Semisupervised Physical Exercise and Lifestyle Counseling in Cardiometabolic Risk Management in Sedentary Adults: Controlled Randomized Trial (BELLUGAT)

in Journal of Physical Activity and Health

Background: The purpose of this study was to evaluate the feasibility and effectiveness of a high-intensity semisupervised exercise program alongside lifestyle counseling as an intervention for managing cardiometabolic risk in sedentary adults. Methods: A 40-week 3-arm randomized controlled clinical trial (16-wk intervention and 24-wk follow-up) was used. Seventy-five sedentary adults (34–55 y) with at least 1 cardiometabolic risk factor were randomized into one of the following arms: (1) aerobic interval training (AIT) plus lifestyle counseling (n = 25), (2) low- to moderate-intensity continuous training plus lifestyle counseling (traditional continuous training, TCT) (n = 27), or (3) lifestyle counseling alone (COU) (n = 23). Metabolic syndrome severity scores, accelerometer-based physical activity, and self-reported dietary habits were assessed at baseline, after the intervention, and at follow-up. Results: AIT was well accepted with high enjoyment scores. All groups showed similar improvements in metabolic syndrome severity scores (standardized effect size = 0.46) and dietary habits (standardized effect size = 0.30). Moderate to vigorous physical activity increased in all study groups, with the number of responders higher in AIT and TCT groups (50%) than in COU group (21%). Both AIT and TCT had a greater impact on sedentary behavior than COU (63.5% vs 30.4% responders). Conclusions: AIT appears to be a feasible and effective strategy in sedentary individuals with cardiometabolic risk factors. AIT could be included in intervention programs tackling unhealthy lifestyles.

Metabolic syndrome (MetS), a cluster of risk factors comprising abdominal obesity, impaired glucose tolerance, hypertension, and dyslipidemia, has a strong negative impact on cardiovascular health. Epidemiological evidence indicates that undesirable lifestyle changes can somewhat explain the increasing prevalence of MetS worldwide.1 Lifestyle modifications have shown positive results in preventing and treating cardiometabolic risk.24

Physical activity is considered a key component of a healthy lifestyle. The health benefits of moderate-intensity exercise are well established.5,6 However, there has been growing interest in the application of high-intensity interval training, particularly aerobic interval training (AIT), in health care settings. Higher intensity physical activity has been reported to offer greater benefits.3,7,8 Several studies analyzing the effects of AIT on individuals with cardiometabolic risk factors, MetS, ischemic cardiomyopathy, or cardiac insufficiency have shown positive changes in body composition, ventricular and vascular functions, plasma adiponectin levels, and glucose intolerance.912

Although these studies support the efficacy of AIT in reducing cardiometabolic risk factors, to the best of our knowledge, there are no data on the effectiveness of AIT as an intervention tool when implemented alongside lifestyle counseling in primary care. Detractors claim that high-intensity interval training is not an appropriate or sustainable form of exercise as it might be associated with a lack of enjoyment, leading to poor compliance.13,14 However, AIT can be tailored to the individual, which could explain why AIT has been observed to be well tolerated by sedentary individuals with or without concomitant diseases.912,15

In addition, participants undergoing AIT interventions report greater satisfaction than participants undergoing traditional continuous training (TCT) interventions.16,17 This could be due to the variable nature of AIT compared with the monotony of TCT sessions, as well as to the fact that the intensity of the physical activities promotes a greater sense of challenge. These affective aspects should be taken into account when trying to improve adherence to exercise interventions.18

Although the efficacy of AIT in inducing physiological improvements in a well-controlled research environment has been demonstrated, questions remain regarding the extent to which its benefits are transferable to a less-controlled clinical environment.13,14

Moreover, for enduring health benefits, physical activity needs to be maintained in the long term (6 mo or more).19 But although evidences support that supervised physical exercise interventions are more effective to improve physical exercise than less supervised ones, dropout rates after cessation of the intervention are high and long-term maintenance of physical activity is poor.

As pointed by Biddle et al,20 interventions should assist the individual in becoming an independent exerciser. In individuals with chronic health conditions, such as risk factors for MetS, this implies that besides the physical exercise program, interventions should consider a range of cognitive-behavioral strategies for both physical activity self-management and health condition (ie, risk factors) management. Self-monitoring, feedback, and goal setting have been shown to be effective to promote physical activity and to support physical activity maintenance.19 In addition, health condition management–related strategies, such as pacing and acceptance of limitations related to physical activity, also support its maintenance.19

The primary aim of this study was to evaluate the effectiveness of a 16-week high-intensity semisupervised exercise program alongside lifestyle counseling (AIT) as a primary care intervention for managing cardiometabolic risk factors in sedentary adults. This intervention was compared with low- to moderate-intensity continuous training plus lifestyle counseling (TCT) and lifestyle counseling (COU) alone. The secondary aim of this study was to investigate the effects of these interventions on physical activity, sedentary behavior (SB), and dietary habits. Moreover, the maintenance of the effects at 24-week follow-up was also investigated.

Methods

Design and Participants

A 3-arm randomized controlled clinical trial was implemented in a primary care setting over a period of 16 weeks, with a 24-week follow-up. It included 2 semisupervised exercise groups with different levels of exercise intensity (AIT and TCT) and nonexercise control group (COU).

This study was conducted between January 2016 and November 2017, in Lleida (140,000 inhabitants, Spain).

Participants were recruited from primary health care centers and advertisements through the media, community centers, and e-mail. To be eligible, potential participants should be between 30 and 55 years old, spent most of their awake time in SB (sitting and lying down) or performing light-intensity activities (slow walking), and have at least one cardiometabolic risk factor (waist circumference [WC] >94.5 cm for men and >89.5 cm for women; blood pressure ≥130/85 mm Hg; triglycerides [TG] in plasma ≥150 mg/dL; high-density lipoprotein cholesterol [HDLc] in plasma <40 mg/dL for men and <50 mg/dL for women; and fasting glucose ≥100 mg/dL). Individuals with severe health problems (morbid obesity and past/current history of substantial cardiovascular, respiratory, neuromuscular, or psychiatric diseases) or disorders that may contraindicate physical exercise were excluded. All eligible participants were fully informed of the experimental procedures and provided written informed consent prior to enrollment.

This study comprised 75 volunteers (34–55 y) who met eligibility criteria, and the participants were randomly allocated to one of the study groups (AIT [n = 25], TCT [n = 27], or COU [n = 23]) using a computer-generated random number.

Study procedures were approved by the clinical research ethics committee of the Primary Healthcare Research Institute Jordi Gol (Institut de Investigació en Atenció Primària [IDIAP]; registration number: P15/122) and conformed with the Declaration of Helsinki.21

The study methods were consistent with the CONSORT guidelines for the reporting of randomized trials.22

Intervention

The trial comprised a 16-week intervention and a 24-week follow-up period. During the intervention phase, all groups (AIT, TCT, and COU) were offered lifestyle counseling, with the 2 exercise groups (AIT and TCT) also participating in a semisupervised physical exercise program.

The counseling program, which was identical for all the study groups, consisted of six 50-minute group sessions and 4 individual sessions lasting 40 to 50 minutes each. The 6 group sessions and 3 individual sessions took place during the intervention period and the fourth individual session took place at the end of the follow-up period. Briefly, the aim of the counseling program was to offer advice on healthy lifestyle, including physical activity (both nonexercise and exercise physical activity),23 SB, and dietary habits; to give the opportunity to exchange experiences; and to help the participants to become aware of the importance of a healthy lifestyle and of the need for self-management. Strategies for behavior change were also introduced as a way of empowering them. The sessions were based on the transtheoretical model for behavior change24 and the motivational interviewing25 approaches. The specific content and psychobehavioral strategies have been described in more detail elsewhere.26 The sessions were conducted at the primary health care center by a nurse trained in healthy lifestyles and psychological strategies for behavioral change. A validated interactive participants’ booklet was used.27

The semisupervised physical exercise program was offered to the exercise groups (AIT and TCT) and consisted of 16 supervised group training sessions lasting 50 minutes each and 32 self-administered sessions. Experts in physical exercise supervised the training sessions. The structured pattern of the exercise program declined along the 16-week intervention phase. While the frequency of supervised exercise sessions decreased, the frequency of the prescheduled self-administered sessions increased (Figure 1), and participants had to become more responsible of their own exercise habits. The aim of progressively limiting supervised exercise sessions was to empower participants with respect to their own exercise habits.

