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Pre-elderly families experience a significant impact on their quality of life as they transition to old age. This study aimed to identify and analyze the preparatory measures taken by families as they enter aging and develop an index to measure their readiness for aging based on physical, economic, and social aspects.
Methods
This study employed a quantitative approach, drawing secondary data from the 2019 Performance Accountability Survey of the Family Planning and Family Development Program. Binary logistic regression was used to examine the bivariate relationships between demographic characteristics and family readiness.
Results
Physical readiness was the most prevalent among pre-elderly families (88.1%), followed by economic (74.0%) and social (50.2%) readiness. Higher readiness was significantly associated with higher education (adjusted odds ratio [AOR], 3.39), urban residence (AOR, 1.39), health insurance ownership (AOR, 1.95), unemployment (AOR, 1.22), and awareness of aging programs (AOR, 1.62), whereas sex, family structure, and number of children were not significantly associated.
Conclusion
To increase physical readiness, families should be encouraged to use health services such as community health centers and perform regular medical check-ups. Additionally, this study suggests government intervention through outreach and guidance on all dimensions of old-age preparation.
Over the last three decades (1971–2017), the elderly population in Indonesia doubled to 23.4 million (Statistics Indonesia, 2019). According to statistics from the Aging Population of 2023, almost 12% of Indonesia’s population is aged 60 years and above, with the dependency ratio of older adults to the productive population increasing to 17%. Although Indonesia is currently experiencing an aging population, the pre-elderly population aged 45 to 59 years has reached 49 million (17.8%) of the total population [1]. Consequently, those of pre-elderly age will soon contribute to the statistics of the elderly population.
Globally, healthy aging has become a strategic agenda promoted by the World Health Organization (WHO) through the Decade of Healthy Aging 2021–2030. WHO defines healthy aging as the process of maintaining functional abilities that enable individuals to live independently, actively, and achieve well-being in old age [2]. This approach emphasizes the importance of cross-lifecycle interventions, where preparation for healthy aging needs to begin during the productive years and before the elderly stage. Thus, the pre-elderly phase becomes a window of opportunity to develop healthy behaviors, strengthen economic resilience, and increase social support for well-being in old age.
The transition into old age presents significant challenges as people experience substantial physical, mental, health, social, and economic changes. Data indicate that 44% of older adults in Indonesia have multimorbidity, including conditions such as hypertension, stroke, heart disease, kidney failure, and diabetes mellitus [3]. Other studies have demonstrated that multiple diseases in the elderly population reduce quality of life, increase the risk of death, and constitute a socio-economic burden in old age [4]. A longitudinal study of 2,960 older adults in Indonesia revealed that multimorbidity was more prevalent in older adults who were obese, had low levels of education, were unemployed, active smokers, engaged in light physical activity, and had insufficient consumption of vegetables and fruits [5]. Research conducted in three European countries revealed that the most significant influence on older adults’ life satisfaction levels is their health condition, rather than their age [6].
In addition to experiencing various diseases and decreased physical abilities, older adults in Indonesia experience diverse illnesses, economic problems, and financial insufficiencies. One in two older adults engage in paid work to meet their daily needs. Their limited economic capacity also results in many older adults being unable to afford nutritious foods such as fruits and vegetables. Studies have shown that improved health conditions are correlated with higher levels of happiness in pre-elderly and elderly families, followed by economic status and place of residence [7]. Furthermore, the happiness and contentment of older adults are also determined by socialization, as well as the frequency and quality of communication with family members and friends [6].
The challenges faced by the elderly population can be mitigated by proper preparation for aging. The Indonesian government has implemented various programs to support this goal. One such initiative is Bina Keluarga Lansia (BKL), which aims to educate and empower families with older members through physical, spiritual, and social readiness by focusing on enhancing the quality of life of Indonesia’s aging population. Furthermore, the government launched Usaha Peningkatan Pendapatan Keluarga Sejahtera (UPPKS), a program designed to increase family income and well-being, particularly for disadvantaged households [8]. Despite ongoing efforts, current initiatives have not specifically targeted pre-elderly demographics, even though preparing for this stage is crucial for ensuring a healthy and socially fulfilling old age.
