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Aging Clinical and Experimental Research (2019) 31:575–593
https://doi.org/10.1007/s40520-018-1012-z
REVIEW
Effectiveness ofhigh-intensity interval training onglycemic control
andcardiorespiratory fitness inpatients withtype 2 diabetes:
asystematic review andmeta-analysis
Jing‑xinLiu1· LinZhu1,2· Pei‑junLi1· NingLi1· Yan‑bingXu3
Received: 4 May 2018 / Accepted: 14 July 2018 / Published online: 30 July 2018
© The Author(s) 2018
Abstract
Aims The aim of this systematic review and meta-analysis was to quantify the effect of high-intensity interval training (HIIT)
on glycemic control and cardiorespiratory fitness compared with moderate-intensity training (MICT) and no training at all
in patients with type 2 diabetes (T2D).
Methods Relevant articles were sourced from PubMed, Embase, the Web of Science, EBSCO, and the Cochrane Library.
Randomized-controlled trials were included based upon the following criteria: participants were clinically diagnosed with
T2D, outcomes that included glycemic control (e.g., hemoglobin A1c); body composition (e.g., body weight); cardiorespira-
tory fitness (e.g., VO2peak) are measured at baseline and post-intervention and compared with either a MICT or control group.
Results Thirteen trials involving 345 patients were finally identified. HIIT elicited a significant reduction in BMI, body
fat, HbA1c, fasting insulin, and VO2peak in patients with type 2 diabetes. Regarding changes in the body composition of
patients, HIIT showed a great improvement in body weight (mean difference: −1.22kg, 95% confidence interval [CI] − 2.23
to − 0.18, P = 0.02) and body mass index (mean difference: −0.40kg/m2, 95% CI − 0.78 to − 0.02, P = 0.04) than MICT
did. Similar results were also found with respect to HbA1c (mean difference: −0.37, 95% CI − 0.55 to − 0.19, P < 0.0001);
relative VO2peak (mean difference: 3.37ml/kg/min, 95% CI 1.88 to 4.87, P < 0.0001); absolute VO2peak (mean difference:
0.37L/min, 95% CI 0.28 to 0.45, P < 0.00001).
Conclusions HIIT may induce more positive effects in cardiopulmonary fitness than MICT in T2D patients.
Keywords High-intensity interval training· Glycemic control· Cardiorespiratory fitness· Type 2 diabetes
Introduction
Type 2 diabetes (T2D) is a metabolic disease characterized
by hyperglycemia resulting from a resistance to insulin
or a relative insulin insufficiency that can induce cardio-
vascular disease and lead to cardiovascular deterioration.
According to epidemiological survey results, more than
422million people worldwide were living with diabetes
in 2014 [1], with a predicted prevalence of 552million by
2030 [2]. Because of the growing economic and social bur-
dens associated with T2D treatment, effective and accessi-
ble lifestyle interventions for people with T2D have never
been more important. Exercise intervention is recognized
as an integral concept for lifestyle intervention in T2D
patients [3, 4], and it has been recommended by both the
American Diabetes Association and the American College
of Sports Medicine that patients should perform at least
150min/week of moderate-to-vigorous aerobic exercise
[5, 6]. Abundant evidence from randomized-controlled
trials (RCTs) shows the benefits of aerobic exercise in
glycemic control; for example, it reduces fasting glucose
and improves insulin sensitivity, both of which help to
alleviate the development of diabetes complications and
mortality [7–10]. Furthermore, a recent meta-analysis
demonstrated that aerobic exercise training is associated
with a decrease in HbA1c, insulin resistance, and fasting
* Lin Zhu
40848567@qq.com
1 School ofKinesiology, Shanghai University ofSport,
Shanghai200438, China
2 Guangzhou Sport University, Guangzhou510500, China
3 Department ofChild Health Care andRehabilitation,
Lanzhou University Second Hospital, Lanzhou730030,
China
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576 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
glucose, and suggested that high-intensity aerobic exer-
cise is superior to lower intensity exercise in improving
cardiorespiratory fitness in T2D patients [11]. However,
the majority of patients do not typically achieve the rec-
ommended level of physical activity, despite the fact that
increases in physical activity level can improve glycemic
control and cardiorespiratory fitness in T2D patients. In
addition, a lack of time has been identified as one of the
key barriers preventing patients from performing sufficient
physical activity, which means that patients must partici-
pate in more time-efficient training programs to achieve
optimized outcomes.
High-intensity interval training (HIIT), therefore, appears
to be a feasible and time-efficient alternative exercise pro-
tocol to aerobic exercise: it involves alternating, repetitive
short bouts of high-intensity exercise interspersed with less
active or passive recovery periods. Numerous recent studies
have shown HIIT to be superior in improving health ben-
efits compared with lower intensity aerobic exercise in a
variety of populations [12–14]. Støa etal. [15] found that
people with T2D who performed a supervised HIIT pro-
gram at an intensity of 85–95% of their maximal heart rate
with 52% VO2peak interval experienced a significant increase
in VO2peak and a reduction in hemoglobin A1c (HbA1c),
body weight, and body mass index (BMI) compared with
those who performed moderate-intensity continuous train-
ing (MICT), though no significant changes in insulin resist-
ance or blood lipid levels were found. Karstoft etal. [16]
compared the efficacy of HIIT with energy expenditure-
matched continuous-walking training in people with T2D
and observed greater improvements in VO2peak, body weight,
fat mass, and glycemic control with the former. Mitranun
etal. [17] also found that HIIT improved HbA1c, maximal
aerobic capacity, and other cardiovascular risk factors in
T2D patients, even if the total exercise time was reduced
to half of that recommended. Similar to the current study, a
recent meta-analysis by Jelleyman etal. demonstrated that
HIIT is more effective than MICT for improving insulin sen-
sitivity and cardiorespiratory fitness in healthy individuals
[18]. However, this study did not determine the suitability
of HIIT in individuals with T2D. Indeed, although a few
RCTs have demonstrated the efficiency of HIIT in the pre-
vention and treatment of T2D patients, no consensus has yet
been reached that HIIT is a superior training protocol for
the improvement of glycemic control, body composition,
and cardiorespiratory fitness compared with moderate-inten-
sity continuous aerobic training among patients with T2D.
Therefore, we performed a meta-analysis to determine the
impact of HIIT on body composition, glycemic control, and
cardiorespiratory fitness, and to compare it to that of MICT
and that of no intervention in randomized-controlled trials in
T2D patients, which we hope can provide clinical evidence
to enable patients to achieve optimal outcomes.
Patients andmethods
Search strategy
The databases which we searched included PubMed,
the Web of Science, EBSCO, Embase, and the Cochrane
Library. All of the databases were searched from their date
of inception until April 2018. We included only studies writ-
ten in English. We used combined key phrases and Medical
Subject Heading (MeSH) terms as follows: “type 2 diabetes
mellitus,” “diabetes mellitus, type II,” “type 2 diabetes,”
“T2D,” “T2DM,” “high-intensity interval training,” “high-
intensity aerobic interval exercise,” “high-intensity interval
training,” “aerobic interval training,” “high-intensity inter-
mittent exercise,” “HIT,” and “HIIT.” Supporting informa-
tion appendix in S1 gives a detailed description of the search
strategy. In addition, the reference lists of included studies
and reviews were also examined for additional potentially
eligible studies.
Inclusion andexclusion criteria
Type ofstudy
This review included studies with randomized-controlled tri-
als. We excluded matched controlled trial designs, uncon-
trolled trials, observational studies, and animal studies.
Type ofparticipant
The study participants were clinically diagnosed with type
2 diabetes. Patients with type 1 diabetes and gestational dia-
betes were excluded. There was no limitation on the age,
gender, or ethnicity of the study participants.
Intervention variables andoutcome measures
The studies included here were required to report at least one
outcome measure, measured at baseline and post-interven-
tion, and compared to either a moderate-intensity exercise
intervention or control group. The HIIT program had to be
prescribed at least two times per week for 4 weeks, with
moderate-intensity continuous training or another treatment
(e.g., drug therapy) as the control group.
