Healthy Longevity: Introduction 101

A beginner-level chapter built from a Web of Science title-search corpus

Prof. Dr. Mehmet Nurullah Kurutkan, Düzce Üniversitesi İşletme Fakültesi Sağlık Yönetimi Bölümü

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Learning objectives

After working through this chapter, the reader should be able to:

1. distinguish lifespan, healthspan, healthy life expectancy and healthy longevity, and explain why the difference matters;
2. describe three ways of measuring healthy longevity: at the level of the population, the person and the cell;
3. summarise the main biological, behavioural, social and environmental determinants discussed in the literature;
4. explain what large ageing cohorts, above all the Chinese Longitudinal Healthy Longevity Survey, add to the evidence;
5. outline how healthy longevity medicine, health systems and public policy try to turn evidence into practice;
6. judge the strength of the evidence and name the main gaps that remain.

Contents

• 1. Why healthy longevity?
• 2. Core concepts and vocabulary
• 3. A short history of the idea
• 4. Measuring healthy longevity
• 5. The biology of healthy longevity
• 6. Lifestyle: nutrition, movement and cardiometabolic health
• 7. Social and environmental determinants: what the cohorts show
• 8. From evidence to practice and policy
• 9. Reading the evidence critically
• 10. Where to go next
• Key takeaways
• Review questions
• Appendix A. How the corpus was built and analysed
• References

1. Why healthy longevity?

1.1 From adding years to life to adding life to years

Human populations have never lived as long as they do today. Longer lives are one of the great achievements of public health, medicine and economic development. Yet a long life and a healthy life are not the same thing. The literature reviewed in this chapter starts from one uncomfortable observation: the number of years lived in good health has not kept pace with the total number of years lived, so that multimorbidity, frailty and disability are rising together with life expectancy (Polidori, 2024; B. L. D. Wang, Szücs, Sandalova, Horberg, et al., 2025). A study of 13,859 Chinese adults aged 65 and over, including more than two thousand centenarians, found that only 5% of centenarians were free of frailty, and its authors concluded that a large gap separates longer life from healthy ageing (H. Y. Wang et al., 2021).

The term healthy longevity names the goal of closing that gap. Demographers describe the same goal as distinguishing healthy longevity from longevity without regard to health (Caswell & Zarulli, 2018). Clinicians describe it as reducing the distance between lifespan, the total years lived, and healthspan, the years lived free of disease (B. L. D. Wang, Szücs, Sandalova, Horberg, et al., 2025). Policy writers use the old public health formula of adding life to years and not only years to life (Braune et al., 2026). An early article in the corpus already argued that if people in midlife and later life could maintain long-term healthy practices, illness could be squeezed into a shorter period at the end of life and disability-free years extended (Walker & Walker, 1993). That idea is known as the compression of morbidity (Fries, 1980).

Why should a student of health management, public health, nursing or medicine care? There are three reasons. First, years lived in poor health are costly for individuals, families and care systems. Second, the potential gain is large: one economic analysis in the corpus estimates that slowing ageing so that life expectancy rises by one year would be worth about 4% to 5% of gross domestic product annually (Scott et al., 2023). Third, healthy longevity is unequally distributed. Differences by income, education, place of residence and early-life conditions run through almost every part of the literature, as later sections show.

1.2 What this chapter is built on

This chapter follows the Introduction 101 approach: it does not assume prior knowledge, it builds the topic from a transparent body of literature, and it cites every claim so that the reader can go back to the source. The body of literature is a Web of Science Core Collection search for the exact phrase “healthy longevity” in the title, limited to articles and reviews. The search returned 288 records: 231 articles and 57 reviews, published between 1991 and 2026 (Appendix A gives the full method).

One feature of the corpus must be understood before reading further. A title search finds the phrase wherever it appears, including inside the proper name of a survey. In 146 of the 288 records the phrase occurs because the study used a cohort or programme that carries it in its name: the Chinese Longitudinal Healthy Longevity Survey in 134 records, the Gan-Dau Healthy Longevity Plan of Taiwan in 8, and three other cohorts in 4. These studies investigate cognition, depression, frailty or mortality in older adults; they do not necessarily discuss healthy longevity as a concept. The remaining 142 records treat healthy longevity itself as the object of study. The chapter therefore separates a concept-centred family from a survey-named family (Figure 2) and uses each for what it does best.