In Indonesia, a dearth of research examines how families with members aged 45 to 59 years (pre-elderly) prepare for their later years. While previous studies have focused largely on the elderly population, this study aimed to bridge this gap by exploring the preparatory measures taken by pre-elderly families as they approach old age. The research objectives included identifying and analyzing these preparations, delineating the characteristics of pre-elderly families in Indonesia, and developing an index to measure their readiness for aging. Furthermore, this study sought to elucidate the factors that influence pre-elderly families’ preparedness for later life. The findings of this study can inform strategies to support pre-elderly families in maintaining productivity and ensuring a high quality of life as they transition to old age.
Methods
This study employed a quantitative approach, drawing on secondary data from the 2019 Performance Accountability Survey of the Family Planning and Family Development Program (SKAP KKBPK). The survey utilized a nationally representative Family Questionnaire Module designed to yield provincial-level data. A stratified multi-stage sampling approach was adopted for the survey. The initial sampling frame comprised a list of villages/subdistricts across Indonesia, categorized by urban/rural classification and the wealth index. Subsequent stages involved cluster selection within the selected villages/subdistricts, followed by door-to-door household listings within these clusters. The survey encompassed 34 provinces, including 67,594 households and 69,662 families across 1,935 clusters. The unit of analysis in this study was the household heads in the pre-elderly age group (45–59 years) with a sample of 25,378 respondents (weighted data). Details on the SKAP KKBPK method has been provided elsewhere [9].
The dependent variable in this study was the pre-elderly readiness index. This composite index encompasses three primary dimensions: physical (health maintenance and risk avoidance), economic (financial preparation and health insurance coverage), and social (interpersonal engagement, mental wellness, and spiritual well-being). The index was calculated using the Booysen scoring method [10]. The process involved aggregating responses across each aspect of pre-elderly preparation: physical, economic, and social. The total score (Xij) was determined by identifying the minimum and maximum values from the aggregated pre-elderly responses. These scores were then combined to yield a value for each aspect, resulting in an average score for old-age preparation dimensions, with equal weighting applied. This uniform weighting approach can be used to determine the cause-effect relationships among unspecified indicators [11]. The scoring formula for each aspect of elderly preparation was as follows:
Iij=Xij-Min{Xjk}Max{Xjk}-Min{Xjk}×100
The composite index value for pre-elderly old-age preparation was then calculated by averaging the old-age preparation indices in physical, economic, and social aspects using the following formula:
Preparation index for pre-elderly=I1+I2+I33
With definition:
I1=preparation index for physical aspect
I2=preparation index for economic aspect
I3=preparation index for social aspect
Categorization of the three aspects was carried out using the World Health Organization Quality of Life (WHOQOL) 2012 scale [12]; however, not all variables could be included because of data limitations. Among the six domains outlined in the framework, this study was able to operationalize only three primary dimensions based on the variables available in the SKAP 2019 dataset (Table 1). The physical aspect was represented by indicators of preventive health behavior and physical health maintenance. The economic aspect was reflected through financial preparedness and health insurance ownership, representing material and economic security in later life. Meanwhile, the social aspect encompassed participation in social, community, and religious activities, reflecting interpersonal engagement, mental wellness, and spiritual well-being.
The readiness index ranges from 0 to 100, where 0 indicates the lowest level of readiness for old age and 100 indicates optimal readiness. On the basis of the composite score, the readiness level is categorized into two groups: (1) high readiness: score ≥mean total score (≥47.55) and (2) low readiness: score <mean total score (<47.55). The independent variables included sex, education, place of residence, economic status, family type, number of children, work status, and participation in elderly focused programs such as elderly family development (BKL) and prosperous family economic empowerment efforts (UPPKS).
Data processing and statistical analyses were performed using the IBM SPSS ver. 25.0 (IBM Corp.). Univariate analysis (frequency) was conducted to provide a descriptive summary of all variables. Binary logistic regression was subsequently employed in Model 1 to examine the bivariate relationship between pre-elderly demographic characteristics and readiness for aging, yielding odds ratios and 95% confidence intervals. In the multivariate logistic regression analysis, only independent variables with P<0.05 were considered to assess their association with the outcome while controlling for other variables in the model. In Model 2, binary logistic regression was applied to examine the factors influencing pre-elderly readiness and yielded adjusted odds ratios with 95% confidence intervals at a significance level of P<0.05.
This study analyzed secondary data from a government-administered survey. As the data were fully anonymized and publicly available, ethical review and informed consent were not required.
