The effects of high-intensity interval training on glucose regulation and insulin resistance: a meta-analysis Academic research paper on "Health sciences"

obesity reviews

doi: 10.1111/obr. 12317

Physical Activity/Metabolic Effects

The effects of high-intensity interval training on glucose regulation and insulin resistance: a meta-analysis

C. Jelleyman1'2, T. Yates12, G. O'Donovan1, L. J. Gray3, J. A. King24, K. Khunti15 and M. J. Davies12

1Diabetes Research Centre, University of Leicester, Leicester, UK; 2NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit, Leicester & Loughborough, UK; 3Department of Health Sciences, University of Leicester, Leicester, UK; 4School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK; 5NIHR Collaboration for Leadership in Applied Health Research and Care - East Midlands (NIHR CLAHRC - EM), Leicester, UK

Received 15 May 2015; revised 6 August 2015; accepted 6 August 2015

Address for correspondence: Miss C Jelleyman, Diabetes Research Centre, Leicester General Hospital, Leicester LE5 4PW, UK. E-mail: cj136@le.ac.uk

Summary

The aim of this meta-analysis was to quantify the effects of high-intensity interval training (HIIT) on markers of glucose regulation and insulin resistance compared with control conditions (CON) or continuous training (CT). Databases were searched for HIIT interventions based upon the inclusion criteria: training >2 weeks, adult participants and outcome measurements that included insulin resistance, fasting glucose, HbAlc or fasting insulin. Dual interventions and participants with type 1 diabetes were excluded. Fifty studies were included. There was a reduction in insulin resistance following HIIT compared with both CON and CT (HIIT vs. CON: standardized mean difference [SMD] = -0.49, confidence intervals [CIs] -0.87 to -0.12, P = 0.009; CT: SMD = -0.35, -0.68 to -0.02, P = 0.036). Compared with CON, HbAlc decreased by 0.19% (-0.36 to -0.03, P = 0.021) and body weight decreased by 1.3 kg (-1.9 to -0.7, P < 0.001). There were no statistically significant differences between groups in other outcomes overall. However, participants at risk of or with type 2 diabetes experienced reductions in fasting glucose (-0.92 mmol L-1, -1.22 to -0.62, P < 0.001) compared with CON. HIIT appears effective at improving metabolic health, particularly in those at risk of or with type 2 diabetes. Larger randomized controlled trials of longer duration than those included in this meta-analysis are required to confirm these results.

Keywords: High-intensity interval training, physical activity, weight loss, type 2 diabetes.

obesity reviews (2015) 16, 942-961

Introduction

Obesity and type 2 diabetes are inextricably linked, with over 80% of people with type 2 diabetes classed as overweight or obese based on body mass index (BMI) thresholds (1). Diet and physical activity interventions are the cornerstones for management of both conditions. However, while the effects of exercise on type 2 diabetes and insulin sensitivity are well established (2-4), the effects on weight regulation are more controversial (5,6). The prevailing rec-

16, 942-961, November 2015

ommendation for meaningful improvements in cardiorespiratory fitness and metabolic health to occur in adults is engaging in a minimum of 150 min of moderate-intensity or 75 min of vigorous-intensity physical activity per week, accumulated in bouts of 10 min or more (7-9). The guidelines for weight loss are greater, suggesting that 200300 min per week are required for long-term reductions (10). Given that less than 50% of the population in industrialized societies (11), with estimates falling to as low as 5% when objective measures of physical activity are

employed (12,13), meet the shorter physical activity recommendations for health, it is becoming more important to elucidate what is the minimum amount of physical activity required to promote health benefits. This notion is supported by findings from surveys investigating perceived barriers to participation in physical activity which consistently highlight 'lack of time' as a common barrier for not being more active, a finding applicable to the general population (14,15) as well as those with type 2 diabetes (16).

High-intensity interval training (HIIT) has been proposed as a time-efficient exercise intervention that may bring about similar benefits to moderate-intensity aerobic exercise (17,18). Sprint interval training (SIT) using the Wingate protocol is a well-defined form of HIIT involving just 3 min of activity per session not including warm-up or cool-down (19). Although this version of HIIT has been shown to improve fitness in a variety of populations (2022), the repeated maximal efforts this protocol requires may limit practicality for sedentary individuals (23). Stemming from this, protocols using longer, submaximal intervals have been developed, a form of HIIT described as 'aerobic interval training' (24,25). For the purpose of this review, any form of interval training that incorporates high-intensity exercise within or above the range categorized as vigorous (64-90% VO2max or 77-95% HRmax) in the American College of Sports Medicine guidelines (26) shall be collectively referred to as HIIT (i.e. SIT, aerobic interval training).

While HIIT tends to have a potent effect on cardiorespi-ratory fitness in a variety of populations (27-29), benefits to obesity and markers of metabolic health, such as glucose regulation and insulin sensitivity, are less well defined. One narrative review concluded that, despite a reduction in total work volume, HIIT has positive effects on blood glucose control and insulin sensitivity compared with continuous exercise (30). This literature review was limited as it did not provide quantification of the effect of HIIT on metabolic health outcomes, nor did it assess the impact of varying HIIT characteristics. The aim of this systematic review was therefore to quantify the impact of HIIT on glucose and insulin regulation, body weight and cardiorespiratory fitness compared with control conditions (CON) or continuous exercise training (CT) using meta-analysis. A secondary aim was to assess whether observed metabolic changes were mediated by characteristics of the training protocol (i.e. interval intensity, training volume) or concurrent changes in participant physiology (e.g. cardiorespira-tory fitness, body weight).

Methods

This meta-analysis has been reported according to the preferred reporting items for systematic reviews and meta© 2015 World Obesity

analyses (PRISMA) guidelines (31). See Supporting Information Appendix S1 for the checklist.

Search strategy and inclusion criteria

Medline (1946-13/03/2015), Embase (1970-13/03/2015) and SPORTDiscus (1953-30/03/2015) were searched for HIIT intervention studies that reported a measure of glycaemic control. There is no universal definition of HIIT; therefore, based upon a brief overview of the literature, we applied the following criteria to our search: at least two bouts of vigorous or higher intensity exercise (26) interspersed with periods of lower intensity exercise or complete rest. 'High-intensity interval training' is not a MeSH term; therefore, words and phrases commonly used to describe HIIT were searched in titles and abstracts using the following search terms: 'high-intensity interval', 'aerobic interval' and 'sprint interval'. These were then combined with the following terms using Boolean commands: intermittent, Wingate, supramaximal, exercise, training, programme, glucose, insulin, glycaemic, and HbA1c. Wildcards: *; ? and $ were used so that both English and American spellings would be returned. Supporting Information Appendix S2 gives a detailed description of the search strategy. Titles and abstracts of returned articles were evaluated based upon the following inclusion criteria: human participants aged 18 years or over, participants receiving a HIIT intervention and at least one measure of glycaemic control defined as HbA1c, fasting glucose, fasting insulin, postprandial or post-challenge glucose response, or any measure of insulin resistance assessed pre- and postintervention. HIIT had to be prescribed at least three times per week for 2 weeks. Two weeks were deemed the minimum period needed to show training adaptations, defined as a temporary or extended change in structure or function that results from performing repeated bouts of exercise and that is independent of the immediate or short-term effects produced by a single bout of exercise (32). Both controlled and uncontrolled studies were included. Articles were excluded if HIIT was prescribed in combination with another intervention, e.g. diet restriction, resistance training; if participants had diagnosed type 1 diabetes (studies of people with type 2 diabetes were included); or if medication had been altered throughout the intervention. Abstracts, case reports, observational studies and studies not published in English were also excluded.

Risk of bias and study quality

Risk of bias was evaluated based on the PRISMA recommendations (31) which suggest assessing randomized controlled trial quality using the Cochrane risk of bias tool (33). This tool consists of five items that have been shown to have an effect on biasing the results of an intervention.

Studies with control groups were checked for random sequence generation, allocation concealment, blinding, participants lost to follow-up and whether an intention-to-treat analysis had been performed. A score of one point was given for each item fulfilled such that studies could score a maximum of five points. Studies without clear descriptions of these processes were considered not to have satisfied these criteria. Uncontrolled trials were not assessed.

Data extraction and synthesis

Reviewers were not blinded to study authors, institutions, or manuscript journals. If the abstract was considered to be relevant to the review, or did not contain enough information regarding the inclusion or exclusion criteria, full texts were retrieved for further evaluation. References included in the identified studies and previous reviews or commentaries were also hand searched. Where there was uncertainty by the first reviewer regarding appropriate studies, the full text was obtained and a second reviewer (T.Y.) approached for discussion. If evidence of participant repetition was evident, participants were only included once; however, if necessary, multiple articles were used to obtain all required data.

If, according to the methodology, relevant measurements had been taken but the results not reported, or values had been presented in graph form, the authors were contacted and asked to provide the missing data. When no reply was received, the study/outcome was either omitted from the analysis (34) or values estimated from figures (35-37). Where only pre- and post-intervention data were presented, change data were imputed based upon guidelines from the Cochrane Handbook for Systematic Reviews of Interventions (38).

A data extraction form was created and data regarding participant characteristics and disease status, protocol specifics, CT interventions, markers of glucose regulation, insulin resistance, VO2max, body composition and compliance, attrition and adverse events were entered independently by two reviewers (C.J. and G.O.). Discrepancies were resolved by consensus or by a third reviewer (T.Y.). A number of studies reported results from both acute (up to 48 h) and longer-term (72 h) blood samples. If this was the case, the 72-h reading was included in the analysis. Since the study by Lunt et al. (39) had two HIIT groups and a CT group, as per the Cochrane guidelines, the number of participants in the CT group was halved so that pairwise comparisons between HIIT and continuous exercise could be made for each HIIT protocol. One study compared two HIIT groups and these were entered as separate, uncontrolled trials (40).

All models of insulin sensitivity were expressed as insulin resistance to account for the directional effect of exercise, since a beneficial effect would increase sensitivity and

decrease resistance. HOMA-IS% (homeostatic model assessment - insulin resistance) values were inverted (100/ HOMA-IS%) (41), and change scores for other models of insulin sensitivity (n = 9, 20%) were multiplied by -1.

Statistical analysis

Stata v.13 (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX, USA) was used to conduct the meta-analyses. Pairwise comparisons comparing the effect of HIIT on glucose/insulin parameters and VO2max to that of either CT or CON were carried out on studies that had two or more groups. In keeping with other exercise-related meta-analyses of continuous outcomes (3,42), and following best practice (38), weighted mean differences were calculated in the pairwise comparisons for glucose/ insulin parameters and VO2max. Standardized mean differences were used to account for the different measures of insulin resistance.