Figure 1
Figure 1

—Schedule of training sessions and counseling sessions along the 16-week intervention. The fourth individual session that took place at the end of the 24-week follow-up period is not represented.

Citation: Journal of Physical Activity and Health 17, 7; 10.1123/jpah.2019-0409

Exercise intensity was prescribed as a percentage of peak oxygen consumption (VO2peak). Before the intervention, a voluntary maximal graded exercise on a cycle ergometer (Monark 828E; Monark, Vansbro, Sweden) was performed.26 During the graded exercise, oxygen uptake was measured using the Oxycon Mobile metabolic system (Oxycon Mobile; CareFusion GmbH, Hoechberg, Germany). VO2peak was considered as the highest oxygen uptake achieved during the test.

Structure of the Training Sessions

All the training sessions comprised 5 parts: introduction, warm-up, main part (see below), cooldown, and conclusion. The main part, which differed between the AIT and TCT groups, consisted of the following:

  1. AIT group: Four 4-minute repetitions at 80% of the VO2peak, with active pauses of 2 minutes at 60% of the VO2peak between them. Given that maintaining the anticipated exercise intensity might, at the beginning of the program, excessively burden the participants, some adaptations were made. During the first week, intensity was established at 80% of the VO2peak as planned, but the repetitions lasted 1 minute, and its number was increased to give an effective exercise time of 24 minutes. In each successive session, the repetition period was increased by 1 minute (until reaching 4 min), whereas the number of repetitions was reduced, therefore not changing the exercise time of 24 minutes.
  2. TCT group: Participants performed activities such as walking, cycling, team activities, and toning exercises, always at an intensity of 60% of their VO2peak.

Training Session Schedule and Monitoring

Both exercise groups performed the first 8 sessions on indoor cycles, the next 4 sessions on a treadmill, and the remaining 4 sessions running outside in green areas of the city. Across the 16 sessions, participants were able to practice and become familiar with a variety of exercise forms, while keeping to the training goals. The aim was to offer them ideas/tools that could be implemented in their self-administered exercise sessions.

During supervised and self-administered training sessions, participants used a heart rate monitor (Geonaute, ONRhythm 110; Oxylane, Villeneuve-d’Ascq, France) to monitor exercise intensity.

Enjoyment of the supervised sessions was measured with the physical activity enjoyment scale28 at the end of the sessions.

Compliance With the Offered Sessions

Attendance at the offered sessions (counseling and supervised exercise sessions) was recorded to assess compliance with the program. Attendance ≥80% corresponded to good compliance, attendance between 50% and 79% corresponded moderate compliance, and attendance <50% corresponded to poor compliance.

Compliance with the self-administered sessions was determined by asking the participants during their counseling sessions.

Outcome Measurements

Outcome measurements were recorded at baseline (T0 = week 0), after the intervention (T1 = week 16), and after the 24-week follow-up (T2 = week 40).

At all times, the assessments were divided into 2 appointments. In the first appointment at the primary health care center, fasting blood samples were obtained through an antecubital vein, and anthropometric data (body mass, height, and WC) were collected. Also during this appointment, accelerometers were placed on the participants’ waist, and they were given instructions on how to complete a 7-day activity log. Accelerometers were retrieved at the end of the 7-day period.

In the second appointment, participants completed the self-reported questionnaires on their dietary habits, and their resting sitting blood pressure was measured.

Metabolic Syndrome Severity Score

A continuous metabolic syndrome severity score (MetSSS) was calculated, as described by Wiley and Carrington.29 The score contains the following 6 risk factors: WC, TG levels, HDLc levels, glycemia, systolic blood pressure (SBP), and diastolic blood pressure (DBP).

The WC was measured in triplicate following World Health Organization guidelines.30 Blood samples for determining plasma TG, HDLc, and glucose levels were obtained after an overnight fast and analyzed with automated methods at the University Hospital Arnau de Vilanova (Lleida, Catalonia, Spain). Blood pressure was measured at the level of the brachial artery of the dominant arm using an automated device (Omron M; Omron Healthcare Europe B.V., Hoofddorp, The Netherlands), with the participant in a relaxed sitting position.

Physical Activity and SB

Active and SB was assessed objectively using the ActiGraph GT3X+ accelerometer (ActiGraph LLC, Pensacola, FL) over 7 consecutive days. Accelerometers were positioned laterally on each participant’s waist and attached with an elastic belt. To improve accelerometer wear compliance, we used a 24-hour protocol.31,32 Participants were instructed to wear the accelerometer all day and to complete a 7-day activity log where they specified waking up and going to sleep times. Accelerometers were programmed to record movement in 60-second intervals. The data from the accelerometers were downloaded and analyzed with the ActiLife software (version 6.0; ActiGraph LLC). Sleeping hours and 20-minute bouts of consecutive zero counts were excluded from the analysis after checking the 7-day activity log.

The data were analyzed to yield an overall physical activity index (expressed as vector magnitude [VM] in mean counts per minute [CPM]) and the percentage of time spent undertaking different levels of physical activity. The cutoff points for categorizing movement intensity were defined as follows: SB (<100 CPM), light physical activity (LPA; 100–2019 CPM), and moderate to vigorous physical activity (MVPA; ≥2020 CPM).33

Dietary Habits

At each assessment, a 3-day 24-hour dietary record was completed to monitor the dietary habits of each participant. Two of these days were weekdays, whereas the other one was on a weekend day. The software PCN-CESNID 1.0 (Barcelona, Spain) was used to analyze the dietary records.

The Healthy Eating Index (HEI) was calculated to assess the participant’s dietary status.34 The HEI comprises 12 items, each representing different features of a healthy diet according to U.S. Department of Agriculture guidelines.35,36 Items 1 to 8 measure the degree of agreement with national dietary guidelines for the consumption of grains, vegetables, fruit, milk, and meat; items 9 to 11 measure cholesterol and sodium intake; and item 12 examines empty calories. A final score ranging from 0 to 100 is obtained. Values >80 indicate a good dietary status, values ranging from 51 to 80 indicate an average dietary status, and values <51 indicate a low dietary status.

Other Measures

Sociodemographic characteristics, medical history, and medication treatment of the participants were also recorded.

Statistical Methods

Quantitative variables are expressed as the mean and SD, and qualitative variables are expressed as frequency (n) and percentages (%), unless otherwise specified. Intention-to-treat analysis was performed for all the participants who completed the baseline assessment (N = 75), regardless of whether they subsequently completed the whole protocol or not. Missing outcome values were replaced by data from the previous assessment (last observation carried forward technique). The initial analysis evaluated the homogeneity of baseline (T0) characteristics between study groups using nonparametric tests. To evaluate the impact of the intervention on outcome parameters, 2 time frameworks were established: efficacy (after the intervention; T1) and maintenance (at 24-wk follow-up; T2). The change from basal values was calculated, and significant differences between groups were determined using nonparametric tests. The standardized effect size (SES) was computed as the mean difference between T0 and T1 or T2 divided by the pooled SD being the cutoffs small (0.2–0.5), moderate (0.5–0.8), and large (>0.8).37

Spearman correlation rank (rs) was used to assess associations between the quantitative variables.

Significance was set at .05. For all analyses, SPSS (version 17.0; SPSS, Chicago, IL) was used.

Results

Participants

Baseline assessment was completed satisfactorily for 75 participants (Figure 2). The participants (26 men and 49 women) were mainly nonsmokers, with an undergraduate or postgraduate degree and a relatively high family income (Table 1). Tables 2 and 3 show the outcome measurements at baseline. There were no differences between the study groups regarding sociodemographic characteristics and outcome measurements at baseline.

Figure 2
Figure 2

—Participant’s flowchart. AIT indicates aerobic interval training; COU, counseling; T0, baseline; T1, after 16-week intervention; T2, after 24-week follow-up; TCT, traditional continuous training.