Results
The results revealed that the vast majority of pre-elderly household heads were male (90.4%), with most having primary (47.9%) or secondary (42.4%) education (Table 2). No significant difference was observed between pre-elderly families living in urban and those living in rural areas. Most pre-elderly families were employed (93.8%) and belonged to the middle-income class (52.0%). Nearly all pre-elderly families (99.1%) had 0–2 children living with them and 89.5% were from intact families. Interestingly, a larger proportion of pre-elderly families (57.8%) lacked exposure to elderly oriented programs, whereas 42.2% were familiar with initiatives such as BKL and UPPKS.
Figure 1 depicts the economic, physical, and social readiness of pre-elderly adults facing old age. The physical aspect was the most common among pre-elderly adults (88.1%). A large majority of pre-elderly families (85.0%) reported efforts to maintain physical health. However, less than 40% of pre-elderly adults avoided risky behaviors. Economic preparation was slightly less prevalent than physical preparation, with 74.0% of pre-elderly families addressing this aspect. Although 66.7% of pre-elderly families claimed to be economically ready, only 26.1% had health insurance coverage. In addition, the social aspect had the lowest level of preparation (50.2%). Only 26.4% of pre-elderly participants were actively engaged in building social networks or activities. In comparison, 42.0% of pre-elderly adults took measures to maintain their mental and spiritual well-being.
The readiness index was constructed from three key dimensions–physical, economic, and social, with an overall score calculated for each demographic group (Table 3). The analysis revealed disparities in old age preparedness across population subgroups. Males and Females showed comparable readiness scores (47.61 vs. 47.04); female scored slightly higher in the physical and social aspects, whereas male were economically more prepared. Higher education was strongly associated with better readiness (63.19 vs. 41.97 for primary education), particularly in the economic domain (73.14 vs. 36.60). Urban residents also scored higher than their rural counterparts (51.27 vs. 43.77), as did pre-elderly adults with higher income (56.14 vs. 42.36 for low-income). Nuclear families demonstrated marginally greater readiness than single-parent families, and smaller families (0–2 children) were better prepared than larger ones, likely because of lower financial strain. In addition, nonworking pre-elderly adults showed higher readiness, especially socially, than did those employed. Finally, awareness of elderly activity programs contributed substantially to readiness, with informed individuals achieving much higher social scores (40.43 vs. 29.68, respectively).
Table 4 summarizes the socio-demographic and economic factors associated with pre-elderly families’ aging preparedness. Families with lower education focused more on physical readiness, whereas those with higher education showed better social preparedness. Urban residents demonstrated higher economic and physical readiness than their rural counterparts. Middle-income and wealthier families were generally more prepared, particularly in economic and social aspects, whereas lower-wealth groups emphasized physical readiness. Families with fewer children and those employed were better prepared than larger or nonworking families. Awareness of elderly activity programs (BKL and UPPKS) was also linked to greater social preparedness. Statistical analysis (P<0.001) showed that education, residence, wealth quintile, employment status, and awareness of elderly activities were significantly associated with physical readiness, whereas economic and social readiness were influenced by overlapping but distinct sets of socio-demographic factors.
As shown in Table 5, the logistic regression analysis revealed that pre-elderly families with a high level of education are 3.39 times more likely to engage in preparations for old age than those with lower educational attainment. Similarly, the likelihood of pre-elderly adults residing in urban areas preparing for old age is 1.39 times greater than that of their counterparts in rural areas. Additionally, pre-elderly adults with health insurance have a 1.95 times greater probability of preparing for old age than those without health insurance. Notably, nonworking pre-elderly adults are 1.22 times more likely to prepare for old age than are their working counterparts. Families familiar with elderly focused programs (BKL and UPPKS) demonstrate a 1.62 times greater likelihood of preparing for old age. Factors such as sex, family composition, and number of living children did not significantly affect preparedness for old age.
The correlation analysis shows that all three readiness aspects (physical, economic, and social) are positively and significantly associated with the readiness index (P<0.01) (Table 6). The social aspect has the strongest correlation (r=0.721), followed by the physical (r=0.641) and economic (r=0.594) aspects. These results indicate that social participation, physical health maintenance, and financial preparedness each contribute meaningfully to pre-elderly readiness for old age, highlighting the multidimensional nature of aging preparedness.
