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Precision Exercise Intensity Zoning for Cardiovascular Health Optimization

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Introduction

Regular cardiovascular exercise confers robust reductions in all-cause and cardiovascular mortality, yet the magnitude and quality of adaptation depend strongly on how intensity is prescribed, distributed, and monitored over time. Population-level guidelines typically operationalise “moderate” and “vigorous” intensity using simple thresholds, but such binary categorisation overlooks the continuous and domain-based nature of physiological stress, particularly in individuals with cardiometabolic risk or established disease. Exercise intensity zone models were developed to translate complex physiological responses into discrete, clinically usable categories that can guide training load, safety limits, and progression across a spectrum from athletes to patients in rehabilitation.

Multiple, partially overlapping frameworks are now used to define and label these zones, including three-zone models anchored by ventilatory or lactate threshold, five-zone heart-rate systems popularised in sports training, and power- or-pace-based schemas derived from critical power or functional thresholds. Each model relies on different calculation methodologies, such as percentage of maximal heart rate, heart rate reserve, percentage of maximal or reserve oxygen uptake, or directly measured thresholds from gas exchange or blood lactate testing, leading to potential discrepancies in zone assignment and training dose for the same individual. These methodological choices interact with underlying physiological determinants, cardiac output, ventilatory control, substrate utilisation, lactate kinetics, and autonomic regulation to shape both the acute internal load and the chronic cardiovascular and metabolic adaptations elicited by a given training prescription.

At the same time, the rapid diffusion of wearable sensors and AI-enabled analytics has shifted intensity zoning from laboratory-centric protocols to continuous, real-world monitoring. Modern devices can estimate heart rate, heart rate variability, movement, power, and respiratory surrogates at scale, enabling dynamic zone estimation, adaptive exercise prompts, and fine-grained feedbacks loops for behavioural coaching in both healthy and clinical populations. However, variability in algorithm, signal quality, and model transparency raises questions about validity, personalisation, and equity of access, particularly when these tools are used to make clinical decisions in higher-risk groups.

This review aims to provide a comprehensive analysis of exercise intensity zone models in cardiovascular training, with three specific objectives. First, it summarises the conceptual and physiological foundations of intensity zoning, including key domains and thresholds relevant to cardiovascular and metabolic responses. Second, it critically examines the principal calculation methodologies, heart rate, oxygen uptake, lactate and ventilatory thresholds, power and pace and perceptual scales, highlighting their strengths, limitations and areas of divergence. Third, it explores clinical and digital health applications of intensity zones, including cardiovascular prevention, rehabilitation, and AI-driven exercise prescription, and outlines future research directions for integrating continuous physiology, advanced analytics, and health outcomes into next-generation zoning frameworks.

Conceptual Foundations of Exercise Intensity

Internal vs External Load and Absolute vs Relative Intensity

The distinction between internal and external load  represents a fundamental conceptual framework for understanding exercise intensity in both athletic and clinical contexts. External load refers to the objective physical work performed by an individual, quantifiable through metrics such as distance covered, power output in watts, running velocity, number of accelerations or decelerations, or total training duration. These variables can be measured with tools including global positioning systems, power meters, and accelerometers, and provide information about the performance capacities and physical demands placed upon the exerciser. In contrast, internal load captures the psychophysiological response to that external stimulus, reflecting how the body and mind adapt to the imposed workload. Common markers of internal load include heart rate, heart rate variability, blood lactate concentration, oxygen uptake, and subjective ratings of perceived exertion. Crucially, the relationship between external and internal load is not fixed: for a given external workload, internal responses can vary substantially depending on an individual’s fitness level, training status, fatigue, environmental conditions, and health. Monitoring both dimensions in tandem allows clinicians and coaches to adjust intensity based on the individual’s condition, avoid overtraining or accumulated fatigue, and detect early signs of maladaptation or injury risk [1,2].

A parallel distinction exists between absolute and relative intensity, each of which has important implications for exercise prescription and population health research. Absolute intensity refers to the energy expenditure required to perform an activity irrespective of individual cardiorespiratory fitness, commonly expressed as metabolic equivalents (METs), where one MET approximates the resting metabolic rate of approximately 3.5 mL O₂/kg/min. By this standard, moderate-intensity activity is typically defined as 3–6 METs and vigorous intensity as greater than 6 METs. While absolute intensity is convenient and widely used for public health messaging, it fails to account for interindividual variability in aerobic capacity. Relative intensity, on the other hand, expresses exercise demand proportional to an individual’s maximal capacity, using metrics such as percentage of maximal heart rate (%HRmax), percentage of heart rate reserve (%HRR), percentage of maximal oxygen uptake (%VO₂max), or percentage of oxygen uptake reserve (%VO₂R). Studies have demonstrated substantial disagreement between absolute and relative intensity classifications at the individual level, with as little as 43–60% agreement when the same activity is categorised by both methods. For example, an obese older adult may reach relative vigorous intensity at an absolute workload that would be classified as only light or moderate for a young, fit individual; conversely, a highly fit person may require a much higher absolute workload to achieve the same relative physiological stress. This divergence underscores the importance of individualised, relative intensity prescription particularly in clinical populations with reduced fitness or cardiometabolic disease to ensure that exercise elicits the intended physiological stimulus and adaptation [3,4,5,6,7].

Physiological Intensity Domains: Moderate, Heavy, and Severe

Beyond categorical labels, exercise physiologists have identified distinct intensity domains defined by qualitatively different metabolic and cardiorespiratory responses, providing a more physiologically grounded framework for training and prescription. The three-domain model are moderate, heavy, and severe are anchored by two key demarcation points: the first threshold (often termed lactate threshold 1 [LT1] or first ventilatory threshold [VT1]) and the second threshold (LT2, VT2, or critical power/velocity) [8,9,10].

The moderate domain encompasses all intensities below LT1/VT1, where oxygen uptake rises rapidly at exercise onset and then stabilises within approximately two to three minutes, blood lactate remains at or near resting baseline levels, and a true metabolic steady state is achieved. Exercise in this domain can be sustained for prolonged durations many hours in trained individuals without progressive metabolic disturbance, making it the foundation of aerobic base training and general health promotion [8,9,10].

