What’s in the web for family physicians − Transforming healthcare for an aging population: strategies for enhancing healthspan

Sio-pan Chan 陳少斌，Wilbert WB Wong 王維斌，Alfred KY Tang 鄧權恩

Introduction

The global population is aging at an unprecedented rate, with projections estimating that by 2050, nearly 25% of individuals will be over 60. This demographic shift calls for a fundamental transformation in healthcare, focusing not only on extending lifespan but on enhancing healthspan - the years lived in good health. Family physicians play a vital role in this transition, leveraging innovations in wearable technology, Artificial intelligence (AI)-driven analytics, and precision medicine to enable early interventions and personalised anti-aging strategies. Central to these efforts is the measurement of physiological age, which reflects biological resilience rather than chronological years, providing a foundation for targeted interventions to slow aging and mitigate disease risk. This article will discuss:

1. Quantifying physiological age and assessing aging.

2. Evidence-based integrated strategies for healthspan maintenance.

3. The potential of smart wearables and AI in agingrelated healthcare.

1. Quantifying physiological age and assessing aging

1. Studenski, S., Faulkner, K., Inzitari, M.,et al. (2011). Gait speed and survival in older adults. JAMA, 305(1), 50–58. https://doi.org/10.1001/jama.2010.1923
2. Leong, D. P., et al. (2015). Prognostic value of grip strength: A systematic review and meta-analysis. The Lancet, 386(9990), 266–273. https://doi.org/10.1016/S0140-6736(14)62629-62637.
3. Selvin, E., et al. (2004). Glycemic control and coronary heart disease risk in persons with and without diabetes. Annals of Internal Medicine, 141(6), 413–420. https://doi.org/10.7326/0003-4819-141-6-200409210-00006
4. SPRINT Research Group. (2015). A randomized trial of intensive versus standard blood-pressure control. The New England Journal of Medicine, 373(22), 2103–2116. https://doi.org/10.1056/NEJMoa1511939
5. Knowler, W. C., et al. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. The New England Journal of Medicine, 346(6), 393–403. https://doi.org/10.1056/NEJMoa012512
6. Zhang, D., et al. (2020). Resting heart rate and allcause mortality. Heart, 106(2), 107–114. https://doi.org/10.1136/heartjnl-2019-315837
7. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2022). Hallmarks of aging. Cell, 185(16), 2739–2755. https://doi.org/10.1016/j.cell.2022.06.006

Physiological age can be assessed through clinical evaluations, biomarkers, and advanced tools like epigenetic clocks. We shall only focus on clinical evaluation.

Functional metrics below provide critical insights into biological aging by quantifying resilience across multiple physiological systems. These measures are weighted (%) based on their predictive value for mortality, disability, and disease risk:

1. Gait Speed (25%): gait speed reflects cardiovascular, muscle and nervous system health. Slower walking speeds (< 0.8 m/s) are linked to a 2–3x higher mortality risk in older adults. Each 0.1 m/s increase reduces mortality by 12%.
2. Grip Strength (20%): Low strength (< 37 kg men, < 22 kg women) raises cardiovascular death risk by 17% per 5 kg decline.
3. HbA1c (15%): Levels >7.0% increase all-cause mortality by 50% in seniors. Tight control (< 7%) cuts complication.
4. Blood Pressure (15%): Keeping systolic BP < 120 mmHg reduces heart disease and stroke risk by 25%.
5. Waist Circumference (15%): A 5 cm reduction lowers diabetes risk by 15% (men ≥102 cm, women ≥88 cm).
6. Resting Heart Rate (10%): Rates >80 bpm predict higher cardiovascular and mortality risks.
7. CRP and cognitive tests such as MMSE carry less weighted predictive value and are not included here.

AI-Driven Scoring Systems

Clinicians can utilise weighted scoring charts to estimate physiological age by integrating these parameters. AI simplifies this process by generating auto-calculating templates that quantifying physiological age enabling clinicians to identify high-risk patients and tailor therapies to delay age-related decline.

Biomarkers and Epigenetic Clocks

Advanced biomarkers, such as telomere length and senescence-associated secretory phenotype (SASP) proteins provide valuable insights into aging process. Epigenetic clocks, which analyse DNA methylation patterns, offer precise biological age measurements, helping to predict morbidity and mortality risks. While these tools are expensive and not yet routinely available in primary care, they are beyond the scope of this article.

2. Integrated Strategies for Healthspan Maintenance

1. López-Otín, C., Blasco, M. A., Partridge, L., et al. (2022). Hallmarks of aging. Cell, 185(16), 2739–2755. https://doi.org/10.1016/j.cell.2022.06.006
2. World Health Organization. (2020). Ageing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health

The World Health Organization emphasises a holistic approach to aging through:

1. Health: Physical and mental well-being, including disease prevention and management.
2. Participation: Social engagement and community involvement.
3. Security: Financial stability and access to healthcare.
4. Lifelong Learning: Intellectual stimulation and skill development.

i. Personal-Level Strategies

• Diet and Nutrition: A balanced diet with 1.0–1.2 g/kg protein daily for older adults to prevent sarcopenia and enough essential nutrients such as calcium, Omega 3 and vitamin D for bone and cardiovascular health.
• Exercise and Balance Training: Maintains cardiovascular health and prevents falls.
• Sleep Optimisation: 7–9 hours/night with 20% REM sleep; treat sleep apnea (reduces dementia risk by 50%).
• Stress Management: Mindfulness and meditation can lower stress cortisol by 25%, so are social integration, and nature exposure.

ii. Therapeutic Interventions Targeting Disease- Aging Axis

Combining metabolic therapies with anti-aging agents offers synergistic benefits. Here are some evidence based therapeutic proposals:

Metformin: This widely used medication for type 2 diabetes has shown potential in extending healthspan by improving insulin sensitivity, reducing inflammation, and potentially influencing aging pathways.