Primary outcomes
Outcome measures included glycemic control (e.g., HbA1c,
fasting glucose, and fasting insulin); body composition [e.g.,
body weight, BMI, body fat (%), and waist circumference];
cardiorespiratory fitness (e.g., VO2peak). The criteria which
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577Aging Clinical and Experimental Research (2019) 31:575–593
1 3
we used complied with the PICO concept (patient/problem/
population; intervention; comparison/control/compara-
tor; outcome). For articles reported in more than two pub-
lications, only one full copy was used for meta-analysis.
Abstracts presented at academic conferences, case reports,
observational studies, examples of animal research, and
studies of which the full text could not be obtained were
excluded.
Evaluation ofbias andquality assessment
The risk of bias and methodological quality of the included
trials were assessed independently by two reviewers (Liu
and Li), who used the Cochrane Collaboration’s tool [19]
to check for concealed allocation, allocation concealment,
blinding, incomplete outcome data, selective reporting, and
other biases. Each reviewer was required to award one of
three grades (either unclear, low risk, or high risk) to each
item. The Grading of Recommendations Assessment, Devel-
opment, and Evaluation (GRADE) system [20] was used
to assess the quality of the evidence from very low to high
based on risk of bias, inconsistency, indirectness, impreci-
sion, and publication bias. A third reviewer was consulted
if any disagreement occurred.
Data extraction
The two investigators assessed each article’s title or abstract
for eligibility. When a disagreement happened, a third inves-
tigator participated in a discussion to reach a final consensus.
For studies that met the inclusion criteria, full papers were
obtained for further analysis. The two authors independently
extracted data from the published works using standard data
extraction forms. If there were any inconsistencies in the
process of data extraction, the two authors would check the
original text and reach an agreement through discussion or
through verification by a third author. Information on trial
design, characteristics of the patients, HIIT protocol, and
relevant results was noted according to a redesigned form.
We recorded the name of the first author and the year of
publication; the number of patients/participants and their
ages, gender, and BMIs; the duration of diagnosis; and the
experimental and control interventions (e.g., exercise inten-
sity and duration, interval intensity and duration, session
time, and duration in weeks). When data were insufficient or
inapplicable, we attempted to contact the authors by e-mail
or used an equation to reveal all available data.
Data analysis
The Review Manager software (RevMan 5.3; Cochrane,
London, UK) was used to conduct the meta-analysis. The
statistical heterogeneity of the treatment effect among the
included studies was assessed using the chi-squared test and
I2 test. A threshold of P < 0.10 was considered to be statisti-
cally significant and an I2 value > 50% was indicative of high
heterogeneity. We used the weighted mean difference (MD)
or standardized MD (SMD) with 95% confidence intervals
(CIs) for summary statistics and derived such for the com-
parison of HIIT with MICT or other treatment methods. MD
was used when all studies reported the same outcome using
the same scale, while SMD was used when studies reported
different units or scales for the outcome. If heterogeneity did
not exist between studies, we incorporated a fixed-effects
model approach to combined outcome measures. A random-
effects model was used when there was a large degree of
heterogeneity between studies. To account for within-group
intervention effect sizes, we used fixed-effects modeling to
estimate the change from baseline. Potential heterogeneity
sources were identified by sensitivity analyses conducted by
omitting one study successively and comparing the influence
of each study on the overall pooled estimate if I2 > 50%.
Data were analyzed using the change from baseline for
both groups. If the study did not contain change data, we
used the following two equations for conversion:
where M is the effect mean, M1 is the mean of the baseline,
and M2 is the end value mean;
where S is the standard deviation of the effect, S1 is the
standard deviation of the baseline value, S2 is the final stand-
ard deviation, and R is constant (0.4 or 0.5).
Results
Search results
The initial database searches returned a total of 484 articles
(PubMed, n = 84; EMBASE, n = 30; The Cochrane Library,
n = 63; EBSCO, n = 30; the Web of Science, n = 277) that
were each screened and evaluated for eligibility based on
their respective titles only. Following removal of duplicates,
421 articles underwent further identification and screen-
ing. In total, 378 non-relevant articles were excluded after
screening the titles and abstracts. Of the remaining articles,
43 were selected to be read in full. At this point, 30 addi-
tional articles were excluded for varying reasons (e.g., the
study was not randomized, there were reduplicative partici-
pants, the study was observational in nature, the research
was performed on animals, the study was presented at an
academic conference, and/or the study had no required data),
rendering a final sample of 13 papers. Figure1 describes the
study selection flow.
(1)
M
=
|
M
1
−M
2|,
(2)
S
2=S2
1
+S2
2
−2×R×S
1
×S
2,
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578 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
Characteristics ofincluded trials
A total of 345 participants were included in the analysis,
of which 163 (47.2%) participants underwent a HIIT inter-
vention. The characteristics of the study participants, the
HIIT training protocols used, and the main results from the
included studies are described in Table1. The countries or
regions of publication were mainly the United Kingdom
(n = 2), Norway (n = 2), the Republic of Korea (n = 1),
Chile (n = 1), Denmark (n = 2), France (n = 1), Thailand
(n = 1), Australia (n = 1), Italy (n = 1), and Canada (n = 1).
The main HIIT intervention ranged in duration from 11 to
16 weeks (16 weeks in 4 studies, 12 weeks in 8 studies,
and 11 weeks in 1 study) and occurred two-to-five times
weekly (median: three times). Total training duration per
session ranged from 30s to 4min, and interval duration
ranged from 30s to 3min.
