The remarkable increase in human life expectancy over the past century represents one of the greatest achievements of modern medicine and public health. Advances in disease prevention, vaccination, sanitation, and healthcare have substantially reduced premature mortality worldwide. However, increased lifespan has not been accompanied by a proportional increase in years lived in good health. Instead, population ageing has been associated with a growing prevalence of chronic diseases, multimorbidity, frailty, cognitive impairment, disability, and rising healthcare costs. Consequently, the focus of contemporary healthcare has shifted from extending lifespan to promoting healthy longevity, in which healthspan, the period of life spent in good physical, cognitive, psychological, and social health, has become a fundamental objective [1–3].

Although healthspan has emerged as a central concept in geroscience, its definition and measurement remain inconsistent. A recent systematic review identified substantial heterogeneity in its conceptualization and operationalization, limiting comparability across studies and hindering clinical translation. Consequently, developing practical frameworks that translate healthspan into measurable and actionable clinical objectives has become an important research priority [4].

Lifestyle factors play a pivotal role in extending healthspan. Regular physical activity, healthy nutrition, adequate sleep, stress management, smoking cessation, weight control, and social engagement substantially reduce the risk of chronic diseases while preserving physical function, cognitive performance, functional independence, and quality of life. These findings have led to the development of Precision Lifestyle Medicine, which seeks to personalize lifestyle interventions according to each individual's biological, physiological, behavioral, environmental, and psychosocial characteristics [2], [5].

Despite these advances, selecting the optimal lifestyle intervention for an individual patient remains challenging. Patients with apparently similar demographic characteristics or clinical profiles often exhibit markedly different responses to identical interventions because of complex interactions among genetic, metabolic, physiological, behavioral, and environmental factors. Consequently, clinical decision-making in healthy longevity should be viewed as a complex multi-objective process performed under substantial uncertainty [3], [5], [6].

Healthcare professionals must simultaneously balance multiple competing objectives, including disease prevention, preservation of physical and cognitive function, maintenance of functional independence, improvement of quality of life, and efficient use of healthcare resources. These decisions are further complicated by biological variability, incomplete clinical information, measurement errors, behavioral adherence, and temporal [6], [7].

This study was conducted as a narrative review with the objective of developing a decision-oriented conceptual framework for selecting mathematical modeling approaches to support healthy longevity. Unlike conventional systematic reviews that primarily summarize existing evidence, this review integrates findings from multiple disciplines, including healthy longevity, healthspan, precision lifestyle medicine, artificial intelligence, mathematical modeling, and operations research.

A comprehensive literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar to identify relevant studies published in English. The search strategy combined keywords related to healthy longevity, healthspan, Precision Lifestyle Medicine, clinical decision-making, artificial intelligence, machine learning, mathematical modeling, operations research, optimization, uncertainty, digital twins, Bayesian networks, grey system theory, and fuzzy systems. Priority was given to recent high-quality review articles, landmark methodological papers, and influential clinical studies published in peer-reviewed journals.

Studies were selected based on their relevance to personalized healthcare, computational decision support, uncertainty modeling, and mathematical approaches applicable to healthy ageing. Rather than comparing algorithmic performance, the extracted evidence was analyzed to identify common decision problems, major sources of uncertainty, data characteristics, and computational strategies used in different healthcare contexts. Finally, the identified methods were synthesized into a decision-oriented conceptual framework that classifies mathematical modeling approaches according to clinical objectives, uncertainty sources, and data availability. The proposed framework is intended to provide practical guidance for researchers and healthcare professionals in selecting appropriate computational methods to support personalized interventions aimed at improving healthspan and promoting healthy longevity.

Promoting healthy longevity requires healthcare professionals to make complex clinical decisions that extend beyond disease prevention. The primary objective is to maximize healthspan while preserving physical function, cognitive performance, psychological well-being, functional independence, and quality of life. Achieving these outcomes is challenging because clinical decisions must simultaneously address multiple, and often competing, objectives within highly heterogeneous patient populations.

A major challenge arises from the substantial variability among individuals. Patients with similar demographic characteristics, body mass index, laboratory findings, or disease status frequently exhibit markedly different responses to identical lifestyle interventions. Such variability is influenced by numerous interacting factors, including genetic background, metabolic flexibility, immune function, ageing biology, environmental exposures, behavioral adherence, and socioeconomic conditions. Consequently, personalized lifestyle recommendations cannot rely solely on population-based evidence but require individualized decision-making strategies.

Clinical decision-making is further complicated by multiple sources of uncertainty. Biological variability, incomplete or missing clinical data, measurement errors, temporal changes in physiological status, and unpredictable behavioral responses all contribute to uncertainty regarding intervention outcomes. As a result, deterministic decision-making approaches are often insufficient to capture the dynamic and adaptive nature of human health.

