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Original Research

Comparison of Linear and Reverse Linear Periodized Programs With Equated Volume and Intensity for Endurance Running Performance

Bradbury, Duncan G.1; Landers, Grant J.1; Benjanuvatra, Nat1; Goods, Paul S.R.1,2

Author Information
Journal of Strength and Conditioning Research: May 2020 - Volume 34 - Issue 5 - p 1345-1353
doi: 10.1519/JSC.0000000000002805
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Periodization is the process of planning a training program that considers all factors that influence the overall performance of an individual (5,30,34). Fitness variables that govern performance are often ordered within a training program to achieve peak performance for a specific event or competition (24,34,35,38). This is performed while considering the interrelationship between the key performance variables (32). As such, the achievement of a specific training goal may enable other training goals to progress, resulting in improvement in the overall performance (12,19,28,29). As a result, periodization is widely regarded as the principle method of developing an athlete's peak performance (15,20,24,30). Crucial to periodization is the inverse relationship between volume and intensity (24).

Although most agree on the concept of periodization, there is debate on how the structure of a training plan should be organized to deliver the optimal performance (20). There is a plethora of periodization methods that have been used in many sports (e.g., linear or undulating) (31), with the optimal structure likely to be dependent on a number of factors including the type of sport or level of athletes. With this in mind, this investigation focuses on the implementation of linear periodization and reverse linear periodization for endurance running performance in recreational runners.

When an athlete is training for an endurance event, the commonly used method of periodization is linear periodization (32). The typical linear periodized program aims to build aerobic capacity first through a period of high-volume/low-intensity training before increasing the proportion of high-intensity training (23). The logic of this approach is to ensure sufficient level of aerobic capacity, so the athlete can better tolerate high-intensity work in later training. The increase in aerobic respiration reduces the need for anaerobically derived energy that reduces metabolite accumulation and enables athletes to exercise at higher intensity for longer (39) but also enhances the rate of recovery between high-intensity bouts during interval training sessions (37). This improves the consistency of high-intensity training efforts in the latter half of the linear periodization model.

By contrast, the reverse linear periodization method is less common but has been used in short- to middle-distance track events (1) and begins with a period of high-intensity training. The primary purpose of the high-intensity work early on is to enhance anaerobic threshold (AT), lactate buffering, and lactate tolerance (1). An investigation by Cantos, Liedtke, and Palomo-Vélez (1) showed reverse linear periodization to be more effective than block periodization for improving middle-distance performance. The authors also suggested that introducing high-intensity running training earlier innervates more muscle fibers, which synergizes the activation of working muscles therefore eliciting improvements in running economy (RE) (1). Continuous training or long intervals are then gradually introduced to further the development of aerobic capacity and fatigue tolerance (1,24). Advocates for reverse linear periodization argue that beginning an endurance training program with shorter efforts will allow subsequent longer training efforts to be performed faster because of an improved AT and RE (1). In addition, longer intervals performed at a greater speed may be more specific to the demands of competitive endurance racing and therefore be more appropriate in later preparatory phases (18).

Although linear periodization traditionally has been used in endurance training programs (31), there is a lack of empirical evidence supporting a particular periodization method for endurance running performance (20). Consequently, within the coaching community, there is reliance on past experience and anecdotal evidence to guide the planning process (20). The aim of this study, therefore, was to examine the effects of the linear periodization and reverse linear periodization models on a 5,000-m time trial (TT) performance and the key physiological factors, which underpin endurance performance. It was hypothesized that, in recreational endurance runners, (a) the reverse linear periodization method would elicit greater improvements in AT and RE after a short period of training than the linear periodization method and an unregulated group and (b) both periodization methods would demonstrate greater improvements than unregulated training across all examined performance measures.


