorced or separated. Participants living in bungalows tended to have better well-being than those living in sanheyuan. Participants who currently chewed betel nuts tended to have higher well-being than those who had chewed betel nuts in the past but had quit. Participants who were retired tended to have higher well-being than those who had no work or retirement.

Table 2.

Parameter Estimations of the Association Between Multidimensional Factors and Well-being Using TLSA in Both the 2015 and 2019 Wave Surveys.

Factors	Estimate	95% CI
Degree of town digital development
High	Ref
Medium	−0.43	(−0.79, −0.07)*
Low	−0.47	(−0.84, −0.10)*
Marriage
Divorce and (formal) separation
Never married	−0.62	(−1.15, −0.09)*
House type
Sanheyuan
Bungalow	0.78	(0.24, 1.33)**
House or villa	0.47	(−0.03, 0.97)
Work status
No work and no retirement
Retirement	0.36	(0.15, 0.57)***
Health status
Very poor
Good	0.99	(0.42, 1.55)***
Hyperlipidemia
Yes
No	0.24	(0.05, 0.42)*
Chewing betel nuts
Ever, quit
Yes	0.88	(0.34, 1.42)**
Exercise
No
Yes	0.23	(0.08, 0.39)**
Vision
Unclear
Clear	0.29	(0.07, 0.50)**
CES-D scale	−0.23	(−0.25, −0.21)***
Loneliness scale	−0.19	(−0.23, −0.14)***
Economic satisfaction
No
Yes	0.60	(0.36, 0.84)***
Ordinary	0.19	(−0.02, 0.41)
Socioeconomic level
Low
High	0.46	(0.08, 0.84)*
Drinking in the past year
No
Yes	0.15	(−0.02, 0.33)
Emotional support	−0.04	(−0.10, 0.01)

The table displays the results of the GEE for variables that have significance or boundary significance in relation to well-being.

P-value: *.05, **.01, and ***.001.

Abbreviation: CI, Confidence Interval; CES-D Scale, Center for Epidemiological Studies Depression Scale.

Emotional and attentive support explained 18.4% and 17.8% of the mediating effects of the association between economic satisfaction and well-being, respectively (Figure 2). Emotional and attentive support mediated the association between satisfaction with cohabitants living together and well-being, with significant percentages of 43.5% and 34.1%, respectively. Emotional, supervisory, and attentive support explained 4.8%, 1.3%, and 26.5% mediation effect of the association between IADL and well-being, respectively. Emotional support and attentive support mediated the association between mental health and life satisfaction, and well-being, with significant percentages of 13.8% and 10.7%, respectively.

Figure 2.

Analytical framework for conducting mediation analysis of social support and well-being: (a) economic satisfaction, (b) satisfaction with cohabitant living together, (c) IADL, and (d) mental health and life satisfaction.

Discussion

Our results showed that residential area, house type, socioeconomic level, work and retirement status, physical and mental health status, health behavior, and marriage were associated with well-being. Comparing our study with those conducted in Yunlin County, Taiwan, on-site face-to-face surveys are challenging.¹⁶ Although the outcome variable, well-being, was the same, the factors exploring their association with well-being were not comparable. They considered factors included social connectedness, economic support, and technology acceptance. However, we delved into more dimensions. In addition to social support, we explored environmental, physical, and mental statuses.

According to the World Health Organization (WHO) definition, well-being is a resource for daily life and is determined by environmental, economic, and social conditions.²²