When studies had a HIIT group only, within-group intervention effect sizes were calculated to estimate the change from baseline. All studies with a control group were included in both the between- and within-group comparisons. Since this within-group comparison is based upon unstandardized data, only HOMA-derived insulin resistance measures could be assessed in this analysis.

Participants were stratified by health characteristics based upon the descriptions given by each included study as follows: healthy (well-trained/recreationally active/ sedentary); overweight/obese; metabolic syndrome (MetS)/ type 2 diabetes; with another chronic disease. Data were presented according to disease status.

We also performed two sensitivity analyses for insulin resistance: (i) using HOMA scores only to determine whether results were attenuated when more sensitive measures of peripheral insulin resistance were removed and (ii) by the length of time elapsed before blood was sampled following the last training session, i.e. <24, >24 and <72 or >72 h; the latter analysis was undertaken using the within-group comparisons only due to lack of data for the between-group comparisons.

Random effects models using Cohen's d were carried out to account for the differences in study protocol and duration. Statistical heterogeneity of the treatment effect among studies was assessed using the chi-squared test. A threshold a value of <0.05 was considered statistically significant and an I2 test with values greater than 50% were indicative of high heterogeneity.

Publication bias

Publication bias based upon reporting of the main outcomes was assessed using a contour-enhanced funnel plot of each trial's effect size against the standard error (43).

Funnel plot asymmetry was assessed by visual interpretation. If publication bias was apparent, Begg & Egger tests were used as a secondary determinant (44,45). Significant publication bias was deemed apparent if P < 0.1.

Meta-regression

Where significant results were found, meta-regression was performed in an attempt to determine whether baseline levels, exercise volume variables and changes to body weight and VO2max mediated the observed changes.

Interval intensity and weekly high-intensity exercise duration and total training period (weeks) were deemed the most relevant components of HIIT protocols. Where possible, using regression equations derived from early works (46,47), we converted interval intensity to a percentage of VO2max in order to be able to directly compare exercise prescriptions. High-intensity exercise duration was estimated by multiplying the number of high-intensity intervals X interval length X the number of sessions per week and controlled for intensity and the number of weeks the study was run.

Changes in body weight and cardiorespiratory fitness were also entered into the regression given their association with the primary outcomes (48,49).

For within-group regression, change summary data were used as the dependent variable and were weighted by the standard error. In studies with a control group, the dependent variable was the mean difference calculated from the pairwise comparison, with each study weighted by the standard error of its effect size.

Results

Studies retrieved

Study selection flow is presented in Fig. 1. The initial searches returned a total of 6,269 articles (Medline n = 3,569, Embase n = 1,933, SportsDiscuss n = 707), of which 4,523 were original articles. Titles and abstracts of returned articles were searched for suitability, leading to the retrieval of 317 full texts. Of these, 266 did not fulfil the inclusion criteria and 4 were excluded due to the nature of the methods used. The total number of papers included in the analysis was 50, as described in Table 1. Fourteen (28%) studies did not have a control group and were therefore only included in the within-group analyses. Of the 36 (72%) controlled trials, 14 (30%) had a CT group, 9 (18%) a CON group, 11 (22%) had both, 1 (2%) had two HIIT groups and a CT group, and 1 (2%) compared two HIIT groups.

Study quality and risk of bias

The 36 controlled trials were assessed for risk of bias. The median quality score was 1/5 (see Supporting Information Table S1). Of the included studies, 13/36 (36%) presented adequate sequence generation, 9 (25%) reported allocation concealment and 11 (31%) blinded where possible. It was unclear in three (8%) studies how many participants were lost to follow-up and five (14%) used the intention-to-treat principle for statistical analysis.

Returned by search n = 6269

Duplicates (n = 1746) Did not satisfy criteria (n = 4206)

Full text articles retrieved for eligibility n = 317

Included (n = 3)

Figure 1 Study selection.

Did not fulfil inclusion criteria (n = 266) Methods inappropriate (n = 3) Implausible data (n = 1)

References and citations searched

Included in analysis n = 50

Table 1 Studies included in the analysis

Authors and year Country Participants Control

Sprint interval training

Adamson etai., 2014™ UK

Babraj etai., 200964 UK

Cocks efa/., 201362 UK

Harmer etai., 2007101 Australia

Heydari etai., 2012102 Australia

Hood eia/., 201 186 Canada

Hovanloo etai, 2013103 Iran

Keating etai, 2014104 Australia

Metcalfe etai., 2012™ UK

Mitranun etai, 2013106 Thailand

Richards etai, 201063 USA

Sandvei etai, 2012® Norway

Serpiello etai, 2010106 Australia

Shaban etai, 20 1 436 Canada

Skleryk etai, 201360 Australia

Healthy; 43 years old (nHIIT = 8, nCON = 6) Healthy; 21 years old (nHIIT = 16, nCON = 9) Sedentary; 21 years old (nHIIT = 8, nCT = 8) Healthy; 24 years old (nHIIT = 7)

Overweight M; 25 years old

(nHIIT = 25, nCON = 21) Sedentary; 45 years old (nHIIT = 7)

Healthy, active; 22 years old (nHIIT = 8, nCT = 8) Overweight; 42 years old (nHIIT = 11, nCT= 11, nCON = 11)

Sedentary; 25 years old (nHIIT = 15, nCON = 14) T2DM; 61 years old (nHIIT = 14, nCT= 14, nCON = 15)

Healthy; 25 years old

(nHIIT = 11, nCON = 9)

Healthy; 25 years old (nHIIT = 11,

nCON = 12) Healthy, active; 22 years old (nHIIT = 10) T2DM; 40 years old (nHIIT = 9)

Sedentary, obese; 39 years old (nHIIT = 8, nCT = 8)

CON Normal activity (RCT)

CON Non-exercising (RCT)

CT 40-60 min @ 65% V02peak (RCT)

HIIT type 1 diabetes; excluded

CON Non-exercising

CT 90-120 min @ 65% V02max (RCT)

CT 45-60 min @ 65% V02peak CON Stretching and flexibility

exercises (RCT)

CON Non-exercising (RCT)

CT 30 min @ 60-65% V02peak CON Non-exercising (RCT)

CON Non-exercising (NRCT)

CT 30-60 min @ 70-80% HRpeak

CT 30 min @ 65% V02peak (RCT)

Intervals

Recovery

Total time (min)

Bike 10 x 6 s @ 6.5% BW 60 s rest

Bike 4-6x30 s @ 7.5% BW 4 min rest

Bike 4-6x30 s @ 7.5% BW 4.5 min @ 30 W

Bike 4-10 x 30 s @ 7.5% BW 3^ min rest

Bike 60 x 8 s @ 80-90% HRpeak 12 s rest

Bike 10 x 60 s @ 60% PPO 60 s rest

Bike 4-6x30 s @ 7.5% BW 4 min rest

Bike 4-6x30 s @ 120% V02peak 2 min @ 30 W

22-30 24

2 6 7 12 2 2 12

Bike 2 x 20 s @7.5% BW

TM 4-6 x 60 s @ 80-85% V02peak

Bike 4-7 x30 s @ 7.5% BW

Walk 5-10 x 30 s @ max. effort

TM 3 x (5 x 4 s) @ max effort

Bike 4 x 30 s @ 100% eWLra„

Bike 8-12 x 10 s @ 5% BW

100 s @ 60 W

4 min @ 50-60%

V02peak

4 min rest 3 min rest 4.5 min (20 s) rest 30 s @ 25% eWLma; 80 s rest

22-36 20^0 20 22

Authors and year Country Participants Control

Trapp eia/., 2008107 Australia

Whyte eia/., 201069 UK

High-intensity Interval training

Boyd eia/., 201340 Canada

Clolac eia/., 201068 Italy

Earnest eia/., 201369 UK

Eguchi eia/., 2012™ Japan

Gunnarson and Denmark

Bangsbo, 201264

Larsen eia/., 2015109 Denmark

Leggate eia/., 201268 UK

Little eia/., 201126 Canada

Morelra eia/., 2008s6 Brazil

Nybo eia/., 2010110 Denmark

Talanlan eia/., 2010111 Canada

Sedentary; 22 years old (nHIIT = 11, nCT = 8, nCON = 15)

Overweight/Obese, sedentary M;

32 years old (nHIIT = 10)

Overweight/Obese; 23 years old (nHIIT 100% = 9, nHIIT 70% = 10)

Healthy; 24 years old

(nHIIT = 16, nCT= 16, nCON = 12)

Risk of IR; 48 years old

(nHIIT = 21, nCT= 16)

Healthy F; 51 years old

(nHIIT = 10, nCT= 10, nCON = 10)

Recreatlonally trained; 34 years old (nHIIT = 10, nCON = 8) Overweight; 38 years old (nHIIT = 10)

Overweight/Obese; 24 years old (nHIIT = 12) T2DM; 63 years old (nHIIT = 8)

Overweight; 40 years old (nHIIT = 8, nCT = 8, nCON = 7)

Healthy; 37 years old

(nHIIT = 8, nCT = 9, nCON= 11)

Healthy F; 22 years old (nHIIT = 10)

CT 15-40 min @ 60% VO; CON Non-exercising (RCT) (NCT)

HIIT 100% aerobic power HIIT 70% aerobic power (RCT)

CT 40 min @ 60-70% V02max CON Non-exercising (RCT)

CT 30 min @ 50-70% V02max (RCT)

CT 30 min @ 50% V02max CON Non-exercising (RCT)

CON Normal activity

CT 20-60 min @ 90% AT CON Non-exercising (RCT)

CT 60 min @ 80% HRmax CON Non-exercising (NRCT) (NCT)

Intervals

Recovery

Total time (min)

Bike 60 x 8 s @ 5% BW

12 s @ 20-30 rpm @ 0 W 5-20

Bike 4-6 x 30 s @ 6.5% BW 4.5 min rest

Bike 8-1 Ox 60s

60 s rest

TM 12 x 60 s @ 80-90% VCW 2 min @ 50-60% V02max 40

TM 2-8x2 min @ 90-95%

V02max

Bike 9 x 30 s @ 75% VCW

2 min @ 50% VOimax 10-34 2.5 min @ 45% VCW 30

Track 20 x 10 s @ 90-100% VCW 50 s @ 30-60% V02r

Bike 5 x 60 s @ 128% PPO 90 s @25 W

Bike 10 x 4 min @ 85% V02peak 4 min rest

Bike 10 x 60 s @ 90% HRmax 60 s rest

Bike 6-18x2 min @ 120% AT 60 s rest

20 15 60 24 20-60

7 6 2 2 12

TM 5x2 min @ >95% HRm

2 min rest

Bike 10 x 4 min @ 90% V02peak 2 min rest

Authors and year

Country Participants

Control

Terada etal., 201366 Canada

Aerobic Interval training Conraads eia/., Belgium

2015112

Fu etal., 20131

Taiwan

Grleco eia/., 201367 USA

Heggelund etal., Norway

2011114

Helgerud etal, 200767 Norway

Holleklm-Strand etal., Norway 2014116

Hwang etal., 20 1 266 lellamo etal., 2013116

lellamo etal., 201461

Karstoft etal., 201361

Taiwan Italy

T2DM; 62 years old (nHIIT = 7, nCT = 7)

Coronary artery disease patients;

58 years old (nHIIT = 85, nCT = 89) Heart failure patients; 68 years old (nHIIT = 14, nCT= 13, nCON = 13)

Healthy; 22 years old

(nHIIT = 12, nCT= 10, nCON = 7)

Schizophrenia; 31 years old (nHIIT = 12, nCON = 7) Healthy, active; 25 years old (nHIIT = 9, nCT = 8) T2DM; 56 years old (nHIIT = 20, nCT= 17)

Cancer; 61 years old (nHIIT = 13, nCON = 11) Heart failure; 62 years old (nHIIT = 17, nCT= 16)

Heart failure; 67 years old (nHIIT = 8, nCT = 8)

Denmark T2DM; 58 years old

(nHIIT = 12, nCT= 12, nCON = i

CT 30-60 min @ 40% V02

reserve (RCT)

CT 37 min @ 70-75% HRpeak (RCT)

CT 30 min @ 60% V02peak CON Non-exercising (RCT)

CT 40 min @ 75% HRR CON Non-exercising (RCT)

CON Computer game task (NRCT)

CT 45 min @ 70 & HRmax (RCT)

CT Home-based moderate Intensity exercise, 210 min weelc1 (RCT)

CON Non-exercising (RCT)

CT 35-40 min @ 45-60%

HRreserve

CT 35-40 min @ 45-60%

HRreserve

CT Walking 60 min @ >55% CON Non-exercising (RCT)

Intervals

Recovery

Total time (min)

TM/B 7-14 x 1 min @ 100%

VO2 reserve

3 min @ 20% V02l

Bike 4 x 4 min @ 90-95% HR„eak 3 min @ 50-70% HR„eak 37

Bike 5x3 min @ 80% V02I

3 min @ 40% V02peak 33

Bike 5x5 min @ 90-100%

HRreserve

TM 4 x 4 min @ 80-85% HRpeak TM 4 x 4 min @ 90-95% HRmax TM 4 x 4 min @ 90-95% HRmax

5 min @ 50% HRreservt

3 min @ 70% HRpeak 3 min @ 70% HRmax 3 min @ 70% HRmax

31 38 37

TM/B 3-12 x 2-5 min @ 80% 1-3 min @ 60% V02peak 30^0 V02peak

TM 4 x 4 min @ 70-85% HRrese™ 3 min @ 45-50% 37

HRreserve

TM 4 x 4 min @ 70-85% HRrese™ 3 min @ 45-50% 37

HRreserve

Walk 10 x 3 min @ >70% PEER 3 min @ <70% PEER 60

Authors and year Country Participants

Control

Mode HIIT intervention

Intervals

Recovery

Total time (min)

Lunt eia/., 2014s9 NZ Overweight; 52 years old

(nSIT = 9, nAIT = 9, nCT = 7)

Madssen eia/., 20146S Norway

Madssen eia/., 2014117 Norway

Moholdt eia/., 201260 Norway

Mora-Rodriguez eia/., Spain 2014118

Morikawa eia/., 201162 Japan

Perry eia/., 2008119 Canada

Stensvold eia/., Norway 2010120

Tjonna eia/., 200866 Norway

Tjonna eia/., 2013121 Norway

Venables and UK

Jeukendrup, 2007s7

Coronary artery disease patients; 64 years old (nHIIT = 24, nCON = 25) Coronary artery disease patients;

64 years old (nHIIT= 15, nCT = 21) Myocardial infarction patients;

57 years old (nHIIT = 30, nCT = 59) Metabolic syndrome; 52 years old (nHIIT = 48) Angina; 65 years old (nHIIT = 666) Healthy; 24 years old (nHIIT = 8)

Metabolic syndrome; 50 years old (nHIIT = 11, nCON = 10) Overweight M; 52 years old (nHIIT = 12, nCT= 10, nCON = 10)

Overweight, sedentary; 42 years

old (nHIIT = 13) Obese M; 40 years old (nHIIT = 8, nCT = 8)

HIIT Maximal effort running HIIT 90% HRmax CT 33 min @ 65-75% HRm® (RCT)

CON Usual care (RCT)

CT 46 min I (RCT)

1 70% HR„,

CT 60 min moderate-vigorous

exercise (RCT) (NCT)

CON Non-exercising (RCT)

CT 47 min @ 70% HFma>; CON Non-exercising (RCT)

HIIT Single interval; excluded (RCT)

CT 30-60 min @ 100% FATmax

(-65% V02ma>;) (NRCT)

AIT 4x4 min @ 85-90% HRma

Walk 3-6 x 30 s @ 100% effort SIT Walk

TM 4 x 4 min @ 85-95% HRma

3 min walking 3 min walking

3 min @ 70% HRm

TM 4 x 4 min @ 85-95% HRma, 3 min @ 70% HRm

Walk 4 x 4 min @ 85-90% HRm® 3 min @ 70% HRm

Bike 4x4 min @ 90% HRm

3 min @ 70% HRm

Walk 4 x 3 min @ 70-85% V02peak 3 min @ 40% VO;

Bike 10x4 min @ 90 @ V02peak 2 min rest

TM 4x4 min @ 90% HRP,

3 min @ 70% HRP,

TM 4 x 4 min @ 90-95% HRma, 3 min @ 70% HRm

TM 4x4 min @ 90% HRm

3 min @ 70% HRm

TM 3-6 x 5 min @ 120% FATm® 5 min @ 80% FATm

36^1 60 43 40

16 16 6 12 16

AIT, aerobic interval training; AT, anaerobic threshold; B, bike; BW, bodyweight; CON, non-exercising control group; CT, continuous training group; eWLmax, estimated maximum work load; F, female; FATma>:, intensity equal to maximal fat oxidation; HIIT, high-intensity interval training; IT, interval training; M, male; NCT, uncontrolled trial; NRCT, non-randomized control trial; PEER, peak energy expenditure rate; PPO, peak power output; RCT, randomized control trial; SIT, sprint interval training; TM, treadmill; W, watts.

Publication bias

Visual interpretation of funnel plots suggested limited publication bias, and as a result, no statistical adjustment was made. See Supporting Information Appendix S3 for figures.

heart failure; n = 461, 23%). For subgroup analysis, we stratified participants by disease status: healthy, n = 1,042 (51%), overweight/obese, n = 230 (11%), metabolic syndrome (MetS)/type 2 diabetes, n = 300 (15%) and other chronic disease, n = 461 (23%).

Heterogeneity

Heterogeneity statistics are presented in Table 2. I2 values were generally high, with all the within-group comparisons indicative of wide heterogeneity (mean score = 89.1%). Controlled trials scored lower, with some showing homogeneous statistics (mean CON = 49.2%; CT = 31.3%).

Participants

There was a total of 2,033 participants included in the analysis, of which 1,383 (68%) underwent a HIIT intervention. Participants were aged 21-68 years and spanned a wide range of health and disease characteristics: from well-trained individuals (n = 61, 3%) through recreationally active (n = 895, 44%), sedentary but otherwise healthy (n = 86, 4%), overweight/obese (n = 230, 11%), with metabolic syndrome (n = 157, 8%), type 2 diabetes (n = 143, 7%) or with another chronic disease (e.g. cancer,

Overview of exercise interventions

Exercise interventions are described briefly in Table 1. Study protocols varied widely between both HIIT and CT interventions. HIIT interventions included aerobic interval training (e.g. (39,50,51)), SIT (e.g. (52-54)) and HIIT (e.g. (25,40,55)). The number (range 2->60), duration (range 4 s-5 min) and intensity (range from 65%VO2max to Wingate effort) of 'high-intensity' intervals, as well as duration (range 12 s-5 min) and intensity (range from complete rest to 70%HRmax) of recovery intervals varied widely between studies. Exercise session duration (mean 34 min, range 10-60 min), total training volume (range 85,040 min) and total length of intervention (range 2-16 weeks) also varied widely between studies. Not all studies reported how the continuous training intervention had been selected, although some were energy matched to HIIT (e.g. (56-58)) or based upon the global recommendations for moderate intensity exercise (e.g. (59,60)). Continuous

Table 2 Effect sizes of comparisons of high-intensity interval training (HIIT) after training compared with control and continuous training

Within groups*

Compared to CON

Compared to CT

Insulin resistance

Fasting glucose

Fasting insulin

Body weight

ES (95% CI) 12 (%) P N

ES (95% CI) I2 (%) P N

ES (95% CI) I2 (%) P N

ES (95% CI) I2 (%) P N

ES (95% CI) I2 (%) P N

ES (95% CI) I2 (%) P

-0.33 (-0.47, -0.18)

<0.001

-0.13 (-0.19, -0.07)

<0.001

-0.13 (-0.27, 0.01) 99.0 0.068 28

-0.93 (-1.39, -0.48)

<0.001

-0.72 (-1.19, -0.25) 93.0 0.002 44

0.30 (0.25, 0.35) 97.9 <0.001

-0.49 (-0.87, -0.12)

56.4 0.009

-0.17 (-0.34, 0.01) 67.8 0.067 6

-0.19 (-0.36, -0.03) 0.0 0.021 11

-1.0 (-2.32, 0.32)

57.5 0.138

-1.29 (-1.90, -0.68) 21.4 <0.001 18

0.28 (0.12, 0.44) 91.8 0.001

-0.35 (-0.68, -0.02) 58.7 0.036 23

-0.07 (-0.17, 0.03) 4.9 0.178 7

0.02 (-0.07, 0.11) 18.5 0.678 16

-0.34 (-1.42, 0.73) 0.0 0.531

0.32 (-0.17, 0.81)

33.2 0.201

0.16 (0.07, 0.25)

76.3 0.001

*Within-group effect sizes reflect the pooled difference before and after the intervention in the HIIT arm of each study including both controlled and non-controlled trials. CI, confidence interval; CON, non-exercising control; CT, continuous training; ES, effect size; i2, study heterogeneity statistic; N, number of studies included in analysis; SMD, standardized mean difference; WMD, weighted mean difference.

training ranged from 30 to 120 min per session at intensities between 55%VO2max/HRmax and 80%HRmax.

Training modalities

In most cases, HIIT was carried out in an exercise laboratory supervised by an investigator or trained exercise physiologist. Three studies investigated the practicality of home-based HIIT interventions (61-63). An exercise bike was used in 26 (52%) studies, 15 (30%) used a treadmill, one (2%) an athletics track (64) and six (12%) a free-living walking environment (39,50,61,62,65). Two (4%) studies allowed participants to choose between treadmill and exercise bike throughout the intervention (55,66).