Risk ofbias amongtheselected articles
The 13 studies were assessed for risk of bias; the evalu-
ation results are shown in Table2. Among the included
studies, the method of randomization was only clearly
stated in four studies [21, 25, 26, 28], while three reported
allocation concealment [25, 26, 28], five blinded partici-
pants or personnel [15, 16, 21, 23, 28], and three did not
employ assessor blinding [22, 24, 27]. Only one study did
not clearly state complete outcomes data and employed
selective reporting [22]; no other bias in all studies. The
evaluation of the overall quality of evidence and results is
shown in Table4, and the level of evidence for RCTs is
Fig. 1 Flowchart of the study
selection process Records identified though
database searching (n = 484)
Additional records identified
through other sources (n = 5)
30 full-text articles were excluded
for the following reasons:
Studydesign (20)
Reduplicative participants (2)
Included animal researches (2)
Participation in academic
conferences (2)
Required data not available(4)
Records after duplicates
removed(n = 421)
Studies included in
quantitative synthesis (n =13)
Studies included in
qualitative synthesis (n = 13)
Records screened (n =421)
Records excluded
for irrelevant studies
(n =378)
Full-text articles assessed
for eligibility (n = 43)
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579Aging Clinical and Experimental Research (2019) 31:575–593
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Table 1 Characteristics of the included trials
Article, year Country Main characteristics of the
subjects
HIIT MICT or CON No. of
patient
dropouts
Exercise intensity and
interval
Frequency and duration Exercise intensity Frequency and duration
Alvarez
2016 [21]
Chile HIIT: mean age was
45.6 ± 3.1 years, mean
duration of diagnosis was
3.14 ± 1.1 years, mean
BMI was 30.8 ± 1.0kg/m2,
n = 13
CON: mean age was
43.1 ± 1.5 years, mean
duration of diagnosis was
3.6 ± 1.1 years, mean BMI
was 30.4 ± 0.4kg/m2,
n = 10
Train: running at 90–100%
HRmax intensity for
30–120s; Interval: low-
intensity walking for
30–120s
2.2–37.5min/time, three
times per week for 16
weeks
CON: non-exercise 5
Hollekin
2014 [22]
Norway Total of 47 patients
55.9 ± 6.0 years; 36%
female; mean dura-
tion of diagnosis was
3.6 ± 2.5years
HIIT: mean BMI was
30.2 ± 2.8kg/m2, 5% in
mild stage, 90% in moder-
ate stage, 5% in severe
stage, n = 24
MICT: mean BMI was
29.7 ± 3.7kg/m2, 29.4%
in mild stage, 70.6% in
moderate stage, n = 23
Protocol was 4×4min
exercise at 90–95% HRmax
with 3min of low-inten-
sity exercise at 70% HRmax
40min /bout; three times
per week for 12 weeks
MICT: Moderate-intensity
aerobics training
210min per week for 12
weeks
10
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580 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
Table 1 (continued)
Article, year Country Main characteristics of the
subjects
HIIT MICT or CON No. of
patient
dropouts
Exercise intensity and
interval
Frequency and duration Exercise intensity Frequency and duration
Karstoft
2013 [16]
Denmark HIIT: mean age was
57.5 ± 2.4 years, 41.7%
female, mean BMI was
29.0 ± 1.3kg/m2, mean
duration of diagnosis was
3.5 ± 0.7 years, n = 12
MICT: mean age was
60.8 ± 2.3 years, 33.3%
female, mean BMI was
29.9 ± 1.6kg/m2, mean
duration of diagnosis was
6.2 ± 1.5 years, n = 12
CON: mean age was
60.8 ± 2.3 years, 37.5%
females, mean BMI was
29.7 ± 1.9kg/m2, mean
duration of diagnosis was
4.5 ± 1.5 years, n = 8
Alternating 3min intervals
of fast (≥ 70% of VO2peak)
and slow (40% of VO2peak)
walking
Five times per week,
60min/time for 4 months
MICT: walking at ≥ 55%
VO2peak 60min/session
CON: non-exercise
Five times per week, for 4
months
0
Lee 2015 [23] Korea Mean age was 15.3 ± 2.2
years, mean BMI was
24.0 ± 3.8kg/m2, mean
duration of diagnosis was
4.0 ± 2.2 years, n = 20
Exercise at ≥ 80% HRR,
train program includ-
ing 30-s sprint and 30-s
recovery
400 Kcal/session, three
sessions per week for 12
weeks
MICT: Exercise at ≤ 40%
HRR, 200kcal/session
Six sessions per week for
12 weeks
0
Maillard
2016 [24]
France Included 16 postmenopausal
women with T2D, mean
age was 69 ± 1 years, mean
BMI was 31 ± 1kg/m2
HIIT: mean age was
68.2 ± 1.9 years, mean
BMI was 32.6 ± 1.7kg/
m2, N = 8
MICT: mean age was
70.1 ± 2.4 years, mean
BMI was 29.7 ± 1.2kg/
m2, n = 8
Repeated cycles of sprinting
for 8s (at around 80%
HRmax) followed by pedal-
ing slowly (20–30rpm)
for 12s (maximum of 60
cycles per 20-min session)
Two times per week for 16
weeks
MICT: Exercise at 55–60%
HRR for 40min
Two times/week, 16 weeks 1
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581Aging Clinical and Experimental Research (2019) 31:575–593
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Table 1 (continued)
Article, year Country Main characteristics of the
subjects
HIIT MICT or CON No. of
patient
dropouts
Exercise intensity and
interval
Frequency and duration Exercise intensity Frequency and duration
Mitranun
2014 [17]
Thailand Total of 43 adults with T2D
(64.4% females), HIIT:
mean age was 61.2 ± 2.8
years, mean BMI was
29.6 ± 0.5kg/m2, mean
duration of diagnosis was
19.5 ± 1.5 years; n = 14.
MICT: mean age was
61.7 ± 2.7 years, mean
BMI was 29.4 ± 0.7kg/m2,
mean duration of diagnosis
was 20.5 ± 1.5 years;
n = 14. CON: mean age
was 60.9 ± 2.4 years, mean
BMI was 29.7 ± 0.4kg/m2,
mean duration of diagnosis
was 21.1 ± 2.32 years;
n = 15
Protocol was 1-min high-
intensity exercise at
50–85% VO2peak with
4-min low-intensity at
50–60% VO2peak interval
20min /session, three
sessions per week for 12
weeks
MICT: exercise intensity
at 50–60% VO2peak for
25–30min
Three times/week, 12 weeks
CON: non-exercise
2
Ramos
2016 [25]
Australia HIIT: mean weight was
99 ± 18kg, n = 9
MICT: mean weight was
98 ± 17kg, n = 6
Protocol was 4*4min bouts
at 85–95% HRmax, interval
with 3min of active recov-
ery at 50–70% HRmax
38min per session, 3 times
per week for 16 weeks
MICT: 30min at 60–70%
HRpeak
5 times/week, 16 weeks 0
Støa
2017 [15]
Norway HIIT: mean age was 59 ± 11
years, mean BMI was
32.0 ± 4.7kg/m2, mean
duration of diagnosis was
9 ± 7 years, n = 19
MICT: mean age was
59 ± 10 years, mean BMI
was 31.1 ± 4.5kg/m2,
mean duration of diagnosis
was 6 ± 5 years, n = 19
Protocol was 4*4min
of walking or uphill at
85–95% of HRmax
Three times per week for 12
weeks
MICT: 60-min walking at
70–75% of HRpeak
Three times per week for 12
weeks
5
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582 Aging Clinical and Experimental Research (2019) 31:575–593
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Table 1 (continued)
Article, year Country Main characteristics of the
subjects
HIIT MICT or CON No. of
patient
dropouts
Exercise intensity and
interval
Frequency and duration Exercise intensity Frequency and duration
Terada
2013 [26]
Canada HIIT: mean age was 62 ± 3
years, mean BMI was
28.4 ± 4.1kg/m2, 50%
female, mean duration of
diagnosis was 6 ± 4 years,
n = 8
MICT: mean age was 63 ± 5
years, mean BMI was
33.1 ± 4.5kg/m2, 42.9%
female, mean duration of
diagnosis was 8 ± 74 years,
n = 7
HIIT protocol involved
alternating 1-min intervals
100% VO2peak with 3-min
recovery intervals at 20%
VO2peak
30–60min/day, 5 days per
week for 12 weeks
MICT: continuous exer-
cise at 40% VO2peak,
30–60min/day
5 days per week for 12
weeks
0
Backx
2011 [27]
UK Involved 15 males and 4
females; total median age
was 59.6 (44.0–69.0) years
HIIT: median BMI was 30.0
(25.3–40.1) kg/m2, n = 10
MICT: median BMI was
32.3 (26.4–40.5) kg/m2,
n = 9
Protocol was 1–2min at
40–50% HRR and 1, 2, or
3min at 80–90% HRR
60min/day, 3 days per week
for 12 weeks
MICT: Exercise at moder-
ate-to-high-intensity for
30min
Five times/week, 12 weeks 2
Cassidy
2016 [28]
UK HIIT: mean age was 61 ± 9
years, mean BMI was
31 ± 5kg/m2, mean dura-
tion of diagnosis was 5 ± 3
years, n = 12
MICT: mean age was 59 ± 9
years, mean BMI was
32 ± 6kg/m2, mean dura-
tion of diagnosis was 4 ± 2
years, n = 11
Training: Pedal
cadence > 80 rev/min,
ranching a RPE 16–17
(very hard); interval:
3-min recovery cycle
Three sessions per week for
12 weeks
Non-exercise 5
Bellia 2017 [29] Italy HIIT: mean age was
58.8 ± 7.9, mean BMI was
27.7 ± 2.8kg/m2, mean
duration of diagnosis was
5.9 ± 4.4 years, n = 11
CON: mean age was
56.3 ± 6.4, mean BMI was
29.9 ± 3.4kg/m2, mean
duration of diagnosis was
3.4 ± 3.7 years, n = 11
Protocol involved a 4-min
walk at 75–80% HRmax to
be repeated two-to-four
times, interval with 3-min
active recovery at 45–50%
HRmax
Two–three times per week
for 12 weeks
MICT: protocol was 10,000 steps per day or 70,000 steps
per week for 12 weeks
7
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583Aging Clinical and Experimental Research (2019) 31:575–593
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downgraded due to inconsistency and imprecision in most
of the studies.