Citation: Journal of Physical Activity and Health 17, 7; 10.1123/jpah.2019-0409

Table 1

Demographic Characteristics of Participants at T0

All (N = 75)Men (n = 26)Women (n = 49)
Sociodemographic characteristicsFrequency, %Frequency, %Frequency, %
Smoking
 Nonsmoker64 (85.3)22 (84.6)42 (85.7)
 Smoker11 (14.7)4 (15.4)7 (14.3)
Education level completed
 Primary school1 (1.3)1 (3.8)
 Bachelor/high school11 (14.7)6 (23.1)5 (10.2)
 College/vocational training11 (14.6)5 (19.2)6 (12.3)
 Degree38 (50.7)11 (42.3)27 (55.1)
 Master or PhD14 (18.7)3 (11.5)11 (22.4)
Economic income status, euros
 <10,0005 (6.7)2 (7.7)5 (6.7)
 10,001–20,00015 (20.0)5 (19.2)15 (20.0)
 20,001–30,00021 (28.0)4 (15.4)17 (34.7)
 >30,00033 (44.0)15 (57.7)18 (36.7)
Medication treatment
 Hypertension10 (13.3)3 (11.5)7 (14.3)
 Hyperlipidemia1 (1.3)0 (0.0)1 (2.0)
 Hyperglycemia2 (2.7)1 (3.8)1 (2.0)
 Others13 (17.3)4 (15.4)9 (178.4)

Abbreviation: T0, baseline. Note: Data are shown as frequency (%).

Table 2

Cardiometabolic Risk Factors and MetSSS at T0

Study groupAll (N = 75)Men (n = 26)Women (n = 49)P value
Risk factorsnMeanSDnMeanSDNMeanSDMen vs women
Waist circumference, cmAll7589.1114.552699.3312.694983.6912.48<.001
AIT2588.0114.27996.0813.651683.4712.87.03
TCT2792.4914.3510104.6811.461785.3210.65<.001
COU2386.3314.93795.8612.191682.1614.37.05
Glycemia, mg/dLAll7588.977.612692.316.984987.207.39.01
AIT2588.327.09989.226.671687.817.48.73
TCT2790.268.101094.907.371787.537.39.02
COU2388.177.70792.576.051686.257.70.09
HDLc, mg/dLAll7555.4911.772649.6210.944958.6111.07<.001
AIT2557.0410.79953.5612.011659.009.89.23
TCT2753.1912.551047.109.121756.7613.13.06
COU2356.5211.93748.1412.051660.1910.17.03
TG, mg/dLAll7596.9545.4826120.8155.604984.2933.26<.001
AIT2597.4443.839127.4448.121680.5631.53.02
TCT27103.4448.7710117.0066.621795.4734.44.60
COU2388.7843.917117.7155.081676.1332.45.08
SBP, mm HgAll75126.3214.4026136.2315.6749121.0610.50<.001
AIT25125.7614.129136.1114.0816119.9410.62.01
TCT27127.9615.8010137.7017.0517122.2412.16.01
COU23125.0013.397134.2917.7516120.948.90.05
DBP, mm HgAll7578.5110.602682.9611.494976.149.37.02
AIT2577.4410.79979.0011.621676.5610.58.69
TCT2778.8111.671083.9012.581775.8210.32.15
COU2379.309.37786.719.571676.067.43.02
MetSSSAll751.541.50262.321.46491.131.37<.001
AIT251.441.5091.741.20161.281.66.16
TCT271.771.64102.881.80171.121.17.01
COU231.381.3672.251.04161.001.33.02

Abbreviations: AIT, aerobic interval training; BMI, body mass index; COU, counseling; DBP, diastolic blood pressure; HDLc, high-density lipoprotein cholesterol; MetSSS, metabolic syndrome severity score according to Wiley29; SBP, systolic blood pressure; T0, baseline; TCT, traditional continuous training; TG, triglycerides. Note: Data shown as mean and SD. P value according to Mann–Whitney U nonparametric test.

Table 3

Physical Activity Behavior and Healthy Eating Index at T0

Study groupAll (N = 75)Men (n = 26)Women (n = 49)P value
Physical activity and eating behaviornMeanSDnMeanSDNMeanSDMen vs women
Overall activity—VM, CPMAll75658.45157.2926685.91178.5049643.87144.65.46
AIT25644.38124.569628.5186.7716653.30143.43.53
TCT27692.35218.3110788.89238.6117635.56190.18.04
COU23633.9487.797612.5991.7816643.2887.34.42
Sedentary time,a min/hAll7537.23.92637.34.054937.23.86.51
AIT2537.13.75938.42.441636.44.21.19
TCT2737.34.781036.45.461737.94.43.92
COU2337.32.97737.33.561637.32.80.74
LPA,a min/hAll7520.23.822619.94.384920.33.52.29
AIT2520.33.42918.92.791621.13.57.14
TCT27204.81020.55.991719.74.12.65
COU2320.23720.43.681620.12.79>.99
MVPA,a min/hAll752.61.24262.81.31492.51.21.38
AIT252.61.0792.71.07162.51.10.53
TCT272.71.39103.11.69172.41.18.34
COU232.51.2872.40.99162.61.41.89
Steps/d, nAll758348.22271.36268385.21935.12498328.52449.92.73
AIT258052.01943.2197940.41602.07168114.82158.97.82
TCT278579.92764.02109124.32440.09178259.72961.64.48
COU238398.12009.2177901.31330.16168615.42246.67.59
Healthy Eating IndexAll7544.6317.402638.2113.624948.0318.33.02
AIT2545.1421.59935.7815.281650.4123.22.20
TCT2746.6715.321041.7013.481749.5915.96.24
COU2341.6714.76736.3612.481644.0015.43.28

Abbreviations: AIT, aerobic interval training; COU, counseling; CPM, counts per minute; LPA, light physical activity; MVPA, moderate to vigorous physical activity; T0, baseline; TCT, traditional continuous training; VM, vector magnitude. Note: Data shown as mean and SD. P value according to Mann–Whitney U nonparametric test.

aMinutes per hour during awaken time.

Interventions Course

During the 16 supervised exercise sessions, participants completed a total of 189 questionnaires (AIT = 74; TCT = 115). Enjoyment was higher in the AIT than TCT group in men, but not in women (Table 4). Enjoyment was lower at the beginning of the intervention, gradually increasing in the AIT group (Spearman rs = .380, P < .01), but not in the TCT group (Spearman rs = .160, P = .1). Enjoyment was not affected by the location (indoor [n = 12] or outdoor [n = 4]) where the session was held.

Table 4

Enjoyment With the Exercise Sessions for AIT and TCT Study Groups (N = 189 Questionnaires of the 16 Sessions)

EnjoymemtStudy groupAll (N = 189 questionnaires)Men (n = 81 questionnaires)Women (n = 108 questionnaires)P value
nMeanSDnMeanSDnMeanSDMen vs women
Enjoyment, arbitrary unitsAll18997.213.48199.715.210895.411.7.46
AIT74101.712.044103.514.53099.16.4.53
TCT11594.413.63795.315.07894.012.9.04
P value AIT vs TCT <.001  <.001 .15  

Abbreviations: AIT, aerobic interval training; TCT, traditional continuous training. Note: Data shown as mean and SD values. P value according to Mann–Whitney U nonparametric test.

No adverse effects related to health (nausea, dizziness, or injuries) were reported during the intervention phase.

There were no differences between the study groups or genders regarding attendance at the offered sessions (Table 5). However, the number of highly compliant (>80% of the offered sessions) participants was greater for group counseling (77.3%) and individual counseling (92%) sessions than for the physical exercise sessions (49%). Only one individual was poorly compliant with the group counseling sessions, while 9 participants (17.6%) were poorly compliant with the exercise sessions. Participants compliant with the exercise sessions were also compliant with the group counseling sessions (Spearman rs = .483, P < .001).

Table 5

Adherence to Offered Sessions During the Intervention Period (Percentage of Sessions Attended)

Study groupAll (N = 75)Men (n = 26)Women (n = 49)P value
SessionsnMeanSDnMeanSDnMeanSDMen vs women
Supervised exercise sessions (16 offered)All5172.221.411966.423.223275.619.85.16
AIT2471.120.84968.118.601572.922.49.45
TCT2773.122.251065.027.71777.917.56.33
COUnananananananananana
Group COU sessions (6 offered)All7584.916.252680.119.454987.413.83.11
AIT2587.313.84987.011.111687.515.52.72
TCT2782.719.331071.723.641789.213.10.04
COU2384.815.00783.319.251685.413.44.97
Individual COU sessions (4 offered)All7598.06.832698.16.794998.06.92.94
AIT2598.06.929100.00.001696.98.54.64
TCT2798.16.671095.010.5417100.00.00.41
COU2397.87.207100.00.001696.98.54.67

Abbreviations: AIT, aerobic interval training; COU, counseling; na, not applicable; TCT, traditional continuous training. Note: Data shown as mean and SD. P value according to Mann–Whitney U nonparametric test.