Experimental Approach to the Problem

A 3 (group) × 3 (period) repeated-measures design with a controlled sample was used to examine the effects of linear periodization, reverse linear periodization, and nonregulated endurance training program on a 5,000-m running performance. Thirty subjects were assigned to either the control group (CG), the linear periodized group (LPG), or the reverse linear periodized group (RPG). Groups were matched based on sex, V̇O2max, and 5,000-m TT results. Subjects in LPG and RPG performed the assigned training program 3 times per week (2 supervised and 1 unsupervised sessions), whereas subjects assigned to the CG continued with their own training. Endurance running performance was monitored through changes in TT performance and key physiological variables: V̇O2max, AT, and RE. Assessments occurred before commencing (week 0), at the end of the first 6-week training block (week 7), and at the end of the second training block (week 14). All subjects were instructed to record any unsupervised training in exercise diaries throughout the training intervention.


Thirty-five recreational runners, male (n = 26) and female (n = 9), age range 19–45 (mean ± SD: 25.2 ± 7.4 years; 175.4 ± 8.1 cm; 69.0 ± 9.8 kg) with no less than 2-year running experience and a 5,000-m personal best less than 25 minutes (22.5 ± 2.1 minutes) were recruited from local sporting clubs and social running groups. Subjects were also required to have run at least 15 km per week without complete discontinuation for more than 2 consecutive weeks, for 2 years or more (14). They were assigned to 1 of 3 groups, LPG (n = 11), RPG (n = 11), and CG (n = 13), using a block randomization technique to ensure that the groups were matched by sex, 5,000-m TT performance, and V̇O2max. Five subjects (1 from LPG, 1 from RPG, and 3 from CG) withdrew from the study because of illness. Thus, only data from the 30 remaining subjects (n = 10 in each group) who completed the entire study were used in the analyses. A power analysis was conducted (G*Power version 3.1.10), which suggested 10 subjects were required to yield a statistical power of 0.8 based on the data reported by Denadai et al. (13). The investigation was conducted in accordance with the National Statement on Ethical Conduct in Human Research and was approved by the University of Western Australia Ethics Committee before recruitment of subjects. Subjects were informed of the benefits and risks of participation before the start of the study. All subjects gave written consent.


All subjects attended 3 testing sessions, at week 0 (pre-training), week 7 (midpoint, separating 2 training blocks), and week 14 (post-training). In the weeks between testing periods (1–6, 8–13), the LPG and RPG completed 6-week training blocks, which consisted of 2 supervised training sessions and 1 unsupervised training session per week (Table 1). Subjects in the CG were asked to continue with their regular training independently. Investigators monitored the unsupervised training of all participants through training diaries.

Table 1.:
Linear periodized training program.*

Testing Orientation

During each testing period, the following tests were conducted: Submaximal test for RE, incremental treadmill test to exhaustion, and a 5,000-m TT. Both the submaximal test for RE and the incremental treadmill test to exhaustion were conducted on the calibrated treadmill (H/P Cosmos; Quasar 3p Medical treadmill, Nussdorf-Traunstein, Germany) at a gradient of 1%, in a climate-controlled laboratory (temperature was maintained at 22° C, and relative humidity varied between 50 and 60%). The incremental treadmill test to exhaustion was used to determine the V̇O2max, velocity at the maximal oxygen uptake (vV̇O2max), and AT for each participant. The 5,000-m TT was conducted on a 400-m grass track at least 48 hours after laboratory tests. The time between the last training session of a training block and the first testing session of a testing week was no fewer than 3 days and no more than 5 days. Subjects were instructed to refrain from unsupervised training and alcohol in the 24 hours before a test and caffeine 3 hours before testing sessions. Subjects were instructed to record dietary intake in the first testing week and replicate it in the second and third testing weeks. At the beginning of each testing period, subject anthropometric data (height, mass, and sum of 8 skinfolds) were collected. All subsequent testing sessions were completed at the same time of day.

Training Sessions

Each subject attended 2 supervised training sessions per week at a minimum of 48 hours apart. At supervised training sessions, subjects completed a standardized warm-up (2 km at 60–70% of maximum heart rate [HRmax]) before commencing their assigned training set (Table 1). Training intensity was determined based on the 5,000-m TT performance, and the rate of perceived exertion (RPE) was recorded at the conclusion of sessions using the modified CR-10 scale (6).