Effects ofHIIT onbody composition
The included studies assessed body weight (11/13; 84.6%);
BMI (11/13; 84.6%); body fat (6/13; 46.2%); waist circum-
ference (7/13; 53.8%) as outcomes. Of these, 8/11 (72.7%
of body weight studies); 8/11 (72.7% of BMI studies); 5/6
(83.3% of body fat studies); 6/7 (85.7% of waist circumfer-
ence studies) compared HIIT to MICT. The meta-analyses
showed (Table3) a significant reduction in body weight of
1.22kg [95% CI − 2.23 to − 0.18, P = 0.02] for patients in
the HIIT group as compared with those in the MICT group.
Furthermore, in comparison with baseline, there was a
reduction in BMI of 0.85kg/m2 (95% CI − 1.57 to − 0.12,
P = 0.02) (Table3), and, as compared with the MICT group,
the reduction was 0.40kg/m2 (95% CI − 0.78 to − 0.02,
P = 0.04) (Table3). In addition, as compared with baseline,
there was a reduction in body fat of 1.86% (95% CI − 3.68 to
− 0.04, P = 0.02) (Table3), but the reduction was not statisti-
cally significant as compared with that in the MICT group.
In addition, there was no significant difference in the waist
circumference reduction following HIIT versus MICT or at
baseline (Table3).
Effects ofHIIT onglycemic control
Ten studies with 220 patients assessed HbA1c. Of these,
nine studies compared changes in HbA1c in HIIT groups to
those in MICT groups, while only three studies compared
such to changes in CON groups. Relative to baseline, there
was a significant reduction in HbA1c (SMD: −0.29, 95%
CI − 0.55 to − 0.04, P = 0.02) (Fig.2a; Table3). Compared
with MICT, the reduction was 0.37% (95% CI − 0.55 to
− 0.19, P < 0.0001, Fig.2a). However, in comparison with
a control intervention, a non-significant change in HbA1c
of -0.39% (95% CI − 0.81 to 0.02, P < 0.06, Fig.2b) was
found. As compared with baseline, there was a significant
reduction in fasting insulin (SMD: −0.46, 95% CI − 0.81 to
− 0.11, P = 0.01, Table3). However, this reduction was not
significantly different as compared with that in the control
intervention or MICT groups (Table3). No significant dif-
ference in the fasting glucose or HOMA-IR (homeostatic
model assessment of insulin resistance) was found for par-
ticipants in the HIIT group as compared with those in the
MICT group (Table3). We further used sensitivity analysis
in HOMA-IR because of the larger heterogeneity (I2 = 73%)
within the group. The results of sensitivity analysis showed
that the heterogeneity (I2 = 0%) was significantly reduced
after exclusion of Lee 2015, but there was no significant
change in results.
Table 1 (continued)
Article, year Country Main characteristics of the
subjects
HIIT MICT or CON No. of
patient
dropouts
Exercise intensity and
interval
Frequency and duration Exercise intensity Frequency and duration
Winding
2018 [30]
Denmark HIIT: mean age was
54 ± 6, mean BMI was
28.1 ± 3.5kg/m2, mean
duration of diagnosis was
8 ± 4 years, n = 13
MICT: mean age was 58 ± 8
years, mean BMI was
27.4 ± 3.1kg/m2, mean
duration of diagnosis was
6 ± 4 years, n = 12
CON: mean age was
57 ± 7, mean BMI was
28 ± 3.5kg/m2, mean dura-
tion of diagnosis was 7 ± 5
years, n = 7
Training: initiated with a
20min of cycling consist-
ing of cycles of 1min
at 95% Wpeak and 1min
of active recovery (20%
Wpeak) was performed
3 days per week for 11
weeks
MICT: 40min of cycling at
50% of Wpeak 3 days/week, 11 weeks 3
RCT randomized-controlled trial, MCT Matched controlled trial designs, HRmax maximal heart rate, HIIT high-intensity interval training, MICT moderate-intensity continuous training, CON
control, HRR heart rate reserve, RPE rating of perceived exertion
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584 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
Effects ofHIIT onlipid control
Seven studies assessed low-density lipoprotein (LDL) cho-
lesterol as an outcome. Of these, five studies compared the
change in the HIIT group to that in the control group and
six studies compared the change in the HIIT to that in the
MICT group. There was also a significant reduction in LDL
cholesterol (MD: −0.25mmol/L 95% CI − 0.46 to − 0.04,
P = 0.02) with HIIT versus with the MICT group (Table3).
Unfortunately, there was no significant change in total cho-
lesterol as compared with both the control and MICT groups
and a similar result was found with respect to high-density
lipoprotein (HDL) cholesterol. LDL cholesterol did not dif-
fer significantly between the HIIT group and the control
group. Because studies comparing HIIT with control inter-
ventions in relation to LDL and HDL cholesterol showed
significantly more heterogeneity, we conducted sensitivity
analysis that showed that the studies heterogeneity changed
significantly (I2 = 20% in LDL cholesterol, I2 = 0 in HDL
cholesterol) after the removal of Alvarez 2016, but there
were no significant changes in the results.
Effects ofHIIT oncardiorespiratory fitness
Cardiorespiratory fitness as measured using absolute VO2peak
(L/min) and relative VO2peak (ml/kg/min) was analyzed
using data from seven studies representing a total of 219
patients. As compared with baseline, there was a 4.75ml/
kg/min (95% CI 2.94 to 6.56, P < 0.0001) (Fig.3a; Table2)
or 0.35L/min (95% CI 0.17 to 0.53, P = 0.0001) increase in
VO2peak with HIIT (Fig.4a; Table3). In addition, there was
a 4.12ml/kg/min (95% CI 2.66 to 5.57, P < 0.0001) (Fig.3b)
or 0.24L/min (95% CI 0.10 to 0.37, P = 0.0005) (Fig.4b)
increase in VO2peak with HIIT over control interventions.
The random-effects model showed (Fig.4c) a significant
improvement in absolute VO2peak of 0.37L/min (95% CI
0.28 to 0.45, P < 0.0001) for patients in HIIT group versus
those in the MICT group and there was a similar increase
seen with respect to relative VO2peak (MD: 3.37ml/kg/min,
95% CI 1.88 to 4.87, P < 0.0001) (Fig.3c). However, there
existed moderate heterogeneity in this analysis (I2 = 48%)
and the results should be interpreted with caution (Table4).
Discussion
The purpose of this study was to evaluate the effectiveness
of HIIT on body composition, glycemic control, and car-
diorespiratory fitness in patients with T2D; to observe the
difference in such compared with MICT or non-exercise;
and to provide information on an ideal time-efficient physi-
cal activity program. The principal finding of the current
meta-analysis was that HIIT was more efficient than MICT
in increasing VO2peak in T2D patients; they also found that
reduction of BMI, body weight, and HbA1c (%) was less
conclusive because of low quality of the evidence.