Intervention Outcomes

Changes from T0 to T1 and T2 are shown in Tables 6 and 7 and Figure 3.

Table 6

Changes in Cardiometabolic Risk Factors and MetSSS at T0, T1, and T2 Periods

Study groupBaseline (T0)16-wk (T1)40-wk (T2)P value

T0 vs T1
P value

T0 vs T2
Risk factorsMeanSDMeanSDMeanSDTimeaGroupbTimeaGroupb
Waist circumference, cmAll (75)89.314.487.814.187.613.6<.001.65<.01.38
AIT (25)88.913.887.913.988.113.5.03 .38 
TCT (27)92.314.490.313.489.613.5<.01 <.01 
COU (23)86.314.984.615.084.613.9.01 .19 
Glycemia, mg/dLAll (75)89.07.689.69.290.09.2.54.37.18.04
AIT (25)88.37.191.18.490.77.4.11 .03 
TCT (27)90.38.191.211.591.012.0.39 .83 
COU (23)88.27.786.26.088.17.0.05 .91 
HDLc, mg/dLAll (75)55.511.853.311.555.011.8.01.03.58.42
AIT (25)57.010.854.710.755.811.1.13 .70 
TCT (27)53.212.552.413.053.512.7.50 .84 
COU (23)56.511.953.010.956.011.7.01 .60 
TG, mg/dLAll (75)96.945.595.851.297.047.1.40.27.83.87
AIT (25)97.443.8101.944.595.838.5.77 .75 
TCT (27)103.448.8100.164.6104.457.4.35 .62 
COU (23)88.843.984.039.089.642.7.45 .84 
SBP, mm HgAll (75)126.314.4121.613.6121.712.0<.01.47<.001.90
AIT (25)125.814.1120.711.2121.315.0.10 .06 
TCT (27)128.015.8123.114.9122.310.1.05 .03 
COU (23)125.013.4120.714.8121.511.0.06 .08 
DBP, mm HgAll (75)78.510.670.18.669.48.7<.001.90<.001.84
AIT (25)77.410.869.27.769.510.1<.01 .01 
TCT (27)78.811.770.19.368.17.5<.01 <.001 
COU (23)79.39.471.39.070.78.4<.001 <.001 
MetSSSAll (75)1.541.500.941.100.901.16<.001.97<.001.55
AIT (25)1.441.500.881.000.951.20.01 .06 
TCT (27)1.771.641.051.100.961.14<.01 <.01 
COU (23)1.381.360.881.240.761.18.01 <.01 

Abbreviations: AIT, aerobic interval training; COU, counseling; DBP, diastolic blood pressure; HDLc, high-density lipoprotein cholesterol; MetSSS, metabolic syndrome severity score according to Wiley29; SBP, systolic blood pressure; T0, baseline; T1, after 16-week intervention; T2, at 24-week follow-up; TCT, traditional continuous training; TG, triglycerides. Note: Data shown as mean and SD. Standardized effect size was computed as the mean difference between T0 and T1 or T2 values divided by the pooled SD. Interpretations: small (0.2–0.5), moderate (0.5–0.8), and large (>0.8).

aP value according to Wilcoxon nonparametric test (T0 vs T1 or T0 vs T2). bP value according to Kruskal–Wallis nonparametric test (between study groups).

Table 7

Changes in Physical Activity Behavior and HEI at T0, T1, and T2 Periods

Study groupBaseline (T0)16-wk (T1)40-wk (T2)P value

T0 vs T1
P value

T0 vs T2
Physical activity and eating behaviorMeanSDMeanSDMeanSDTimeaGroupbTimeaGroupb
Overall activity—VM, CPMAll (75)658.4157.3697.5199.5690.0174.8.04.33.13.66
AIT (25)644.4124.6706.6174.2699.9155.2.13 .13 
TCT (27)692.3218.3739.5259.7709.0216.3.07 .63 
COU (23)633.987.8638.2123.2656.9139.6.99 .58 
Registered time, min/dAll (75)817.178.7856.486.883792.5<.001.84.02.66
AIT (25)797.378.54843.6100.4818.897.5.01 .05 
TCT (27)82483.5864.588.9839.4103.8.02 .40 
COU (23)830.672.1860.968.6853.970.6.05 .16 
Sedentary time, min/hAll (75)37.23.936.54.7736.54.11.06.02.06.45
AIT (25)37.13.7535.54.9435.94.0.06 .09 
TCT (27)37.34.78365.2636.64.84.02 .16 
COU (23)37.32.97383.6437.13.32.16 .93 
LPA, min/hAll (75)20.23.8220.54.520.43.79.29.01.59.58
AIT (25)20.33.4221.64.4520.93.66.05 .34 
TCT (27)204.820.75.0720.34.56.10 .61 
COU (23)20.23193.53202.91.07 .41 
MVPA, min/hAll (75)2.61.2431.5431.51<.01.37<.01.94
AIT (25)2.61.072.91.133.11.43.28 .02 
TCT (27)2.71.393.31.9631.7.01 .14 
COU (23)2.51.282.91.3931.41.07 .15 
Steps/d, nAll (75)834822719555342092303069<.001.61<.01.63
AIT (25)805219439481314293953021.03 .02 
TCT (27)8580276410,049412990893449<.01 .19 
COU (23)839820099056280292192758.11 .11 
HEI, pointsAll (75)45.117.050.615.655.315.0.02.31<.001.37
AIT (25)46.720.550.917.360.415.0.43 .01 
TCT (27)46.715.349.614.152.914.7.57 .09 
COU (23)41.714.851.416.152.714.7<.01 .01 

Abbreviations: AIT, aerobic interval training; COU, counseling; CPM, counts per minute; HEI, Healthy Eating Index; LPA, light physical activity; MVPA, moderate to vigorous physical activity; T0, baseline, T1, after 16-week intervention; T2, at 24-week follow-up; TCT, traditional continuous training; VM, vector magnitude. Note: Data shown as mean and SD. Standardized effect size was computed as the mean difference between T0 and T1 or T2 values divided by the pooled SD. Interpretations: small (0.2–0.5), moderate (0.5–0.8), and large (>0.8).

aP value according to Wilcoxon nonparametric test (T0 vs T1 or T0 vs T2). bP value according to Kruskal–Wallis nonparametric test (between study groups).

Figure 3
Figure 3

—Changes in metabolic syndrome severity score (MetSSS, according to Wiley29) from T0 to T1 and T2. AIT indicates aerobic interval training; COU, counseling; MetSSS, metabolic syndrome severity score; T0, baseline; T1, after 16-week intervention; T2, after 24-week follow-up; TCT, traditional continuous training. Data shown as mean (bar height) and SD (vertical line). *P < .05 according to Wilcoxon nonparametric test.

Citation: Journal of Physical Activity and Health 17, 7; 10.1123/jpah.2019-0409

Metabolic Syndrome Severity Score

In the AIT, TCT, and COU groups, a significant small decrease (SES = −0.46) in the pooled cardiometabolic risk (MetSSS) was observed after the intervention (Figure 3). Changes persisted at the 24-week follow-up (SES = −0.49), with 38.7% and 40% of the participants improving their MetSSS at T1 and T2, respectively. The effects were similar in all the study groups.

Improved MetSSSs were associated with changes in DBP (T0–T1: rs = .41, P < .01 and T0–T2: rs = .41, P < .01); SBP (T0–T2: rs = .38, P < .01); and WC (T0–T1: rs = .34, P < .01 and T0–T2: rs = .27, P = .02), but not compliance with the exercise or counseling sessions.

The intervention improved several cardiometabolic risk factors, with DBP the most improved (large effect sizes ranging from −0.89 to −0.83). Of the participants, 48.0% and 36.7% improved their DBP at T1 and T2, respectively. The effects were similar in all the groups.

Physical Activity and SB

Participants wore the accelerometers for 7.01 days (SD = 0.42) and for 817.1 minutes per day (SD = 78.75) of their awake time. At baseline, they spent most of their time performing sedentary activities (mean = 62.1% of the registered time, SD = 6.5%; Table 3).

Overall physical activity (VM), MVPA, and the number of daily steps taken improved significantly after the intervention (Table 7). The effect sizes were small (SES = 0.22, 0.32, and 0.42, respectively). Only changes in MVPA and the daily steps taken persisted in the medium term. No significant group × time interaction effect was observed. The number of participants with improved overall activity (n = 29; 38.7%) and number of steps per day (n = 47; 62.7%) were similar in all the groups. However, at T1, 50% of the participants in the AIT and TCT groups showed increased MVPA compared with only 21.7% in the COU group (chi-squared; P = .03).

Significant group × time interaction effects were observed at T1 for SB and LPA (Kruskal–Wallis P = .02 and P = .01, respectively). The AIT and TCT groups showed reduced SB and increased LPA, while the COU group presented no changes. At T1, 63.5% and 55.8% of the participants in the exercise groups (AIT and TCT pooled together) improved their SB and LPA, respectively, compared with only 30.4% and 26.1% in the COU group, respectively (chi-squared; P = .03 and P = .05).

At T1, the percentage of participants showing reduced SB, increased MVPA, and increased LPA was higher in the AIT group (60%, 40%, and 60%, respectively) and in the TCT group (66.7%, 59.3%, and 51.9%, respectively) than in the COU group (30.4%, 21.7%, and 26.1%, respectively) (chi-squared; P = .03, P = .03, and P = .05 for SB, MVPA, and LPA, respectively). Significance did not persist in the medium term (T2).

A small but significant association was detected between compliance with group counseling sessions and changes in SB (rs = −.26, P = .02) and LPA (rs = −.25, P = .03).

Participants with higher compliance with the exercise program showed slightly higher improvements in SB (rs = −.32, P = .01); LPA (rs = .27, P = .02); MVPA (rs = .27, P = .02); and daily steps taken (rs = .24, P = .04). These associations did not persist at the 24-week follow-up.

Self-Reported Dietary Habits

After the intervention, the HEI improved slightly in the whole group (SES = 0.30, Wilcoxon P = .024). The percentage of participants with improved HEI scores was similar among the groups (50%, 44.4%, and 69.6% for AIT, TCT, and COU, respectively). At the 24-week follow-up, the HEI improved further, with a small effect size (SES = 0.33). The maintenance of the improvement was similar in all the groups.

Changes in HEI scores were not associated with changes in physical activity and SB; however, a small but significant association was detected between changes in HEI scores and changes in WC (Spearman rs = .25, P = .03).

Discussion

This trial evaluated the feasibility and effectiveness of a 16-week intervention combining different levels of a semisupervised exercise training program (AIT or TCT) and lifestyle counseling on cardiometabolic risk factors and lifestyle habits. The intervention was offered to sedentary adults with metabolic risk factors and implemented in a real-world setting (health care and sport centers).

The AIT was well accepted by the participants. Adherence to the exercise sessions was rather high, with no differences between the AIT and TCT groups. As reported previously,17 the AIT sessions were well tolerated, with no discomfort or incidences reported. It is important to note that interval time was achieved gradually and that running was introduced only after the 8 cycling sessions, which could have prepared the participants’ musculoskeletal system to cope with the higher mechanical stress of running, lessening the risk of injury. The greater enjoyment of the supervised AIT sessions was in accordance with the findings of Barlett et al16 that higher intensity exercise is associated with greater enjoyment.

All study groups exhibited comparable decreases in metabolic syndrome severity scores that persisted in the medium term (24-wk follow-up). This could be due to the decreases in WC and DBP components but not glycemia, HDLc, or TG. Our data are in accordance with others that have reported improvements of metabolic syndrome severity score after supervised continuous and interval training interventions.31 In contrast to other studies,17,38,39 AIT was not more effective than TCT or COU in reducing WC. Changes in WC were associated with changes in dietary habits, not with exercise. Thus, in our study, it seems that the dose of exercise was not enough to produce different outcomes between the study groups. The 3 interventions were equally effective at lowering DBP, as also reported by Tjonna et al40 and Morales-Palomo et al41 in patients with MetS.

The 3 study groups exhibited differences in physical activity and SB. The intervention was effective in producing a small and persistent increase in MVPA. Although no group effect was observed, the proportion of responders was higher in the exercise groups than in the COU group. Likewise, the reduction in SB and the proportion of responders were clearly higher in the exercise groups than in the COU group. As all the groups received the same counseling program, it seems that the exercise sessions were more effective at encouraging sedentary individuals to become more active.

The HEI indicated unhealthy eating habits in our participants, mainly in men, being below the European average of 49.2. After the intervention, the HEI improved, but still reflected unhealthy eating habits. The improvement in the HEI was larger in the COU group than in the exercise groups, suggesting that participants in the COU group were more amenable to changing their dietary habits than their exercise/SB. Changes in the HEI persisted at the 24-week follow-up, showing a greater effect size in all the groups than immediately after the intervention. Although the improvement of the HEI was small, its maintenance in the long term may have positive effects on cardiometabolic risk factors.

A major strength of this study was that the 16-week intervention combined a semisupervised exercise training program with lifestyle counseling to promote behavioral change. This study is unique as, unlike other AIT studies, it considers not only physical exercise, but also links it to strategies, such as increasing literacy related to healthy lifestyle, helping participants in becoming aware of barriers and planning strategies to overcome them, involving participants in decision making and establishing personal goals, and becoming aware of oneself changes that promote behavioral change and empowerment.42 Most AIT interventions have been performed in a highly controlled experimental setting that strictly controls the effects of physical exercise for a limited time period but impedes their application to real-world challenges because they do not consider physical exercise as lifestyle change component. Another strength of this study was assessing the maintenance of the effects. Most studies report the immediate effects of exercise interventions, not analyzing their maintenance in the medium term.43

The semistructured organization of the exercise training could be considered not only a strength, but also a limitation. In this study, the participants were closely supervised by a physical activity professional at the beginning of the program but became more autonomous with time. This may explain the lack of greater effects of the exercise interventions on cardiometabolic risk factors compared with the COU group or other studies. Moreover, participants in the COU group were not stopped from participating in other physical activity programs, and in some cases, they did spontaneously perform exercises. In our opinion, the semistructured organization of the physical exercise training empowered participants, giving them the responsibility to be physically active and not rely solely on the supervisor, but it also increased the risk of noncompliance or of not achieving the desired exercise intensity during the self-administered sessions. We are aware that we did not adequately follow the participants during the self-administered sessions. To overcome this limitation in the future, strategies for tracking self-administered sessions, such as using e-based communication platforms, could be implemented.4345 One major limitation was that most participants volunteered to participate in response to recruitment calls. Only a few participants were referred directly by health care professionals. The participants were highly motivated and willing to change and had a rather high education level and economic status. Although this may limit generalizing our results to the whole population, we feel that this did not affect the assessment of outcomes.

Finally, it should be noted that unlike the accelerometer-based assessments of physical activity, dietary habits were measured using self-reports, which are less reliable. Photos of the food consumed may be a more accurate way of recording dietary habits.

Conclusions

Our results suggest that semisupervised AIT alongside lifestyle counseling could be (1) a feasible strategy for promoting physical exercise in sedentary individuals with cardiometabolic risk factors, (2) as effective as TCT or COU in reversing metabolic syndrome severity scores, and (3) as effective as TCT in promoting a more active and less sedentary lifestyle, being more effective than counseling alone.

The AIT could be included in intervention programs tackling unhealthy lifestyles.