Discussion
This study sought to identify and understand the preparation of pre-elderly families for old age. Although the overall preparation index was low, the physical dimension scored relatively higher than did the economic and social dimensions. This suggests that physical readiness may be better maintained than economic or social readiness. Previous studies have confirmed that preolder adults are commonly physically better prepared than for other aspects such as psychological, financial, environmental, and social aspects [13]. Typically, physical readiness is associated with preventing chronic pain and reducing hopelessness, potentially improving quality of life [14]. Therefore, the natural decline in health and physical condition with age often manifests as various disease symptoms and physical complaints, and finally a diminished quality of life [15]. A Korean study found that awareness of successful aging and good health conditions strongly predicts old-age preparation, emphasizing the importance of health literacy and psychosocial awareness during midlife. In this context, the relatively high index of physical aspects may reflect a growing attention of pre-elderly adults to a healthy lifestyle such as avoiding smoking and other risky behaviors.
However, readiness for aging cannot rely solely on physical well-being. A systematic review concluded that financial and health planning are the two most essential preparations for aging. Thus, financial preparation is equally crucial, as it is closely linked to the ability to meet healthcare, long-term care, and daily life needs [16], which subsequently influence the quality of life of older adults. Arguably, higher income levels, sufficient financial resources, and home ownership contribute positively to physical health and social well-being [17] and improve accessibility to various services, particularly those focused on elderly adults [18]. Equally important, despite its low index, fostering social engagement among pre-elderly adults helps strengthen interpersonal relationships that support overall health and well-being [19]. Ultimately, preparing for old age should encompass all aspects and involve all life stages, including adolescence and middle age.
In this study, several socio-demographic characteristics were associated with the readiness index, including education level, suggesting that those who have attained higher education levels were better prepared to enter old age. A growing body of literature showed that education increases the probability of having a house, healthy diet, and the maintenance of intellectual skills [20]. This can be explained by the evidence that as the education and income of older adults increase, their quality of life also improves. In other words, pre-elderly adults with sound financial standing and higher educational levels display a better preparation index than those with limited financial resources and education [21]. These findings further imply that education shapes a family’s economic capacity and spending behavior such as investments in housing, skill development, and nutrition, which collectively form the foundation for readiness in later life.
A positive association between socio-economic status and the aging-readiness index was also evident in this study. Previous research has shown that higher income and home ownership contribute positively to both physical and social health, as they facilitate access to various services, particularly those related to aging [22]. Therefore, pre-elderly adults from more advantaged families tend to demonstrate better physical and mental health. Similarly, a Korean study found that higher income levels were associated with greater retirement preparedness, which enhanced life satisfaction [18]. Interestingly, the present study also found that unemployed pre-elderly adults indicated a relatively higher level of preparation for old age. This can be explained by several interrelated psychosocial and contextual factors. The absence of work-related activities allows pre-elders to manage their time and engage in emotionally meaningful activities. They tend to have more time for doing regular exercises, maintaining a healthy diet, and avoiding work-related stress [23].
However, unemployment may lead to a reduced regular income and limited access to economic resources such as savings and investments resulting in economic dependence on other family members. This may reflect that health, financial stability, and social participation are key determinants of successful aging transitions, not employment status per se. Previous evidence illustrates that financial insecurity, particularly difficulties in meeting basic needs and saving for the future, is a major barrier to adequate aging preparation [16]. Among working-class groups, income instability often constrains the ability to afford healthcare and other essential needs, which ultimately affect overall well-being and highlight the importance of social protection mechanisms. Creating income-generating activities, including starting a small business in the pre-retirement period and improving financial literacy, are also necessary preparations that should be started in the younger ages.
This study further reveals the important role of insurance ownership in enhancing aging readiness, which is consistent with the findings from previous studies [16]. Although insurance ownership refers to various types of health insurance such as a national health insurance subsidized by the government, individually financed schemes, and private insurance obtained through workplaces or self-enrollment, the findings highlight that awareness and participation in health insurance programs may contribute to aging preparation [24]. Furthermore, health insurance may be associated with financial planning related to healthcare and service utilization. With declining household income due to retirement and simultaneous health deterioration, fulfilling healthcare expenditure becomes essential for the pre-elderly. Insurance may reduce the burden of healthcare expenses.
Another key determinant of aging preparedness is place of residence. Pre-elderly families living in urban areas are better prepared for old age than those living in rural settings, likely because of education levels, better economic status, and easier access to information and services [25]. In contrast, older adults in rural areas often experience economic burdens and must continue working to meet their daily needs. The availability of health facilities and aging-supportive infrastructure such as nursing services and elderly friendly transportation also contributes to greater readiness among urban residents. These findings highlight the need to address regional disparities by strengthening healthcare access and social infrastructure in rural communities to ensure more equitable aging preparedness across regions.