One dimension of well-being, environmental conditions, is influenced by rehibition areas and house conditions, and plays an important role in health, employment, recreation, and social opportunities.²³ Urban areas offer more education, employment, income and consumption opportunities, public transit, healthcare, and infrastructure, which positively affect the subjective well-being of their inhabitants.²⁴ However, urbanization increases living costs, work pressure, pollution, and crime, all of which can decrease subjective well-being.⁴ Results revealed that residential areas with a higher degree of digital development tended to have higher well-being. House type (eg, apartment, bungalow, and shared houses), house quality (eg, number of windows and amount of daylight exposure), neighborhood (eg, condition of pollution and noise), and green spaces have been reported to affect mental health and well-being.²³ People living in high-rise housing or apartment complexes, particularly women and children, were found to have poor overall mental health due to social isolation, limited social interactions, and restricted play opportunities.²³ In our results, we found that people who lived in bungalows tended to have higher well-being than those living in sanheyuan. One reason is that overcrowded households may lack privacy, have insufficient personal space, hinder social interactions, and social receptivity.²⁵

Objective measures of socioeconomic status include income, educational level, and occupation.²⁶ Participants experiencing higher economic satisfaction and belonging to a higher socioeconomic status tended to have higher well-being. People with low socioeconomic status may encounter diminished resources, uncertainty, and unpredictability, which are negatively related to psychological well-being.²⁶ People with a low income or those who are unemployed and experience economic stress tend to have lower life satisfaction.⁸

The transition to retirement is accompanied by challenges and opportunities and influences changes in mental well-being.¹⁵ We found that retired participants tended to have higher well-being than those who had no work or retirement. Retirement may result in positive changes in well-being due to reduced work stress and increased opportunities to engage in favored activities; however, losing the work role and work-related resources may result in negative changes in well-being.¹⁵ A UK longitudinal study reported positive effect of retirement on leisure satisfaction.²⁷ Resources in the work-to-retirement transition and adaptation processes are important for psychological well-being.²⁸

In our findings, participants with better health status, lack of hyperlipidemia, clear vision, and regular exercise tended to have higher well-being. Physical health has been widely proven to influence psychological well-being among older adults, and subjective health is strongly associated with psychological well-being. Furthermore, physical health impairments and biomarkers (eg, hemoglobin and albumin) exhibit independent direct effects on subjective health, thereby establishing an indirect association with psychological well-being among the older adults.²⁹

There is a bidirectional association between physical activity and life satisfaction. It improves physical and mental health, increases self-efficacy, and promotes life satisfaction and psychological well-being. People with higher life satisfaction may engage in more physical activity.^29,30

In our study, participants who chewed betel nuts tended to have higher well-being than those who had chewed betel nuts in the past but quit. Betel nuts contain several psychoactive compounds that stimulate the central and autonomic nervous system.³¹ Habitual users experience euphoria, sense of well-being, increased alertness, and stamina.³¹

We found that participants reporting lower levels of depression and loneliness tended to have higher well-being, similar to the findings of other studies. In a cross-sectional study, 40- to 60-year-old women with severe depression exhibited low happiness levels.⁹ A Nordic study found a strong negative relationship between loneliness and mental health among adolescents.³²

The positive effects of marriage, such as its physical, emotional, social, and economic benefits lead to greater well-being and life satisfaction. In our study, participants who were never married tended to have lower well-being than those who were divorced or separated. Compared with happy marriages, being divorced, widowed, or never married has more disadvantages. However, an unfair or problematic marriage is worse than an unmarried marriage.³³