Acknowledgments

The authors would like to thank Marta Miret for her assistance with the recruitment of participants and the execution of the counseling program; Pep Castarlenas, manager of Ekke Sport center, for his implications and help with the supervised exercise program; Josep-Ramon Marsal for his assistance with randomization and statistical analysis; Gisela Galindo and Susana Sarriegui for their assistance with the recruitment of participants; and Francisco-José Verdejo for his invaluable participation in data collection and data entry. This study is mainly supported by the National Institute of Physical Education (INEFC)—Campus Lleida. The funding covers central organization, design, management, assessments, and analysis and reporting of the study; and supports the printing of educational resources (fundings to research: 2015) and blood analysis reagents (fundings to research: 2016). The Catalonian Health Institute (ICS) provided the setting for the counseling sessions and support for recruitment, phlebotomy, and general blood analysis. Ekke’s sport center (local sport center) provided the setting and the necessary equipment for the physical exercise sessions. The Development of Healthy Organizations and Territories (DOTS) group of the University of Lleida and the Diputació de Lleida provided funding to support partially the execution of physical activity sessions (funding to research: July 25, 2016). G.E.-T. has been granted participation in the study as a PhD student (Ref: 2014-PINEF-00003; PRE/2448/2014, de 30 October, Diari oficial de la Generalitat de Catalunya (DOGC) no. 6743— November 5, 2014). The funding sources had no role in the design of this study, execution, analyses, or interpretation of the data. The authors declare that they have no competing interests. This study is registered at clinicaltrials.gov (no. NTC02832453).

References

  • 1.

    Knaeps S, Bourgois J, Charlier R, Mertens E, Lefevre J, Wijndaele K. Ten-year change in sedentary behaviour, moderate-to-vigorous physical activity, cardiorespiratory fitness and cardiometabolic risk: independent associations and mediation analysis. Br J Sports Med. 2018;52(16):10631068. PubMed ID: 27491779 doi:10.1136/bjsports-2016-096083

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Marcuello C, Calle-Pascual A, Fuentes M, et al. Prevalence of the metabolic syndrome in Spain using regional cutoff points for waist circumference; the di@bet.es study. Acta Diabetol. 2013;50(4):615623. PubMed ID: 23512475 doi:10.1007/s00592-013-0468-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Pattyn N, Cornelissen V, Eshghi RT, Vanhees L. The effect of exercise on the cardiovascular risk factors constituting the metabolic syndrome. Sports Med. 2013;43(2):121133. PubMed ID: 23329606 doi:10.1007/s40279-012-0003-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Pedersen BK, Saltin B. Exercise as medicine—evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. 2015;25(suppl 3):172. doi:10.1111/sms.12581

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    World Health Organization. Physical Activity Strategy for the WHO European Region 2016–2025. 2015. http://www.euro.who.int/en/publications/abstracts/physical-activity-strategy-for-the-who-european-region-20162025. Accessed June 3, 2019.

    • Search Google Scholar
    • Export Citation
  • 6.

    Powell K, King A, Buchner D, et al. The scientific foundation for Physical Activity Guidelines for Americans, 2nd edition. J Phys Act Health. 2019;16(1):111. doi:10.1123/jpah.2018-0618

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Rankin A, Rankin A, MacIntyre P, Hillis W. Walk or run? Is high-intensity exercise more effective than moderate intensity exercise at reducing cardiovascular risk? Scott Med J. 2012;57(2):99102. PubMed ID: 22194404 doi:10.1258/smj.2011.011284

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Arena R, Myers J, Forman D, Lavie C, Guazzi M. Should high-intensity interval training become the clinical standard in heart failure? Heart Fail Rev. 2013;18(1):95105. PubMed ID: 22791516 doi:10.1007/s10741-012-9333-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Helgerud J, Hoydal K, Wang E, et al. Aerobic high-intensity intervals improve VO2max more than moderate training. Med Sci Sports Exerc. 2007;39(4):665671. PubMed ID: 17414804 doi:10.1249/mss.0b013e3180304570

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    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(3):216222. PubMed ID: 15179103 doi:10.1097/01.hjr.0000131677.96762.0c

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Wisloff U, Stoylen A, Loennechen JP, et al. Superior cardiovascular effect of aerobic interval training versus moderate continuous training in heart failure patients: a randomized study. Circulation. 2007;115(24):30863094. PubMed ID: 17548726 doi:10.1161/CIRCULATIONAHA.106.675041

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Weston K, Wisloff U, Coombes J. High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: a systematic review and meta-analysis. Br J Sports Med. 2014;48(1227):1234. doi:10.1007/s40279-014-0180-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    European Comission. Special Eurobarometer 412—Sport and Physical Activity. Brussels, Belgium: European Comission; 2014. Report No.: 2014.3314. doi:10.2766/73002. https://data.europa.eu/euodp/en/data/dataset/S1116_80_2_412. Accessed June 26, 2019.

    • Search Google Scholar
    • Export Citation
  • 14.

    Biddle S, Batterham A. High-intensity interval exercise training for public health: a big HIT or shall we HIT it on the head? Int J Behav Nutr Phys Act. 2015;12(1):95. PubMed ID: 26187579 doi:10.1186/s12966-015-0254-9

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Moholdt TT, Amundsen BH, Rustad LA, et al. Aerobic interval training versus continuous moderate exercise after coronary artery bypass surgery: a randomized study of cardiovascular effects and quality of life. Am Heart J. 2009;158(6):10311037. PubMed ID: 19958872 doi:10.1016/j.ahj.2009.10.003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Barlett J, Close G, MacLaren D, Gregson W, Drust B, Morton JP. High-intensity interval running is perceived as more enjoyable than moderate-intensity continuous exercise: implications for exercise adherence. J Sports Sci. 2011;29(6):547553. doi:10-1080/026-40414.2010.545427

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Reljic D, Wittmann F, Fisher J. Effect of low-volume high-intensity training in a community setting: a pilot study. Eur J Appl Physiol. 2018;118(6):11531167. doi:10.1007/s00421-018-3845-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Ekkekakis P, Parfitt G, Petruzzello S. The pleasure and displeasure people feel when they exercise at different intensities. Sports Med. 2011;41(8):641671. PubMed ID: 21780850 doi:10.2165/11590680-000000000-00000

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Scott SE, Breckon JD, Copeland RJ, Hutchinson A. Determinants and strategies for physical maintenance in chronic health conditions: a qualitative study. J Phys Act Health. 2015;12(5):733740. PubMed ID: 24905976 doi:10.1123/jpah.2013-0286

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Biddle S, Mutrie N, Gorely T. Psychology of Physical Activity: Determinants, Well-Being and Interventions. 3rd ed. Abingdon, UK: Routledge; 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    World Medical Association. Declaration of Helsinki—ethical principles for medical research involving human subjects. 2013. http://www.wma.net/en/30publications/10policies/b3/index.html. Accessed May 5, 2019.

    • PubMed
    • Export Citation
  • 22.

    Schulz K, Altman D, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Ann Intern Med. 2010;152(11):726732. doi:10.7326/0003-4819-152-11-201006010-00232

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Pettee Gabriel K, Morrow J, Woolsey A. Framework for physical activity as a complex and multidimensional behavior. J Phys Act Health. 2012;9(suppl 1):S11S18. doi:10.1123/jpah.9.s1.s11

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51(3):390395. PubMed ID: 6863699 doi:10.1037/0022-006X.51.3.390

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Miller W, Rollnick S. Motivational Interviewing: Helping People Change. 3rd ed. New York, NY: Guilford Press; 2013.

  • 26.

    Ensenyat A, Espigares-Tribo G, Machado L, et al. Metabolic risk management, physical exercise and lifestyle counselling in low-active adults: controlled randomized trial (BELLUGAT). BMC Public Health. 2017;17(1):257272. PubMed ID: 28292282 doi:10.1186/s12889-017-4144-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Espigares G, Ensenyat A. Guia per a la Promoció d’un estil de Vida Saludable (Guide for the Promotion of a Healthy Style). Lleida, Spain: Espigares; 2015.

    • Search Google Scholar
    • Export Citation
  • 28.

    Kendzierski D, DeCarlo K. Physical activity enjoyment scale: two validation studies. J Sport Exerc Psychol. 1991;13(1):5064. doi:10.1123/jsep.13.1.50

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    Wiley J, Carrington M. A metabolic syndrome severity score: a tool to quantify cardio-metabolic risk factors. Prev Med. 2016;88:189195. PubMed ID: 27095322 doi:10.1016/j.ypmed.2016.04.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    World Health Organization. Waist Circumference and Waist-to-Hip Ratio: Report of a WHO Expert Consultation. Geneva, Switzerland: World Health Organization; 2008. https://www.who.int/nutrition/publications/obesity/WHO_report_waistcircumference_and_waisthip_ratio/en/. Accessed September 3, 2019.

    • Search Google Scholar
    • Export Citation
  • 31.