The heavy domains lies  between LT1 and LT2 (or between VT1 and critical power). Within this range, blood lactate is elevated above baseline but eventually stabilises over time, and oxygen uptake exhibits a characteristic “slow component”, a delayed, gradual rise that prolongs the time to steady state but does not drive VO₂ to its maximum. The slow component reflects a progressive loss of muscular efficiency, largely attributable to increased recruitment of less efficient fast-twitch muscle fibres as exercise continues. Exercise in the heavy domain is sustainable for durations ranging from tens of minutes to a few hours depending on fitness, and represents a significant challenge to the aerobic system while not fully depleting anaerobic reserves [9,10,11,12,13].

The severe domain begins above LT2/VT2/critical power and extends up to maximal oxygen uptake (VO₂max). In this range, oxygen uptake rises inexorably via the slow component until VO₂max is attained, blood lactate accumulates continuously without stabilisation, and intramuscular metabolites (phosphocreatine, hydrogen ions) reach critically perturbed levels that ultimately force exercise termination. Exercise tolerance in the severe domain is finite and highly predictable: the higher the intensity above critical power, the more rapidly the finite work capacity above this threshold (termed W′ or D′) is depleted, and the sooner exhaustion occurs. Training studies have demonstrated that exercise in the heavy and severe domains produces greater improvements in VO₂max and threshold parameters compared to training confined to the moderate domain, highlighting the importance of domain-based intensity prescription for eliciting targeted adaptations [10,14,15].

Key Anchors: Ventilatory Thresholds, Lactate Thresholds, and Critical Power/Velocity

Accurate identification of the physiological thresholds that demarcate intensity domains is essential for precise exercise prescription. The first ventilatory threshold (VT1) is identified during incremental exercise testing as the first non-linear increase in minute ventilation relative to oxygen uptake (VE/VO₂), without a concomitant rise in the ventilatory equivalent for carbon dioxide (VE/VCO₂). This point reflects the onset of buffering of hydrogen ions produced alongside lactate accumulation, leading to increased CO₂ production and a compensatory rise in ventilation to maintain acid–base balance. The second ventilatory threshold (VT2), also known as the respiratory compensation point, occurs at a higher intensity and is marked by a disproportionate increase in VE/VCO₂, signifying that hyperventilation alone can no longer adequately buffer metabolic acidosis. VT2 is closely associated with the upper limit of sustainable exercise and approximates the boundary between the heavy and severe domains [16].

The lactate thresholds (LT1 and LT2) are defined analogously using blood lactate kinetics. LT1 represents the lowest exercise intensity at which blood lactate concentration rises significantly above resting baseline, traditionally associated with values around 2.0 mmol/L, though substantial interindividual variation exists. LT2 corresponds to the onset of a rapid, sustained increase in blood lactate, often approximated at 4.0 mmol/L but ranging widely depending on training status, muscle fibre composition, and mode of exercise. The maximal lactate steady state defined as the highest workload or blood lactate concentration that can be maintained over time without continual lactate accumulation operationally, less than 1 mmol/L rise in lactate during the final 20 minutes of constant-load exercise. MLSS is considered the gold-standard marker of sustainable high-intensity exercise and closely corresponds to, though is not identical with, critical power [17,18,19].

Critical power (CP) and its speed-based analogue critical velocity (CS) represent the asymptote of the hyperbolic relationship between power output (or speed) and the tolerable duration of exercise. Physiologically, CP demarcates the boundary between the heavy and severe domains: below CP, metabolic steady states for oxygen uptake, blood lactate, and intramuscular phosphocreatine are achievable; above CP, these variables rise inexorably toward their limiting values and exhaustion ensues within a predictable timeframe. CP may be conceptualised as the greatest metabolic rate at which energy provision remains wholly oxidative at the whole-body level, with substrate-level phosphorylation reaching equilibrium and no progressive depletion of anaerobic reserves. The curvature constant of the power–duration relationship, termed W′ (or D′ for distance), represents a fixed but depletable capacity for work above CP, which is drawn upon at rates proportional to the difference between the exercise intensity and CP. Accurate determination of CP or CS requires multiple exhaustive trials at different intensities or validated single-session protocols, and provides a robust, integrative anchor for prescribing and monitoring high-intensity training in both athletes and clinical populations [15,20].

Together, these anchors ventilatory and lactate thresholds and critical power/ velocity form a physiologically coherent framework for defining intensity zones that can be applied across calculation methodologies, wearable technologies, and clinical contexts, as explored in subsequent sections.

Major Exercise Intensity Zone Models

Three- Zone Threshold-Based Models

The three-zone model represents a physiologically grounded framework that divides exercise intensity into discrete categories anchored by the two primary metabolic thresholds: the first lactate or ventilatory threshold (LT1/VT1) and the second lactate or ventilatory threshold (LT2/VT2). In this schema, Zone 1 encompasses all intensities below LT1/VT1, corresponding to low-intensity or “ base” work where blood lactate remains near resting levels, oxygen uptake attains a true steady state, and exercise can be sustained for many hours without progressive fatigue. This zone forms the foundation of aerobic training volume and is the primary target for recovery sessions and long endurance efforts. Zone 2 occupies the intensity band between LT1 and LT2, representing moderate or “threshold” intensity where lactate is elevated but stabilises over time, and the metabolic cost is sustainable for durations ranging from approximately 30 minutes to two hours depending on training status. This zone targets race-specific efforts for endurance events such as marathons or long-distance triathlons and corresponds closely to the maximal lactate steady state (MLSS). Zone 3 lies above LT2/VT2, characterised by high-intensity efforts where lactate accumulates progressively, oxygen uptake rises toward maximal values, and exercise tolerance is finite typically ranging from several minutes to approximately 30 minutes at sustainable paces [21,22].