Figure 3 shows how young the field is. Only 12 records appeared before 2012. About three quarters of the corpus (213 of 288 records, 74%) has been published since 2022. Growth in the survey-named family follows the release of new waves of cohort data, while growth in the concept-centred family reflects the rise of geroscience and of healthy longevity medicine, both discussed below.

2. Core concepts and vocabulary

2.1 The key terms

The field uses a small set of terms that are easy to confuse. Table 1 defines them in plain language. Three points deserve emphasis. First, lifespan counts years; healthspan counts healthy years. Public knowledge of this difference is limited: in a national survey of 3,034 residents of Singapore, 82.3% of respondents defined lifespan correctly but only 41.3% defined healthspan correctly (Amalaraj et al., 2026). Second, health in later life is increasingly defined by function and not only by the absence of disease. The World Health Organization describes healthy ageing as developing and maintaining the functional ability that enables well-being in older age (World Health Organization, 2015), and the corpus builds on this through the concept of intrinsic capacity (Bautmans et al., 2022). Third, some authors argue that function is still too narrow: a recent conceptual review proposes that healthy longevity should also include the preservation of a life that is recognised, chosen, connected and meaningful (Cai et al., 2026).

2.2 How studies define a “healthy long life” in practice

A concept becomes researchable only when someone turns it into a measurable outcome. The corpus shows that there is no single operational definition of healthy longevity, and that the choice of definition shapes the findings (Table 2). Studies of individuals usually combine a survival threshold with a functional criterion, such as reaching age 90 with intact mobility (Hyde et al., 2025; Jain et al., 2022) or reaching age 85 without cognitive impairment (Richard et al., 2017). Population studies report life expectancy free of disability or of limiting illness (Payne et al., 2022; K. Q. Xu & Payne, 2024). A study in China treated frailty-free (“robust”) life expectancy as its outcome (Y. Y. Chen, 2024). Caswell and van Daalen (2021) show how far this variety can go: in a simple multistate model the possible definitions of healthy longevity outnumber the stars in the visible universe.

Note. Definitions are paraphrased from the cited studies.

For a beginner the practical rule is simple: before comparing two studies of healthy longevity, check what each one counted as healthy and how long counted as long.

3. A short history of the idea

The oldest record in the corpus, published in 1991, is not about cells or clinics. It is an analysis of food and nutrition policies in Finland and Norway, written on the premise that diet-related disease impairs both the quality and the length of life (Milio, 1991). Two years later a review of nutritional and non-nutritional factors linked healthy longevity explicitly to the compression of morbidity (Walker & Walker, 1993). From the beginning, then, healthy longevity was a public health and policy idea.

A second line of work began in the 2000s with the demography of the oldest old in China. Using the survey that would later dominate the corpus, researchers showed that childhood conditions (Yi et al., 2007), late childbearing (Yi & Vaupel, 2004) and access to healthcare (Gu et al., 2009) were associated with healthy survival at very advanced ages. The reference description of the survey itself is the most cited record in the corpus (Zeng, 2012).

A third line, laboratory biology, became visible in the 2010s. A review of adiponectin receptors as targets for obesity-related disease and ageing is the second most cited record (Yamauchi & Kadowaki, 2013), and studies in flies, worms and long-lived rodents followed (Ma et al., 2018; Triplett et al., 2015). The fourth and most recent line is translation: a World Health Organization working definition of vitality capacity (Bautmans et al., 2022), a global consensus on exercise (Izquierdo et al., 2025), a report of the United States National Academy of Medicine setting out a global roadmap (Wong et al., 2023), and a growing literature on healthy longevity clinics (Bonnes et al., 2024; Mironov et al., 2025). The four lines now coexist, which is why a newcomer meets demographers, molecular biologists, geriatricians and economists using the same two words.

4. Measuring healthy longevity

Measurement is the backbone of the field. It is useful to think of three levels: populations, persons and cells.