Excess weight and obesity are important risk factors for
the occurrence of T2D and contribute to the development of
insulin resistance in obese individuals [31, 32]. Even with a
body weight that falls within the normal range, individuals
with an abnormal BMI and waist circumference can also
present with an increased risk of abnormal glucose metabo-
lism [33]. Our work showed that HIIT improved body com-
position, reducing BMI significantly by 0.85kg/m2 and
reducing body fat by 1.86%. Notably, both body weight
and BMI were significantly decreased compared with the
Table 2 Risk-of-bias assessment for the included studies
Study Random sequence
generation
Allocation
concealment
Blinding Incomplete
outcome data
Selective reporting Other bias
Participants or
personnel
Outcome
assessment
Alvarez [21]Low Unclear Low Low Low Low Low
Hollekin [22] Unclear Unclear Unclear Unclear Unclear Unclear Low
Karstoft [16] Unclear Unclear Low Low Low Low Low
Lee [23] Unclear Unclear Unclear Low Low Low Low
Maillard [24] Unclear Unclear Unclear Unclear Low Low Low
Mitranun [17] Unclear Unclear Unclear Low Low Low Low
Ramos [25]Low Low Low Low Low Low Low
Støa [15] High High Low Low Low Low Low
Terada [26]Low Low High Low Low Low Low
Backx [27] High Unclear Unclear Unclear Low Low Low
Cassidy [28]Low Low Low Low Low Low Low
Bellia [29] Unclear Unclear Unclear Low Low Low Low
Winding [30] Unclear Unclear Unclear Low Low Low Low
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585Aging Clinical and Experimental Research (2019) 31:575–593
1 3
MICT group, which suggests that HIIT may be more effec-
tive for improving body composition (even in the absence of
changes in body fat and waist circumference) in individuals
with T2D. The underlying mechanism of HIIT-induced body
weight loss may be related to the consumption and release
of fat from visceral fat stores. Maillard etal. [24] studied
and compared the effects of HIIT and MICT on abdominal
fat in postmenopausal women with T2D, and observed that
only HIIT reduced the subcutaneous and visceral fat mass
significantly following 16 weeks of training. Cassidy etal.
Table 3 Effect of HIIT on body composition, glycemic control, lipid control, and cardiorespiratory fitness in patients with T2D
ES effect sizes, CI confidence interval, MD mean difference, SMD standardized mean difference, ND not enough data
Body composition Within groups Compared to CON Compared to MICT
Body weight N11 6 8
ES (95% CI) MD: −1.65 [−4.76, 1.46] MD: −0.78 [−2.36, 0.80] MD: −1.22 [−2.23, −0.18]
I2 (%) 0 0 0
BMI N11 4 8
ES (95% CI) MD: −0.85 [−1.57, −0.12] MD: −0.80 [−1.86, 0.27] MD: −0.40 [−0.78, −0.02]
I2 (%) 0 0 0
Body fat (%) N6 ND 5
ES (95% CI) MD: −1.86 [−3.68, −0.04] MD: −0.50 [−1.18, 0.19]
I2 (%) 0 0
Waist circumference N7 ND 6
ES (95% CI) MD: −2.23 [−5.00, 0.55] MD: −0.15 [−1.21, 0.91]
I2 (%) 0 0
Glycemic control
HbA1c (%) N10 3 9
ES (95% CI) MD: −0.29 [−0.55, −0.04] MD: −0.39 [−0.81, 0.02] MD: −0.37 [−0.55, −0.19]
I2 (%) 0 0 0
Fasting glucose N9 5 8
ES (95% CI) MD: −0.41 [−0.91, 0.09] SMD: −0.31 [−0.69, 0.06] MD: 0.10 [−0.84, 0.65]
I2 (%) 0 0 0
Fasting insulin N6 5 4
ES (95% CI) SMD: −0.46 [−0.81, −0.11] SMD: −0.46 [−0.91, 0.02] SMD: −0.19 [−0.58, 0.20]
I2 (%) 41 26 0
HOMA-IR N7 4 6
ES (95% CI) MD: −0.43 [−1.18, 0.32] MD: −0.18 [−0.79, 0.42] MD: 0.13 [−0.10, 0.36]
I2 (%) 73 0 0
Lipid control
Total cholesterol N8 6 7
ES (95% CI) SMD: −0.13 [−0.42, 0.15] SMD: 0.02 [−0.32, 037] MD: −0.18 [−0.44, 0.07]
I2 (%) 0 9 0
HDL cholesterol N11 5 9
ES (95% CI) SMD: 0.20 [−0.07, 0.48] SMD: 0.60 [−0.26, 1.45] MD: −0.04 [−0.10, 0.02]
I2 (%) 39 83 0
LDL cholesterol N 7 5 6
ES (95% CI) SMD: −0.15 [−0.44, 0.13] MD: −0.60 [−1.74, 0.54] MD: −0.25 [−0.46, −0.04]
I2 (%) 0 52 0
Cardiorespiratory fitness
VO2peak (ml/kg/min) N7 2 7
ES (95% CI) MD: 4.75 [2.94, 6.56] MD: 4.12 [2.66, 5.57] MD: 3.37 [1.88, 4.87]
I2 (%) 0 0 48
VO2peak (L/min) N5 2 6
ES (95% CI) MD: 0.35 [0.17, 0.53] MD: 0.24 [0.10, 0.37] MD: 0.37 [0.28, 0.45]
I2 (%) 0 0 36
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586 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
[28] reported, in their randomized study, that there was a
39% relative reduction in liver fat following HIIT perfor-
mance and observed that there was a significant correlation
with changes in HbA1c and 2-h glucose. Moreover, Karstoft
etal. [16] found that patients with T2D had greater oxygen
consumption during HIIT training than did those who per-
formed MICT, suggesting that this may be responsible for
their greater weight loss. Recent studies have shown that
the positive effects of exercise on body composition may be
related to the improvement of glycemic control. For exam-
ple, in a long-term randomized trial, Senechal etal. [34]
found that changes in HbA1c were associated with changes
in body weight, waist circumference, and trunk fat mass
in individuals with T2D. Notably, however, although this
review shows that HIIT has favorable effects on body fat
reduction in individuals with T2D, the effects of HIIT on
blood lipids were limited. Only LDL cholesterol showed
significantly lower levels after HIIT than after MICT, while
total cholesterol and HDL cholesterol did not. Thus, more
studies are required to determine whether HIIT could be a
successful training program for lipid control in T2D patients.
HbA1c is not only the most widely used indicator of
glucose: it is also an important risk factor of cardiovas-
cular disease in patients with T2D [35, 36]. The previous
studies have shown that if HbA1c levels are reduced by
1%, the risk of microvascular complications is reduced by
Fig. 2 Forest plot for change in of HbA1c (%), a before and after (within-group) high-intensity interval training (HIIT), b between HIIT and
control (CON) intervention, and c between HIIT and moderate-intensity training (MICT)
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587Aging Clinical and Experimental Research (2019) 31:575–593
1 3
37% and that of death related to diabetes can be reduced
by 21% [35]. A recent meta-analysis has shown that an
increase of 100min in physical activity per week was asso-
ciated with an average change of −0.16% of HbA1c in
individuals with T2D and pre-diabetes subjects [37]. In
our meta-analysis, HbA1c (%) was found to be lower after
HIIT than at baseline (SMD: −0.29, 95% CI −0.55 to
−0.04). Similar to our findings, a recent meta-analysis of
RCTs by Grace etal. identified the positive effects of aero-
bic exercise in reducing HbA1c levels over with controls
[11]. HIIT showed a 0.37% greater reduction of HbA1c
than MICT, which means that HIIT may have additional
benefits on glycemic control. This is inconsistent with the
findings of a meta-analysis conducted by Jelleyman etal.
[18], which found that, while HIIT can reduce the levels
of HbA1c in patients with diabetes and metabolic syn-
drome, there is no significant difference in reduction ver-
sus with continuous training. Furthermore, in a previous
review with a meta-analysis, it was concluded that exercise
intensity was a better predictor of weight MD in HbA1c
than exercise volume in T2D patients [38]. Unfortunately,
we noted no difference in fasting glucose, fasting insulin,
or insulin resistance changes in patients following HIIT as
compared with the CON and MICT groups, even though
the previous studies have shown that the effects of aerobic
training on insulin intensity are more closely influenced
by high-exercise intensity than by low- or moderate-inten-
sity exercise [39]. The inconsistent results could partly
be explained by the difference among methods used to
measure insulin sensitivity, as well as the difference in
the baseline of glycemic control. Further research would
need to include data on the HIIT intervention program
(e.g., training intensity, duration of interval time, fre-
quency of training, and total duration) and the character-
istics of patients (especially with respect to age, duration
Fig. 3 Forest plot for change in VO2peak (ml/kg/min), a before and after (within-group) high-intensity interval training (HIIT), b between HIIT
and control (CON) intervention, and c between HIIT and moderate-intensity training (MICT)
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588 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
of diabetes, and the baseline glycemic control), which all
impact trial results.