In addition to socio-demographic factors, this study highlights the importance of awareness of elderly activity programs initiated by the Indonesian government such as BKL and UPPKS, to prepare pre-elderly adults for old age. Participation in such programs encourages social engagement and supports active aging. Numerous studies have shown that social participation protects against isolation, promotes physical activity, and improves quality of life [18]. Evidence from Hong Kong suggests that declining physical and financial conditions can limit older adults’ participation in social activities, leading to lower life satisfaction [26]. Moreover, research in developed countries emphasizes that social and cultural recognition, along with active communication within the community, are crucial for maintaining well-being among the elderly [27].
Overall, the results demonstrate that aging preparedness involves a multidimensional process, reinforcing the need for actionable approaches to enhance readiness among vulnerable groups. Beyond its statistical function, the readiness index developed in this study holds potential as a practical tool for primary care and community-based health promotion. In clinical practice, family physicians and public health workers could use the composite index as a screening instrument to identify individuals or families at risk of low readiness for aging. This index would allow for early, tailored interventions focusing on preventive health, economic counseling, or social support linkage. In community contexts, the index can serve as a monitoring and evaluation framework for programs such as Posbindu Lansia or BKL, guiding resource allocation and assessing the impact of agingreadiness initiatives [2].
The index’s strength lies in its multidimensional and integrative nature, simultaneously capturing physical, economic, and social preparedness. This comprehensive approach enables health practitioners to detect multidomain vulnerabilities that may go unnoticed. However, its imitations include dependence on self-reported data and the restricted number of available variables in secondary datasets, which may not capture psychosocial nuances such as emotional resilience or family caregiving dynamics [28,29]. Future research should aim to refine this index by incorporating more behavioral and psychological indicators, validating it in clinical settings, and exploring its predictive value for health outcomes and quality of life among pre-elderly and elderly populations.
The gaps across dimensions of aging preparedness indicate the need for comprehensive strategies to enhance readiness among pre-elderly families, not only strengthen economic security but also expand access to health insurance and preventive health services, particularly for low-income groups. Simultaneously, enhancing social participation through community and religious engagement can improve social well-being, which remains the least developed dimension of aging readiness. This is aligned with the policy frameworks enacted by the government, including the national action plan for the elderly (Rencana aksi nasional lanjut usia 2020–2024) and the National Strategy on Aging (Strategi Nasional Kelanjutusiaan) [30]. These frameworks not only emphasized the crucial roles of physical and economic preparedness for maintaining health, independence, and financial stability, but also the significance of social readiness, as reflected in community-activity participation and social supports, for promoting healthy, independent, active, and productive older people. They also highlight a life course approach, considering the essence of preparation for every life stage to embody individual well-being.
This study provides valuable policy-level insights into aging preparedness; however, its findings also offer direct, practice-oriented implications for family physicians who routinely engage with pre-elderly patients. The discussion should be expanded to include specific, actionable recommendations for clinical application. Family physicians can play a pivotal role in assessing and enhancing aging readiness by integrating a brief, multidomain screening or consultation framework that evaluates patients’ preparedness across health, financial, and social dimensions [28,29]. Incorporating short, structured screening questions into routine consultations allows physicians to identify individuals needing targeted support in lifestyle management, financial planning, or social engagement.
Those with low readiness may be referred to appropriate services such as nutrition counseling, psychosocial support, or community-based programs like BKL to enhance coping and planning capacities. Preventive care can be strengthened using educational and counseling strategies emphasizing health promotion, chronic disease control, emotional well-being, and long-term preparedness, by integrating such assessments into existing preventive care protocols, such as annual health checks or chronic disease management for aging. This approach empowers family physicians to promote holistic, anticipatory care that supports successful aging across physical, psychological, and social domains.
Study limitations
Because this study used a nationally representative dataset, the limited variables available in the data are insufficient to fully capture the diverse preparations of pre-elderly families for old age. This constraint restricts the study’s ability to fully capture the multidimensional concept of quality of life as defined in the WHOQOL framework. The cross-sectional design creates difficulty to establish causal relationships. Specifically, the direction of the relationship cannot be determined (e.g., whether higher education leads to better preparation or vice versa) and the temporal relationship between variables remains unclear. Additionally, selection bias may affect the generalizability of the findings to other Indonesian populations or international contexts. Finally, further research is needed to examine various aspects of how pre-elderly adults prepare for later life, including longitudinal designs to track readiness changes over time and intervention studies to test the effectiveness of preparation programs, including specific reliability and validity testing for aging-related constructions. Such research would provide stronger evidence on causal relationships and the long-term impact of specific interventions on health, economic stability, and social engagement among pre-elderly and elderly populations.