4.1 Populations: health expectancy

Health expectancy combines mortality and morbidity into one number: the average years lived in a given health state. The classic method combines a life table with the prevalence of ill health at each age (Sullivan, 1971). Depending on the health measure used, the result is called disability-free life expectancy, healthy life expectancy or something similar. Multistate life tables go further by modelling transitions between health states over time, and they are used in the corpus to compare socio-economic groups in Australia (K. Q. Xu & Payne, 2024) and China (Payne & Xu, 2022), countries across the Asia-Pacific (Payne et al., 2026), and people with and without HIV in South Africa (Payne et al., 2022).

Two methodological advances are worth knowing even at an introductory level. First, an expectancy is an average, and averages hide variation between individuals. Matrix methods make it possible to compute the variance and other moments of healthy longevity, and to handle continuous as well as binary health outcomes (Caswell & van Daalen, 2021; Caswell & Zarulli, 2018). Second, recent work compares whole distributions of healthy lifespan between men and women under different health definitions (Cosulich et al., 2026). The message is that two populations with the same healthy life expectancy can differ in how unequally healthy years are shared.

4.2 Persons: intrinsic capacity and composite indices

At the level of the person, the central concept is intrinsic capacity. A working group convened by the World Health Organization defined its physiological foundation, vitality capacity, and identified candidate biomarkers for monitoring it (Bautmans et al., 2022). Intrinsic capacity performs well as a predictor: in a pooled analysis of four cohorts with 159,840 participants aged 50 and over, it was associated with mortality and predicted healthy longevity with high discrimination (Y. Li et al., 2026). A Healthy Longevity Index developed in Taiwan and validated in Japan combines demographics, lifestyle, intrinsic capacity and chronic conditions to predict disability-free and dementia-free survival, using information that can be collected in a routine primary care visit (Lai et al., 2025).

4.3 Cells: biomarkers and biological clocks

The third level asks whether the pace of ageing can be read from biological markers. Epigenetic clocks, which estimate biological age from DNA methylation, are the best studied. Among 1,813 older women in the United States, higher epigenetic age acceleration was associated with lower odds of surviving to age 90 with intact mobility and cognitive functioning (Jain et al., 2022). Exceptionally long-lived individuals in Israel showed younger methylation profiles than controls (Gutman et al., 2020). Other candidate markers include heart rate variability, an index of autonomic function that was persistently high in healthy long-lived people (Yelisyeyeva et al., 2025; Zulfiqar et al., 2010), lipid profiles (Ademowo et al., 2024) and integrated panels of molecular biomarkers (Yusri et al., 2024).

Biological clocks should be read with caution. In 5,844 women followed in the Women’s Health Initiative Memory Study, all fifteen clocks examined were associated with survival to age 90, but none distinguished survival with preserved cognition from survival with cognitive impairment (LaCroix et al., 2026). A clock that predicts how long people live does not automatically predict how well they live. The same distinction appears in genetics: a whole-genome study of 3,703 adults aged 90 and over concluded that longevity and healthy longevity are different outcomes underlain by partly different mechanisms (Kashtanova et al., 2026).

5. The biology of healthy longevity

The biological literature in the corpus rests on one organising idea: ageing itself is the main risk factor for chronic non-communicable diseases, so slowing the ageing process could delay many diseases at once (Polidori, 2024). This is the central claim of geroscience (Kennedy et al., 2014), and it is usually organised around a list of cellular and molecular hallmarks of ageing (López-Otín et al., 2013). Thirty-two records in the concept-centred family are primarily biological. A beginner does not need the molecular detail, but should recognise the main themes summarised in Table 3.

Three cautions help in reading this literature. First, much of the evidence comes from model organisms such as the nematode Caenorhabditis elegans, the fruit fly and the mouse (Díaz-del Cerro et al., 2022; Ma et al., 2018; Sharifi et al., 2024). Findings in short-lived animals generate hypotheses for humans; they do not establish human benefit. Second, many reviews end by naming candidate compounds, from pharmaceuticals to plant extracts and nutraceuticals (DiNicolantonio et al., 2022; McIntyre et al., 2022). Candidate status is not proof of efficacy, and the reviews themselves call for further research. Third, interventions can interact: a review of geroprotector drugs and exercise warns that taking leading candidate drugs on the same day as exercise may blunt some of the benefits of exercise (Elliehausen et al., 2023).