Each subject was also required to perform one unsupervised training each week during the intervention period. Subjects were instructed to undertake the prescribed training session on flat ground. For these sessions, the target work intensity was based on the subject's HRmax determined during the graded exercise test (GXT). Subjects were required to record running distance, duration, average heart rate (HR), and CR-10 RPE scores.

To calculate training load, session duration in minutes was multiplied by the corresponding CR-10 RPE score. Session loads were then summated to determine weekly training loads (17). The daily mean values and SDs of training load were also calculated for the week (including zero load days). The daily mean was divided by the SD to determine training monotony (16).

Anthropometric Assessment

Body mass was measured with a digital platform scale (Model ED3300; Sauter Multi-Range, Ebingen, West Germany ±10 g) with subjects wearing as little clothing as possible. Skinfold thickness at 8 body landmarks (triceps, biceps, subscapulare, supraspinale, iliocristale, midabdominal, anterior thigh, and medial calf) was measured using Slim Guide spring-loaded calipers to the nearest 0.5 mm on the right side of the body (Creative Health Products, Plymouth, MI, USA). If the difference between duplicate measures exceeded 4% for skinfolds, a third measurement was taken but only after the full profile had been completed in duplicate. The median of duplicate anthropometric measurements was used for subsequent analysis (33).

Measurement of Running Economy

Running economy was defined as the steady state oxygen consumption in ml·kg−1·min−1 obtained at each workload (8). Before the test, participants warmed up at a running speed of 8 km·h−1 for 10 minutes then rested for 5 minutes. Subjects then completed 2 bouts of continuous running on the treadmill, 1 at 9 km·h−1, and another at 11 km·h−1. These speeds were selected to be submaximal as a 25-minute 5,000-m TT equates to 12 km·h−1. Previous literature has used speeds between 8 and 21 km·h−1 (2,40). Each workload was undertaken until steady state was reached and lasted for a minimum of 3 minutes. The 2 running bouts were separated by a 5-minute passive rest period. A 10-minute rest period was given at the conclusion of the 11 km·h−1 trial. Steady state was defined as an increase of <100 ml·O2 used determined through expired gas over the final minute of both stages. Respiratory exchange ratio (RER), V̇o2·kg−1·min−1, and HR averaged over 15-second intervals from steady state of each stage. Rate of perceived exertion was also taken through use of the Borg 6–20 RPE scale (6). Before testing, subjects were familiarized with the RPE scale and received standardized instructions on how to implement the scale. The scale was in full view of each subject during the RE test.

Graded Exercise Test

The initial speed was set at 10 km·h−1 for 3 minutes and then increased 1 km·h−1 every 3 minutes until volitional exhaustion. Each stage of the test was followed by a 1-minute rest period to allow for determination of blood lactate concentration through a capillary blood sample. Throughout each test, pulmonary gas exchange was determined breath-by-breath (Universal ventilation meter; VacuMed, Ventura, CA, USA). Before each test, the oxygen and carbon dioxide analysis system was calibrated with and validated by a 1-L calibration syringe (Model 5,540; Hans Rudolph, Kansas City, MO, USA) in accordance with manufacturer's instructions. Expired oxygen and carbon dioxide concentrations were analyzed using Ametek gas analyzers (Applied Electrochemistry, SOV S-3A11 and COV CD-3A, Pittsburgh, PA, USA) and calibrated immediately before and verified after each test using a certified gas mixture of known concentrations (BOC Gases, Chatswood, Australia). Heart rate was monitored continuously throughout the tests and recorded in the last 15 seconds of each stage (Polar Electro Oy Professorintie, Kempele, Finland). Earlobe capillary samples were analyzed through a Lactate Pro 2 portable blood lactate analyzer (Arkray, KDK, Kyoto, Japan). The V̇O2max is defined as the sum of the highest 4 consecutive 15-second V̇o2 values reached during the incremental test (expressed as ml·kg−1·min−1). To have reached V̇O2max, a subject fulfilled at least 2 of the following criteria: a RER of greater than 1.1, a blood lactate reading above 8 mmol·L−1, and a peak HR at least equal to 90% of age predicted maximum (36). The vV̇O2max is defined as the minimum velocity at which V̇O2max occurred (4). Individual AT was calculated through the D-max method (9). A third-order polynomial regression equation was established on the lactate concentrations against workloads. The D-max was identified as the point on the polynomial regression curve that yielded the maximal distance to the straight line formed by the 2 end data points (41).