Regarding the social aspect, in our results, a positive relationship between emotional support and well-being was of boundary significance, and the relationship between supervisory support, attentive support, and well-being was not significant. A related study found that confidential support, which refers to the availability of help, is more influential on well-being than affective support, which refers to the satisfaction of emotional needs. The emotional needs of older individuals may not be decisive in their perceptions of well-being. Confidential support indirectly helps satisfy emotional needs.³⁴ Older individuals’ emotional needs may not be decisive in their perceptions of well-being. Because confidential support indirectly helps satisfy emotional needs.³⁴ However, another study reported that family support, friend support, and closer social networks were associated with greater emotional well-being.³⁵ Mediation analysis revealed the potential role of social support, including emotional and attentive support, in the relationships among economic satisfaction, cohabitant satisfaction, mental health and life satisfaction, IADL, and overall well-being. One aspect of social support named supervisory support also mediated 1.3% of the association between IADL and well-being. The relationship between social support and well-being has been extensively established, acknowledging that social support provides both physical and psychological benefits.³⁶ A study conducted among university students during the COVID-19 school closure examined the mechanisms of life satisfaction and affective well-being, with perceived social support acting as a mediator. The findings revealed significant and positive associations between life satisfaction and affective well-being. Moreover, perceived social support, as a mediator, exhibited a significant and positive relationship with both life satisfaction and affective well-being.³⁷ Additionally, Zhang et al³⁸ proposed that social support mediates the association between physical and mental health and subjective well-being in older individuals. However, the direct effect of physical health on subjective well-being (SWB) was not significant. Although physical health is not directly associated with well-being, the deterioration of physical health can sometimes lead to decreased mental health, ultimately resulting in lower well-being. This mediating effect validates the value-added spiral effect of Conservation of Resources theory. Older individuals with better physical health are more likely to engage in social activities, thereby expanding their relationships with others, enhancing their self-identification, obtaining higher social support, and perceiving greater well-being. With regard to different cultures, Uchida et al³⁹ pointed out that in North American cultural contexts, happiness tends to be predicted by personal achievement and self-esteem. In contrast, in East Asian cultural contexts, happiness is often defined in terms of interpersonal connectedness. Regional cultural differences should also be considered when influencing well-being.

Strengths

This study has several strengths. First, this was a nationally representative retrospective cohort study with an extremely high survey response rate. The dataset was derived from a large randomly selected sample, which contributes to its high external validity. Second, we considered various multidimensional factors associated with well-being, including socioeconomic status, living environment, health status, health behaviors, job status, and social support. Third, other than urbanization based on population density and housing unit density, the digital development we used was classified by human resource structure, information application, education and culture, environment and information infrastructure, and social economy.

Limitations

First, a potential reporting bias existed, as all data were self-reported and not objectively measured. However, we excluded proxy respondents to increase survey accuracy. Second, subjective well-being is influenced by multidimensional factors such as socioeconomic status, personality, social support, health and physical functioning, religion, and culture, as well as geography and infrastructure. Unfortunately, due to data unavailability, potential confounders were not considered in our analysis. Third, the participants were investigated repeatedly every 3 to 4 years. However, in our study, a few participants were only included once. Therefore, the short-term factors that may influence well-being were not controlled owing to the 4-year survey interval.

Conclusion

This study emphasizes the importance of raising awareness about physical and mental health and actively seeking social resources to promote well-being, especially among the older population. Additionally, enhancing the accessibility of social support and improving the environment are beneficial for increasing well-being.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319241272023 – Supplemental material for Association of Well-Being in Middle-Aged and Older Adults With Enhanced Personal Health and Social Support: A Nationally Representative Cohort Study

Supplemental material, sj-docx-1-jpc-10.1177_21501319241272023 for Association of Well-Being in Middle-Aged and Older Adults With Enhanced Personal Health and Social Support: A Nationally Representative Cohort Study by Ping-Chen Chung, Yen-Sheng Chiang, Yi-Chien Liu, Yi-Fang Chuang, Hsiao-Han Hsu and Ta-Chien Chan in Journal of Primary Care & Community Health

Footnotes

Author’s Note

Yi-Chien Liu is also affiliated to Fu Jen University School of Medicine, Taipei, Taiwan. Yi-Fang Chuang is also affiliated to Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei, Taiwan.

Author Contributions

Ping-Chen Chung: Writing—original draft. Hsiao-Han Hsu: Formal analysis and Project administration. Ta-Chien Chan: Conceptualization, Data curation, Methodology, Supervision, and Writing—review & editing. Yen-Sheng Chiang: Funding acquisition and interpretation of the results. Yi-Chien Liu, Yi-Fang Chuang: Methodology and interpretation of the results.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Academia Sinica, Taiwan (grant number AS-TP-112-H03).