Comparative biology adds a useful reminder about trade-offs. A life-course analysis in pet dogs found that removing the risk of two specific diseases did not translate into longer overall life, and concluded that efforts to promote longevity should look beyond reducing the incidence of a single disease (Waters et al., 2017). Healthy longevity is a property of the whole organism.

6. Lifestyle: nutrition, movement and cardiometabolic health

6.1 Nutrition

Diet is the oldest theme in the corpus and one of the largest. The classic review by Rizza et al. (2014) concludes that calorie restriction and diet quality both influence metabolic and molecular pathways related to ageing, and that some benefits attributed to calorie restriction may reflect the high quality of the diets that practitioners eat. Reviews of dietary patterns converge on plant-rich, minimally processed diets. The Mediterranean and Japanese patterns are compared in two reviews (Santa et al., 2024; Vitale, 2024); a review of Taiwanese studies describes a pattern rich in vegetables, fruit, whole grains, soy and tea that is associated with lower mortality (W. H. Pan et al., 2022); and a recent narrative review links whole and plant-rich foods with early markers of healthy longevity, partly through the gut microbiota (Martini et al., 2026). Newer topics include fermented foods (Mu et al., 2026) and the timing of eating in relation to the body clock (Granero et al., 2027).

Cohort data support the general direction. Among the oldest old in China, daily fruit and vegetable intake was associated with lower mortality, and salt-preserved vegetables with higher mortality (Shi et al., 2015); greater dietary diversity was associated with a lower risk of frailty (X. M. Wang et al., 2023). Alcohol illustrates the limits of observational evidence. In a cohort of 1,344 older adults in California, regular moderate drinking was associated with higher odds of reaching age 85 without cognitive impairment (Richard et al., 2017). An association of this kind can reflect the characteristics of people who drink moderately in a particular community, and the authors themselves note the negative health consequences of excessive drinking.

6.2 Physical activity and exercise

The strongest practice-oriented document in the corpus is the global consensus on exercise for older adults, which treats exercise as medicine and stresses that prescriptions should be personalised because individuals differ in how they adapt (Izquierdo et al., 2025). Measured activity predicts healthy longevity directly: among 2,656 women with a mean age of 83, each standard-deviation increase in daily steps was associated with 75% higher odds of surviving to age 90 with intact mobility as against dying, and more sitting time with lower odds (Hyde et al., 2025). Among the oldest old in China, staying active or becoming active was associated with longer survival (R. Y. Yin et al., 2023). Fitness may also buffer biological risk: high cardiorespiratory fitness and muscle strength attenuated the health risks associated with accelerated biological ageing (Z. Y. Chen et al., 2025). Skeletal muscle is a recurring theme, with sarcopenia described as a critical nexus in the ageing process (L. K. Chen, 2024; Yamada & Arai, 2022).

6.3 Cardiometabolic health

Cardiovascular and metabolic risk factors accumulate over decades. In pooled cohorts from the United States, ten-year cumulative systolic blood pressure in middle age was associated with later cardiovascular disease and all-cause mortality over and above current blood pressure (Reges et al., 2021). Composite scores of cardiovascular health tell a similar story. Ideal cardiovascular health was associated with longer life expectancy in people with and without cardiometabolic disease (C. J. Xu et al., 2022), and in 264,675 participants of the UK Biobank, high scores on Life’s Essential 8 were associated with about 8 to 11 additional years of life expectancy at age 45 compared with low scores, depending on disease status (Huang et al., 2026). The lesson for beginners is that healthy longevity is prepared in midlife and earlier, not at age 80.

7. Social and environmental determinants: what the cohorts show

Healthy longevity is not only a matter of biology and personal behaviour. The demographic studies and the survey-named family of the corpus document how strongly it depends on the conditions in which people are born, grow, live and age. This section draws mainly on the Chinese Longitudinal Healthy Longevity Survey, a nationwide cohort conducted since 1998 with an unusually large sample of people aged 80 and over (Zeng, 2012). Figure 4 shows which outcomes the 146 survey-named records study.