These challenges indicate that healthy longevity should be viewed as a multi-objective clinical decision problem rather than a single disease-management task. Effective decision support therefore requires computational approaches capable of integrating heterogeneous data, managing uncertainty, balancing competing clinical objectives, and generating personalized recommendations. These considerations provide the rationale for examining the mathematical modeling approaches discussed in the following section.

The complexity of healthy longevity decision-making has stimulated growing interest in computational methods capable of integrating heterogeneous data, addressing uncertainty, and supporting personalized interventions. Various mathematical and artificial intelligence approaches have been applied in healthcare; however, their suitability depends on the characteristics of the clinical problem, data availability, and the nature of uncertainty involved.

The growing emphasis on healthy longevity has fundamentally changed the focus of healthcare from treating individual diseases to preserving long-term health, functional independence, and quality of life. This shift requires clinical decision-making strategies that can simultaneously consider multiple health objectives while addressing the substantial uncertainty inherent in human physiology. The findings of this review indicate that no single computational approach is universally applicable to all healthy longevity decision problems. Instead, the effectiveness of a mathematical model depends on the specific clinical objective, the characteristics of the available data, and the dominant sources of uncertainty.

A major contribution of the proposed framework is its transition from an algorithm-centered perspective to a decision-centered perspective. Previous reviews have primarily compared computational methods according to predictive accuracy or methodological characteristics. In contrast, the present framework emphasizes selecting mathematical models based on clinical requirements. This distinction is particularly important in healthy longevity because different clinical scenarios require different analytical strategies. For example, probabilistic approaches may be more suitable for uncertain causal reasoning, whereas multi-objective optimization is better suited to balancing competing health outcomes. Likewise, grey system models may outperform data-intensive approaches when clinical information is limited or incomplete.

Another important implication is the integration of Precision Lifestyle Medicine with computational decision support. Lifestyle interventions are inherently personalized, and identical recommendations often produce markedly different outcomes among individuals because of biological, behavioral, and environmental heterogeneity. By explicitly considering uncertainty, data characteristics, and clinical objectives, the proposed framework may facilitate more personalized and clinically meaningful decision-making, ultimately supporting interventions that improve healthspan rather than merely extending lifespan.

Despite these strengths, several limitations should be acknowledged. First, the proposed framework is conceptual and has not yet been prospectively validated in clinical settings. Second, the recommendations are based on evidence synthesized from diverse disciplines, which may introduce methodological heterogeneity. Third, rapid advances in artificial intelligence and digital health technologies may continuously reshape the relative advantages of different computational approaches.

Future research should focus on validating the proposed framework using real-world clinical datasets, integrating wearable sensor data and electronic health records, and incorporating emerging technologies such as digital twins, multimodal artificial intelligence, and explainable AI. Such developments may enable the creation of next-generation clinical decision-support systems capable of delivering adaptive, personalized interventions that maximize healthspan while improving quality of life and reducing the burden of age-related diseases.

Healthy longevity represents one of the most important goals of modern healthcare, requiring a transition from disease-centered management to personalized strategies that maximize healthspan, preserve functional independence, and improve quality of life. Achieving these objectives is inherently challenging because clinical decision-making must address biological heterogeneity, multiple competing health outcomes, and diverse sources of uncertainty.

This review demonstrates that no single computational methodology is universally appropriate for all healthy longevity decision problems. Instead, the selection of mathematical models should be guided by the clinical objective, the characteristics of the available data, and the dominant sources of uncertainty. By integrating evidence from healthy longevity, Precision Lifestyle Medicine, artificial intelligence, mathematical modeling, and operations research, we proposed a decision-oriented conceptual framework that supports the selection of appropriate computational approaches for specific clinical contexts.

Unlike previous reviews that primarily compare algorithms according to methodological performance, the proposed framework emphasizes clinical applicability and decision suitability. This perspective provides a practical bridge between computational methodology and clinical practice, facilitating the development of intelligent decision-support systems capable of delivering personalized lifestyle interventions.

Future studies should validate the proposed framework using real-world clinical data, wearable technologies, digital health platforms, and longitudinal cohort studies. Integrating emerging technologies, including explainable artificial intelligence, digital twins, and multimodal data analytics, may further enhance personalized healthcare and contribute to extending healthspan while improving quality of life. Ultimately, adopting decision-oriented computational strategies has the potential to transform healthy longevity from a conceptual objective into a practical and evidence-based component of precision healthcare.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

All data are included in the text.

The authors declare no conflict of interest.