Time Trial

At TT sessions, subjects completed a standardized warm-up (1 km at 60–70% of HRmax) followed by a 5-minute rest. Each subject individually completed a 5,000-m TT on a 400-m grass surface track. Subjects were instructed to run at race pace and given verbal encouragement throughout the trial. The time taken to run each distance was recorded using a manual chronometer. Heart rate was recorded immediately after completion of the TT, and 60 seconds later, heart rate recovery (HRR) was recorded. In an attempt to control factors that could influence HR and HRR, subjects were asked to sit passively and remain still for the duration of the recovery period (22).

Statistical Analyses

Time Trial Performance

A 3 (group) × 3 (time point) split-plot analysis of variance (SPANOVA) was used to determine any significant effects of training conditions at different time points. Subsequent 2-way repeated-measures analysis of variances (ANOVAs) located any between-group differences. Post hoc paired sample t-tests were used to identify any differences at specific time points. Cohen's d effect sizes were also calculated where the following descriptors were used: 0–0.2 (trivial); 0.2–0.5 (moderate); and >0.8 (large), with only moderate to large effect sizes reported (11).

Training Response

Comparisons were made for equivalent training weeks where LPG and CG weeks 1–6 and 8–13 were compared with RPG weeks 13–8 and 6–1. A 1-way ANOVA was used to locate any significant differences between groups for both block and weekly training volume, load, and monotony. Post hoc paired sample t-tests were used to identity the nature of any differences.

Physiological Determinants of Endurance Performance

A 3 (group) × 3 (time point) SPANOVA was used to locate any significant effects for V̇O2peak, AT, RE at 9 km·h−1, RE at 11 km·h−1, and sum of 8 skinfolds. Subsequent 2-way repeated-measures ANOVAs located any between-group differences. Post hoc paired sample t-tests identified the nature of any differences.


Training Compliance

All subjects (LPG and RPG) were required to attend a minimum of 90% of supervised training sessions to be included in analysis. Subject compliance was 94.0 ± 3.4%.

Time Trial Performance

A significant time × group interaction (F(4, 54) = 5.423, p = 0.003) was found in the 5,000-m TT performance. Post hoc analyses found that both LPG and RPG had significantly greater improvements in the 5,000 m than CG (F(2, 36) = 6.705, p = 0.009, d = 1.27 and F(2, 36) = 8.801, p = 0.002, d = 1.51, respectively). No significant differences existed between the LPG and RPG groups (F(2, 36) = 1.172, p = 0.321, d = 0.51).

Figure 1 displays the improvements in 5,000-m TT performance that occurred in LPG (76.8 ± 55.8 seconds, 5.5 ± 3.9%), RPG (112.8 ± 83.4 seconds, 8.1 ± 5.5%), and CG (3.6 ± 59 seconds, 0.1 ± 4.6%) after 12 weeks of training.

Figure 1.:
Mean ± SE 5,000-m time trial times between the linear periodized group (LPG), reverse linear periodized group (RPG), and control group (CG) recorded in week 0, week 7, and week 14 of the training intervention. *Significantly different to week 0 value (p < 0.05). †Significantly different to week 7 value (p < 0.05). ‡Significantly different to CG (p < 0.05).

Training Response

Significant differences were detected in training volume and load between groups in training weeks 1–12 (Figure 2).

Figure 2.:
Mean ± SE weekly training volume (A) and weekly training load (B) for the linear periodized group (LPG), reverse linear periodized group (RPG), and control group (CG) during training weeks (1–12).

A significant difference in training load (F(2, 29) = 12.535, p < 0.001) resulted between the 3 groups for total training load during the high-volume training block (HVB). The LPG had a significantly higher total training load than both the RPG (p = 0.015, d = 1.59) and the CG (p < 0.001, d = 1.91). The significant differences in loads were located within HVB in weeks 1–5 (Table 2).