Ethical Approval

Consent for Publication

Not applicable.

ORCID iD

Ta-Chien Chan

Data Source and Study Population

Data Availability Statement

The data that support the findings of this study are available from Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan (). However, restrictions apply to the availability of these data, which are under approval for the current study and thus, are not publicly available. The datasets used in this study had to be analyzed in person at the Health and Welfare Data Science Center, Ministry of Health and Welfare, Taiwan and only the aggregated data and the statistical results can be carried out after the examination process.

Supplemental Material

Supplemental material for this article is available online.

References

Helliwell

Layard

Sachs

Aknin

De Neve

J-E

Wang

eds. World Happiness Report 2023. 11th ed. Sustainable Development Solutions Network; 2023.

Santini

Ekholm

Koyanagi

, et al. Higher levels of mental wellbeing predict lower risk of common mental disorders in the Danish general population. Mental Health Prevent. 2022;26:200233.

McMahon

Douglas

Casey

Ahern

Disruption to well-being activities and depressive symptoms during the COVID-19 pandemic: the mediational role of social connectedness and rumination. J Affect Disord. 2022;309:274-281.

Tay

Ocansey

RTA

. Impact of urbanization on health and well-being in Ghana. Status of research, intervention strategies and future directions: a rapid review. Front Public Health. 2022;10:877920.

Wang

Kim

The relationship between physical-mental comorbidity and subjective well-being among older adults. Clin Gerontol. 2020;43(4):455-464.

Nakamura

Hong

Smith

, et al. Associations between satisfaction with aging and health and well-being outcomes among older US adults. JAMA Network Open. 2022;5(2):e2147797.

Topp

Østergaard

Søndergaard

Bech

The WHO-5 well-being index: a systematic review of the literature. Psychother Psychosom. 2015;84(3):167-176.

Ergin

Mandiracioglu

Demographic and socioeconomic inequalities for self-rated health and happiness in elderly: the situation for Turkey regarding World Values Survey between 1990 and 2013. Arch Gerontol Geriatr. 2015;61(2):224-230.

Allahverdipour

Karimzadeh

Alizadeh

Asghari Jafarabadi

Javadivala

Psychological well-being and happiness among Middle-aged women: a cross-sectional study. Healthc Women Int. 2021;42(1):28-42.

10.

Singh

Kshtriya

Valk

Health, hope, and harmony: a systematic review of the determinants of happiness across cultures and countries. Int J Environ Res Public Health. 2023;20(4):3306.

11.

Han

Shibusawa

Trajectory of physical health, cognitive status, and psychological well-being among Chinese elderly. Arch Gerontolo Geriatr. 2015;60(1):168-177.

12.

Yuxi

Ingviya

Sangthong

Wan

Determinants of subjective well-being among migrant and local elderly in China: a cross-sectional study. BMJ Open. 2022;12(6):e060628.

13.

Fastame

Hitchcott

Penna

MP.

Does social desirability influence psychological well-being: perceived physical health and religiosity of Italian elders? A developmental approach. Aging mental Health. 2017;21(4):348-353.

14.

Ray

TK.

Work related well-being is associated with individual subjective well-being. Ind Health. 2022;60(3):242-252.

15.

Henning

Segel-Karpas

Stenling

Huxhold

Subjective well-being across the retirement transition: historical differences and the role of perceived control. Psychol Aging. 2022;37(3):388-400.

16.

Chung

M-L

Fung

KK-W

Liu

C-L.

Enabling factors of subjective well-being among older adults in Taiwan: the welfare state and beyond?

J Gerontol Soc Work. 2021;64(4):348-371.

17.

Organization

. Wellbeing Measures in Primary Health Care/The DepCare Project: Report on a WHO Meeting: Stockholm, Sweden, 12-13 February 1998. World Health Organization. Regional Office for Europe;1998.

18.

Lin

Lee

Huang

Sun

Tsen

HF.