7.1 The long arm of childhood and socio-economic position

Early-life conditions leave traces that are visible eight or nine decades later. Receiving adequate medical care in childhood and rarely suffering serious childhood illness were associated with better function at the oldest ages (Yi et al., 2007). A later analysis found that high childhood socio-economic status had a stronger protective effect on frailty-free life expectancy than high adult status (Y. Y. Chen, 2024). Across the life course, the gap in total life expectancy at age 45 between Chinese adults with persistently high and persistently low socio-economic status was six years for men and five years for women (Payne & Xu, 2022).

Inequality is not confined to one country. In Japan, municipalities with lower socio-economic conditions had shorter health expectancy at age 65 (Fukuda et al., 2005). In Australia, social disparities in healthy longevity widened between 2001 and 2020 on all three measures of socio-economic position studied (K. Q. Xu & Payne, 2024). In the United States, rising educational attainment increased disability-free life expectancy, but worsening midlife health among people without a high school diploma held back the population-level gain (Shen et al., 2026).

7.2 Health services and social protection

Access to healthcare increased the odds of healthy survival among older Chinese adults by 22% to 68%, and access in childhood by a further 18% (Gu et al., 2009). Antiretroviral therapy offers a striking illustration of what treatment can achieve: older South Africans with virally suppressed HIV could expect nearly as many disability-free years as people without HIV (Payne et al., 2022). At the level of the health system, however, more spending does not automatically buy more healthy years. A panel study of 35 OECD countries found that the positive association between health expenditure and healthy life expectancy disappeared once country and year effects were controlled, which points to allocation efficiency, financing structure and governance and not to the volume of spending alone (Ünlü et al., 2026). Studies of long-term care add that formal home and community services can relieve family caregivers (Y. X. Wang et al., 2024) and that the use of institutional and home care is unequally distributed by income (Z. H. Wang et al., 2022).

7.3 Social relationships

Social connection is one of the most consistent themes. Loneliness predicted later cognitive decline and cognitive decline predicted later loneliness (Zhong et al., 2017), and chronic loneliness was more harmful than transient loneliness (Zhong et al., 2016). Visits from children were associated with a lower incidence of cognitive impairment (S. F. Yin et al., 2020). Frequent social activity was associated with longer survival (Z. Q. Wang et al., 2023), and social isolation with a lower likelihood of becoming a centenarian (Y. Q. Li et al., 2024). Even an animal experiment in the corpus points in the same direction: old mice that spent short periods with adult mice showed improved homeostatic function and longer life (Díaz-del Cerro et al., 2022).

7.4 The physical environment

Where people live matters. In a cohort of 12,873 older Chinese adults, lower residential greenness and higher fine particulate air pollution were each associated with higher mortality, with some indication that the two interact (Ji et al., 2020). Long-term exposure to fine particles was associated with incident frailty in 25,047 participants (Lv et al., 2023). A review of urban health argues that cities concentrate both risks and opportunities, and calls for green infrastructure, better mobility and participatory governance (Cacciatore et al., 2025).

7.5 Selected findings on function, cognition and survival

Table 4 lists further findings, mainly from the survey-named family, to give a sense of the breadth of this evidence. Most are observational associations; Section 9 explains what that means for interpretation.

The Chinese survey has also been used to estimate how much healthy longevity might be gained. One report attributes 73.4% of healthy longevity among adults aged 75 and over to modifiable factors taken together, and 30.4% to lifestyles (Zhou et al., 2023). Such population attributable fractions depend on strong assumptions about causality, but they convey the scale of the opportunity. Other cohorts in the family include the Gan-Dau Healthy Longevity Plan in Taiwan, which links intrinsic capacity with resilience and happiness (Z. J. Chen et al., 2024), and a multi-ethnic longitudinal study in Malaysia in which 11% of participants met criteria for successful ageing at baseline (Shahar et al., 2016).

8. From evidence to practice and policy

8.1 Healthy longevity medicine and longevity clinics

Healthy longevity medicine applies geroscience in clinical settings with the aim of optimising health across the lifespan. Its typical tools are a comprehensive assessment to detect early functional decline, followed by lifestyle modification, supplements, repurposed drugs, and social and environmental interventions (Bonnes et al., 2024). Demand has so far been met mainly by private clinics, and Bonnes et al. (2024) argue for establishing such clinics in publicly funded hospitals. The field is frank about its immaturity: there is no universally accepted standard model for a longevity clinic (Mironov et al., 2025), and a report of the French National Academy of Medicine recommends that advances in geroscience be implemented in practice once they have been validated (Balardy et al., 2026).