Compliance, attrition and adverse events

Adherence to the intervention was reported by 20 (40%) studies and was 90 ± 11% of exercise sessions. Minimum adherence to be included in analysis was specified by 12 (24%) studies and ranged from 66% to 90% attendance of exercise training sessions. Mean dropout from follow-up measurement was 10 ± 10% in the 36 (72%) studies in which attrition was clear. Adverse events were reported in 17 (34%) studies. There were 18 musculoskeletal injuries attributable to the exercise interventions: 14/18 (72%) occurred in the HIIT group. Injuries did not necessarily result in the affected participants having to drop out from the study or discontinue the intervention. No serious adverse events were reported (see Supporting Information Table S1).

Meta-analysis

Data for fasting glucose, fasting insulin, HbA1c, insulin resistance, VO2max and body weight were included in the meta-analysis. Effect sizes for within groups and comparisons with CON and CT are presented in Table 2. Postprandial or post-challenge glucose levels were extracted but not analysed as there were not enough data to perform meaningful comparisons.

Insulin resistance

Insulin resistance was estimated in 29 (58%) studies. Of these, 20/29 (69%) had at least one control group. The HOMA model was employed by 21/29 (72%) studies. Other models of IR used were the QUICKI method (n = 1, 3% (67)), Matsuda index (n = 4, 14% (37,52,68,69)), Cederholm index (n = 2, 7% (70)) and the euglycaemic hyperinsulinaemic clamp (n = 1, 3% (53)). There was a significant reduction in HOMA score of 0.33 (95% CI -0.47 to -0.18, P < 0.001) with HIIT compared with baseline (Supporting Information Fig. S1). With all models of insulin resistance standardized for between-group compari-

sons, there was a significant reduction in insulin resistance compared with both CON and CT groups (Fig. 2a,b).

Sensitivity analyses

When only studies using HOMA were included in pairwise comparisons, the standardized mean differences between HIIT and CT as well as HIIT and CON were somewhat attenuated; however, effects for HIIT versus CT remained significant (data not shown).

When studies were categorized by the time between final exercise session and post-test blood sample, we found that the improvement in insulin sensitivity diminished as the time after exercise increased (Supporting Information Fig. S2).

Fasting glucose

Fasting glucose was reported in 47 (94%) studies. Of these, 30/47 (64%) were compared to at least one control group. There was a reduction in fasting glucose of 0.13 mmol L-1 (-0.19 to -0.07, P < 0.001) with HIIT compared with baseline (Supporting Information Fig. S3), although this reduction was not different compared with the CON or CT groups overall (Fig. 3a,b). Conversely, in those with metabolic syndrome or type 2 diabetes, there was a reduction in fasting glucose of 0.92 mmol L-1 (-1.22 to -0.63, P < 0.001) following HIIT compared to CON (five studies; Fig. 3a).

Baseline and post-intervention HbA1c was reported by 13 (26%) studies. Of these, 6/13 (46%) had a CON group and 7/13 (54%) had a CT group. Compared with baseline, there was no change in HbA1c (Supporting Information Fig. S4); however, within the metabolic syndrome/type 2 diabetes population, there was a significant reduction of -0.25% (-0.27 to -0.23, P < 0.001). Similarly, there was no effect of HIIT compared with CON overall, but a significant reduction of 0.47% (-0.92 to -0.01, P = 0.04) was observed in the metabolic syndrome/type 2 diabetes group (Fig. 4a). There was no change in HbA1c compared with CT overall, or within any of the population subgroups (Fig. 4b).

Fasting insulin

Fasting insulin was reported in 28 (56%) studies. Of these, 19/28 (68%) were compared to at least one control group. There was a significant reduction in fasting insulin from baseline of -0.93 |U L-1 (-1.39 to -0.48, P < 0.001; Supporting Information Fig. S5.1); however, this effect was not present when HIIT was compared with a control group (Supporting Information Fig. S5.2 and S5.3).

Body weight

Studies reported body weight (9/50; 18%), BMI (5/50; 10%) or both (25/50; 50%). Of these, 23/34 (68%; body

Study ID

Healthy

Trapp [2008,107) Ciolac (2010, 58) Richards (2010.53) Metcalfe (2012,70) Grieco (2013, 67) Subtotal [l-squared -

75.9%, p = 0.002)

OverwelghtiObese Heydari (2D12.102) Keating (2014,104) Subtotal (l-squared = 72.8%, p = 0.055)

MetS/T2DM

Tjonna (2008. 56) -

Stensvold (201Q, 120)

Witranun (2014.105)

Subtotal (l-squared = 0 0%. p = 0 544)

Chronic Disease Hwang (2012, 66) Subtotal [[-squared = .%. p = .)

Overall (l-squared = 56.4%, p = D 011)

NOTE: Weights are from random effects analysis

SMD(95<fcCI>

-0.51 (-1.31, 0.28) -1.19 (-2.00,-0.37) -0.75 (-1,66, 0-17) -0.91 (-1-68. -0.14) 1.38(0.34,2.42) -0 44 (-1.22. 0.34)

Weight

9.17 8-24 9.65

7.18 43.65

0.08 (-0.50, 0.66) 11 72

-0.95 (-' I 84,-0 07) 849

-0.38 (-' 1.39, 0.63) 20.21

-1.02 (-' I 92,-0.13) 8.39

-0.58 (-' 1.46. 0,30) 859

-0.37 1.11,0.36) 9.99

-0.62 (-' 1.10, -0.14) 26,97

-0.46 (-' 1.27, 0.35) 9.18

-0.46 (-' 1.27, 0.35) 9.18

-0 49 (-0 87.-0 12) 10D 00

Study ID

Healthy

Trapp (2008, 107) Ciolac (2010, 68) Sanctvei (2012, 65) Hovanlou (2013, 103) Grieco (2013. 67) Cocks (2013, 52) Subtotal (l-squared = 43.1%. p = 0.118)

Overweight/Obese Venables (2008. 37) Skleryk (2013. 60) Lun1_AIT (2014. 39) Keating (2014. 104) Lunt_SIT (2014. 39)

Subtotal (l-squared = 36.6%, p = 0.177) MetS/T2DM

Tjonna (2008. 66) -*—

Earnest (2013, 69) Hollekim-Strand (2014. 116) Mitranun (2014, 105) Subtotal (l-squared = 80 9% p = 0 001)

Chronic Disease lellamo [2013. 116) lellamo (2014, 51) Subtotal (l-squared =

= 0.0%. p = 0-992)

Overall (l-squared = 58.7%. p = 0.001) <j>

NOTE. Weights are from random effects analysis -3.6

SMD (95% CI)

Weight

5.55 7.07 626

■0-78 (-1-73. 0 16) 0.09 (-0.61, 0.78) 0.29 (-0.53.1,11) -1.32 (-2.41,-0.22) 4.81 -0.32 (-1.16, 0.53) 6.13 0 91 ( 1 94, 0 13) -0.40 (-0.88, 0.08)

6.09 34.91

1.20 (0,12, 2.28) 0.34 (-0.65, 1.32) -0.56 (-1.56, 0.46) -0 07 (-0.91. 0.77) -0.18 (-1.17, 0.81) 0 12 (-0 43. 0 67)

4,90 634 5.23 6 19

S33 26 96

-2 39 ( 3 50. -1.27) 4.70 -0.26 (-0.91, 0.39) 7.33 0.15 (-0.50, 0.79) -0.03 (-0.77, 0.71) -0,53 (-1.40. 0-34)

7 37 6.77 26.16

-0 96 (-2.00, 0.09) 5.06

-0.96 (-1.67,-0.23) 6.88

-0-95 (-1 54,-0 36) 11 95

-0.35 (-0.68. -0,02) 100.00

favours HIIT

favours CT

Figure 2 Change in insulin resistance after high-intensity interval training (HIIT) compared with (a) control and (b) continuous training (CT).

Study ID

WMO {96% CI)

Weight

Healthy

Trapp (2008. 107) Nybo(2010 110) Ciolac (2010. S3) Richards (2010. 53) Metcalfe (2012. 70) Eguchi (2012. 108) Gunnarsson (2012, Б4) Grieco (2013. 67) Adamson ¡2014. 100) Subtotal (l-squared - 5.1%, p =

0.393)

Overweight/Obese Heydari (2012, 102) Keating (2014. 104)

Subtotal (l-squared = 75-7%. p = 0 042)

MetS/T2DM Tjonrta (2008. 56) Stensvold (2010, 120) Heggelund (2011 114) Karetoft (2013, Gl) Mitranun (2014. 105)

Subtotal (l-squared = 0.0%. p = 0 692)

Chronic Disease Fu(2013, 113) MadsserrA (2014. 63) Subtotal (l-squared = 0.0%. p =

1.000)

Overall (l-squared = 67.8%. p= 0 000) <j>

WJTE- Weights are from random effects analysis

0 20 (-0.52, 0 92) -tl.70 (-1.60, 0.20) 0,30 (0,02. OSS) -0.05 (-0.31. 0.21) 0 00 (-0 21, 0.21) 0 10 (-0.26, 0.46) 0.20 (-0.39, 0.79) 0 00 (-0,32, 0,32) -0.20 (-0.60, 0.20) 0.03( 0.08, 0 IS)

388 287 8,43 8.73 940 7.43 4 90 7.97 6.93 60.53

0.10 (-0.26, 0.45) 7.60 -0.30 (-0.47. -0.13) 9.81 -0.13 (-0.52. 0.26) 17.41

■1 00 (-1.37. -0.63) О ОО (-1,36, 1,36) -0.30 (-2.57, 1.97) -1 00 (-3.34. 1.34) -0 94 (-1 51. -0.37) -0.92 (-1.22. -0.62)

-0.40 (-1.69. 0.89) -0 40 (-0.93, 0.13) -0.40 (-0.89, 0.09)

732 148 0.58 0.S4 5. ОБ 14.97

1 61 6.47 708

-0.17 (-0 34. 0,01) 10 0 00

favours HIIT

Figure 3 Change in fasting glucose after high-intensity interval training (HIT) compared to (a) control and (b) continuous training.

Healthy

Helgerud [2007, 57) Trapp(2008,107) Nybo{2010. 110) Ciolac (2010.58) Eguchi (2012, 108) Sandvel (2012,65) Grieco (2013. 57)

Subtotal it-squared - 0 0%. p - 0.752)

Ove rwelghtto bese Venables [2008,37) Moreira (2008,35) SMeiys{2013.80) LuntAIT (2014. 39) Keating(2014,104) LunLSIT (2014, 39)

Subtotal [l-squared = 10.4%. p = 0.349)

мевягом Tjonna (2003, 56) Earnest (2013.59) Karstott [2013, 61) Terada{Z013,65) Mitranun (2014.105) Subtotal [l-squared =

54.6%, p - 0 066)

Chronic Disease

Moholdt(2012.50) -

lellamo [2013.116) ♦

Fu (2013,113) HH.