Psychometric properties of the Taiwanese version of the World Health Organization-Five Well-Being index. Acta Psychiatr Scand. 2013;127(4):331.

19.

RStudio Team. RStudio: Integrated Development for R. RStudio, PBC. 2020. Accessed May 2, 2023. http://www.rstudio.com/

20.

Ulrich

Søren

Jun

The R Package geepack for generalized estimating equations. J Stat Softw. 2006;15/2:1-11.

21.

Tingley

Yamamoto

Hirose

Keele

Imai

Mediation: R Package for causal mediation analysis. J Stat Softw. 2014;59(5):1-38.

22.

World Health Organization. Promoting Well-being. 2023. Accessed November 20, 2023. https://www.who.int/activities/promoting-well-being

23.

Riva

Rebecchi

Capolongo

Gola

Can homes affect well-being? A scoping review among housing conditions, indoor environmental quality, and mental health outcomes. Int J Environ Res Public Health. 2022;19(23):15975.

24.

Navarro

D’Agostino

Neri

The effect of urbanization on subjective well-being: explaining cross-regional differences. Soc Econ Plan Sci. 2020;71:100824.

25.

Ruiz-Tagle

Urria

Household overcrowding trajectories and mental well-being. Soc Sci Med. 2022;296:114051.

26.

Navarro-Carrillo

Alonso-Ferres

Moya

Valor-Segura

Socioeconomic status and psychological well-being: revisiting the role of subjective socioeconomic status. Front Psychol. 2020;11:1303.

27.

Kesavayuth

Rosenman

Zikos

The dynamic effects of retirement on well-being. Appl Econ. 2020;52(54):5921-5935.

28.

Kubicek

Korunka

Raymo

Hoonakker

Psychological well-being in retirement: the effects of personal and gendered contextual resources. J Occup Health Psychol. 2011;16(2):230-246.

29.

Cho

Martin

Margrett

Macdonald

Poon

LW.

The relationship between physical health and psychological well-being among oldest-old adults. J Aging Res. 2011;2011:605041.

30.

Chen

Wang

Yang

Huang

Fan

SY.

The relationships between physical activity and life satisfaction and happiness among young, middle-aged, and older adults. Int J Environ Res Public Health. 2020;17(13):4817.

31.

Garg

Chaturvedi

Gupta

PC.

A review of the systemic adverse effects of areca nut or betel nut. Indian J Med Paediatr Oncol. 2014;35(1):3-9.

32.

Lyyra

Thorsteinsson

Eriksson

, et al. The association between loneliness, mental well-being, and self-esteem among adolescents in four Nordic countries. Int J Environ Res Public Health. 2021;18(14):7405.

33.

Williams

Has the future of marriage arrived? A contemporary examination of gender, marriage, and psychological well-being. J Health Soc Behav. 2003;44(4):470-487.

34.

Farriol-Baroni

González-García

Luque-García

Postigo-Zegarra

Pérez-Ruiz

Influence of social support and subjective well-being on the perceived overall health of the elderly. Int J Environ Res Public Health. 2021;18(10):5438.

35.

Ermer

Proulx

CM.

Associations between social connectedness, emotional well-being, and self-rated health among older adults: difference by relationship status. Res Aging. 2019;41(4):336-361.

36.

Feeney

Collins

NL.

A new look at social support: a theoretical perspective on thriving through relationships. Pers Soc Psychol Rev. 2015;19(2):113-147.

37.

Huang

Wang

Socioeconomic status and students’ mental health during the COVID-19 university closure: mediating roles of perceived social support and self-efficacy. Behav Sci. 2023;13(10):871.

38.

Zhang

Sun

The health status, social support, and subjective well-being of older individuals: evidence from the Chinese General Social Survey. Front Public Health. 2024;12:1312841.

39.

Uchida

Norasakkunkit

Kitayama

Cultural constructions of happiness: theory and emprical evidence. J Happiness Stud. 2004;5(3):223-239.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB