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. 2017 Mar;76(3):71–76.

Perceptions of Factors Impacting Longevity among Hawai‘i Older Adults

Michiyo Tomioka 1,2,✉, Kathryn Braun 1,2, Mieko Homma 1,2, Hiroaki Nobuhara 1,2, Tomio Kubota 1,2, Hiromichi Sakai 1,2
PMCID: PMC5349114  PMID: 28352492

Abstract

With increased life expectancy, people need more education about healthy aging. This paper examines older adult perceptions regarding various factors impacting longevity, including genetics, lifestyle, and the environment. Data were collected from 733 Hawai‘i adults age 50 years and older (39% Caucasian, 27% Japanese, 19% Native Hawaiian and Pacific Islander (NHOPI), 9% Chinese, and 7% Filipino) through randomized telephone interviews. Participants were asked to rate a variety of factors as having “great impact,” “some impact,” or “no impact” on lifespan. Regardless of ethnicity, more than half of the participants felt that eating habits, exercise, health information, health care, and the environment had great impact on lifespan. Less than half felt that economic status and community had great impact. Compared to the all ethnic groups, Filipino respondents were significantly less likely to feel that smoking (44%, compared with an average across all race/ethnicities of 64%) and stress (48%, average 62%) had great impact. Chinese participants were more likely to feel that drinking alcohol (64%) had great impact (average 38%). Filipinos and Chinese were more likely to perceive that working conditions have great impact (65% and 56%, respectively; average 45%), and NHOPI and Filipinos were more likely to perceive the natural environment as having great impact (59% and 54%, respectively; average 46%). Findings suggest that cultural values and experiences may shape older adults' perceptions of factors associated with lifespan, providing guidance for health professionals on how to tailor health messages to older adults in different ethnic groups.

Keywords: Lifespan/impact on lifespan, minority, health education

Introduction

Life expectancy is increasing across the United States (US) and in Hawai‘i. With increased life expectancy, people need more education about healthy aging. But what do older adults know about factors that influence longevity and health? In Hawai‘i, a multicultural state, it is useful to assess the health beliefs of different ethnic groups because life expectancy varies dramatically by ethnic group. For example, in 2010, life expectancy at birth was 79 years for males and 86 years for females. However, there was also a 10-year gap in life expectancy between Native Hawaiians (77 years), the shortest living group, and Chinese (88 years), the longest living group, in Hawai‘i.1

Longitudinal studies and research on extremely long-lived individuals, including centenarians, provide insights into the predictors of longevity such as genetics, income, education, familial environment, lifestyle behaviors, and mental health. For example, in an analysis of centenarians and matched controls, paternal and maternal longevity were predictors of long life for both men and women, highlighting the important role of genetics.2 In terms of lifestyle, findings from the Oslo Ischemia Study suggest that smoking status is significantly and independently related to longevity. This study also found that, among non-smokers, overweight and physical fitness are significantly and independently related to longevity after adjusting for age, blood pressure, and cholesterol level.3 Findings from the Honolulu Asia Aging Study (HAAS) suggest that avoidance of overweight, hyperglycemia, hypertension, smoking, and excessive alcohol consumption are associated with overall and exceptional (over 85 years of age) survival.4

Individual socio-economic status also is important. For example, an analysis of a merged tax-record-death-record database found that higher income was associated with greater longevity across the US.5 Also, the aforementioned HAAS study found that high educational attainment was associated with exceptional survival.4

Stress and mental health status may also be important for longevity. For example, analysis of data from the Health Survey for England found a dose response between psychological distress and increased mortality.6 The HAAS study found that otherwise disease-free men reporting depressive symptoms had higher mortality than healthy men without depressive symptoms.7 Longitudinal research in Canada suggests that mortality risk increases for people with a high level of distrust in others, belief that the world is an unjust place, and a pessimistic view of their opportunities in the future.8

Life expectancy is also associated with one's occupation. For example, it is estimated that 10%–38% of the variation in life expectancy across demographic groups can be attributed to the working conditions the groups experience.9 It also is influenced by community characteristics. A study comparing 30 counties, Organization for Economic Co-operation and Development (OECD), identified urbanization and number of hospitals per capita as associated with life expectancy.10 The Diet and Health Study of the National Institutes of Health-American Association of Retired Persons (NIH-AARP), examined the socioeconomics of census tracts and found that risk of premature mortality among healthy adults was associated with neighborhood socioeconomic inequality.11

Media and public health professionals have attempted to disseminate research findings on factors influencing health and longevity. However, it is not known how much of this information has been absorbed by older adults, nor is it known the extent to which these factors are recognized as important by different ethnic groups. Understanding individual and group perceptions of health risk will help in the development and targeting of health promotion messages. In fact, knowledge of health risks is a key construct in many behavior change theories, including the Health Belief Model, the Social-Cognitive Model, the Transtheoretical Model, and the Theory of Planned Behavior.12

This paper describes similarities and differences of older adults' perceptions, across five ethnic groups in Hawai‘i of impact of various factors on longevity, including genetics, lifestyle, and the environment.

Methods

Data were collected as part of a collaboration between the University of Hawai‘i (UH) and Saitama Prefectural University in Saitama, Japan. The latter institution is collecting health belief and behavior data in several cities in Japan, China, Korea, Taiwan, and Hawai‘i. In Honolulu, data were collected in 2015 from 1,266 adults age 18 years and older through random-digit-dial, computer-assisted telephone interview (CATI). The survey team used dual-frame sampling that included both landlines and cell-phones. Several attempts were made to contact a resident, and when the resident did not meet the age eligibility, the interviewer called back at an appropriate time to reach an eligible resident at the household.

This paper reports findings on 733 Hawai‘i adults in the sample who were age 50 years and older. We limited this analysis to older adults for several reasons. First, nearly 73% of adults age 50 and older have at least one chronic condition.13 Second, older people are more aware of their health than younger people because of the increase in recommended health screening after age 50.14 Third, the age distribution of the full sample was dissimilar across ethnic groups, and some ethnic groups had higher proportion of young adults who may have much different health beliefs than older adults. Thus, we limited this analysis to the oldest age group—adults 50+.

The data collection instrument was provided by Saitama Prefectural University based on their interests in health knowledge and behaviors across East Asia and Hawai‘i. Several rounds of pretesting with UH public health students and Hawai‘i residents helped assure that the English-language version of the questionnaire was understandable to Hawai‘i adults. The questionnaire included a total of 99 items on self-rated health, lifestyle behaviors (eg, smoking, drinking alcohol, and exercising), diet (eg, limiting the amount of salt and sugar and eating specific healthy foods), health-seeking behavior, social support, stress, health conditions, BMI (height and weight), and demographics.

This analysis focuses on one section of the questionnaire that asked about factors that impacted longevity. The interviewer read this introduction: “I am going to read several factors that could impact how long you live. Please tell me what impact each factor has on your lifespan.” The 13 queried factors included eating habits, exercise, knowledge about health, stress, drinking alcohol, smoking, genetics, personal economic status, healthcare access, working conditions, home environment, community, and natural environment. Response options were “great impact,” “some impact,” and “no impact.” For purposes of this analysis, responses were dichotomized to great impact vs some or no impact.

Data were managed and analyzed in IBM SPSS V23.0 Armonk, NY. Chi-square tests were used to compare differences by ethnic group in the percentage of individuals who felt each factor had great impact on lifespan. To standardize reporting, data are reported for groups with higher prevalence first, followed by those with a lower prevalence.

Results

Demographic

Of the 733 participants included in this analysis, 39% were Caucasian, 27% were Japanese, 19% were NHOPI, 9% were Chinese, and 7% were Filipino. The mean age of participants was 65.3 years, with slight variations in mean age by ethnic group (Japanese = 66.5, range 50–93; Caucasian = 65.9, range 50–96; Chinese = 64.4, range 50–96; Filipino = 64.0, range 51–89; and NHOPI = 63.2, range 50–87; [P=.013]). As shown in Table 1, there were slightly more female respondents than male respondents in the sample, but the distribution of male and female across ethnic groups did not differ significantly (P=.923). More than half of participants were married (average was 59%), and 72% reported their living standard as “middle” by selecting one from the three categories (upper, middle, or low).

Table 1.

Demographic of the Sample

Caucasian (n=284) n (%) NHOPI (n=141) n (%) Japanese (nN=197) n (%) Filipino (n=48) n (%) Chinese (n=63) n (%) All (n=733) n (%) P-value
Age
50s
60s
70s+		 72 (25.4)
118 (41.5)
94 (33.1)		 51 (36.2)
55 (39.0)
35 (24.8)		 64 (32.5)
62 (31.5)
71 (36.0)		 18 (37.5)
17 (35.4)
13 (27.1)		 17 (27.0)
32 (50.8)
14 (22.2)		 222 (30.3)
284 (38.7)
227 (31.0)		 .035
Female 154 (54.4) 77 (54.6) 100 (50.8) 25 (52.1) 32 (50.8) 388 (53.0) .923
Married 165 (58.9) 79 (56.0) 118 (59.9) 32 (66.7) 36 (58.1) 430 (59.1) .778
Born Overseas 17 (6.0) 13 (9.3) 10 (5.1) 18 (37.5) 12 (19.4) 71 (9.6%) <.001
Education Level
Less than BA
BA or Higher		 108 (38.0)
176 (62.0)		 89 (63.1)
52 (36.9)		 83 (42.1)
114 (57.9)		 30 (62.5)
18 (37.5)		 21 (33.3)
42 (66.7)		 331 (45.2)
402 (54.8)		 <.001
Living Standard
Upper
Middle
Low
Missing		 62 (22.1)
196 (70.0)
22 (7.9)
4		 31 (22.0)
101 (71.6)
9 (6.4)
0		 37 (19.0)
147 (75.4)
11 (5.6)
2		 8 (17.0)
35 (74.5)
4 (8.5)
1		 16 (25.4)
45 (71.4)
2 (3.2)
0		 154 (21.2)
524 (72.2)
48 (6.6)
7		 .823
Self-rated Health
Excellent
Good
Not so Good/Poor
Missing		 86 (30.3)
161 (56.7)
37 (13.1)		 28 (19.9)
82 (58.2)
31 (21.9)		 38 (19.3)
134 (68.0)
25 (12.7)		 9 (18.8)
28 (58.3)
11 (22.9)		 14 (22.6)
42 (67.7)
6 (9.7)
1		 175 (23.9)
447 (61.1)
110 (15.0)
1		 .009
Health Conditions
None
Hypertension
Diabetes
High Cholesterol
Heart Disease
Rheumatoid Arthritis
Allergy		 96 (33.8)
85 (29.9)
27 (9.5)
70 (24.6)
31 (10.9)
27 (9.5)
77 (27.1)		 35 (24.8)
71 (50.4)
38 (27.0)
45 (31.9)
12 (8.5)
18 (12.8)
26 (18.4)		 45 (22.8)
100 (50.8)
39 (19.8)
79 (40.1)
15 (7.6)
15 (7.6)
41 (20.8)		 8 (16.7)
30 (62.5)
12 (25.0)
21 (43.8)
5 (10.4)
4 (8.3)
9 (18.8)		 15 (23.8)
29 (46.0)
10 (15.9)
20 (31.7)
7 (11.1)
3 (4.8)
15 (23.8)		 199 (27.1)
315 (43.0)
126 (17.2)
235 (32.1)
70 (9.5)
67 (9.1)
168 (22.9)		 .020
.001
.001
.003
.761
.364
.246
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Significant ethnic group differences were found by place of birth, education level, self-rated health, and number of chronic conditions. Over one-third (37.5%) of Filipino respondents and 19.4% of Chinese respondents were born overseas, compared to 5.1%–9.3% of other ethnic groups (P<.001). More than half (57.9%–66.7%) of Caucasian, Japanese, and Chinese respondents had college degrees, compared to only a third of NHOPI and Filipino participants (P<.001). More than half of the participants reported their health as good (61.1%). However, 30% of Caucasians reported their health is “excellent”, compared to only 18.8%–22.6% of the other groups (P=.009). Also, 33.8% of Caucasians reported having no chronic health conditions, compared to 16.7%–24.8% of other groups (P=.02). More than half of NHOPI, Japanese, and Filipinos (50%, 51%, and 63%, respectively) reported having hypertension (P<.001). According to self-reported prevalence of chronic conditions, 17.2% of the sample reported having diabetes, but prevalence ranged from 9.5% of Caucasians to 25% of Filipinos and 27% of NHOPI (P<.001).

Genetics and Economic Status

Regardless of ethnicity, about half felt that genetics had great impact on lifespan, and ethnic differences were not significant (Table 2). However, perceptions of impact of economic status varied by ethnicity, with 41%–44% of NHOPI, Filipino, and Chinese respondents feeling that economic status had a great impact on lifespan, vs only 28% of Caucasians and 33% of Japanese (P=.019).

Table 2.

Number and Ppercentage in Each Group that Felt this Factor had a “Great Impact” (vs “some” or “no”) on Life Span