MadssenB(2014,117) -

lellamo [2014.51) -

Conraads (2015,112)

Subtotal (l-squared = 37.6%. p = 0.156) <

Overall [l-squared - 29.1%. p = 0.091) NOTE Weights are Trcm random effects analysis

0.20 (-0.14. 0.54) -0.10 (-0.86, 0.66) 0.00 (-1 04.1.04) 0,10 (-0,11.0.31) 0.00 (-0 31.0.31) 0.00 (-0.22.0.22) -0.20 (-0.53, 0.13) 0,03 (-DOS. 0.14)

0.20 (-0.33.0.73) 0.30 (-0.36,0.96) 0,50 (-0,76.1,76) -0 07 (-0.57, 0 44) -020 (-0.33,-0.07) 0,04 (-0 50.0.58) -0 09 (-0.25, 0.07)

-0.70 <-1.14, -0.26) 0.00 (-0 26,0.26) -0 40 (-2 48, 1.68) 0.50 (-0 63,1.63) -0 06 (-0.65, О 53) -0.18 (-0 58. 0.22)

-0 20 (-0.97, 0 57) -0.83 (-0.99.-0.27) -D10 (-0.89, 0 69) 0.20 (-0.64.1.04) -0.07 (-1.11, 0.97) 0.01 (-0.33.0.35) -0.20 (-0.50, 0.11)

3.07 2.10 0.63 3.31 13-80 2.96 25.86

8.25 0.24 0.77 252 15.95

1 57 559 1.51 1.35 0.91 6.06 16.99

-0.07 (-0,17.0.03) 100.00

Healthy

Egucni (2012, 108)

-0.14 (-0.39,0.11) 44.21

Healthy

Egutfii (2012, 108} Subtotal {4-wuar«d » %, p ■ .)

MetS T2DM Kautoft (2013. 61) Tera as (2013, £5) Hollcfcirrv-Straod (2014.115)

Mitranun (2014, 105) <-

Subtotal ;I-M)uarea ■ û 0*, p - 0 906)

Clvonio Disease

Fu (2013, 113)

MadssanB {2014. 117)

Subtotal (<-*(y#fe<J = 0.0*. P = 0 651)

Overall (l-sguar&a = 1« 5*. p = 0 289) HOTE Weight 91e from random effect analyst

0.01 14.12, {1.14} 29 20

0.01 №12, 0.14) 29 20

10 (4.41Î. 0 23) ■0 20 (4 51. 0 11) 4 20(4.00.0 20) 4 30 (-1.24. 0 04) 4 13(4 311.0 03)

-0 10 (4.75. 0 55) 0.10)0.03.0.17] 0-11X0-0?. 0-17)

0 <12 10 07 fl 11)

5.31 7 43 4.80 0.50

1.E6 50 SO

Figure 4 Change in HbA1c after high-intensity interval training (HIT) compared to (a) control and (b) continuous training (CT).

weight) and 23/31 (74%; body mass index) compared HIIT to at least one control group. Compared with baseline, there was a 0.7 kg reduction in weight following HIIT (-1.19, -0.25, P = 0.002; Supporting Information Fig. S6.1). Compared with CON, the reduction was 1.3 kg (-1.90, -0.68, P < 0.001; Supporting Information Fig. S6.2). A greater effect of 2.3 kg (-3.27 to -1.22, P < 0.001) was observed in the metabolic syndrome/ type 2 diabetes subgroup. In contrast, there was no difference in weight loss following HIIT compared with CT overall (WMD = 0.32, -0.17, 0.81, P = 0.20; Supporting Information Fig. S6.3). As expected, a

similar pattern of changes was observed for BMI (data not shown).

Cardiorespiratory fitness

Cardiorespiratory fitness, expressed as VO2max, was reported in 42 (84%) studies. Of these, 31/42 (74%) compared change in VO2max to a control group. Compared with baseline, there was a 0.30 L min-1 increase in VO2max with HIIT (0.25-0.35, P < 0.001; Supporting Information Fig. S7.1). This increase was similar in comparison to CON (WMD = 0.28, 0.12-0.44, P = 0.001; Supporting Information Fig. S7.2) and attenuated but still significant when

compared to CT (WMD = 0.16, 0.07-0.25, P = 0.001; Supporting Information Fig. S7.3).

Meta-regression

Table 3 shows the P coefficients and confidence intervals for the regression analyses. HIIT characteristics, interval intensity and weekly high-intensity exercise did not predict the improvements observed in insulin resistance, fasting glucose, fasting insulin or HbAlc. Baseline levels of insulin resistance, fasting glucose and fasting insulin predicted changes in these outcomes overall. Using the regression equation, we calculated that baseline insulin resistance would have to be >3.18 to experience a reduction in HOMA-IR (homeostatic model assessment - insulin resistance) of -0.5 or greater. Similarly, for a 0.1 mmol L-1 or greater reduction in fasting glucose, baseline glucose would have to be >4.92 mmol L-1. When compared to non-exercising control groups, there was an inverse association between baseline level and change in fasting glucose. Changes in body weight did not predict changes in insulin resistance or glucose regulation. VO2max was associated with a reduction in fasting glucose in studies that included a non-exercising control group; however, VO2max did not predict other outcomes.

Discussion

The results of this meta-analysis suggest that HIIT is effective at improving measures of insulin resistance compared with continuous exercise and a non-exercising control group. Importantly, the largest effects were seen in those with type 2 diabetes or metabolic syndrome. Furthermore, in those with type 2 diabetes or metabolic syndrome, there was a 0.92 mmol L-1 reduction in fasting glucose and a 0.47% (5 mmol L-1) reduction in HbA1c when compared to studies with a non-exercising control group. The results for these measures and fasting insulin were less conclusive among the cohort as a whole and when compared to continuous exercise. There was a significant reduction of 1.3 kg in body weight compared with the non-exercising control group, an effect largely observed in those described as overweight, obese, with, or at risk of type 2 diabetes. In addition, cardiorespiratory fitness improved compared with both controls, to an extent comparable with previous meta-analyses of HIIT interventions (22,27).

The primary modifiable elements of HIIT protocols, defined here as interval intensity and weekly time spent at high-intensity, did not significantly alter intervention effectiveness in terms of insulin resistance, fasting glucose, HIbA1c or fasting insulin. Consistent with the results observed in the meta-analysis, those with the highest baseline values experienced the greatest benefits in insulin resistance and glucose regulation, although these associations

LO CO Г— CM LO CQ

О CO CD O ^ CO

^ * см ^ * см c\i CM

CO o~ xf o~ o~

I I I I I I I I I I I I

о о о о

I О О I I о о

о о со о

о о о о о о о

со ср

i i i i i i i Si i ° 2-

CMCM^^CM^^^^CMxTCM

o o o oo oo o oo oo I I I I I I I I I I I I

G)OCOOOCMCQ"^CMO[— CM^[— ^w^o^wo^o O ^ ^ OO OO O O^ CO CM

CD CD CD К CM~ CO C0~ o' ^ 1 ^ 1 CQ CM~ On^O^^O^M'ÍOlO

o o ^ oo oo o oo oo

J, -L J, J, -L J, -L J, -L -L J, -L

OLONWm^^OJ^OlCD^ ^COCMOOOOOOCMLOr-

o o o oo oo o oo ^o

^NCOOOINIOM

I I I I I I I I

r-OO^OOLOO

o o ^o oo P

CM I I I I I I o

CDCMCOLOI— [— COLO

o o o o

СО ОТ СО LO CM со

Gl СО СО О

СМ со со ^

^ СО СО СО

I I I I

OOl-ÖOhOOl-ÖOh ЗооЗооЗооЗоо

were, largely, not present in controlled studies. Body weight and cardiorespiratory fitness both improved following HIIT, but changes in these outcomes did not tend to predict improvements in insulin resistance or glucose regulation. There was, however, an inverse relationship between change in VO2max and fasting glucose in controlled studies.

As far as we are aware, this is the first systematic review and meta-analysis of the effects of HIIT on outcomes related to metabolic health. The findings extend the conclusions made by Adams (30) who inferred that HIIT resulted in similar acute physiological adaptations to continuous training despite a lower energy expenditure. Here, we provide a quantified estimation of the training effects of HIIT on insulin resistance, HbAlc, fasting glucose, fasting insulin and body weight.

Clinical application

Our study suggests that HIIT may reduce insulin resistance compared to both continuous exercise training and control conditions. Insulin resistance is a recognized precursor to type 2 diabetes (71) and has been identified as an independent risk factor for cardiovascular disease (72-74). We have shown that under supervised laboratory conditions, HIIT is effective in improving insulin sensitivity and potentially therefore improving glycaemic control and diabetes-related outcomes. Indeed, while data on HbA1c in this area are limited, the reduction following HIIT in those with or at risk of type 2 diabetes in this study was 0.47%, which is consistent with previous observations that report clinically significant reductions of up to 0.6% in HbAlc after a minimum of 8 weeks of exercise training (2-4).

Meta-regression results suggest that to achieve the observed reduction in HOMA-IR of 0.5 units, baseline HOMA needs to be at least 3.18, a value that has been consistently associated with the 50% most insulin-resistant individuals within a population (72,73) and indicating that HIIT may improve insulin sensitivity only in those who are insulin resistant. HIIT therefore has the potential to be used as an alternative therapeutic strategy to traditional physical activity interventions for those with or at risk of type 2 diabetes.

Potential mechanisms

Improvement in peripheral insulin sensitivity is one of the main mechanisms that has been used to explain the enhancement in glycaemia following exercise training and has been widely demonstrated following both acute and chronic exercise training (75).

Improvements in insulin sensitivity have often been associated with a reduction in body weight (76). We found that HIIT reduced both insulin resistance and body weight, although meta-regression did not reveal an association

between these two factors. This is in agreement with the findings of Karstoft et al. (77) who found that changes in body composition following HIIT explained less than 25% of improvements in insulin sensitivity in patients with type 2 diabetes. However, we were unable to determine whether body composition or fat distribution was affected by HIIT. A reduction in abdominal adiposity - often achieved with exercise training (78) - may cause an improvement in hepatic insulin sensitivity (79), and it may be this that resulted in an improvement in HOMA-IR scores rather than overall weight loss.

Furthermore, given the protective effect of cardiorespiratory fitness on HbAlc (80), morbidity (81) and mortality (82) in type 2 diabetes, it is notable that change in VO2max also did not predict changes in insulin resistance or glycaemic control in this study. It therefore appears that some adaptations associated with increased muscle oxida-tive capacity may be independent of those that promote metabolic health. Nonetheless, by providing evidence that HIIT may lead to greater reductions in insulin resistance than continuous exercise training, our study suggests that either the interval modality or the greater exercise intensity facilitates benefits observed with continuous moderate-intensity exercise training. There are a number of established metabolic pathways that are likely to be enhanced by HIIT, with some support from recent investigations. These include skeletal muscle glucose uptake (83), GLUT-4 content (84,85) and muscle glycogen depletion-induced insulin sensitivity (70,86).