The appeal of the three-zone model lies in its direct correspondence to the physiological intensity domains (moderate, heavy, and severe) and its simplicity for practitioners. Because each zone is defined by objectively measurable thresholds rather than arbitrary percentage cut-offs, the model accounts for substantial interindividual variability in threshold location, an advantage over percentage-based systems that assume uniform physiology across populations. In terms of heart rate, Zone 1 typically corresponds to below approximately 82% of maximal heart rate, Zone 2 to approximately 82–87% HRmax, and Zone 3 to 87–97% HRmax, although these percentages vary considerably based on individual threshold positions. The three-zone model is widely used in scientific literature examining training intensity distribution (TID) and forms the conceptual basis for both polarized and pyramidal training paradigms, discussed below [22.23].

Five- Zone and Multi-Zone Models in Endurance Training

While the three-zone model offers physiological precision, many commercial and coaching frameworks employ five-zone or multi-zone systems that subdivide the intensity continuum into finer gradations, often based on percentage of maximal heart rate (%HRmax), heart rate reserve (%HRR), or functional threshold power (FTP). The classic five-zone heart rate model, popularised by wearable device manufacturers and fitness organisations, typically defines zones as follows: Zone 1 (50–60% HRmax), Zone 2 (60–70% HRmax), Zone 3 (70–80% HRmax), Zone 4 (80–90% HRmax), and Zone 5 (90–100% HRmax). Each zone is associated with distinct training objectives: Zone 1 for warm-up, cool-down, and active recovery; Zone 2 for aerobic base building and fat oxidation; Zone 3 for tempo or “aerobic power” development; Zone 4 for threshold and lactate tolerance training; and Zone 5 for maximal aerobic and anaerobic capacity work [22,24,25].

In cycling, the seven-zone powerl model developed by Dr Andrew Coggan has become a widely adopted standard, with zones defined as percentages of functional threshold power: Zone 1 (Active Recovery, <55% FTP), Zone 2 (Endurance, 55–75% FTP), Zone 3 (Tempo, 76–90% FTP), Zone 4 (Lactate Threshold, 91–105% FTP), Zone 5 (VO₂max, 106–120% FTP), Zone 6 (Anaerobic Capacity, 121–150% FTP), and Zone 7 (Neuromuscular Power, >150% FTP). This system provides granular distinctions between submaximal intensities and explicitly separates threshold, VO₂max, and anaerobic efforts, enabling precise periodisation of training stimuli. The addition of a “sweet spot” zone, typically defined as 84–97% of FTP, straddling the upper tempo and lower threshold ranges has gained popularity as an efficient means of accumulating training stress with manageable fatigue, though its placement relative to individual lactate thresholds varies [26].

Multi-zone models offer practical advantages for day-to-day training prescription and athlete communication, allowing coaches to assign specific workout targets with numerical precision. However, a significant limitation is that percentage-based zones may not align with individual physiological thresholds, leading to potential misclassification of training intensity. For example, two athletes with the same maximal heart rate but different lactate threshold positions may be assigned identical Zone 3 targets despite experiencing markedly different metabolic stresses. Mapping between multi-zone and three-zone models reveals that Zones 1–2 of the five-zone system correspond roughly to Zone 1 (below LT1) of the three-zone model; Zone 3 and part of Zone 4 correspond to Zone 2 (between LT1 and LT2); and the upper part of Zone 4 and Zone 5 correspond to Zone 3 (above LT2). Understanding these correspondences is essential for clinicians and coaches who must translate research findings often reported using three-zone frameworks into practical prescriptions for athletes using commercial wearables with five- or seven-zone displays [22,25,27,28,29].

Alternative Frameworks: Continuous “Threshold-to-threshold” and Pyramidal/Polarized Models

Beyond static zone definitions, training intensity distribution (TID) models describe how athletes allocate training time across intensity zones over a mesocycle or macrocycle, with profound implications for adaptation and performance. The three dominant TID paradigms pyramidal, polarized, and threshold, each prescribe distinct proportions of low-, moderate-, and high-intensity work [23,30].

The pyramidal model distributes training volume in a stepwise fashion, with the greatest time spent in Zone 1 (low intensity), a moderate amount in Zone 2 (threshold/tempo), and the least in Zone 3 (high intensity), often approximated as 75–80% Zone 1, 15–20% Zone 2, and 5–10% Zone 3. This approach mirrors the intuitive progression of most endurance training programmes and has been the de facto standard among elite endurance athletes across sports including rowing, cycling, running, and cross-country skiing. The rationale is that high volumes of low-intensity work build aerobic capacity and exercise economy, while moderate threshold sessions develop lactate clearance and race-specific fitness, and limited high-intensity work provides top-end speed without excessive fatigue accumulation [23,31,32].

In contrast, the polarized model popularised by exercise physiologist Dr Stephen Seiler and often termed “80/20 training” advocates spending approximately 80% of training time in Zone 1 (below LT1), very little (approximately 0–5%) in Zone 2 (between thresholds), and the remaining 15–20% in Zone 3 (above LT2). The polarized approach deliberately avoids the “threshold” or “tempo” zone, hypothesising that moderate-intensity training accumulates disproportionate fatigue relative to its adaptive benefit, whereas low-intensity work builds aerobic foundation and high-intensity intervals maximally stimulate VO₂max and anaerobic capacity. A landmark study by Stöggl and Sperlich (2014) compared high-volume, high-intensity interval, threshold, and polarized training over nine weeks and found that polarized training produced the greatest improvements in VO₂max, time to exhaustion, and peak power among well-trained endurance athletes. Subsequent research has demonstrated that polarized TID benefits both elite and recreational athletes, with one study concluding that “polarized training can stimulate greater training effects than between-thresholds training in recreational runners” [23,30,32]

The threshold model (also termed “sweet spot” or continuous moderate-intensity training) inverts the polarized distribution, emphasising sustained work in Zone 2, between LT1 and LT2 with comparatively less time in Zones 1 and 3. Proponents argue that training near the lactate threshold is the most time-efficient stimulus for improving threshold power and race performance, particularly for athletes with limited training hours. Sweet spot training, defined as efforts at 84–97% of FTP, sits at the upper edge of Zone 2 and lower edge of Zone 4, allowing athletes to accumulate significant training stress without crossing into unsustainable intensities. However, critics note that excessive threshold work may compromise recovery and reduce the quality of subsequent high-intensity sessions, potentially leading to stagnation or overtraining [32,33].