Both VO2peak and HbA1c are important predictors of car-
diovascular events in T2D patients [35], and the previous
studies have shown that low cardiorespiratory fitness was
associated with an increased risk for impaired glycemic con-
trol [40, 41]. Aerobic exercise training represents an effec-
tive means to improve VO2peak and HbA1c, and a previous
meta-analysis has revealed that aerobic exercise intensity
is the primary stimulus for improved VO2peak in people
with T2D [11]. Our study further compared the difference
between HIIT and MICT in increasing peak VO2 and found
that the improvement of 3.37ml/kg/min in relative VO2peak
and 0.37L/min in absolute VO2peak following HIIT is supe-
rior to those seen with MICT. Our findings are similarly to
those from other recent studies. A meta-analysis focused
mainly on cardiac patients by Xie etal. [42] showed that
HIIT is more effective than continuous training in improv-
ing VO2peak [MD: 1.76ml/kg/min, 95% CI 1.06 to 2.46ml/
kg/min]. Another systemic analysis analyzing 65 studies by
Batacan etal. [43] revealed that HIIT yielded a significant
increase in VO2peak by a large amount in normal-weight pop-
ulations and a medium effect in overweight/obese popula-
tions, with an aggregate improvement of 3.8 and 4.43ml/
kg/min, respectively. A more recent meta-analysis includ-
ing 594 coronary artery disease patients by Gomes-Noto
etal. [44] reported that a higher improvement in VO2peak
(MD: 1.3ml/kg/min, 95% CI 0.6 to 1.9ml/kg/min) was seen
with HIIT versus with MICT. The underlying physiologi-
cal mechanisms of HIIT that improve peak VO2 could not
be ascertained from the present study, but may involve a
combination of central and peripheral adaptations, includ-
ing an increase in cardiac output, an improvement in vas-
cular/endothelial function, and increased muscle oxidation,
which together promote the enhanced availability, extrac-
tion, and use of oxygen during exercise [45, 46]. Revdal
etal. [47] studied the impact of HIIT on cardiac structure
and function in T2D patients, and observed a 12% relative
increase in left-ventricular wall mass and increased end-
diastolic blood volume, thus demonstrating improvements
Fig. 4 Forest plot for change in VO2peak (L/min), a before and after (within-group) high-intensity interval training (HIIT), b between HIIT and
control (CON) intervention, and c between HIIT and moderate-intensity training (MICT)
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589Aging Clinical and Experimental Research (2019) 31:575–593
1 3
Table 4 Summary of GRADE’s approach to rating quality of evidence
Outcomes Quality assessment
Comparison Participants
(studies) follow
up
Risk of bias Inconsistency Indirectness Imprecision Publication bias Overall quality of
evidence
Body weight MICT 185 (eight stud-
ies)
None None Serious Serious Undetected ⊕⊕⊝⊝ Low due
to indirectness
and imprecision
CON 136 (six studies) None None Serious Serious Undetected ⊕⊕⊝⊝ Low due
to indirectness
and imprecision
BMI MICT 207 (eight stud-
ies)
None None Serious Serious Undetected ⊕⊕⊝⊝ Low due
to indirectness
and imprecision
CON 72 (three stud-
ies)
None None Serious Serious Undetected ⊕⊕⊝⊝ Low due
to indirectness
and imprecision
Body fat (%) MICT 138 (five stud-
ies)
Serious Very serious None Serious Undetected ⊕⊝⊝⊝ Very low
due to risk of
bias, incon-
sistency and
imprecision
CON ND ND ND ND ND ND ND
Waist circum-
ference
MICT 140 (six studies) None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
CON ND ND ND ND ND ND ND
HbA1c (%) MICT 209 (nine stud-
ies)
None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsist-
ency, impreci-
sion
CON 63 (three stud-
ies)
None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
Fasting glucose MICT 162 (eight stud-
ies)
None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
CON 114 (five stud-
ies)
None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
Fasting insulin MICT 103 (five stud-
ies)
None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
CON 85 (four studies) None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
HOMA-IR MICT 182 (seven stud-
ies)
None Very serious None Serious Undetected ⊕⊝⊝⊝ Very low
due to incon-
sistency and
imprecision
CON 99 (four studies) None Very serious None Serious Undetected ⊕⊝⊝⊝ Very low
due to incon-
sistency and
imprecision
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590 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
in systolic function, as indicated by raised stroke volume
and left-ventricular ejection fraction. A similar finding was
found by Hollekin etal. [22], who observed that both MICT
and HIIT groups showed improved diastolic function at rest,
but that the HIIT group showed greater improvement than
did the MICT group. Moreover, Little etal. [48] found that
people with T2D who performed six sessions of low-volume
HIIT at an intensity of 90% of the maximal heart rate with
60-s rest over 2 weeks experienced an increase in maximal
activity of citrate synthesis and skeletal muscle mitochon-
drial protein content, suggesting that the increases in skel-
etal muscle mitochondrial content and function following
low-volume HIIT may be contributing factors to improved
VO2peak.
Table 4 (continued)
Outcomes Quality assessment
Comparison Participants
(studies) follow
up
Risk of bias Inconsistency Indirectness Imprecision Publication bias Overall quality of
evidence
Total choles-
terol
MICT 165 (seven stud-
ies)
None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
CON 137 (six studies) None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
HDL choles-
terol
MICT 204 (nine stud-
ies)
None Serious None Serious Reporting bias
strongly sus-
pected
⊕⊝⊝⊝ Very
low due to
inconsistency,
imprecision and
publication bias
CON 114 (five stud-
ies)
None Serious None Serious Reporting bias
strongly sus-
pected
⊕⊝⊝⊝ Very
low due to
inconsistency,
imprecision and
publication bias
LDL MICT 150 (six studies) None Serious None Serious Reporting bias
strongly sus-
pected
⊕⊝⊝⊝ Very
low due to
inconsistency,
imprecision and
publication bias
CON 114 (five stud-
ies)
None Very serious None Serious None ⊕⊝⊝⊝ Very low
due to incon-
sistency and
imprecision
VO2peak (L/
min)
MICT 159 (six studies) None Serious None Serious Undetected ⊕⊕⊕⊝ Moder-
ate due to
inconsistency,
imprecision and
large effect
CON 40 (two studies) None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
VO2peak (ml/
kg/min)
MICT 182 (seven stud-
ies)
Serious Serious None Serious Undetected ⊕⊕⊕⊝ Moder-
ate due to
inconsistency,
imprecision and
large effect
CON 40 (two studies) None Serious None Serious Undetected ⊕⊕⊝⊝ Low due
to inconsistency
and imprecision
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591Aging Clinical and Experimental Research (2019) 31:575–593
1 3
Strengths andlimitations
Our meta-analysis of randomized trials has several strengths.
First, to our knowledge, this is the first existing systematic
review to compare the effects of HIIT and MICT or non-
exercise on glycemic control (e.g., HbA1c, insulin, and fast-
ing glucose); body composition (e.g., body weight, body fat,
BMI, and waist circumference); and cardiorespiratory fitness
(e.g., VO2peak) among people with T2D. Second, this system-
atic review involved a large number of literature searches by
two reviewers who independently screened studies, assessed
their quality, and extracted data to decrease publishing bias
and increase credibility.