Table 2.:
Mean ± SD weekly training load, weekly training monotony, and weekly training volume (km) for the linear periodized group (LPG), reverse linear periodized group (RPG), and control group (CG).

Significantly higher training loads were found in the HVB than the high-intensity training block (HIB) within both the LPG (p < 0.001, d = 3.52) and the RPG (p < 0.001, d = 2.21).

No significant differences were found between groups for training monotony in the HVB (F(2, 29) = 1.121, p = 0.341) or the HIB (F(2, 29) = 0.671, p = 0.519).

Significant differences in training volume were found between groups in weeks 1–12 (Table 2). No significant differences were found between LPG (24.59 ± 2.49 km), RPG (25.23 ± 2.48 km), or CG (24.26 ± 5.56 km) for mean weekly training volumes (F(2, 29) = 0.166, p = 0.848).

Physiological Determinants of Endurance Performance

For the GXT, no significant time x group interaction was found for V̇O2peak (F(4, 54) = 0.166, p = 0.955) and AT (F(4, 54) = 0.680, p = 0.609). However, all groups demonstrated improvement in these variables (F(2, 54) = 4.998, p = 0.010 and F(2, 54) = 10.639, p < 0.001, d = 0.64 for V̇o2peak and AT, respectively).

For the RE tests, no significant interaction effects were found for the speeds 9 km·h−1 (F(4, 54) = 1.183, p = 0.329) or 11 km·h−1 (F(4, 54) = 0.757, p = 0.558). However, significant time effects for RE at 9 km·h−1 (F(4, 54) = 9.795, p < 0.001, d = 0.69) and 11 km·h−1 (F(4, 54) = 5.999, p = 0.004) indicate improvements at both speeds.

No interaction was found between groups and time for skinfolds (F(4, 54) = 1.312, p = 0.283). A significant time effect was found (F(2, 54) = 31.961, p < 0.001) indicating a reduction within groups in the sum of 8 skinfolds measurement. Significant decreases were seen in all 3 groups (Table 3).

Table 3.:
Mean ± SD V̇O2peak, anaerobic threshold (AT), running economy at 9 km·h−1 (
), running economy at 11 km·h−1 (
), and sum of 8 skinfolds (S8SF) for the linear periodized group (LPG), reverse linear periodized group (RPG), and control group (CG) recorded in week 0, week 7, and week 14 of the training intervention.


To the best of our knowledge, this was the first study focused on analyzing periodized endurance running training and subsequent changes to long-distance running performance. The results displayed a lack of group differences in the key performance measures: V̇O2peak, RE, AT, and body composition. Consequently, the hypothesis that recreational endurance runners in the RPG would elicit greater improvements in AT and RE after a short period of training than the LPG and CG was not supported. With regard to V̇O2peak, subject training history may have placed them close to their physiological upper limits of oxygen consumption (3). The current group of subjects already had high V̇O2peak scores as demonstrated at initial tests. Therefore, limited increases to V̇O2peak could be expected, reducing the potential for between-group differences. As these measures have been shown to account for a large portion of interindividual variance in endurance performance (12,13,21,26), it is likely that improvements in these subqualities accounted for a significant proportion of the improvements to 5,000-m TT performance.

The hypothesis that both periodized groups would be more effective in improving 5,000-m TT performance than the CG was confirmed. This concurs with previous research that shows that a structured training plan is better than unstructured exercise (7,25,28). It has been previously reported that recreationally active individuals performing a 6-week program including 20- to 30-minute continuous and interval-based training at 67 and 77% of vV̇O2peak under supervision 3–5 times per week improves V̇O2peak and AT by 3 and 8%, respectively, supporting the use of this type of training for improving endurance performance (7). In addition, increasing training intensity and using more interval training sessions has been demonstrated to induce greater improvements in endurance performance than moderate continuous running over an 8-week period in recreational runners (25). This may be due to the anaerobic nature of intervals and the subsequent improvement to the important AT. A structured program can also combine running training with resistance training to attain even greater improvements to endurance performance (28). The underlying purpose of periodization is to split the training program into specific objectives, focusing on one objective before moving on the next (20). An advantage of splitting training into objectives is gaining the ability to use periods of high load training as they may be followed by optimized recovery periods (5,20). This will result in improvements to performance. Furthermore, the manipulation or variation of training can reduce the likelihood of training stagnation, monotony, and boredom (5,27). By contrast, the CG had relatively constant week to week training loads for entirety of the intervention.