Comparative evidence suggests that the optimal TID may depend on athlete characteristics and goals. A meta-analysis by Rosenblat and colleagues found that competitive athletes may benefit most from polarized TID, while recreational athletes may respond equally well to pyramidal approaches. A study of half-Ironman triathletes found that polarized and pyramidal programmes produced nearly identical finishing times, with the authors noting that “coaches should not rule out training prescription in zone 2, since training time in this zone positively correlated with performance.” Practically, periodisation strategies may incorporate elements of both models, for example, following a pyramidal distribution during the base phase and transitioning to a more polarized approach as competition approaches. The key insight is that intensity distribution matters as much as total training volume, and that deliberate manipulation of TID can be a powerful lever for optimising cardiovascular and metabolic adaptations in both athletic and clinical populations [23,30,32].

Calculation Methodologies

Heart Rate-Based Methods: %HRmax, heart rate reserve, and age-predicted vs measured maxima

Heart rate remains the most widely used surrogate for exercise intensity due to its accessibility, non-invasiveness, and strong linear relationship with oxygen consumption across a broad range of submaximal workloads. Two principal approaches exist for translating heart rate into prescriptive intensity zones: percentage of maximal heart rate (%HRmax) and percentage of heart rate reserve (%HRR), also known as the Karvonen method. The %HRmax approach assigns zones based on simple fractions of the highest heart rate achievable during maximal exercise, with common guideline recommendations targeting 64-65% HRmax for moderate-intensity and 77-95% HRmax for vigorous-intensity exercise. While straightforward, this method does not account for individual differences in resting heart rate, potentially leading to systematically higher or lower relative workloads for individuals at the extremes of resting heart rate distribution [34,35,36,37].

The Karvonen or heart rate reserve method addresses this limitation by incorporating resting heart rate into the calculation: Target HR = HRrest + (HRmax − HRrest) × desired intensity fraction. Heart rate reserve (HRR) represents the total dynamic range available for cardiovascular response and has been shown to correlate more closely with percentage of oxygen uptake reserve (%VO₂R) than %HRmax does with %VO₂max, making HRR-based prescription preferable when the goal is to match metabolic intensity across individuals. According to the American College of Sports Medicine (ACSM), light-intensity exercise corresponds to approximately 30–40% HRR, moderate intensity to 40–60% HRR, and vigorous intensity to 60–90% HRR. The Karvonen method was developed by Finnish physiologist Martti Karvonen in 1957, who identified that training at approximately 60% of HRR represented a threshold intensity for producing measurable improvements in cardiorespiratory fitness [34,37,38,39].

A critical determinant of accuracy in both methods is how maximal heart rate is established.  The most commonly cited formula, HRmax = 220 − age, was never derived from rigorous original research and has been shown to exhibit a standard deviation of 10–12 beats per minute, systematically overestimating HRmax in younger individuals and underestimating it in older adults. Analyses involving thousands of subjects have demonstrated that the 220 − age equation has “no scientific merit for use in exercise physiology and related fields.” Alternative equations with improved accuracy have been proposed, including Tanaka’s formula (HRmax = 208 − 0.7 × age) derived from a meta-analysis of 18,712 subjects, and the HUNT formula (HRmax = 211 − 0.64 × age) based on direct measurement in 3,320 healthy adults. Nonetheless, all age-based predictions show wide limits of agreement with measured HRmax, and directly measured values from graded exercise testing remain the gold standard for precision prescription, particularly in clinical populations where the consequences of intensity misclassification may be significant [36,40,41].

Oxygen uptake-based methods: %VO2max and %VO2 Reserve

Oxygen uptake (VO₂) provides a direct measure of metabolic rate and whole-body aerobic energy expenditure, making it the criterion standard for quantifying exercise intensity in research settings. Historically, exercise prescription guidelines expressed intensity as a percentage of maximal oxygen uptake (%VO2max),  with moderate intensity typically defined as 40–60% VO₂max and vigorous intensity as 60–85% VO₂max. However, this approach assumes that resting oxygen consumption is negligible relative to the exercise value, which introduces systematic error—particularly in individuals with low aerobic capacity for whom resting VO₂ represents a substantial fraction of VO₂max [37,40].

The concept of oxygen uptake reserve (%VO2R),  analogous to heart rate reserve, was introduced to correct this limitation. VO₂R is calculated as VO₂max minus resting VO₂, and target intensity is expressed as a percentage of this reserve added to resting VO₂. Research has demonstrated that %VO₂R is more closely equivalent to %HRR than %VO₂max is to %HRmax, supporting the interchangeable use of HRR and VO₂R when prescribing exercise intensity. The ACSM revised its guidelines in 1998 to recommend %VO₂R as the preferred oxygen-based metric for intensity prescription [37,39,40].

A practical consequence of this distinction is that the minimal effective training intensity differs depending on baseline fitness. Swain and Franklin’s analysis of training studies found that individuals with aerobic capacity above 40 mL/kg/min require intensities of at least 45% VO₂R to achieve improvements in VO₂max, whereas those with lower baseline fitness experience gains at intensities as low as 30% VO₂R. This threshold-dependent response underscores the importance of individualised prescription and highlights the risk of prescribing fixed percentage intensities without accounting for baseline cardiorespiratory fitness [37,40].

Lactate- and Ventilatory-Threshold-Based Prescriptions (VT1, VT2, LT1, LT2)

Threshold-based methods anchor exercise intensity to objective physiological demarcation points rather than arbitrary percentages of maximal values, offering a more physiologically grounded approach to zone assignment. The first ventilatory threshold (VT1) is identified during cardiopulmonary exercise testing as the intensity at which minute ventilation begins to increase disproportionately relative to oxygen uptake (rising VE/VO₂ without a rise in VE/VCO₂), reflecting the onset of metabolic buffering of lactate-associated hydrogen ions. VT1 marks the upper boundary of the moderate-intensity domain and is considered ideal for high-volume, low-intensity aerobic training [42,43].