Caucasian (n=284) n (%) NHOPI (n=141) n (%) Japanese (n=197) n (%) Filipino (n=48) n (%) Chinese (n=63) n (%) All (n=733) n (%) P-value
Genetics 151 (53.7) 58 (42.3) 99 (50.8) 21 (43.8) 29 (46.0) 358 (49.4) .207
Economic status 79 (28.0) 58 (41.7) 65 (33.2) 21 (43.8) 26 (41.3) 249 (34.2) .019
Eating habits 192 (67.8) 101 (71.6) 130 (66.0) 32 (66.7) 43 (68.3) 498 (68.0) .867
Exercise 199 (70.1) 101 (71.6) 137 (69.5) 34 (70.8) 46 (73.0) 517 (70.5) .983
Drinking alcohol 101 (36.2) 44 (31.2) 71 (36.4) 21 (43.8) 40 (63.5) 277 (38.2) <.001
Smoking cigarettes 202 (71.6) 75 (53.2) 125 (63.5) 21 (43.8) 48 (76.2) 471 (64.4) <.001
Stress 181 (63.7) 85 (60.7) 111 (56.6) 23 (47.9) 50 (79.4) 450 (61.6) .005
Health knowledge 163 (57.4) 99 (70.2) 109 (55.3) 32 (66.7) 38 (60.3) 441 (60.2) .047
Healthcare access 181 (63.7) 103 (73.0) 116 (58.9) 39 (81.3) 46 (73.0) 485 (66.2) .006
Working conditions 113 (40.4) 67 (47.9) 80 (40.8) 30 (65.2) 35 (55.6) 325 (44.8) .005
Home environment 152 (53.3) 90 (63.8) 101 (51.3) 35 (72.9) 37 (58.7) 415 (56.6) .020
Community 67 (23.6) 60 (42.6) 43 (22.1) 18 (37.5) 14 (22.2) 202 (27.6) <.001
Natural environment 139 (49.3) 83 (59.3) 63 (32.0) 26 (54.2) 26 (41.3) 337 (46.2) <.001
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Lifestyle Factors

In terms of lifestyle factors, more than half of the participants felt that eating habits and exercise had great impact on lifespan, and ethnic differences were not significant. However, significant ethnic differences were seen for drinking and smoking. Almost two-thirds (63.5%) of Chinese felt drinking alcohol had great impact on health compared to 43.8% of Filipinos, and 31%–36% of the other groups (P<.001). The pattern for smoking was a bit different, with 72%–76% of Caucasians and Chinese feeling this had great impact on lifespan, vs 43.8% of Filipinos, 53.2% of NHOPI, and 63.5% of Japanese (P<.001). Significant differences also were seen in impact of stress, with 79.4% of Chinese agreeing that stress had great impact on lifespan, vs 47.9% of Filipinos, 56.6% of Japanese, and 61–64% of NHOPI and Caucasians (P=.005).

Health Knowledge and Healthcare Access

Ethnic differences were seen in the importance of health knowledge and healthcare access on longevity. Specifically, 70.2% of NHOPI felt health knowledge had a great impact, compared to fewer in other groups (P=.047). In terms of healthcare access, 81.3% of Filipinos felt this had a great impact on longevity, compared to significantly lower percentages of Japanese (58.9%) and Caucasians (63.7%) (P=.006).

Environmental Factors

The last category was environmental factors, which included working conditions, the home environment, the community, and the natural environment. All ethnic differences were significant, with higher percentages seen for NHOPI and Filipinos compared to the other groups. For example, 65.2% of Filipinos felt working conditions had a great impact on lifespan, vs only 41% of Caucasians and Japanese (P=.005). Also, 72.9% of Filipinos felt the home environment had a great impact on longevity, compared to a low of 51.3% among Japanese (P=.020); 42.6% of NHOPI felt the community had a great impact on longevity, compared to a low of 22.1% among Japanese; and 59.6% of NHOPI felt the natural environment had great impact, compared to a low of 32.0% among Japanese.

Discussion

This paper explored ethnic differences in perceived impact of various factors on longevity with a goal of guiding the development of culturally relevant health messages and interventions. This study found a number of interesting differences across ethnic groups, which are discussed here.

Two-thirds or more of participants in all ethnic groups felt that eating habits and exercise had great impact on lifespan. Still, educational interventions are needed to help older adults translate diet and exercise knowledge into action, as the proportion of older adults who are overweight or obese is relatively high. Specifically, among adult age 50+, only 23.0% of NHOPI, 38.9% of Caucasians, 43.4% of Filipinos, 44.9% of Japanese, and 55.5% of Chinese meet recommendations for BMI (18.5–25).15

In contrast, compared to other ethnic groups, smaller proportions of Filipinos and NHOPI felt smoking had great impact on longevity. At the same time, adults age 50+ in these two groups had a relatively high smoking prevalence: 18.7% in NHOPI and 12.5% in Filipinos (compared to 10.8% of Caucasians, 8.7% of Japanese, and 4.7% of Chinese age 50+).16 This suggests that educational messages about the dangers of smoking need to be better communicated to these two groups. Almost 40% of our Filipino sample was born overseas, presumably in the Philippines where anti-smoking legislation is more lax.17 Health professionals should be aware that immigrants from counties where smoking is more accepted may need basic education about its risks. Smoking education and cessation programs, including the Hawai‘i Tobacco Quitline, should outreach to these priority groups.