Training adaptations have been associated with changes in body composition, muscle physiology (83-85,87,88) and glucose metabolism (89). There is some evidence that, while muscle glycogen content is not greatly affected following moderate-intensity continuous activity lasting less than 1 h (90), glycogen depletion is observed following vigorous-intensity exercise (91) and is one way HIIT may enhance acute insulin sensitivity superior to moderate-intensity continuous exercise (70). It is unclear whether this acute response promotes chronic adaptations that enhance insulin sensitivity, although it is possible that repeated acute improvements may be as beneficial (92).

The mechanisms that may be enhanced following HIIT compared with continuous exercise training need further elucidation as there is disagreement as to the optimum volume and intensity of exercise that stimulates the greatest benefits (93,94), and which of these factors is more important in metabolic health. We found no relationship between exercise intensity or time spent at high-intensity, and changes in glucose/insulin parameters, meaning that we are unable to determine which characteristics of HIIT protocols induce the observed improvements in these outcomes. HIIT presents a unique challenge to optimizing exercise prescription given the range of variables that can be manipulated. Some (42), but not all (94) studies suggest that exercise

intensity is the primary factor determining the degree of metabolic adaptations, although these investigations have not assessed HIIT programmes specifically which, as discussed, introduce more nuanced exercise variables.

Strengths and limitations

The strengths of this review include the comprehensive search strategy employed, the use of random effects meta-analysis and the focus on metabolic outcomes. Of note, none of the individual studies of metabolic syndrome or type 2 diabetes patients reported a significant reduction in HbA1c compared with control, whereas the pooled effect showed this may occur following HIIT training. This demonstrates the advantages of meta-analysis and highlights the importance of conducting adequately powered trials.

However, this meta-analysis is not without limitation. Firstly, study quality was poor with only 4/36 (11%) controlled studies deemed to have low risk of bias. Secondly, there was wide heterogeneity between participants, HIIT protocol, and intervention length as well as CT interventions, making it difficult to generalize conclusions and make direct comparisons between HIIT and CT. This issue was addressed to the best of our ability by stratifying results by participant disease status and using meta-regression. Nonetheless, we highlight the need for more robust randomized controlled trials to be carried out in the future using standardized continuous training protocols. Thirdly, the length of time between the last bout of exercise and post-test blood samples was not reported by many of the studies measuring insulin resistance. This is significant since we demonstrated that the improvement in HOMA-IR score diminished with increasing time to assessment. In addition, it is possible that the use of HOMA-IR may underestimate the impact of HIIT on insulin sensitivity given that HOMA is more representative of hepatic insulin resistance (41), and exercise is more likely to affect peripheral insulin resistance (95). Indeed, our sensitivity analysis indicated that this may have been the case in the included studies. It is also difficult to apply the reduction in HOMA-IR score found in this meta-analysis in a wider context, and the clinical relevance of a change of -0.33 unit is unclear.

The number of participants who underwent a HIIT intervention and who were likely to be insulin resistant represented just 23% of the study population. This could mean that the potential of HIIT to reduce insulin resistance is not fully illustrated by this study, as demonstrated by our metaregression, and emphasizes the need for more trials to be carried out in those at risk of or with type 2 diabetes.

Despite the safety concerns associated with HIIT, few studies reported pre-screening results or adverse events. There were more exercise-related injuries reported in the HIIT interventions than control conditions, but it is difficult to draw conclusions from the limited data available.

Finally, HIIT has been promoted as being a time-efficient exercise modality. This review provides some support that exercise-induced health benefits can be achieved with as little as 21 ± 16 min of vigorous-intensity physical activity performed three times per week. However, it is worth noting that in total, exercise sessions took 34 ± 13 min to complete (including warm-up, recovery intervals and cool down). It is important to elucidate whether the requirement to set aside 35 min three times per week to perform HIIT addresses the perceived barrier to physical activity of 'lack of time'.

Suggestions for future research

Our results suggest that HIIT per se has the potential to improve health outcomes, regardless of the precise protocol employed. However, it is clear that more studies should be conducted that compare the effects of HIIT to those of continuous training, particularly in people at risk of or with type 2 diabetes given these were where the strongest effects were observed. To this end, studies should be of long enough duration and adequately powered to detect any potential changes in clinically relevant outcomes such as HbA1c. A greater understanding of the potential mechanisms stimulating the more potent effects of HIIT compared with continuous training should be elucidated so they can be maximized through exercise training.

Just six studies included in this review were conducted in either a 'free-living' or 'real world' context (39,50,6163,65). If HIIT is to be recommended to the general population, it must be made practical and accessible. Interventions in community settings, requiring minimal specialist equipment and supervision, should be conducted to assess uptake, adherence and compliance to the protocol. Few studies have measured effort and enjoyment of completing HIIT, with some positive responses (96-98), including in sedentary populations (99). The results should be extended to populations averse to exercise in order to determine whether HIIT would be taken up as a health-promoting form of physical activity.

Conclusions

In conclusion, we have demonstrated that HIIT conveys benefits to cardiometabolic health which in the cases of insulin resistance and VO2max may be superior to the effect of traditional continuous training. HIIT may therefore be suitable as an alternative to continuous exercise training in the promotion of metabolic health and weight loss, particularly in those with type 2 diabetes or metabolic syndrome. However, given the identified limitations, more research is needed to determine both behavioural responses and clinical benefits over the longer term.

Funding

The primary author was funded for a PhD in the Diabetes Research Centre, University of Leicester by the Leicester-Loughborough Diet, Lifestyle and Physical Activity BRU.

Authors' contribution

CJ and TY had the original idea for the review. CJ developed and revised the protocol with input from all authors. CJ developed the search strategy, performed the searches and statistical analyses and wrote the first draft of the article. CJ and GO reviewed and extracted data. All authors contributed to the writing of the paper, provided input throughout the study and approved the final manuscript.

Copyright statement

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide licence to the Publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future), to (i) publish, reproduce, distribute, display and store the Contribution, (ii) translate the Contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution, (iii) create any other derivative work(s) based upon the Contribution, (iv) to exploit all subsidiary rights in the Contribution, (v) the inclusion of electronic links from the Contribution to third party material where-ever it may be located; and, (vi) licence any third party to do any or all of the above.

Conflict of interest statement

No conflict of interest was declared.

Acknowledgements

The authors acknowledge support from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care -East Midlands (NIHR CLAHRC - EM), the Leicester Clinical Trials Unit and the NIHR Leicester-Loughborough Diet, Lifestyle and Physical Activity Biomedical Research Unit, which is a partnership between University Hospitals of Leicester NHS Trust, Loughborough University and the University of Leicester.

Supporting information

Additional Supporting Information may be found in the online version of this article, http://dx.doi.org/10.1111/ obr.12317

Figure S1. Insulin resistance within-group comparison.

Figure S2. Insulin resistance sensitivity analysis.

Figure S3. Fasting glucose within-group comparison.

Figure S4. HbA1c within-group comparison.

Figure S5. 1: Fasting insulin within-group comparison. 2 &

3: Fasting insulin between-group comparisons.

Figure S6. 1: Body weight within-group comparison. 2 &

3: Body weight between-group comparisons.

Figure S7. 1: VO2max within -group comparison. 2 & 3:

VO2max between-group comparisons.

Table S1. Study quality and adherence, attrition and adverse events.

Appendix S1. PRISMA checklist. Appendix S2. Search strategy. Appendix S3. Publication bias.

References

1. Daousi C, Casson IF, Gill GV, MacFarlane IA, Wilding JPH, Pinkney JH. Prevalence of obesity in type 2 diabetes in secondary care: association with cardiovascular risk factors. Postgrad Med J 2006; 82: 280-284.

2. Umpierre D, Ribeiro PA, Kramer CK et al. Physical activity advice only or structured exercise training and association with HbA1c levels in type 2 diabetes: a systematic review and metaanalysis. JAMA 2011; 305: 1790-1799.

3. Boule NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus - a meta-analysis of controlled clinical trials. JAMA 2001; 286: 1218-1227.

4. Gillies CL, Abrams KR, Lambert PC et al. Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ 2007; 334: 299.

5. Shaw K, Gennat H, O'Rourke P, Del C. Exercise for overweight or obesity. Cochrane Database Syst Rev 2006; 4: CD003817.

6. Franz MJ, VanWormer JJ, Crain AL et al. Weight-loss outcomes: a systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up. J Am Diet Assoc 2007; 107: 1755-1767.

7. Sigal RJ, Kenny GP, Wasserman DH, Castaneda-Sceppa C, White RD. Physical activity/exercise and type 2 diabetes - a consensus statement from the American diabetes association. Diabetes Care 2006; 29: 1433-1438.

8. Albright A, Franz M, Hornsby G et al. American College of Sports Medicine position stand. Exercise and type 2 diabetes. Med Sci Sports Exerc 2000; 32: 1345-1360.

9. Davies S, Burns H, Jewell T, McBride M. Start active, stay active: a report on physical activity from the four home countries' Chief Medical Officers. Department of Health, Physical activity, Health improvement and protection. 2011 Available at: https:// www.gov.uk/government/uploads/system/uploads/attachment _data/file/216370/dh_128210.pdf.

10. Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. Appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc 2009; 41: 459-471.

11. Roth M. Self-reported physical activity in adults. In: Craig R, Mindell J, Hiram V (eds). Health Survey for England. National Centre for Social Research: London, 2008, pp. 21-43.

12. Chaudhury M, Esliger D. Accelerometry in Adults. In: Craig R, Mindell J, Hirani V (eds). Health Survey for England. National Centre for Social Research: London, 2008, pp. 61-88.

13. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008; 40: 181-188.

14. Stutts WC. Physical activity determinants in adults. Perceived benefits, barriers, and self efficacy. AAOHN J 2002; 50: 499-507.

15. Koeneman MA, Verheijden MW, Chinapaw MJM, Hopman-Rock M. Determinants of physical activity and exercise in healthy older adults: a systematic review. Int J Behav Nutr Phys Act 2011; 8: 142-156.

16. Korkiakangas EE, Alahuhta MA, Laitinen JH. Barriers to regular exercise among adults at high risk or diagnosed with type 2 diabetes: a systematic review. Health Promot Int 2009; 24: 416-427.

17. Gibala MJ, Little JP, Macdonald MJ, Hawley JA. Physiological adaptations to low-volume, high-intensity interval training in health and disease. J Physiol 2012; 590: 1077-1084.