The second ventilatory threshold (VT2) also termed the respiratory compensation point, occurs at a higher intensity and is characterised by a disproportionate rise in both VE/VO₂ and VE/VCO₂, signifying that hyperventilation can no longer fully compensate for progressive metabolic acidosis. VT2 corresponds closely to the transition from the heavy to the severe intensity domain and sets a critical upper limit for sustainable high-intensity interval training. In clinical cardiac rehabilitation, VT1 is often used to define the lower boundary of moderate-intensity training, while VT2 delineates the transition to high-intensity effort [42,43,44].

Lactate thresholds (LT1 and LT2) are analogous constructs derived from serial blood lactate measurements during incremental exercise. LT1 corresponds to the first sustained increase in blood lactate above baseline, often approximated at 2.0 mmol/L, while LT2 marks the onset of rapid, exponential lactate accumulation, traditionally estimated at 4.0 mmol/L but varying substantially among individuals. The maximal lactate steady state (MLSS) represents the highest intensity at which blood lactate can stabilise over prolonged exercise (typically defined as <1 mmol/L rise in the final 20 minutes of constant-load work) and closely aligns with, though is not identical to, VT2 and critical power. While gas-exchange analysis requires expensive laboratory equipment, lactate testing is more accessible to coaches and field practitioners, making it a popular method for individualised zone prescription in endurance sports [9,45,46,47].

A key advantage of threshold-based prescription is that it accounts for interindividual variability in the location of metabolic breakpoints. Studies have demonstrated that fixed percentage targets (e.g., 70% HRmax) can misclassify exercise intensity relative to true physiological thresholds, with some individuals training below VT1 and others inadvertently exceeding VT2 when following percentage-based guidelines. Milani and colleagues found that VT1 occurred at a median of 73% HRpeak and 58% VO₂peak in patients with cardiovascular disease, values that would be classified as “low to moderate” intensity by some guidelines and “moderate to high” by others, highlighting the discordance between percentage-based and threshold-based approaches [37,43].

Power and Pace-Based Zoning in Cycling and Running (FTP, Critical Power, Critical Speed)

In cycling and running, power output (measured in watts) and pace (expressed as speed or time per distance) provide direct, objective measures of external workload that are unaffected by day-to-day variations in heart rate response due to fatigue, hydration, temperature, or psychological state. Two principal constructs underpin power- and pace-based zoning: functional threshold power (FTP) and critical power/ critical speed (CP/CS) [28].

Functional threshold power  is commonly defined as the highest power output a cyclist can sustain for approximately 60 minutes, and is often estimated as 95% of average power during a maximal 20-minute time trial or 90% of an 8-minute effort. FTP serves as the anchor for widely used training zone systems, such as the seven-zone model popularised by Dr Andrew Coggan: Zone 1 (Active Recovery, <55% FTP), Zone 2 (Endurance, 56–75% FTP), Zone 3 (Tempo, 76–90% FTP), Zone 4 (Lactate Threshold, 91–105% FTP), Zone 5 (VO₂max, 106–120% FTP), Zone 6 (Anaerobic Capacity, 121–150% FTP), and Zone 7 (Neuromuscular Power, >150% FTP). The “sweet spot” zone, typically defined as 84–97% FTP, straddles the upper tempo and lower threshold ranges and has gained popularity as a time-efficient stimulus for improving sustainable power [28,48,49,50].

Critical power and its running analogue critical speed represent the asymptote of the hyperbolic power–duration (or speed–duration) relationship, derived from multiple exhaustive trials at different intensities or a single three-minute all-out test. Physiologically, CP/CS demarcates the boundary between sustainable (heavy) and unsustainable (severe) exercise domains: below CP, metabolic steady states for VO₂ and lactate are achievable; above CP, these variables rise inexorably until exhaustion. CP is typically slightly lower than FTP—approximately 95–102% of FTP depending on athlete fitness reflecting subtle methodological differences between the two constructs. The curvature constant of the power–duration relationship (W′ or D′) quantifies the finite capacity for work above CP and can inform pacing strategy, interval design, and recovery prescription [51,52].

In running, critical speed serves the same physiological and prescriptive role. Workouts below CS target the heavy domain for aerobic development, while intervals above CS engage the severe domain for VO₂max and anaerobic capacity stimulation. Because pace is directly measurable and unaffected by cardiac drift, critical speed–based zones offer practical advantages for field-based training prescription, particularly for runners training on variable terrain where heart rate may lag behind actual metabolic demand [9,53,54].

Rating of perceived Exertion and Talk Test as Low-Tech Intensity Markers

When objective physiological measurement is unavailable, rating of perceived exertion (RPE) and the talk test provide accessible, low-cost alternatives for monitoring and prescribing exercise intensity. The Borg RPE scale, developed by Swedish psychologist Gunnar Borg, exists in two principal versions: the original 6–20 scale, where ratings are designed to approximate heart rate when multiplied by 10 (e.g., RPE 13 ≈ 130 bpm), and the modified category-ratio 0–10 scale (CR10), which anchors perceptions of effort to descriptive categories ranging from “nothing at all” to “maximal} [55,56,57].

Research consistently demonstrates strong linear relationships between RPE and objective physiological markers, including heart rate, oxygen uptake, and blood lactate concentration. Correlation coefficients between RPE and VO₂ typically exceed 0.85–0.95 during graded exercise testing, supporting the validity of RPE as a proxy for metabolic intensity. On the Borg 6–20 scale, an RPE of 12–14 corresponds approximately to moderate intensity (50–70% VO₂max), while ratings of 15–17 indicate vigorous effort approaching lactate threshold. The ACSM and other guideline bodies recommend RPE as an adjunct or alternative to heart rate monitoring, particularly in populations taking heart rate–altering medications or those with irregular cardiac rhythms [43,55,56,57].

The talk test rovides an even simpler approach by leveraging the relationship between ventilatory drive and the ability to speak comfortably during exercise. Below the first ventilatory threshold (VT1), carbon dioxide production is matched by ventilation and individuals can converse or recite passages without difficulty. As intensity increases toward and beyond VT1, rising ventilatory demand compromises speech, providing a practical marker of the transition from moderate to vigorous intensity. Studies have validated the talk test as a reliable surrogate for VT1 in both healthy and clinical populations, with individuals reporting they “can talk but not sing” at moderate intensity and “can only speak a few words” at high intensity. The talk test aligns well with the three-zone training model: Zone 1 corresponds to comfortable speech, Zone 2 to increasingly difficult conversation, and Zone 3 to the inability to maintain continuous speech [43,56,58,59].