Conclusions
Aging preparation is a multidimensional concept. Each aspect of preparation supports the others and is equally important. This study confirms that awareness of old age readiness among pre-elderly families in Indonesia remains relatively low. Social readiness scores were the lowest among the physical and economic aspects, highlighting the need to promote pre-elderly participation in social, religious, and community activities. Vocational training can improve job skills for those with limited educational backgrounds to increase economic readiness. To increase physical readiness, pre-elderly families should be urged to use health services such as community health centers and undergo regular medical checkups. Health education should emphasize physical activity and discourage risky behaviors such as casual sex, smoking, and substance abuse. Family physicians can also help promote aging preparedness by incorporating brief, multidimensional assessments into routine consultations to identify and support pre-elders with lower readiness levels. Ultimately, this study suggests government intervention through outreach and guidance on all dimensions of old-age preparation. Comprehensive planning for aging is crucial for achieving healthy aging and should be initiated before individuals reach old age.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported
Acknowledgments
The authors would like to thank the Research and Development Center for Family Planning and Prosperous Families, National Population and Family Planning Board (BKKBN), for providing access to the 2019 SKAP dataset and supporting the implementation of this study.
Funding
None.
Data availability
Data used for study are available from the National Population and Family Planning board. Restrictions apply to the availability of these data, which were used under license for this study. Data are available by request to the Centre for Services of Information and Data (PPID), National Population and Family Planning board through https://e-ppid.bkkbn.go.id/.
Author contribution
Conceptualization: MMPN. Formal analysis: SLN. Visualization: AR. Writing–original draft: MMPN, SLN, RP, DNF. Writing–review & editing: MMPN, SLN, RP, DNF, AR. Final approval of the manuscript: all authors.
Figure 1
Readiness on physical, economic, and social aspects. Values are presented as %.
Table 1
Comparison between WHOQOL (2012) domains and indicators used in the SKAP 2019 study
WHOQOL domain (2012)
Domains and indicators used in the SKAP 2019
Physical health
Pain and discomfort
Energy and fatigue
Sleep and rest
Physical aspect
Health maintenance through regular check-ups or use of health facilities.
Results of binary logistic regression analysis on the impact of socio-economic characteristics on the preparation for aging among preelderly families, Indonesia 2019
Variable
Unadjusted OR (95% CI)
P-value
Adjusted OR (95% CI)
P-value
Sex
Male
1.095 (0.937–1.279)
0.255
-
-
Female
1
Education level
Low
1
1
Middle
1.529 (1.439–1.624)
<0.001
1.533 (1.443–1.628)
<0.001
High
3.388 (2.978–3.854)
3.391 (2.981–3.858)
Residence area
Rural
1
1
Urban
1.397 (1.320–1.478)
<0.001
1.393 (1.317–1.474)
<0.001
Insurance ownership
No
1
1
Yes
1.957 (1.849–2.070)
<0.001
1.956 (1.848–2.069)
<0.001
Family structure
Incomplete
1.019 (0.897–1.182)
0.799
-
-
Complete
1
Economic status
Low
1
1
Middle
1.108 (1.037–1.184)
<0.001
1.109 (1.038–1.184)
0.002
High
1.408 (1.283–1.546)
1.410 (1.285–1.548)
<0.001
No. of children
0–2
1.251 (0.942–1.661)
0.122
-
-
>2
1
Working status
No
1.287 (1.138–1.455)
<0.001
1.226 (1.096–1.371)
<0.001
Yes
1
1
Knowledge of elderly activities
No
1
1
Yes
1.623 (1.538–1.712)
<0.001
1.624 (1.539–1.713)
<0.001
Data processed by author based on data from SKAP 2019.
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Mental wellness and spiritual well-being (religious and spiritual activities)
Environment
Physical safety and security
Home environment
Financial resources
Access to health and social care services
Opportunities for leisure and recreation
Physical environment (pollution, noise, climate, and traffic)
Transport
Economic aspect
Financial preparation
Health insurance coverage (ownership of health insurance [BPJS] or private insurance)
Psychological health
Positive and negative feelings
Self-esteem
Body image and appearance
Thinking, learning, memory, and concentration
No data on stress, self-esteem, or overall life satisfaction.