18. Gibala MJ. High-intensity interval training: a time-efficient strategy for health promotion? Curr Sports Med Rep 2007; 6: 211-213.

19. Burgomaster KA, Hughes SC, Heigenhauser GJF, Bradwell SN, Gibala MJ. Six sessions of sprint interval training increases muscle oxidative potential and cycle endurance capacity in humans. J Appl Physiol 2005; 98: 1985-1990.

20. Gist NH, Fedewa MV, Dishman RK, Cureton KJ. Sprint interval training effects on aerobic capacity: a systematic review and meta-analysis. Sports Med 2013; 44: 269-279.

21. Sloth M, Sloth D, Overgaard K, Dalgas U. Effects of sprint interval training on VO2max and aerobic exercise performance: a systematic review and meta-analysis. Scand J Med Sci Sports 2013; 23: e341-e352.

22. Weston M, Taylor KL, Batterham AM, Hopkins WG. Effects of low-volume high-intensity interval training (HIT) on fitness in adults: a meta-analysis of controlled and non-controlled trials. Sports Med 2014; 44: 1005-1017.

23. Hardcastle SJ, Ray H, Beale L, Hagger MS. Why sprint interval training is inappropriate for a largely sedentary population. Front Psychol 2014; 5: 1505.

24. Rognmo O, Hetland E, Helgerud J, Hoff J, Slordahl SA. High intensity aerobic interval exercise is superior to moderate intensity exercise for increasing aerobic capacity in patients with coronary artery disease. Eur J Cardiovasc Prev Rehabil 2004; 11: 216-222.

25. Little JP, Gillen JB, Percival ME etal. Low-volume high-intensity interval training reduces hyperglycemia and increases muscle mitochondrial capacity in patients with type 2 diabetes. J Appl Physiol 2011; 111: 1554-1560.

26. Garber CE, Blissmer B, Deschenes MR et al. Quantity and quality of exercise for developing and maintaining cardiorespira-tory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc 2011; 43: 1334-1359.

27. Weston KS, Wisloff U, Coombes JS. High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: a systematic review and meta-analysis. Br J Sports Med 2013; 48: 1227-1234.

28. Cornish AK, Broadbent S, Cheema BS. Interval training for patients with coronary artery disease: a systematic review. Eur J Appl Physiol 2011; 111: 579-589.

29. Elliott AD, Rajopadhyaya K, Bentley DJ, Beltrame JF, Aromataris EC. Interval training versus continuous exercise in patients with coronary artery disease: a meta-analysis. Heart Lung Circ 2015; 24: 149-157.

30. Adams OP. The impact of brief high-intensity exercise on blood glucose levels. Diabetes Metab Syndr Obes 2013; 6: 113-122.

31. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009; 151: 264-269.

32. Haskell WL. Health consequences of physical activity - understanding and challenges regarding dose-response. Med Sci Sports Exerc 1994; 26: 649-660.

33. Higgins JPT, Altman DG, Gotzsche PC et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928.

34. Schjerve IE, Tyldum GA, Tjonna AE et al. Both aerobic endurance and strength training programmes improve cardiovascular health in obese adults. Clin Sci 2008; 115: 283-293.

35. Moreira MM, Souza HP, Schwingel PA, Sa CK, Zoppi CC. Effects of aerobic and anaerobic exercise on cardiac risk variables in overweight adults. Arq Bras Cardiol 2008; 91: 200-206,19-26.

36. Shaban N, Kenno KA, Milne KJ. The effects of a 2 week modified high intensity interval training program on the homeo-static model of insulin resistance (HOMA-IR) in adults with type 2 diabetes. J Sports Med Phys Fitness 2014; 54: 203-209.

37. Venables MC, Jeukendrup AE. Endurance training and obesity: effect on substrate metabolism and insulin sensitivity. Med Sci Sports Exerc 2008; 40: 495-502.

38. Higgins JPT, Green S (eds). Cochrane handbook for systematic reviews of interventions (V5.1.0) [updated March2011]. The Cochrane Collaboration 2011. URL www.cochrane-handbook.org.

39. Lunt H, Draper N, Marshall HC et al. High intensity interval training in a real world setting: a randomized controlled feasibility study in overweight inactive adults, measuring change in maximal oxygen uptake. PLoS ONE 2014; 9: e83256.

40. Boyd JC, Simpson CA, Jung ME, Gurd BJ. Reducing the intensity and volume of interval training diminishes cardiovascular adaptation but not mitochondrial biogenesis in overweight/obese men. PLoS ONE 2013; 8: e68091.

41. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985; 28: 412-419.

42. Umpierre D, Ribeiro PAB, Schaan BD, Ribeiro JP. Volume of supervised exercise training impacts glycaemic control in patients with type 2 diabetes: a systematic review with meta-regression analysis. Diabetologia 2013; 56: 242-251.

43. Palmer TM, Peters JL, Sutton AJ, Moreno SG. Contour-enhanced funnel plots for meta-analysis. Stata J 2008; 8: 242-254.

44. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50: 10881101.

45. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997; 315: 629-634.

46. Swain DP, Leutholtz BC, King ME, Haas LA, Branch JD. Relationship between % heart rate reserve and % VO2 reserve in treadmill exercise. Med Sci Sports Exerc 1998; 30: 318-321.

47. Byrne NM, Hills AP. Relationships between HR and VO2 in the obese. Med Sci Sports Exerc 2002; 34: 1419-1427.

48. Larsen FJ, Anderson M, Ekblom B, Nystrom T. Cardiorespi-ratory fitness predicts insulin action and secretion in healthy individuals. Metabolism 2012; 61: 12-16.

49. Myers J, McAuley P, Lavie CJ, Despres J-P, Arena R, Kokkinos P. Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: their independent and interwoven importance to health status. Prog Cardiovasc Dis 2015; 57: 306-314.

50. Moholdt T, Bekken Vold M, Grimsmo J, Slordahl SA, Wisloff U. Home-based aerobic interval training improves peak oxygen uptake equal to residential cardiac rehabilitation: a randomized, controlled trial. PLoS ONE 2012; 7: e41199.

51. Iellamo F, Caminiti G, Sposato B et al. Effect of High-Intensity interval training versus moderate continuous training on 24-h blood pressure profile and insulin resistance in patients with chronic heart failure. Intern Emerg Med 2014; 9: 547-552.

52. Cocks M, Shaw CS, Shepherd SO et al. Sprint interval and endurance training are equally effective in increasing muscle microvascular density and eNOS content in sedentary males. J Physiol 2013; 591: 641-656.

53. Richards JC, Johnson TK, Kuzma JN et al. Short-term sprint interval training increases insulin sensitivity in healthy adults but does not affect the thermogenic response to beta-adrenergic stimulation. J Physiol 2010; 588: 2961-2972.

54. Babraj JA, Vollaard NB, Keast C, Guppy FM, Cottrell G, Timmons JA. Extremely short duration high intensity interval training substantially improves insulin action in young healthy males. BMC Endocr Disord 2009; 9: 3.

55. Terada T, Friesen A, Chahal BS, Bell GJ, McCargar LJ, Boule NG. Feasibility and preliminary efficacy of high intensity interval training in type 2 diabetes. Diabetes Res Clin Pract 2013; 99: 120-129.

56. Tjonna AE, Lee SJ, Rognmo O et al. Aerobic interval training versus continuous moderate exercise as a treatment for the metabolic syndrome: a pilot study. Circulation 2008; 118: 346-354.

57. Helgerud J, Hoydal K, Wang E et al. Aerobic high-intensity intervals improve VO2max more than moderate training. Med Sci Sports Exerc 2007; 39: 665-671.

58. Ciolac EG, Bocchi EA, Bortolotto LA, Carvalho VO, Greve JMD, Guimaraes GV. Effects of high-intensity aerobic interval training vs. moderate exercise on hemodynamic, metabolic and neuro-humoral abnormalities of young normotensive women at high familial risk for hypertension. Hypertens Res 2010; 33: 836843.

59. Earnest CP, Lupo M, Thibodaux J et al. Interval training in men at risk for insulin resistance. Int J Sports Med 2013; 34: 355-363.

60. Skleryk JR, Karagounis LG, Hawley JA, Sharman MJ, Laursen PB, Watson G. Two weeks of reduced-volume sprint interval or traditional exercise training does not improve metabolic functioning in sedentary obese men. Diabetes Obes Metab 2013; 15: 1146-1153.

61. Karstoft K, Winding K, Knudsen SH et al. The effects of free-living interval-walking training on glycemic control, body composition, and physical fitness in type 2 diabetic patients: a randomized, controlled trial. Diabetes Care 2013; 36: 228-236.

62. Morikawa M, Okazaki K, Masuki S et al. Physical fitness and indices of lifestyle-related diseases before and after interval walking training in middle-aged and older males and females. Br J Sports Med 2011; 45: 216-224.

63. Madssen E, Arbo I, Granoien I, Walderhaug L, Moholdt T. Peak oxygen uptake after cardiac rehabilitation: a randomized controlled trial of a 12-month maintenance program versus usual care. PLoS ONE 2014; 9: e107924.

64. Gunnarsson TP, Bangsbo J. The 10-20-30 training concept improves performance and health profile in moderately trained runners. J Appl Physiol 2012; 113: 16-24.

65. Sandvei M, Jeppesen PB, Stoen L et al. Sprint interval running increases insulin sensitivity in young healthy subjects. Arch Physiol Biochem 2012; 118: 139-147.

66. Hwang CL, Yu CJ, Shih JY, Yang PC, Wu YT. Effects of exercise training on exercise capacity in patients with non-small

cell lung cancer receiving targeted therapy. Support Care Cancer 2012; 20: 3169-3177.

67. Grieco CR, Swain DP, Colberg SR et al. Effect of intensity of aerobic training on insulin sensitivity/resistance in recreationally active adults. J Strength Cond Res 2013; 27: 2270-2276.

68. Leggate M, Carter WG, Evans MJC, Vennard RA, Sribala-Sundaram S, Nimmo MA. Determination of inflammatory and prominent proteomic changes in plasma and adipose tissue after high-intensity intermittent training in overweight and obese males. J Appl Physiol 2012; 112: 1353-1360.

69. Whyte LJ, Gill JMR, Cathcart AJ. Effect of 2 weeks of sprint interval training on health-related outcomes in sedentary overweight/obese men. Metabolism 2010; 59: 1421-1428.

70. Metcalfe RS, Babraj JA, Fawkner SG, Vollaard NBJ. Towards the minimal amount of exercise for improving metabolic health: beneficial effects of reduced-exertion high-intensity interval training. Eur J Appl Physiol 2012; 112: 2767-2775.

71. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. DiabetMed 1998; 15: 539-553.