A practical advantage of RPE and the talk test is their applicability across exercise modes, settings, and populations without requiring any equipment. However, both methods are inherently subjective and may be influenced by psychological factors, prior experience, and individual differences in effort perception. For greatest accuracy, RPE and talk test ratings should be calibrated against objective thresholds during initial assessment and periodically re-evaluated as fitness improves [43,55,60].

Physiological Determinants Across Zones

Across the classic exercise-intensity domains, cardiovascular, respiratory, metabolic, and autonomic systems adjust in coordinated but zone-specific ways that determine both acute tolerance and long-term adaptation. Understanding these physiological determinants clarifies why seemingly similar external workloads can elicit different internal responses in individuals with distinct fitness, cardiometabolic risk, or autonomic profiles [61,62,63,64].

Cardiovascular responses show a characteristic pattern across intensity zones, driven by changes in stroke volume, heart rate, and arteriovenous oxygen difference. Stroke volume rises rapidly from rest and typically plateaus by about 40–60% of VO₂max in untrained individuals, whereas endurance-trained athletes can continue to augment stroke volume into higher intensities through enhanced diastolic filling, myocardial contractility, and blood volume expansion. Cardiac output (product of stroke volume and heart rate) increases almost linearly with VO₂, supporting progressively higher muscle blood flow and oxygen delivery, but during prolonged moderate-to-heavy exercise a “cardiovascular drift” emerges in which stroke volume declines and heart rate drifts upward at a constant workload, often accompanied by small reductions in mean arterial pressure. With increasing intensity, systolic blood pressure rises substantially due to elevated cardiac output, while diastolic pressure remains stable or slightly decreases in dynamic exercise, maintaining or increasing mean arterial pressure and perfusion pressure across tissues. These hemodynamic adjustments underpin the capacity to maintain oxygen delivery in moderate and heavy domains, but in the severe domain the ability to further augment stroke volume or redistribute blood flow is progressively exhausted, contributing to a finite tolerance time [62,65,66,67,68,69].

Pulmonary and ventilatory responses also scale with intensity, closely tracking metabolic demand and acid–base status. At low-to-moderate intensities, tidal volume and breathing frequency increase efficiently to maintain arterial oxygenation with a relatively stable ventilatory equivalent for oxygen (VE/VO₂) and carbon dioxide (VE/VCO₂). Around the first ventilatory threshold (VT1), VE begins to rise disproportionately relative to VO₂, reflecting increased CO₂ production from bicarbonate buffering of accumulating hydrogen ions, and VE/VO₂ increases while VE/VCO₂ remains near its nadir. At higher intensities near the second ventilatory threshold (VT2), both VE/VO₂ and VE/VCO₂ rise sharply as a respiratory compensation for metabolic acidosis, marking a transition to the severe domain where ventilation approaches maximal capacity and breathing becomes more rapid and shallow. In this range, ventilatory efficiency declines and dyspnoea increases, particularly in individuals with ventilatory or gas-exchange limitations such as COPD or heart failure, making ventilatory equivalents and end-tidal gas tensions valuable clinical markers of functional reserve [70,71,72].

Metabolic and muscular responses across zones are characterised by progressive shifts in substrate utilisation, lactate kinetics, and mitochondrial engagement. In the moderate domain below LT1, skeletal muscle relies predominantly on fat oxidation, with high mitochondrial efficiency, stable lactate concentrations, and maintenance of intramuscular phosphocreatine and pH, supporting long-duration steady-state exercise. As intensity enters the heavy domain between LT1 and LT2, carbohydrate contribution to ATP resynthesis increases (the “crossover” concept), lactate production rises but reaches a new steady state, and a VO₂ slow component emerges, reflecting recruitment of less efficient type II fibres and reduced muscle efficiency. At intensities above LT2 in the severe domain, glycolytic flux and lactate production outstrip clearance capacity, leading to progressive lactataemia, phosphocreatine depletion, and intracellular acidosis that directly impair excitation–contraction coupling and cross-bridge cycling, thereby limiting time to exhaustion. Chronic training within and across these domains induces mitochondrial biogenesis, increased capillary density, enhanced lactate transport (MCT1 and MCT4), and improved metabolic flexibility, shifting thresholds to higher absolute workloads and delaying the onset of fatigue-generating perturbations [73,74,75].

Autonomic and neuroendocrine responses integrate cardiovascular, ventilatory, and metabolic adjustments via dynamic modulation of sympathetic and parasympathetic activity and stress signalling pathways. At low intensities, parasympathetic (vagal) withdrawal is the primary driver of heart-rate increases, while sympathetic activation remains modest, preserving high heart-rate variability (HRV) and low circulating catecholamine levels. As intensity progresses into the moderate and heavy domains, sympathetic outflow rises and vagal activity declines, reducing HRV indices such as root mean square of successive differences (RMSSD) and high-frequency power, and driving increased catecholamine release (epinephrine, norepinephrine) that supports glycogenolysis, lipolysis, and vascular tone. Near and above VT2, sympathetic dominance becomes pronounced, with marked reductions in HRV, elevated catecholamines, and activation of hypothalamic–pituitary–adrenal axis signalling (e.g., cortisol), which together facilitate maximal cardiorespiratory performance but also increase allostatic load. Repeated training exposure, particularly in moderate and heavy domains, enhances resting vagal tone, improves HRV, and attenuates sympathetic responses to a given absolute workload, whereas chronic overload without adequate recovery can lead to sustained autonomic imbalance, blunted HRV, and pro-inflammatory states associated with increased cardiovascular risk [64,68,76,77].

Clinical Applications in Cardiovascular and Metabolic Care

Exercise intensity zoning has direct implications for cardiovascular and metabolic prevention, rehabilitation, and safety across risk strata. Translating physiological zone models into clinical prescriptions requires adaptation to comorbidities, medications, and baseline fitness, with special attention to threshold-based and symptom-limited approaches in higher-risk groups [43,77,78,79].