Three conditions for responsible translation emerge from the corpus. The first is public understanding and preference. Qualitative work in Singapore found enthusiasm for extending healthspan together with concerns about lifestyle change and loss of autonomy (B. L. D. Wang, Szücs, Sandalova, Lim, et al., 2025), and interest in clinics was higher among younger, more educated and higher-income respondents (Amalaraj et al., 2026), which raises an equity question. The second is professional education: physicians who completed accredited training reported greater confidence and more frequent conversations about healthspan with patients, although only 113 of 590 invited physicians responded and the outcomes were self-reported (Bischof et al., 2026). The third is integration with existing geriatric care and with disease-specific frameworks, for example in obesity (Canecki-Varzic et al., 2026), oral health (Fukai et al., 2017; Iijima et al., 2026) and skin ageing (Wyles et al., 2025).

8.2 Community and digital approaches

A scoping review of 21 studies of community practices found that most were published after 2020 and conducted in high-income settings, and that effective programmes were usually multicomponent and involved several stakeholders (Leung et al., 2026). Digital technologies are promoted as enablers, but a policy agenda in the corpus warns that their benefits are unevenly distributed because of gaps in access, usability and representation in data and design, and that technology should strengthen and not replace social relationships and professional care (Braune et al., 2026). Machine learning is increasingly applied to cohort data to predict cognitive decline, loneliness or frailty (Ren et al., 2025), with the modest gains noted in Table 4.

8.3 Policy and economics

The most comprehensive policy document represented in the corpus is the Global Roadmap for Healthy Longevity (National Academy of Medicine, 2022). Its authors describe a virtuous cycle: whole-of-society investment in social infrastructure, the physical environment, health systems, work and volunteering, and lifelong learning, with equity and social cohesion at the centre, produces good health across the life course and releases a longevity dividend of human, social and economic capital (Wong et al., 2023). The economic case is supported by estimates of the value of slower ageing (Scott et al., 2023). Table 5 summarises the policy levers that recur across the corpus.

Policy also has a normative side. Proxy measures of human rights conditions at different life stages were associated with healthy survival in China (Brown et al., 2012), and legal scholars have begun to ask whether international law does or should recognise a right connected with considerably extended lifespans (Truby & Kantiana, 2026). For health managers the practical implication is that healthy longevity cannot be delivered by the health sector alone; it requires coordination with education, urban planning, social protection and labour policy.

9. Reading the evidence critically

An introductory chapter should leave the reader able to doubt as well as to summarise. Six limitations of this literature deserve attention.

1. No agreed definition. Table 2 shows that healthy longevity is measured in many incompatible ways. Results cannot be pooled or compared without checking definitions (Caswell & van Daalen, 2021).
2. Observational designs dominate. Most cohort findings are associations. Reverse causation and confounding are plausible in several well-known results, for example the inverse association between LDL cholesterol and mortality in the oldest old (Lv et al., 2015) and the association between moderate drinking and cognitively healthy longevity (Richard et al., 2017). Ecological analyses carry a further warning from their own authors: causal interpretation requires longitudinal or intervention studies (Y. Z. Pan et al., 2026).
3. One cohort carries much of the empirical weight. In this corpus 134 of 288 records use a single Chinese survey. Its size and its coverage of the oldest old are great strengths, but findings from one country, one period and one questionnaire need replication elsewhere.
4. Survival bias. People who reach 90 or 100 are selected survivors. Factors that look protective among them may partly reflect who was able to survive that long (Yi & Vaupel, 2004).
5. Translation gap. Much of the biology rests on model organisms and candidate compounds (Section 5). Biomarkers that predict length of life do not yet predict quality of life (LaCroix et al., 2026).
6. Equity and commercial interest. Interest in longevity clinics is concentrated among advantaged groups (Amalaraj et al., 2026), standards are lacking (Mironov et al., 2025), and widening social gaps in healthy longevity have been documented (K. Q. Xu & Payne, 2024). An approach that improves the average while widening inequality would be a partial success at best.