Drinking was considered to have a great impact on lifespan among Chinese. Although it was not clear whether drinking was thought to impact lifespan in a positive or negative way, research suggests that the drinking of alcohol in Chinese culture is done socially, eg, in family gatherings and at special occasions like graduation, rather than every day or by oneself.18 Only about one-third of other groups felt that drinking had a great impact on lifespan. It should be noted, however, that about 6.5% of adults age 50+ in Hawai‘i report to be heavy drinkers19 and about 11.0% report to be binge drinkers.20

Compared with other ethnic groups, significantly smaller percentages of Caucasians and Japanese felt that socio-economic status, working conditions, and healthcare access had a great impact on health. In Hawai‘i, relatively high proportions of Caucasians and Japanese have college degrees, which help them qualify for professional (rather than blue collar) jobs in relatively safe working environments and with good access to health insurance and healthcare. From their position of privilege, they may not be aware of the abundance of data linking socio-economic status to health and longevity.5, 21, 22 In contrast, large proportions of NHOPI and Filipinos (especially new immigrants) work in agricultural, construction, and service industries, which tend to be lower paying and more physically demanding, and many work more than one job.23–25 Research has found that blue-collar workers are at higher risk for chronic disease than white-collar workers.26–27 Also, many Filipinos often remit part of their incomes back to the Philippines to support relatives living there, reducing their access to disposable income in Hawai‘i.28 Broad policy changes could help to build economic and social welfare systems to help reduce these disparities.29,30 Meanwhile, health professionals play an important role in connecting low-income individuals with government, clinic, and non-profit programs to increase access to health.

All ethnic groups except Filipinos perceived that stress has a great impact on lifespan. As noted by Guerrero and colleagues, Filipinos tend to draw upon humor, faith, and family as a means of coping with the burdens of everyday life and accepting events over which they have no control.28 NHOPI were most likely to believe that the community and natural environment had a great impact on lifespan. The ‘aina (land) is very important in Native Hawaiian traditions, in that the natural environment was respected, cared for with prudent stewardship, and never owned.31 Again, health educators should be knowledgeable about cultural traditions and build on cultural strengths as they educate and promote interventions to improve health.32 It is promising that most professional schools now include courses in cultural competence.33

These findings add to the growing Hawai‘i-based literature on the importance of tailoring health education messages for different groups. Researchers at the UH and at community-based projects like ‘Imi Hale Native Hawaiian Cancer Network and Pacific Diabetes Today (both based at Papa Ola Lokahi) have published findings on the best ways to deliver educational programs to specific ethnic groups and in specific communities. For example, local research continues to find that: (1) education should be offered through the clubs, churches, and social and family networks appropriate to the target groups; (2) public service announcements, paid advertising, and ideas for stories should be directed at the preferred mainstream or ethnic media outlet, and (3) lay educator and navigation programs are especially effective at changing behavior.34–39

There were several limitations in this study. First, the sample was not equally distributed across ethnic groups. Chinese and Filipino participants numbered less than 100. Having more participants in these groups would increase our confidence in the findings. Second, the sample may not represent the entire ethnic group. Although this study used random-digit dialing, participants must have had a phone (landline or mobile), answered the phone, and agreed to participate. Third, data were self-reported, so social desirability bias may be an issue. Fourth, the survey did not ask whether each factor negatively or positively contributed to lifespan, and different individuals and ethnic groups may have interpreted the questions differently. Additional research is necessary to clearly understand the association of each factor with personal perception on lifespan. Lastly, our analysis was limited to the subsample age 50 years or older. However, analysis of the full sample found similar patterns.

Conclusions

Although there were limitations, this study confirms the value of educating adults on the multiple factors influencing lifespan, including social determinants of health, and of tailoring educational messages to our different ethnic groups in Hawai‘i. Older adults are more likely than other age groups to have chronic conditions, and it is important to incorporate the cultural beliefs and practices into developing interventions and policies. Continuing education is needed for health professionals and researchers on how to tailor interventions to our different ethnic communities. Future research will be needed to further understand how educational needs differ by length of time in the Hawai‘i and for older adults with multiple ethnic backgrounds.

Acknowledgement

This study was a part of cross-international study funded by Saitama Prefectural University. Data were collected through SMS. This study was approved by University of Hawai‘i Institutional Review Board and Saitama Prefectural University Institutional Review Board.

Conflict of Interest

None of the authors identify any conflict of interest.

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