However, some limitations were still present in our
evaluation. First, there are some inconsistencies among the
included studies with respect to HIIT protocols and MICT
protocols, which may have affected the results obtained
with respect to the intervention and control groups. Second,
considering the low quality of evidence, these results may
have some limitations in guiding clinical applications. Third,
an important limitation is that most of the included studies
reported the pre- and post-intervention parameters but not
the differences between the baselines. Therefore, consider-
ing the different baseline values that may be present between
the intervention and control groups in some studies, we used
equations to calculate the mean difference whenever it was
not reported to address the discrepancy of the baseline in
each group, and this could have resulted in a bias. Fourth,
the results of this meta-analysis are limited by the lack of
high-quality studies and the small number of patients in each
included study. Only four of the included studies clearly
indicated random sequence generation, while three studies
reported allocation concealment, and five studies blinded
participants in their experimental procedures.
Conclusions
In conclusion, we here demonstrated that HIIT is an effective
strategy for improving cardiorespiratory fitness in patients
with T2D, preferable to MICT. Results related to other
parameters associated with the prognosis of T2D, such as
HbA1c, body weight, and BMI, were not conclusive. This
review can still provide some suggestions for the clinical
application of HIIT in T2D patients. Future studies should
investigate the effects of HIIT in T2D patients through mul-
ticenter RCTs with large sample sizes over the long term.
Acknowledgements We would like to thank LetPub (http://www.letpu
b.com) for providing linguistic assistance during the preparation of
this manuscript.
Author contributions Jing-xin Liu contributed to study conception and
design, drafting the submitted article, and critically revising the draft
for important intellectual content. Lin Zhu revised the draft critically
for important intellectual content and gave final approval of the version
for publication. Pei-jun Li, Ning Li, and Yan-bing Xu contributed to
acquisition, analysis, and interpretation the data. All authors contrib-
uted at all stages of this study, gave final approval of the version for
publication, and agree to be accountable for all aspects of the work.
Funding This work was supported by the National Planning Office of
Philosophy and Social Science of China (No. 18BTY075), the research
projects of the Social Science and Humanity on Young Fund of the
Ministry of education of China (No. 13yjc890050), the research pro-
jects of the Department of Education of Guangdong Province (No.
2015KTSCX079), the research projects of the Department of Science
and Technology of Guangdong Province (No. 2015A020219010 and
No. 2014A020220010).
Compliance with ethical standards
Conflict of interest The authors declare that there is no conflict of in-
terest regarding the publication of this paper.
Statement of human and animal rights This review does not contain
any experiments involving human participants or animals performed
by any of authors.
Informed consent For this review, formal consent forms were not
required.
Open Access This article is distributed under the terms of the Crea-
tive Commons Attribution 4.0 International License (http://creat iveco
mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribu-
tion, and reproduction in any medium, provided you give appropriate
credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
References
1. WHO (2016) Global Report on Diabetes. WHO. http://www.who.
int/diabe tes/publi catio ns/grd-2016/en/. Accessed 6 April 2016
2. Whiting DR, Guariguata L, Weil C etal (2011) IDF diabetes atlas:
global estimates of the prevalence of diabetes for 2011 and 2030.
Diabetes Res Clin Pract 94:311–321. https ://doi.org/10.1016/j.
diabr es.2011.10.029
3. Johansen MY, MacDonald CS, Hansen KB etal (2017) Effect of
an intensive lifestyle intervention on glycemic control in patients
with type 2 diabetes: a randomized clinical trial. JAMA 318:637–
646. https ://doi.org/10.1001/jama.2017.10169
4. Ribisl PM, Gaussoin SA, Lang W etal (2012) Lifestyle interven-
tion improves heart rate recovery from exercise in adults with
type 2 diabetes: results from the Look AHEAD study. J Obes
2012:309196. https ://doi.org/10.1155/2012/30919 6
5. Colberg SR, Sigal RJ, Fernhall B etal (2010) Exercise and type
2 diabetes: the American College of Sports Medicine and the
American Diabetes Association: joint position statement executive
summary. Diabetes Care 33:2692–2696. https ://doi.org/10.2337/
dc10-1548
6. Anderson JE, Greene MA, Griffin JW etal (2012) Diabetes and
employment. Diabetes Care 35:S94–S98. https ://doi.org/10.2337/
dc12-s094
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
592 Aging Clinical and Experimental Research (2019) 31:575–593
1 3
7. Sigal RJ, Kenny GP, Boule NG etal (2007) Effects of aerobic
training, resistance training, or both on glycemic control in type 2
diabetes: a randomized trial. Ann Intern Med 147:357–369. https
://doi.org/10.7326/0003-4819-147-6-20070 9180-00005
8. Church TS, Blair SN, Cocreham S etal (2010) Effects of aerobic
and resistance training on hemoglobin A1c levels in patients with
type 2 diabetes: a randomized controlled trial. JAMA 304:2253–
2262. https ://doi.org/10.1001/jama.2010.1710
9. Jorge ML, de Oliveira VN, Resende NM etal (2011) The effects
of aerobic, resistance, and combined exercise on metabolic con-
trol, inflammatory markers, adipocytokines, and muscle insulin
signaling in patients with type 2 diabetes mellitus. Metabolism
60:1244–1252. https ://doi.org/10.1016/j.metab ol.2011.01.006
10. Motahari-Tabari N, Ahmad Shirvani M, Shirzad EAM etal (2014)
The effect of 8 weeks aerobic exercise on insulin resistance in
type 2 diabetes: a randomized clinical trial. Global J Health Sci
7:115–121. https ://doi.org/10.5539/gjhs.v7n1p 115
11. Grace A, Chan E, Giallauria F etal (2017) Clinical outcomes
and glycaemic responses to different aerobic exercise training
intensities in type II diabetes: a systematic review and meta-anal-
ysis. Cardiovasc Diabetol 16:37. https ://doi.org/10.1186/s1293
3-017-0518-6
12. Ellingsen O, Halle M, Conraads V etal (2017) High-intensity
interval training in patients with heart failure with reduced ejec-
tion fraction. Circulation 135:839–849. https ://doi.org/10.1161/
circu latio naha.116.02292 4
13. Wisloff U, Stoylen A, Loennechen JP etal (2007) Superior car-
diovascular effect of aerobic interval training versus moderate
continuous training in heart failure patients: a randomized study.
Circulation 115:3086–3094. https ://doi.org/10.1161/circu latio
naha.106.67504 1
14. Phillips BE, Kelly BM, Lilja M etal (2017) A practical and
time-efficient high-intensity interval training program modifies
cardio-metabolic risk factors in adults with risk factors for type II
diabetes. Front Endocrinol 8:229. https ://doi.org/10.3389/fendo
.2017.00229
15. Støa EM, Meling S, Nyhus LK etal (2017) High-intensity aerobic
interval training improves aerobic fitness and HbA1c among per-
sons diagnosed with type 2 diabetes. Eur J Appl Physiol 117:455–
467. https ://doi.org/10.1007/s0042 1-017-3540-1
16. Karstoft K, Winding K, Knudsen SH etal (2013) 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 36:228–236. https ://
doi.org/10.2337/dc12-0658
17. Mitranun W, Deerochanawong C, Tanaka H etal (2014) Con-
tinuous vs interval training on glycemic control and macro- and
microvascular reactivity in type 2 diabetic patients. Scand J Med
Sci Sports 24:e69–e76. https ://doi.org/10.1111/sms.12112
18. Jelleyman C, Yates T, O’Donovan G etal (2015) The effects of
high-intensity interval training on glucose regulation and insulin
resistance: a meta-analysis. Obes Rev 16:942–961. https ://doi.
org/10.1111/obr.12317
19. Higgins JP, Altman DG, Gotzsche PC etal (2011) The Cochrane
Collaboration’s tool for assessing risk of bias in randomised trials.