    Herrmann S, Barrerira T, Kang M, Ainsworth BE. Impact of accelerometer wear time on physical activity data: a NHANES semi simulation data approach. Br J Sports Med. 2014;48(3):278282. PubMed ID: 22936409 doi:10.1136/bjsports-2012-091410

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Meredith-Jones K, Williams S, Galland B, Kennedy G, Taylor R. 24-h Accelerometry: impact of sleep-screening methods on estimates of sedentary behaviour and physical activity while awake. J Sports Sci. 2016;34(7):679685. PubMed ID: 26194337 doi:10.1080/02640414.2015.1068438

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Troiano R, Berrigan D, Dodd K, Masse L, Tilert T, McDowel M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181188. PubMed ID: 18091006 doi:10.1249/mss.0b013e31815a51b3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Basiotis PP, Carlson A, Gerrior SA, Juan WY, Lino M. The Healthy Eating Index: 1999–2000. Washington, DC: U.S. Department of Agriculture, Center for Nutrition Policy and Promotion; 2002. Report No.: CNPP-12. www.cnpp.usda.gov. Accessed April 2, 2018.

    • Search Google Scholar
    • Export Citation
  • 35.

    U.S Department of Agriculture, U.S Department of Health and Human Services. Dietary Guidelines for Americans. 7th ed. Washington, US: Government Printing Office; 2010. https://health.gov/our-work/food-nutrition/previous-dietary-guidelines/2010. Accessed April 30, 2019.

    • Search Google Scholar
    • Export Citation
  • 36.

    Guenther PM, Kirkpatrick SI, Reedy J, et al. The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 dietary guidelines for Americans. J Nutr. 2014;144(3):399407. PubMed ID: 24453128 doi:10.3945/jn.113.183079

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Cohen J. A power primer. Psychol Bull. 1992;112(1):155159. PubMed ID: 19565683 doi:10.1037/0033-2909.112.1.155

  • 38.

    Boucher S. High-intensity intermittent exercise and fat loss. J Obes. 2011;868305. doi:10.1155/2011/868305

  • 39.

    Tjonna AE, Stolen TO, Bye A, et al. Aerobic interval training reduces cardiovascular risk factors more than a multitreatment approach in overweight adolescents. Clin Sci. 2009;116(4):317326. doi:10.1042/CS20080249

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    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(4):346354. PubMed ID: 18606913 doi:10.1161/CIRCULATIONAHA.108.772822

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Morales-Palomo F, Ramirez-Jimenez M, Ortega J, Mora-Rodriguez R. Effectiveness of aerobic exercise programs for health promotion in metabolic syndrome. Med Sci Sports Exerc. 2019;51(9):18761883. PubMed ID: 31415443 doi:10.1249/MSS.0000000000001983

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Castro EM, Regenmortel TV, Vanhaecht K, Sermeus W, Hecke AV. Patient empowerment, patient participation and patient-centeredness in hospital care: a concept analysis based on a literature review. Patient Educ Couns. 2016;99(12):19231939. PubMed ID: 27450481 doi:10.1016/j.pec.2016.07.026

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Kim CJ, Schlenk EA, Kang SW, Park JB. Effects of an internet-based lifestyle intervention on cardio-metabolic risks and stress in Korean workers with metabolic syndrome: a controlled trial. Patient Educ Couns. 2015;98(1):111119. PubMed ID: 25468401 doi:10.1016/j.pec.2014.10.013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Biddle S, Brehm W, Verheijden M, Hopman-Rock M. Population physical activity behaviour change: a review for the European College of Sport Science. Eur J Sport Sci. 2012;12(4):367383. doi:10.1080/17461391.2011.635700

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45.

    Braithwaite J. Changing how we think about healthcare improvement. BMJ. 2018;361:k2014. doi:10.1136/bmj.k2014

If the inline PDF is not rendering correctly, you can download the PDF file here.

Ensenyat and Machado-Da-Silva are with the Complex Systems and Sport Research Group, National Institute of Physical Education of Catalonia—Lleida Campus, Lleida, Spain. Espigares-Tribo, Sinfreu-Bergués, and Blanco are with the National Institute of Physical Education of Catalonia—Lleida Campus, Lleida, Spain.

Ensenyat (aensenat@inefc.udl.cat) is corresponding author.
  • View in gallery

    —Schedule of training sessions and counseling sessions along the 16-week intervention. The fourth individual session that took place at the end of the 24-week follow-up period is not represented.

  • View in gallery

    —Participant’s flowchart. AIT indicates aerobic interval training; COU, counseling; T0, baseline; T1, after 16-week intervention; T2, after 24-week follow-up; TCT, traditional continuous training.

  • View in gallery

    —Changes in metabolic syndrome severity score (MetSSS, according to Wiley29) from T0 to T1 and T2. AIT indicates aerobic interval training; COU, counseling; MetSSS, metabolic syndrome severity score; T0, baseline; T1, after 16-week intervention; T2, after 24-week follow-up; TCT, traditional continuous training. Data shown as mean (bar height) and SD (vertical line). *P < .05 according to Wilcoxon nonparametric test.

  • 1.

    Knaeps S, Bourgois J, Charlier R, Mertens E, Lefevre J, Wijndaele K. Ten-year change in sedentary behaviour, moderate-to-vigorous physical activity, cardiorespiratory fitness and cardiometabolic risk: independent associations and mediation analysis. Br J Sports Med. 2018;52(16):10631068. PubMed ID: 27491779 doi:10.1136/bjsports-2016-096083

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Marcuello C, Calle-Pascual A, Fuentes M, et al. Prevalence of the metabolic syndrome in Spain using regional cutoff points for waist circumference; the di@bet.es study. Acta Diabetol. 2013;50(4):615623. PubMed ID: 23512475 doi:10.1007/s00592-013-0468-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Pattyn N, Cornelissen V, Eshghi RT, Vanhees L. The effect of exercise on the cardiovascular risk factors constituting the metabolic syndrome. Sports Med. 2013;43(2):121133. PubMed ID: 23329606 doi:10.1007/s40279-012-0003-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Pedersen BK, Saltin B. Exercise as medicine—evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. 2015;25(suppl 3):172. doi:10.1111/sms.12581

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    World Health Organization. Physical Activity Strategy for the WHO European Region 2016–2025. 2015. http://www.euro.who.int/en/publications/abstracts/physical-activity-strategy-for-the-who-european-region-20162025. Accessed June 3, 2019.

    • Search Google Scholar
    • Export Citation
  • 6.

    Powell K, King A, Buchner D, et al. The scientific foundation for Physical Activity Guidelines for Americans, 2nd edition. J Phys Act Health. 2019;16(1):111. doi:10.1123/jpah.2018-0618

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Rankin A, Rankin A, MacIntyre P, Hillis W. Walk or run? Is high-intensity exercise more effective than moderate intensity exercise at reducing cardiovascular risk? Scott Med J. 2012;57(2):99102. PubMed ID: 22194404 doi:10.1258/smj.2011.011284

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Arena R, Myers J, Forman D, Lavie C, Guazzi M. Should high-intensity interval training become the clinical standard in heart failure? Heart Fail Rev. 2013;18(1):95105. PubMed ID: 22791516 doi:10.1007/s10741-012-9333-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    Helgerud J, Hoydal K, Wang E, et al. Aerobic high-intensity intervals improve VO2max more than moderate training. Med Sci Sports Exerc. 2007;39(4):665671. PubMed ID: 17414804 doi:10.1249/mss.0b013e3180304570

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    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(3):216222. PubMed ID: 15179103 doi:10.1097/01.hjr.0000131677.96762.0c

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Wisloff U, Stoylen A, Loennechen JP, et al. Superior cardiovascular effect of aerobic interval training versus moderate continuous training in heart failure patients: a randomized study. Circulation. 2007;115(24):30863094. PubMed ID: 17548726 doi:10.1161/CIRCULATIONAHA.106.675041

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Weston K, Wisloff U, Coombes J. High-intensity interval training in patients with lifestyle-induced cardiometabolic disease: a systematic review and meta-analysis. Br J Sports Med. 2014;48(1227):1234. doi:10.1007/s40279-014-0180-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    European Comission. Special Eurobarometer 412—Sport and Physical Activity. Brussels, Belgium: European Comission; 2014. Report No.: 2014.3314. doi:10.2766/73002. https://data.europa.eu/euodp/en/data/dataset/S1116_80_2_412. Accessed June 26, 2019.