Differences between structured training and nonstructured training groups may have occurred as a result of training history. In practical sense, coaches must take the level of the athlete in consideration when developing a training program because training experience can dictate the response to a training stimulus (27). Athletes may stagnate more quickly than untrained people when exposed to new training stimuli (10). As subjects in this study all had a minimum of 2-year training experience, it can be assumed that subjects in this study would stagnate without appropriate training variation. This is due to the likely repetitive nature of training in recreational runners and may be seen in the weekly training loads of the CG. The training structure and variation of volume and intensity likely prevented stagnation in the LPG and RPG, whereas the CG performed relatively constant loads throughout the training period. This principle may be responsible for differences in 5,000-m TT performance between the periodized groups and the CG. Furthermore, there was a lack of significant differences between total group training volumes indicating training structure was likely responsible for group differences. This highlights the importance in the role of the coach, and subsequent training program and session design that seem crucial in improving key endurance parameters and subsequent running performance (7,25,28).

Although there were no significant differences between LPG and RPG 5,000-m TT results, the differences between programs was evident with significant differences in weekly training volume and weekly training load in training weeks 1–12. Furthermore, the different mechanisms of linear periodization and reverse linear periodization were evident as session equivalent RPE scores were lower in the RPG than LPG for HVB sessions. Although the LPG relied on the introduction of high-volume training early to improve aerobic capacity thereby improving HIB repetition consistency and interval recovery (5), the RPG introduced high-intensity training first, which may have induced improvements in lactic acid buffering and tolerance (peripheral adaptation). This is one possible reason for the lower RPE scores seen in the RPG high-volume training sessions (2). A previous investigation that demonstrated reverse linear periodization to be more effective than block periodization for short-/middle-distance running attributed the success of reverse linear periodization to the principle of managing lactate through training tolerance followed by clearance (1). Authors also advocated early introduction of high-intensity interval training to maximize motor recruitment thereby training muscle coordination and improving synchronization (1). By contrast, the LPG had higher RPE scores than the CG and RPG, which suggest a greater training stimulus. This highlights that while there were no significant differences between the LPG and RPG for TT results, the mechanisms that allowed significant improvement are different.

There were some limitations to this study, which are worth noting. There was a high intersubject variability across all performance variables that may have impaired the ability to detect statistical significance. However, groups were matched on pre-training test results allowing for group comparisons. Because of the moderate effect size between the linear and reverse periodized groups for TT performance, it is possible that a greater number of subjects recruited for this investigation may have yielded statistical significance for this performance variable. In addition, as periodization generally refers to periods of a season or more, it may be logical for future research to use longer periods, so that differences after each training block can become more pronounced. This would allow the first trained key performance variable to have a larger effect on subsequent training, therefore having a greater impact on other interrelated variables that may also have a greater impact on performance.

In conclusion, structured endurance running training was superior in improving endurance TT performance compared with unregulated training, which shows the importance of training periodization and session structure. Although no differences existed in between-group training volumes, differences existed in training loads, which may indicate variances in physiological adaptations. These differences suggest the order of equated (volume and intensity) training sessions does have an impact on physiological adaptations but is also important in preventing stagnation.

Practical Applications

From the results, this study may be used to support previous research showing structured training will improve endurance running performance compared with training load matched unstructured training. The RPG improved by the same magnitude as the LPG with lower training RPE scores. This confirms the desired mechanism of the reverse linear periodization method and is therefore a valid method of structuring an endurance running program for recreational level endurance athletes.


The authors thank all the athletes who volunteered to participate in the study. The authors have no outside funding or conflicts of interest to disclose. The results of this study do not constitute endorsement of the product by the authors or the National Strength and Conditioning Association.


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running economy; V̇O2max; time trial; training load; linear periodization

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