Level of independence
Mobility
Activities of daily living
Dependence on medication or treatment
Work capacity
No variables on mobility, dependence on medication, or ability to perform daily tasks.
Spirituality/religion/personal beliefs
Meaning and purpose of life
Spiritual satisfaction
Personal beliefs related to life and health
No explicit measurement of life meaning, personal values, or spiritual fulfillment.
WHOQOL, World Health Organization Quality of Life; SKAP, Survei Kinerja dan Akuntabilitas Program; BPJS, Badan Penyelenggara Jaminan Sosial.
Demographic, social, and economic characteristics of pre-elderly families, Indonesia 2019
Characteristic
No. (%)
Sex
Male
22,942 (90.4)
Female
2,436 (9.6)
Education level
Primary
12,162 (47.9)
Secondary
10,765 (42.4)
Tertiary
2,451 (9.7)
Residence area
Rural
12,559 (49.5)
Urban
12,819 (50.5)
Working status
Yes
23,811 (93.8)
No
1,567 (6.2)
Economic status
Low
5,875 (23.1)
Middle
13,187 (52.0)
High
6,315 (24.9)
Family structure
Single-parent family
2,658 (10.5)
Nuclear family
22,719 (89.5)
No. of children
0–2
25,147 (99.1)
>2
231 (0.9)
Knowledge of elderly activity
No
14,676 (57.8)
Yes
10,702 (42.2)
Total
25,378 (100.0)
Data processed by author, based on Data from (SKAP) 2019.
SKAP, Survei Kinerja dan Akuntabilitas Program.
Preparation index for old age among pre-elderly families, Indonesia 2019
Characteristic
Mean preparation index
Total
Physical aspecta)
Economic aspect
Social aspect
Sex
Male
62.05
46.60
34.18
47.61
Female
62.27
44.36
34.49
47.04
Education level
Primary
58.32
36.60
31.00
41.97
Secondary
64.24
51.35
35.32
50.30
Tertiary
71.13
73.14
45.29
63.19
Residence area
Rural
59.16
39.95
32.20
43.77
Urban
64.91
52.70
36.18
51.27
Economic status
Low
58.26
36.32
32.49
42.36
Middle
61.47
42.65
33.17
45.76
High
66.87
63.56
37.98
56.14
Family structure
Single-parent family
62.09
44.11
33.68
46.62
Nuclear family
62.07
46.65
34.27
47.66
No. of children
0–2
62.07
46.44
34.23
47.58
>2
62.10
40.31
31.90
44.77
Working status
Yes
61.84
46.22
34.05
47.37
No
65.52
48.93
36.63
50.36
Knowledge of elderly activities
Yes
67.07
50.13
40.43
52.54
No
58.42
43.66
29.68
43.92
Total
62.07
46.39
34.21
47.55
Data processed by author based on data from SKAP 2019.
SKAP, Survei Kinerja dan Akuntabilitas Program.
a)Mean score index.
Socio-demographic characteristics and their association with aging preparation among pre-elderly adults, Indonesia 2019
Characteristic
Physical aspect
Economic aspect
Social aspect
No. (%)
χ2
P-value
No. (%)
χ2
P-value
No. (%)
χ2
P-value
Sex
0.53
0.468
5.37
0.021*
0.53
0.467
Male
20,190 (90.4)
17,016 (90.7)
11,510 (90.3)
Female
2,156 (9.6)
1,754 (9.3)
1,241 (9.7)
Education level
216.08
<0.001***
292.23
<0.001***
Low
10,343 (46.3)
8,077 (43.0)
881.09
<0.001***
5,586 (43.8)
Middle
9,718 (43.5)
8,438 (45.0)
5,593 (43.9)
High
2,284 (10.2)
2,254 (12.0)
1,572 (12.3)
Residence area
114.54
<0.001***
337.21
<0.001***
86.06
<0.001***
Rural
10,782 (48.3)
8,647 (46.1)
5,941 (46.6)
Urban
11,563 (51.7)
10,123 (53.9)
6,810 (53.4)
Economic status
146.20
<0.001***
768.22
<0.001***
99.44
<0.001
Low
5,003 (22.4)
3,875 (20.6)
2,783 (21.8)
Middle
11,532 (51.6)
9,423 (50.2)
6,459 (50.7)
High
5,810 (26.0)
5,472 (29.2)
3,509 (27.5)
Family structure
0.001
0.978
7.83
0.005**
0.49
0.825
Incomplete
2,340 (10.5)
1,906 (10.2)
1,341 (10.5)
Completed
20,006 (89.5)
16,864 (89.8)
11,411 (89.5)
No. of children
1.81
0.179
2.67
0.102
0.06
0.799
0–2
22,136 (99.1)
18,610 (99.1)
12,633 (99.1)
>2
209 (0.9)
160 (0.9)
118 (0.9)
Working status
13.22
<0.001***
0.69
0.405
17.24
<0.001***
Yes
20,920 (93.6)
17,597 (93.8)
11,885 (93.2)
No
1,425 (6.4)
1,173 (6.2)
867 (6.8)
Knowledge of elderly activities
284.71
<0.001***
131.32
<0.001***
351.22
<0.001***
Yes
9,853 (44.1)
8,311 (44.3)
6,114 (47.9)
No
12,492 (55.9)
10,459 (55.7)
6,637 (52.1)
Total
22,345 (100.0)
18,770 (100.0)
12,751 (100.0)