These limitations point to a research agenda: harmonised outcome definitions, cohorts from under-represented regions (the corpus contains very little from Africa, Latin America, the Middle East or Türkiye), randomised and quasi-experimental evaluations of community and clinical interventions, biological clocks validated against function, and studies that report distributional as well as average effects.

10. Where to go next

Table 6 proposes a reading path of ten papers from the corpus for a reader who wants to move beyond this introduction. They are ordered from concept to practice.

Key takeaways

• Healthy longevity is long life with preserved function and well-being. Its opposite is not short life but long life in poor health.
• There is no single definition. Always check what a study counted as healthy and how long counted as long.
• It can be measured in populations (health expectancy), persons (intrinsic capacity) and cells (biological clocks). The three levels do not yet line up.
• Determinants operate from childhood onwards and at every level from the cell to public policy. Diet, physical activity, cardiometabolic health, social connection, clean and green environments, and access to care recur throughout the evidence.
• Healthy longevity is socially patterned, and in several countries the gaps are widening.
• Clinical translation has begun but lacks standards. Much of the evidence is observational or pre-clinical, and should be read with that in mind.

Review questions

1. Explain in your own words the difference between lifespan, healthspan and healthy life expectancy.
2. Two studies report on healthy longevity. One uses survival to age 90 with intact mobility and the other uses disability-free life expectancy at age 65. Why can their results not be compared directly?
3. Why did all fifteen epigenetic clocks in one study predict survival to age 90 but fail to distinguish survivors with and without preserved cognition? What does this imply for the use of biological age in clinics?
4. Choose one finding from Table 4 and list two alternative explanations other than a causal effect.
5. A hospital manager proposes to open a healthy longevity clinic. Using Section 8, list three conditions that should be met first.
6. Half of the records in the corpus were retrieved because of a survey name. How would the conclusions of this chapter change if those records had been deleted, and how if they had been mixed in without comment?

Appendix A. How the corpus was built and analysed

Search. The Web of Science Core Collection was searched with the query TI=(“healthy longevity”) AND DT=(“Article” OR “Review”). The full records were exported on 12 November 2026. The export contained 288 records: 225 articles, 55 reviews, and 8 early-access items (6 articles and 2 reviews), which were classified with their document type.

Classification. Each record was assigned to the survey-named family if the phrase occurred in its title as part of the proper name of a cohort, survey or programme, and to the concept-centred family otherwise. Each record was then assigned to one theme by rules applied to its title, and the assignments were checked by reading. Classification from titles is fast and transparent but imperfect: some concept-centred studies also analyse the Chinese survey without naming it in the title. Borderline cases, such as a trial or an initiative whose name contains the phrase but whose subject is healthy longevity itself (for example the HOPE trial and the HELO initiative), were kept in the concept-centred family.

Descriptive profile. The 288 records had received 6,242 citations in the Core Collection at the date of export (3,156 for the concept-centred family and 3,086 for the survey-named family; median 8 and 9 citations per record). All 57 reviews belong to the concept-centred family. Open access versions were recorded for 210 records (73%). Authors based in China contributed to 157 records and authors based in the United States to 82 (Figure A1); within the concept-centred family the United States leads (50 records), followed by China (30), Italy (19), England (18) and Japan (17). The most frequent journals were BMC Geriatrics (15 records), BMC Public Health (11), and Frontiers in Public Health, Ageing Research Reviews and Nutrients (8 each).

Note. Citations are Web of Science Core Collection counts at the date of export.

Limitations of the method. A title search is precise but narrow: it misses work on the same subject that uses healthspan, healthy ageing, successful ageing or active ageing in the title. The corpus is drawn from one database and is almost entirely in English (287 of 288 records). The chapter is an introductory narrative synthesis; it did not appraise the risk of bias of individual studies, and the numerical results quoted are taken from the abstracts of the cited records. A small number of background sources from outside the corpus are cited for foundational concepts and are marked with an asterisk in the reference list.

References

Sources marked with an asterisk (*) are background works from outside the Web of Science corpus.