Senolytics: These agents work by selectively eliminating senescent cells, which contribute to chronic inflammation and tissue dysfunction. Intermittent senolytic cycles can enhance metabolic and muscle function, potentially improving overall healthspan.

SGLT2 Inhibitors: These medications protect cardiac and renal health while activating AMPK, mimicking the effects of caloric restriction. They have been shown to reduce the risk of heart failure and improve kidney function in diabetic patients.

NAD+ Boosters: NAD+ precursors are essential for cellular energy metabolism and DNA repair. By restoring NAD+ levels, these supplements can improve endothelial function, lower blood pressure, and enhance overall metabolic health.

GLP-1 Agonists: Probably only applicable to obese patients.

iii. Vaccinations

Vaccination strategies are essential in mitigating the effects of immunosenescence, which is the gradual decline of the immune system associated with aging. Recommended vaccinations include:

Influenza Vaccine: Annual flu shots are crucial for older adults, as they are at higher risk for severe complications from influenza.

Pneumococcal Vaccine: This vaccine protects against pneumonia, meningitis, and bloodstream infections caused by pneumococcal bacteria.

Shingles Vaccine: The shingles vaccine significantly reduces the risk of developing shingles and its complications, such as postherpetic neuralgia, which can severely impact quality of life in older adults.

RSV Vaccine: The respiratory syncytial virus (RSV) vaccine can help prevent hospitalisations and complications associated with RSV infections.

By integrating these strategies, patients can achieve not only disease control but also delayed aging, preserving cognitive, metabolic, and physical function into later life. Ongoing research will further refine this paradigm.

3. The Potential of Smart Wearables and AI in Aging-Related Healthcare

1. Manafò, K., Kaczorowski, J., & Turner, S. (2021). Older adults' experiences with using wearable devices: Qualitative systematic review and metasynthesis. JMIR mHealth and uHealth, 9(6), e22214. https://pmc.ncbi.nlm.nih.gov/articles/PMC8212622/
2. Sorell, T., & Draper, H. (2024). Wearable technologies for healthy ageing: Prospects, challenges, and ethical considerations. Journal of Medical Internet Research, 26, e38616371. https://pubmed.ncbi.nlm.nih.gov/38616371/

Modern wearable devices have evolved into sophisticated health monitoring systems that offer unprecedented capabilities for aging populations. These devices incorporate advanced biosensors capable of continuous physiological monitoring, including electrocardiogram tracking for cardiac arrhythmia detection, blood oxygen saturation measurement, and subtle skin temperature variations that may indicate emerging health issues. The technology utilises photoplethysmography and accelerometer data with accuracy comparable to medical-grade equipment, enabling reliable health assessments outside clinical settings.

A particularly valuable application is in fall prevention and detection. Contemporary wearables integrate microelectromechanical systems that analyse gait patterns with artificial intelligence algorithms capable of predicting fall risk days before potential incidents occur. These systems can automatically alert caregivers or emergency services when falls are detected, providing crucial response time advantages. For medication management, wearable solutions now work in tandem with smart dispensers and transdermal sensors to monitor adherence and even detect drug metabolite levels through sweat analysis, significantly improving treatment compliance among older adults with complex medication regimens.

Sleep monitoring technology has made remarkable progress in recent years. Modern systems now use multi-spectral optical sensors to distinguish between sleep stages and employ sophisticated audio analysis to detect breathing irregularities linked to sleep apnea. These features give clinicians detailed data to better address sleep-related health issues, especially those common in older adults.

AI serves as the cognitive framework that transforms raw wearable data into actionable medical insights. Machine learning models can predict hospitalisation likelihood with remarkable accuracy and identify early signs of cognitive decline long before clinical symptoms emerge. AI systems create personalised treatment plans by synthesising genomic data, real-time physiological metrics, medication interactions, and individual lifestyle patterns. Emerging digital twin technology takes this further by constructing virtual patient models that simulate treatment outcomes and allow safe testing of intervention strategies.

Virtual assistants powered by AI offer cognitive support through medication reminders, conversational therapy, and detection of subtle neurological changes through speech pattern analysis. These systems are getting smarter, offering companionship and mental health support while monitoring cognitive function.

Despite these advances, significant challenges remain in implementing these technologies widely. Data privacy concerns require robust security frameworks for continuous health monitoring. Large-scale clinical validation studies are needed to establish standardised protocols for AI-driven diagnostics. User interface design must accommodate varying levels of technological literacy among older adults while maintaining ease of use.

Future developments point toward even more integrated solutions, including smart clothing with woven nanosensors for comprehensive physiological monitoring and brain-computer interfaces for early detection of neurodegenerative conditions. The ultimate vision is autonomous care coordination systems that seamlessly connect patients, wearable data, and healthcare providers.

The convergence of wearable technology and AI represents a fundamental shift in geriatric care, enabling truly personalised medicine based on continuous health monitoring rather than episodic clinical visits. These technologies promise to extend healthspan by facilitating earlier interventions, improving treatment adherence, and maintaining independence for aging populations. As these systems mature, they will redefine aging care from reactive treatment to proactive wellness management, fundamentally transforming how we approach healthy aging.