    • Search Google Scholar
    • Export Citation
  • 14.

    Biddle S, Batterham A. High-intensity interval exercise training for public health: a big HIT or shall we HIT it on the head? Int J Behav Nutr Phys Act. 2015;12(1):95. PubMed ID: 26187579 doi:10.1186/s12966-015-0254-9

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Moholdt TT, Amundsen BH, Rustad LA, et al. Aerobic interval training versus continuous moderate exercise after coronary artery bypass surgery: a randomized study of cardiovascular effects and quality of life. Am Heart J. 2009;158(6):10311037. PubMed ID: 19958872 doi:10.1016/j.ahj.2009.10.003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 16.

    Barlett J, Close G, MacLaren D, Gregson W, Drust B, Morton JP. High-intensity interval running is perceived as more enjoyable than moderate-intensity continuous exercise: implications for exercise adherence. J Sports Sci. 2011;29(6):547553. doi:10-1080/026-40414.2010.545427

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Reljic D, Wittmann F, Fisher J. Effect of low-volume high-intensity training in a community setting: a pilot study. Eur J Appl Physiol. 2018;118(6):11531167. doi:10.1007/s00421-018-3845-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 18.

    Ekkekakis P, Parfitt G, Petruzzello S. The pleasure and displeasure people feel when they exercise at different intensities. Sports Med. 2011;41(8):641671. PubMed ID: 21780850 doi:10.2165/11590680-000000000-00000

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Scott SE, Breckon JD, Copeland RJ, Hutchinson A. Determinants and strategies for physical maintenance in chronic health conditions: a qualitative study. J Phys Act Health. 2015;12(5):733740. PubMed ID: 24905976 doi:10.1123/jpah.2013-0286

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Biddle S, Mutrie N, Gorely T. Psychology of Physical Activity: Determinants, Well-Being and Interventions. 3rd ed. Abingdon, UK: Routledge; 2012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    World Medical Association. Declaration of Helsinki—ethical principles for medical research involving human subjects. 2013. http://www.wma.net/en/30publications/10policies/b3/index.html. Accessed May 5, 2019.

    • PubMed
    • Export Citation
  • 22.

    Schulz K, Altman D, Moher D. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Ann Intern Med. 2010;152(11):726732. doi:10.7326/0003-4819-152-11-201006010-00232

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Pettee Gabriel K, Morrow J, Woolsey A. Framework for physical activity as a complex and multidimensional behavior. J Phys Act Health. 2012;9(suppl 1):S11S18. doi:10.1123/jpah.9.s1.s11

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 24.

    Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51(3):390395. PubMed ID: 6863699 doi:10.1037/0022-006X.51.3.390

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25.

    Miller W, Rollnick S. Motivational Interviewing: Helping People Change. 3rd ed. New York, NY: Guilford Press; 2013.

  • 26.

    Ensenyat A, Espigares-Tribo G, Machado L, et al. Metabolic risk management, physical exercise and lifestyle counselling in low-active adults: controlled randomized trial (BELLUGAT). BMC Public Health. 2017;17(1):257272. PubMed ID: 28292282 doi:10.1186/s12889-017-4144-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Espigares G, Ensenyat A. Guia per a la Promoció d’un estil de Vida Saludable (Guide for the Promotion of a Healthy Style). Lleida, Spain: Espigares; 2015.

    • Search Google Scholar
    • Export Citation
  • 28.

    Kendzierski D, DeCarlo K. Physical activity enjoyment scale: two validation studies. J Sport Exerc Psychol. 1991;13(1):5064. doi:10.1123/jsep.13.1.50

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 29.

    Wiley J, Carrington M. A metabolic syndrome severity score: a tool to quantify cardio-metabolic risk factors. Prev Med. 2016;88:189195. PubMed ID: 27095322 doi:10.1016/j.ypmed.2016.04.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    World Health Organization. Waist Circumference and Waist-to-Hip Ratio: Report of a WHO Expert Consultation. Geneva, Switzerland: World Health Organization; 2008. https://www.who.int/nutrition/publications/obesity/WHO_report_waistcircumference_and_waisthip_ratio/en/. Accessed September 3, 2019.

    • Search Google Scholar
    • Export Citation
  • 31.

    Herrmann S, Barrerira T, Kang M, Ainsworth BE. Impact of accelerometer wear time on physical activity data: a NHANES semi simulation data approach. Br J Sports Med. 2014;48(3):278282. PubMed ID: 22936409 doi:10.1136/bjsports-2012-091410

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Meredith-Jones K, Williams S, Galland B, Kennedy G, Taylor R. 24-h Accelerometry: impact of sleep-screening methods on estimates of sedentary behaviour and physical activity while awake. J Sports Sci. 2016;34(7):679685. PubMed ID: 26194337 doi:10.1080/02640414.2015.1068438

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 33.

    Troiano R, Berrigan D, Dodd K, Masse L, Tilert T, McDowel M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181188. PubMed ID: 18091006 doi:10.1249/mss.0b013e31815a51b3

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 34.

    Basiotis PP, Carlson A, Gerrior SA, Juan WY, Lino M. The Healthy Eating Index: 1999–2000. Washington, DC: U.S. Department of Agriculture, Center for Nutrition Policy and Promotion; 2002. Report No.: CNPP-12. www.cnpp.usda.gov. Accessed April 2, 2018.

    • Search Google Scholar
    • Export Citation
  • 35.

    U.S Department of Agriculture, U.S Department of Health and Human Services. Dietary Guidelines for Americans. 7th ed. Washington, US: Government Printing Office; 2010. https://health.gov/our-work/food-nutrition/previous-dietary-guidelines/2010. Accessed April 30, 2019.

    • Search Google Scholar
    • Export Citation
  • 36.

    Guenther PM, Kirkpatrick SI, Reedy J, et al. The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 2010 dietary guidelines for Americans. J Nutr. 2014;144(3):399407. PubMed ID: 24453128 doi:10.3945/jn.113.183079

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 37.

    Cohen J. A power primer. Psychol Bull. 1992;112(1):155159. PubMed ID: 19565683 doi:10.1037/0033-2909.112.1.155

  • 38.

    Boucher S. High-intensity intermittent exercise and fat loss. J Obes. 2011;868305. doi:10.1155/2011/868305

  • 39.

    Tjonna AE, Stolen TO, Bye A, et al. Aerobic interval training reduces cardiovascular risk factors more than a multitreatment approach in overweight adolescents. Clin Sci. 2009;116(4):317326. doi:10.1042/CS20080249

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 40.

    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(4):346354. PubMed ID: 18606913 doi:10.1161/CIRCULATIONAHA.108.772822

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 41.

    Morales-Palomo F, Ramirez-Jimenez M, Ortega J, Mora-Rodriguez R. Effectiveness of aerobic exercise programs for health promotion in metabolic syndrome. Med Sci Sports Exerc. 2019;51(9):18761883. PubMed ID: 31415443 doi:10.1249/MSS.0000000000001983

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 42.

    Castro EM, Regenmortel TV, Vanhaecht K, Sermeus W, Hecke AV. Patient empowerment, patient participation and patient-centeredness in hospital care: a concept analysis based on a literature review. Patient Educ Couns. 2016;99(12):19231939. PubMed ID: 27450481 doi:10.1016/j.pec.2016.07.026

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 43.

    Kim CJ, Schlenk EA, Kang SW, Park JB. Effects of an internet-based lifestyle intervention on cardio-metabolic risks and stress in Korean workers with metabolic syndrome: a controlled trial. Patient Educ Couns. 2015;98(1):111119. PubMed ID: 25468401 doi:10.1016/j.pec.2014.10.013

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 44.

    Biddle S, Brehm W, Verheijden M, Hopman-Rock M. Population physical activity behaviour change: a review for the European College of Sport Science. Eur J Sport Sci. 2012;12(4):367383. doi:10.1080/17461391.2011.635700

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 45.

    Braithwaite J. Changing how we think about healthcare improvement. BMJ. 2018;361:k2014. doi:10.1136/bmj.k2014

All Time Past Year Past 30 Days
Abstract Views 3 3 0
Full Text Views 283 283 123
PDF Downloads 83 83 41