BMJ 343:d5928. https ://doi.org/10.1136/bmj.d5928
20. Balshem H, Helfand M, Schunemann HJ etal (2011) GRADE
guidelines: 3. Rating the quality of evidence. J Clin Epidemiol
64:401–406. https ://doi.org/10.1016/j.jclin epi.2010.07.015
21. Alvarez C, Ramirez-Campillo R, Martinez-Salazar C et al
(2016) Low-volume high-intensity interval training as a therapy
for type 2 diabetes. Int J Sports Med 37:723–729. https ://doi.
org/10.1055/s-0042-10493 5
22. Hollekim-Strand SM, Bjorgaas MR, Albrektsen G etal (2014)
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
64:1758–1760. https ://doi.org/10.1016/j.jacc.2014.07.971
23. Lee SS, Yoo JH, So YS (2015) Effect of the low- versus high-
intensity exercise training on endoplasmic reticulum stress and
GLP-1 in adolescents with type 2 diabetes mellitus. J Phys Ther
Sci 27:3063–3068. https ://doi.org/10.1589/jpts.27.3063
24. Maillard F, Rousset S, Pereira B etal (2016) High-intensity
interval training reduces abdominal fat mass in postmenopausal
women with type 2 diabetes. Diabetes Metab 42:433–441. https
://doi.org/10.1016/j.diabe t.2016.07.031
25. Ramos JS, Dalleck LC, Borrani F etal (2016) The effect of dif-
ferent volumes of high-intensity interval training on proinsu-
lin in participants with the metabolic syndrome: a randomised
trial. Diabetologia 59:2308–2320. https ://doi.org/10.1007/s0012
5-016-4064-7
26. Terada T, Friesen A, Chahal BS etal (2013) Feasibility and pre-
liminary efficacy of high intensity interval training in type 2 diabe-
tes. Diabetes Res Clin Pract 99:120–129. https ://doi.org/10.1016/j.
diabr es.2012.10.019
27. Backx K, McCann A, Wasley D etal (2011) The effect of a sup-
ported exercise programme in patients with newly diagnosed Type
2 diabetes: a pilot study. J Sports Sci 29:579–586. https ://doi.
org/10.1080/02640 414.2010.54466 6
28. Cassidy S, Thoma C, Hallsworth K etal (2016) High intensity
intermittent exercise improves cardiac structure and function and
reduces liver fat in patients with type 2 diabetes: a randomised
controlled trial. Diabetologia 59:56–66. https ://doi.org/10.1007/
s0012 5-015-3741-2
29. Bellia A, Iellamo F, De Carli E etal (2017) Exercise individu-
alized by TRIMPi method reduces arterial stiffness in early
onset type 2 diabetic patients: a randomized controlled trial with
aerobic interval training. Int J Cardiol 248:314–319. https ://doi.
org/10.1016/j.ijcar d.2017.06.065
30. Winding KM, Munch GW, Iepsen UW etal (2018) The effect
on glycaemic control of low-volume high-intensity interval train-
ing versus endurance training in individuals with type 2 diabetes.
Diabetes 20:1131–1139. https ://doi.org/10.1111/dom.13198
31. Kahn SE, Hull RL, Utzschneider KM (2006) Mechanisms linking
obesity to insulin resistance and type 2 diabetes. Nature 444:840–
846. https ://doi.org/10.1038/natur e0548 2
32. Heianza Y, Kato K, Kodama S etal (2015) Risk of the develop-
ment of Type 2 diabetes in relation to overall obesity, abdominal
obesity and the clustering of metabolic abnormalities in Japanese
individuals: does metabolically healthy overweight really exist?
The Niigata Wellness Study. Diabetic Med 32:665–672. https ://
doi.org/10.1111/dme.12646
33. Li S, Xiao J, Ji L etal (2014) BMI and waist circumference are
associated with impaired glucose metabolism and type 2 diabetes
in normal weight Chinese adults. J Diabetes Complicat 28:470–
476. https ://doi.org/10.1016/j.jdiac omp.2014.03.015
34. Senechal M, Swift DL, Johannsen NM etal. (2013) Changes in
body fat distribution and fitness are associated with changes in
hemoglobin A1c after 9 months of exercise training: results from
the HART-D study. Diabetes Care 36:2843–2849. https ://doi.
org/10.2337/dc12-2428
35. Stratton IM, Adler AI, Neil HA etal (2000) Association of gly-
caemia with macrovascular and microvascular complications of
type 2 diabetes (UKPDS 35): prospective observational study.
BMJ 321:405–412. https ://doi.org/10.1136/bmj.321.7258.405
36. Woerle HJ, Neumann C, Zschau S etal (2007) Impact of fasting
and postprandial glycemia on overall glycemic control in type 2
diabetes Importance of postprandial glycemia to achieve target
HbA1c levels. Diabetes Res Clin Pract 77:280–285. https ://doi.
org/10.1016/j.diabr es.2006.11.011
37. Boniol M, Dragomir M (2017) Physical activity and change
in fasting glucose and HbA1c: a quantitative meta-analysis
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
593Aging Clinical and Experimental Research (2019) 31:575–593
1 3
of randomized trials. Acta Diabetol 54:983–991. https ://doi.
org/10.1007/s0059 2-017-1037-3
38. Boule NG, Kenny GP, Haddad E etal (2003) Meta-analysis of the
effect of structured exercise training on cardiorespiratory fitness
in Type 2 diabetes mellitus. Diabetologia 46:1071–1081. https ://
doi.org/10.1007/s0012 5-003-1160-2
39. DiPietro L, Dziura J, Yeckel CW et al (2006) Exercise and
improved insulin sensitivity in older women: evidence of the
enduring benefits of higher intensity training. J Appl Physiol
(Bethesda Md 1985) 100:142–149. https ://doi.org/10.1152/jappl
physi ol.00474 .2005
40. Nojima H, Yoneda M, Watanabe H etal (2017) Association
between aerobic capacity and the improvement in glycemic con-
trol after the exercise training in type 2 diabetes. Diabetol Metab
Syndr 9:63. https ://doi.org/10.1186/s1309 8-017-0262-9
41. Wei M, Gibbons LW, Mitchell TL etal (1999) The association
between cardiorespiratory fitness and impaired fasting glucose
and type 2 diabetes mellitus in men. Ann Intern Med 130:89–96.
https ://doi.org/10.7326/0003-4819-130-2-19990 1190-00002
42. Xie B, Yan X, Cai X (2017) Effects of high-intensity interval
training on aerobic capacity in cardiac patients. Syst Rev Meta
Anal. https ://doi.org/10.1155/2017/54208 40
43. Batacan RB, Duncan MJ, Dalbo VJ etal (2017) Effects of high-
intensity interval training on cardiometabolic health: a systematic
review and meta-analysis of intervention studies. Br J Sports Med
51:494–503. https ://doi.org/10.1136/bjspo rts-2015-09584 1
44. Gomes-Neto M, Duraes AR, Reis H etal (2017) High-intensity
interval training versus moderate-intensity continuous training
on exercise capacity and quality of life in patients with coronary
artery disease: A systematic review and meta-analysis. Eur J Pre-
vent Cardiol 24:1696–1707. https ://doi.org/10.1177/20474 87317
72837 0
45. Ramos JS, Dalleck LC, Tjonna AE etal (2015) The impact of
high-intensity interval training versus moderate-intensity con-
tinuous training on vascular function: a systematic review and
meta-analysis. Sports Med (Auckland NZ) 45:679–692. https ://
doi.org/10.1007/s4027 9-015-0321-z
46. Moholdt T, Aamot IL, Granoien I etal (2012) Aerobic inter-
val training increases peak oxygen uptake more than usual
care exercise training in myocardial infarction patients: a ran-
domized controlled study. Clin Rehabil 26:33–44. https ://doi.
org/10.1177/02692 15511 40522 9
47. Revdal A, Hollekim-Strand SM, Ingul CB (2016) Can time effi-
cient exercise improve cardiometabolic risk factors in type 2 dia-
betes? a pilot study. J Sports Sci Med 15:308–313
48. Little JP, Gillen JB, Percival ME etal (2011) Low-volume high-
intensity interval training reduces hyperglycemia and increases
muscle mitochondrial capacity in patients with type 2 diabetes.
J Appl Physiol (Bethesda Md 1985) 111:1554–1560. https ://doi.
org/10.1152/jappl physi ol.00921 .2011
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