*P<0.05,
**P<0.01, and
***P<0.001 (Statistically significant).
Results of binary logistic regression analysis on the impact of socio-economic characteristics on the preparation for aging among preelderly families, Indonesia 2019
Variable
Unadjusted OR (95% CI)
P-value
Adjusted OR (95% CI)
P-value
Sex
Male
1.095 (0.937–1.279)
0.255
-
-
Female
1
Education level
Low
1
1
Middle
1.529 (1.439–1.624)
<0.001
1.533 (1.443–1.628)
<0.001
High
3.388 (2.978–3.854)
3.391 (2.981–3.858)
Residence area
Rural
1
1
Urban
1.397 (1.320–1.478)
<0.001
1.393 (1.317–1.474)
<0.001
Insurance ownership
No
1
1
Yes
1.957 (1.849–2.070)
<0.001
1.956 (1.848–2.069)
<0.001
Family structure
Incomplete
1.019 (0.897–1.182)
0.799
-
-
Complete
1
Economic status
Low
1
1
Middle
1.108 (1.037–1.184)
<0.001
1.109 (1.038–1.184)
0.002
High
1.408 (1.283–1.546)
1.410 (1.285–1.548)
<0.001
No. of children
0–2
1.251 (0.942–1.661)
0.122
-
-
>2
1
Working status
No
1.287 (1.138–1.455)
<0.001
1.226 (1.096–1.371)
<0.001
Yes
1
1
Knowledge of elderly activities
No
1
1
Yes
1.623 (1.538–1.712)
<0.001
1.624 (1.539–1.713)
<0.001
Data processed by author based on data from SKAP 2019.
Correlation analysis between physical, economic, and social readiness aspects and the pre-elderly readiness index
Variable
Physical aspect
Economic aspect
Social aspect
Aging readiness index
Physical aspect
1
0.084**
0.218**
0.641**
Economic aspect
0.084**
1
0.116**
0.594**
Social aspect
0.218**
0.116**
1
0.721**
Readiness index
0.641**
0.594**
0.721**
1
**P<0.01 (Statistically significant).
Table 1
Comparison between WHOQOL (2012) domains and indicators used in the SKAP 2019 study
WHOQOL, World Health Organization Quality of Life; SKAP, Survei Kinerja dan Akuntabilitas Program; BPJS, Badan Penyelenggara Jaminan Sosial.
Table 2
Demographic, social, and economic characteristics of pre-elderly families, Indonesia 2019
Data processed by author, based on Data from (SKAP) 2019.
SKAP, Survei Kinerja dan Akuntabilitas Program.
Table 3
Preparation index for old age among pre-elderly families, Indonesia 2019
Data processed by author based on data from SKAP 2019.
SKAP, Survei Kinerja dan Akuntabilitas Program.
Mean score index.
Table 4
Socio-demographic characteristics and their association with aging preparation among pre-elderly adults, Indonesia 2019
P<0.05,
P<0.01, and
P<0.001 (Statistically significant).
Table 5
Results of binary logistic regression analysis on the impact of socio-economic characteristics on the preparation for aging among preelderly families, Indonesia 2019
Data processed by author based on data from SKAP 2019.
, Sri Lilestina Nasution