Intensity Zoning for Primary Prevention in At-Risk but Asymptomatic Adults

For asymptomatic adults with elevated cardiovascular risk (e.g., hypertension, dyslipidaemia, family history), major societies recommend at least 150–300 minutes/week of moderate-intensity or 75–150 minutes/week of vigorous-intensity aerobic activity, which can be reframed using zone models. Moderate intensity aligns broadly with Zone 1–2 work (below or around VT1/LT1, ~40–59% VO₂R or HRR), while vigorous intensity corresponds to upper Zone 2 to lower Zone 3 (around or above VT2, ~60–89% VO₂R/HRR). For primary prevention, most weekly volume is ideally accumulated below VT1 (low–moderate zone) to maximise adherence and minimise injury risk, with smaller but regular doses of higher-intensity intervals (near/above VT2) to augment VO₂max and endothelial function. In sedentary or deconditioned at-risk adults, initial targets may be as low as 30–40% VO₂R/HRR, progressing toward guideline levels as tolerance improves, while tracking symptoms, blood pressure, and perceived exertion within each zone [37,43,80,81,82,83,84]

Exercise Prescription in Cardiovascular Rehabilitation and Heart Failure

In secondary prevention and heart failure (HF), intensity zoning becomes more tightly anchored to ventilatory and ischemic thresholds to balance efficacy and safety. Cardiac rehabilitation programmes commonly prescribe continuous aerobic training at 40–70% HRR or VO₂R, which corresponds approximately to intensities between VT1 and slightly below VT2 in many patients. When thresholds are directly measured using cardiopulmonary exercise testing, VT1 is often used as the lower anchor for training, with steady-state sessions set slightly below or around VT1 and interval bouts prescribed between VT1 and VT2, depending on clinical stability. In patients with documented myocardial ischemia, exercise heart rate is typically maintained at least 10 beats/min below the ischemic or angina threshold, effectively limiting training to the lower zones even if ventilatory thresholds would permit higher intensities. Emerging data support carefully supervised high-intensity interval training (HIIT) in selected HF and coronary patients, with short intervals near or above VT2 interspersed with longer recovery at or below VT1, producing larger improvements in VO₂peak than moderate-intensity continuous training, provided that comprehensive risk stratification and monitoring are in place [43,79,81,85,86].

Considerations in Obesity, Type 2 Diabetes, and Metabolic Syndrome

In obesity, type 2 diabetes (T2D), and metabolic syndrome, intensity zoning must account for reduced cardiorespiratory fitness, mechanical loading, and frequent comorbidities such as hypertension and NAFLD. Guidelines for T2D recommend at least 150 minutes/week of moderate-to-vigorous aerobic activity plus 2–3 sessions/week of resistance training, with additional benefit from higher weekly volumes (e.g., ≥5–7 hours/week) for glycaemic control and weight management. For many individuals with metabolic syndrome, intensities just below VT1 (low Zone 2, ~40–59% VO₂R/HRR) offer an optimal trade-off between fat oxidation, glycaemic benefits, and tolerability, while brief excursions into higher zones (near VT2) via HIIT can further improve insulin sensitivity and cardiorespiratory fitness in clinically stable patients. Obesity and T2D are often associated with autonomic dysfunction and blunted heart-rate responses, so perceived exertion (e.g., Borg 12–14), the talk test (comfortably talking but not singing), and, where available, ventilatory thresholds derived from CPET or heart-rate variability can provide safer anchors for intensity zoning than %HRmax alone. Weight-bearing modalities may require lower mechanical loads for the same cardiometabolic stimulus (e.g., cycling or aquatic exercise versus running), and joint pain or neuropathy may constrain how patients can safely reach higher zones [87,88,89,90,91,92,93,94].

Safety, Contraindications, and Risk Stratification by Zone

Safety in clinical exercise hinges on structured risk stratification and zone-appropriate monitoring. For low-risk, asymptomatic adults, unsupervised training in Zones 1–2 using guideline-based targets (e.g., 40–59% VO₂R/HRR or “can talk but not sing”) is generally safe, provided that vigorous efforts (upper Zone 2–3) are introduced gradually and warning symptoms (chest pain, undue dyspnoea, palpitations, syncope) prompt immediate cessation and evaluation. In established cardiovascular disease or HF, high-intensity Zone 3 work is usually reserved for supervised settings, with pre-participation assessment (history, ECG, echocardiography, CPET where feasible) used to categorise patients into low, intermediate, or high risk before prescribing any near-VT2 or above-VT2 intervals. Absolute contraindications to training in higher zones include unstable angina, decompensated HF, uncontrolled arrhythmias, severe aortic stenosis, and acute myocarditis, while relative contraindications (e.g., uncontrolled hypertension, severe autonomic neuropathy, proliferative retinopathy) often limit patients to lower zones until stabilised. Across all groups, integrating zone-based prescription with personalised progression (e.g., 5–10% increases in weekly volume or intensity), regular symptom and blood-pressure monitoring, and re-assessment of thresholds over time helps ensure that the cardiometabolic benefits of structured exercise are realised with acceptable risk [43,81,83,89].

Digital Health, Wearables, and AI-Driven Zone Estimation

Consumer and clinical wearables now provide a rich sensor stack for exercise-intensity zoning, including optical or ECG-based heart rate, step counts and accelerometry, cycling power, and increasingly, heart rate variability (HRV) and respiratory surrogates such as breathing rate or chest motion. Heart rate and power data allow direct mapping to traditional percentage-based and threshold-based zones, while HRV-derived metrics (e.g., rMSSD, DFA-α1) and respiratory rate can be used to approximate ventilatory thresholds (VT1, VT2) and autonomic recovery in real time. Recent respiratory wearables have demonstrated close agreement with laboratory ergospirometry for detecting ventilatory thresholds during graded cycling tests, suggesting feasibility of threshold estimation on smart trainers and potentially in semi–free-living conditions [95,96,97,98,99].