72. Hanley AJG, Williams K, Stern MP, Haffner SM. Homeostasis model assessment of insulin resistance in relation to the incidence of cardiovascular disease: the San Antonio heart study. Diabetes Care 2002; 25: 1177-1184.

73. Bonora E, Formentini G, Calcaterra F et al. HOMA-estimated insulin resistance is an independent predictor of cardiovascular disease in type 2 diabetic subjects - prospective data from the Verona Diabetes Complicated Study. Diabetes Care 2002; 25: 1135-1141.

74. Kim J, Chae YK, Chernoff A. The risk for coronary heart disease according to insulin resistance with and without type 2 diabetes. Endocr Res 2013; 38: 195-205.

75. Henriksen EJ. Exercise effects of muscle insulin signaling and action - Invited review: effects of acute exercise and exercise training on insulin resistance. J Appl Physiol 2002; 93: 788-796.

76. Goodpaster BH, Kelley DE, Wing RR, Meier A, Thaete FL. Effects of weight loss on regional fat distribution and insulin sensitivity in obesity. Diabetes 1999; 48: 839-847.

77. Karstoft K, Winding K, Knudsen S et al. Mechanisms behind the superior effects of interval vs continuous training on glycaemic control in individuals with type 2 diabetes: a randomised controlled trial. Diabetologia 2014; 57: 2081-2093.

78. Vissers D, Hens W, Taeymans J, Baeyens J-P, Poortmans J, Van Gaal L. The effect of exercise on visceral adipose tissue in overweight adults: a systematic review and meta-analysis. PLoS ONE 2013; 8: e56415.

79. Barzilai N, She L, Liu BQ et al. Surgical removal of visceral fat reverses hepatic insulin resistance. Diabetes 1999; 48: 94-98.

80. Boule NG, Kenny GP, Haddad E, Wells GA, Sigal RJ. Meta-analysis of the effect of structured exercise training on cardiores-piratory fitness in type 2 diabetes mellitus. Diabetologia 2003; 46: 1071-1081.

81. Wei M, Gibbons LW, Mitchell TL, Kampert JB, Lee CD, Blair SN. The association between cardiorespiratory fitness and impaired fasting glucose and type 2 diabetes mellitus in men. Ann Intern Med 1999; 130: 89-96.

82. Church TS, Cheng YJ, Earnest CP et al. Exercise capacity and body composition as predictors of mortality among men with diabetes. Diabetes Care 2004; 27: 83-88.

83. Etgen GJ, Brozinick JT, Kang HY, Ivy JL. Effects of exercise training on skeletal-muscle glucose-uptake and transport. Am J Physiol 1993; 264: C727-C733.

84. Etgen GJ, Jensen J, Wilson CM, Hunt DG, Cushman SW, Ivy JL. Exercise training reverses insulin resistance in muscle by

enhanced recruitment of GLUT-4 to the cell surface. Am J Physiol 1997; 272: E864-E869.

85. Hood MS, Little JP, Tarnopolsky MA, Myslik F, Gibala MJ. Low-volume interval training improves muscle oxidative capacity in sedentary adults. Med Sci Sports Exerc 2011; 43: 1849-1856.

86. Bogardus C, Thuillez P, Ravussin E, Vasquez B, Narimiga M, Azhar S. Effect of muscle glycogen depletion on in-vivo insulin action in man. J Clin Invest 1983; 72: 1605-1610.

87. Kristiansen S, Hargreaves M, Richter EA. Exercise-induced increase in glucose transport, GLUT-4, and VAMP-2 in plasma membrane from human muscle. Am J Physiol 1996; 270: E197-E201.

88. Kristiansen S, Hargreaves M, Richter EA. Progressive increase in glucose transport and GLUT-4 in human sarcolemmal vesicles during moderate exercise. Am J Physiol 1997; 272: E385-E389.

89. Goodyear LJ, Kahn BB. Exercise, glucose transport, and insulin sensitivity. Annu Rev Med 1998; 49: 235-261.

90. Kang J, Robertson RJ, Hagberg JM et al. Effect of exercise intensity on glucose and insulin metabolism in obese individuals and obese NIDDM patients. Diabetes Care 1996; 19: 341-349.

91. Romijn JA, Coyle EF, Sidossis LS et al. Regulation of endogenous fat and carbohydrate metabolism in relation to exercise intensity and duration. Am J Physiol 1993; 265: E380-E391.

92. Boule NG, Weisnagel SJ, Lakka TA etal. Effects of exercise training on glucose homeostasis. Diabetes Care 2005; 28:108-114.

93. Colberg SR. The impact of exercise on insulin action in type 2 diabetes mellitus: relationship to prevention and control. Insulin 2006; 1: 85-98.

94. Houmard JA, Tanner CJ, Slentz CA, Duscha BD, McCartney JS, Kraus WE. Effect of the volume and intensity of exercise training on insulin sensitivity. J Appl Physiol 2004; 96: 101-106.

95. Hawley JA, Lessard SJ. Exercise training-induced improvements in insulin action. Acta Physiol (Oxf) 2008; 192: 127-135.

96. Bartlett JD, Close GL, MacLaren DPM, Gregson W, Drust B, Morton JP. High-intensity interval running is perceived to be more enjoyable than moderate-intensity continuous exercise: implications for exercise adherence. J Sports Sci 2011; 29: 547-553.

97. Oliveira BRR, Slama FA, Deslandes AC, Furtado ES, Santos TM. Continuous and high-intensity interval training: which promotes higher pleasure? PLoS ONE 2013; 8: e79965.

98. Tritter A, Fitzgeorge L, Cramp A, Valiulis P, Prapavessis H. Self-efficacy and affect responses to Sprint Interval Training. Psychol Sport Exerc 2013; 14: 886-890.

99. Jung ME, Bourne JE, Little JP. Where does HIT fit? An examination of the affective response to high-intensity intervals in comparison to continuous moderate- and continuous vigorous-intensity exercise in the exercise intensity-affect continuum. PLoS ONE 2014; 9: e114541.

100. Adamson S, Lorimer R, Cobley JN, Lloyd R, Babraj J. High intensity training improves health and physical function in middle aged adults. Biology (Basel) 2014; 3: 333-344.

101. Harmer AR, Ruell PA, McKenna MJ et al. Effects of sprint training on extrarenal potassium regulation with intense exercise in type 1 diabetes. J Appl Physiol 2006; 100: 26-34.

102. Heydari M, Freund J, Boutcher SH. The effect of high-intensity intermittent exercise on body composition of overweight young males. J Obes 2012; 2012: 480467.

103. Hovanloo F, Arefirad T, Ahmadizad S. Effects of sprint interval and continuous endurance training on serum levels of inflammatory biomarkers. J Diabetes Metab Disord 2013; 12: 22.

104. Keating SE, Machan EA, O'Connor HT etal. Continuous exercise but not high intensity interval training improves fat distribution in overweight adults. J Obes 2014; 2014: 834865.

105. Mitranun W, Deerochanawong C, Tanaka H, Suksom D. Continuous vs interval training on glycemic control and macro-

and microvascular reactivity in type 2 diabetic patients. Scand J Med Sci Sports 2014; 24: e69-e76.

106. Serpiello FR, McKenna MJ, Bishop DJ et al. Repeated sprints alter signaling related to mitochondrial biogenesis in humans. Med Sci Sports Exerc 2012; 44: 827-834.

107. Trapp EG, Chisholm DJ, Freund J, Boutcher SH. The effects of high-intensity intermittent exercise training on fat loss and fasting insulin levels of young women. Int J Obes (Lond) 2008; 32: 684-691.

108. Eguchi Y, Ohta M, Inoue T et al. Effects of transitory stimulation interval exercise on physical function: a randomized controlled pilot study among Japanese Subjects. J UOEH 2012; 34: 297-308.

109. Larsen S, Danielsen JH, Sondergard SD etal. The effect of high-intensity training on mitochondrial fat oxidation in skeletal muscle and subcutaneous adipose tissue. Scand J Med Sci Sports 2015; 25: e59-e69.

110. Nybo L, Sundstrup E, Jakobsen MD et al. High-intensity training versus traditional exercise interventions for promoting health. Med Sci Sports Exerc 2010; 42: 1951-1958.

111. Talanian JL, Holloway GP, Snook LA, Heigenhauser GJ, Bonen A, Spriet LL. Exercise training increases sarcolemmal and mitochondrial fatty acid transport proteins in human skeletal muscle. Am J Physiol 2010; 299: E180-E188.

112. Conraads VM, Pattyn N, De Maeyer C et al. Aerobic interval training and continuous training equally improve aerobic exercise capacity in patients with coronary artery disease: the SAINTEX-CAD study. Int J Cardiol 2015; 179: 203-210.

113. Fu TC, Wang CH, Lin PS et al. Aerobic interval training improves oxygen uptake efficiency by enhancing cerebral and muscular hemodynamics in patients with heart failure. Int J Cardiol 2013; 167: 41-50.

114. Heggelund J, Nilsberg GE, Hoff J, Morken G, Helgerud J. Effects of high aerobic intensity training in patients with schizophrenia - a controlled trial. Nord J Psychiatry 2011; 65: 269-275.

115. Hollekim-Strand SM, Bjorgaas MR, Albrektsen G, Tjonna AE, Wisloff U, Ingul CB. High-intensity interval exercise effectively improves cardiac function in patients with type 2 diabetes mellitus and diastolic dysfunction a randomized controlled trial. J Am Coll Cardiol 2014; 64: 1758-1760.

116. Iellamo F, Manzi V, Caminiti G et al. Matched dose interval and continuous exercise training induce similar cardiorespiratory and metabolic adaptations in patients with heart failure. Int J Cardiol 2013; 167: 2561-2565.

117. Madssen E, Moholdt T, Videm V, Wisloff U, Hegbom K, Wiseth R. Coronary atheroma regression and plaque characteristics assessed by grayscale and radiofrequency intravascular ultrasound after aerobic exercise. Am J Cardiol 2014; 114: 15041511.

118. Mora-Rodriguez R, Ortega JF, Hamouti N et al. Time-course effects of aerobic interval training and detraining in patients with metabolic syndrome. Nutr Metab Cardiovasc Dis 2014; 24: 792-798.

119. Perry CG, Heigenhauser GJ, Bonen A, Spriet LL. High-intensity aerobic interval training increases fat and carbohydrate metabolic capacities in human skeletal muscle. Appl Physiol Nutr Metab 2008; 33: 1112-1123.

120. Stensvold D, Tjonna AE, Skaug EA et al. Strength training versus aerobic interval training to modify risk factors of metabolic syndrome. J Appl Physiol 2010; 108: 804-810.

121. Tjonna AE, Leinan IM, Bartnes AT et al. Low- and highvolume of intensive endurance training significantly improves maximal oxygen uptake after 10-weeks of training in healthy men. PLoS ONE 2013; 8: e65382.