Building on these data streams, AI and machine-learning models are increasingly used to estimate thresholds and dynamically adjust zones outside the laboratory. Approaches include training algorithms on large cardiopulmonary exercise-test datasets to detect breakpoints in heart rate, respiratory patterns, or HRV that correspond to VT1/VT2, and then applying these models to wearable data during field exercise. In free-living settings, supervised and unsupervised learning methods can segment accelerometer and heart rate traces into bouts of light, moderate, and vigorous activity, infer time spent in each zone, and estimate energy expenditure with higher accuracy than traditional cut-point methods. Emerging HRV-guided training systems adjust daily intensity targets and session composition based on overnight or resting HRV, with trials showing that HRV-based prescriptions can personalise training loads, optimise performance, and improve adherence compared with fixed plans [95,98,100].

AI-enabled health platforms overlay these physiological insights with behavioural science to drive personalisation and long-term engagement. Digital coaching systems use wearable-derived zone data to provide just-in-time feedback, adaptive goals, and tailored exercise sessions, often combining automated recommendations with video content, gamification, and social features. Randomised studies of AI or voice-assisted coaching integrated with fitness trackers have demonstrated increases in daily steps and moderate-to-vigorous physical activity, suggesting that scalable, algorithm-driven counselling can partially replicate the benefits of human coaching for sedentary individuals. HRV- or readiness-based recommendations can also support recovery management, discouraging high-zone sessions when physiological stress markers are elevated and reinforcing consistency when recovery is adequate [98,101].

However, several limitations and equity concerns temper enthusiasm for AI-driven zone estimation. Device-level issues include variable accuracy of optical heart-rate sensors during high-intensity or irregular movements, proprietary algorithms that obscure how zones are derived, and inconsistent validation across populations with darker skin tones, arrhythmias, or chronic disease. Data fragmentation between platforms, limited interoperability with electronic health records, and privacy/security concerns can hinder clinical integration and trust. From a public-health perspective, reliance on smartphones and premium wearables risks exacerbating digital and socioeconomic disparities, as those with the greatest cardiometabolic burden may have the least access to high-quality devices and broadband connectivity. Addressing these challenges will require open validation frameworks, regulatory standards for algorithm transparency, inclusive datasets that capture diverse users, and implementation strategies that make AI-driven zone estimation available in low-resource and high-risk communities, not only among early adopters [97,102].

Practical Frameworks for Implementation

Translating laboratory-derived zones into real-world prescriptions starts with converting VT1/VT2 or LT1/LT2 from cardiopulmonary testing into simple heart-rate bands, pace/power ranges, and perceived-exertion cues (for example, “most weekly work slightly below VT1; brief intervals near VT2”). For patients who cannot access full testing, pragmatic field tests (submaximal walk tests, time trials) and validated equations can approximate three broad zones (below, around, and above the first threshold), with clear written rules for progression and when to stop because of symptoms such as chest pain, severe breathlessness, or dizziness [43,103].

In day-to-day practice, combining markers is more robust than relying on a single metric. Heart rate or power provide quantitative anchors, while RPE and symptom monitoring (dyspnoea, angina, unusual fatigue) act as safety and calibration checks, especially in those on rate-limiting drugs or with autonomic dysfunction. A practical prescription might be “walk or cycle at 40–60% heart-rate reserve, RPE 12–13, talk-test positive,” with clinicians adjusting targets over time based on observed responses and adherence [104,105].

Communication strategies for patients and digital-app users work best when they translate zones into intuitive language (“easy, moderate, hard”), use visuals to show weekly targets, and provide real-time feedback (“you’re in your green zone today”). Digital coaching solutions that combine short, clear explanations, progress dashboards, reminders, and two-way messaging have been shown to improve adherence and confidence compared with static paper prescriptions, particularly when linked to personalised goals (blood pressure, glycaemia, daily-function milestones) [106].

Future Directions and Research Gaps

Future research on exercise intensity zones will likely focus on three converging directions. First, multi‑omics, imaging, and dense longitudinal sensor data (HR, HRV, CGM, BP, muscle oxygenation) will be integrated into intensity models to define mechanistically distinct “response phenotypes” and explain why individuals with similar VO₂max or thresholds show different adaptations and risks. Second, adaptive, closed‑loop systems will use real‑time physiological inputs to update zones and prescriptions within and between sessions, analogous to closed‑loop insulin delivery, moving from static CPET‑based targets to continuously optimised, AI‑guided training plans. Third, there is a major gap in long-term data linking specific zone distributions and digital prescriptions to hard outcomes such as incident CVD, diabetes, frailty, and all‑cause mortality, so future large, pragmatic trials must compare different intensity mixes and algorithmic strategies on cardiometabolic risk, longevity, and health span rather than surrogate metrics alone [99].

Conclusion

Exercise-intensity zone models provide a unifying framework to translate complex cardiopulmonary, metabolic, and autonomic responses into practical prescriptions that can be tailored from athletic performance to cardiometabolic prevention and rehabilitation. Three‑zone, five‑zone, and power‑ or pace‑based schemes each offer advantages, but differ in how they anchor intensity (percentages of maximal heart rate or oxygen uptake, heart‑rate or oxygen‑uptake reserve, ventilatory and lactate thresholds, or functional/critical power), which can lead to divergent training doses for the same individual and reinforces the need for threshold‑informed, relative prescriptions rather than fixed population cut‑points. Across zones, distinct cardiovascular, ventilatory, metabolic, and neuroautonomic profiles underlie differences in adaptation and risk, supporting the strategic use of predominantly low‑intensity volume with targeted bouts of heavy and severe‑domain work in appropriately screened individuals. In clinical practice, zone‑based approaches enable more precise primary prevention, cardiac rehabilitation, and management of obesity, type 2 diabetes, and metabolic syndrome, especially when combined with safety stratification by symptoms, comorbidities, and medications. The rapid proliferation of wearables and AI‑enabled analytics extends these concepts into real‑world settings through continuous estimation of intensity, thresholds, and recovery, but also introduces challenges around measurement validity, algorithm transparency, and equitable access that future research must address by integrating multi‑omics and imaging, developing adaptive closed‑loop prescriptions, and linking specific intensity distributions to long‑term cardiometabolic risk, longevity, and health span.

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