Journal of Obesity & Metabolic Syndrome

Search

Article

March, 2024 | Vol.33 No.1

J Obes Metab Syndr 2024; 33(1): 20-26

Published online March 30, 2024 https://doi.org/10.7570/jomes23020

Copyright © Korean Society for the Study of Obesity.

Effects of Individualized Exercise on Risk Factors of Metabolic Syndrome: A Scoping Review

Kyoung-Bae Kim, Harim Choe, Hoyong Sung *

Department of Physical Education, Korea Military Academy, Seoul, Korea

Correspondence to:
Hoyong Sung
https://orcid.org/0000-0002-8325-5206
Department of Physical Education, Korea Military Academy, 574 Hwarang-ro, Nowon-gu, Seoul 01805, Korea
Tel: +82-2-2197-2410
E-mail: hys@kma.ac.kr

Received: May 2, 2023; Reviewed : August 7, 2023; Accepted: November 29, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Exercise intervention is effective in alleviating the severity of metabolic syndrome (MetS). However, the results of previous studies on the effect of exercise on MetS have demonstrated considerable individual variability in response to a specific dose of exercise, which was attributed to the lack of a personalized approach to exercise prescription. It is essential to consider individual factors to enhance the effectiveness of exercise in addressing MetS. This scoping review assesses the effectiveness of individualized exercise on the risk factors associated with MetS. Various databases and articles were examined based on eligibility criteria and nine studies were chosen for this review. Personal and adjusted factors were predominantly analyzed to tailor exercise prescriptions to individual needs. This review proposes that personal factors can be classified into three categories: fixed factors, adaptation factors, and response factors, considering both clinical and exercise science perspectives. It also suggests that a two-way communication approach between specialists and individuals is more effective for prescribing exercise to address MetS compared to a one-way method. A one-way communication approach relies solely on an expert’s decision, even whether or not he or she fully considers a client’s lifestyle and preferences. If the individualized selection of exercise prescriptions is achieved through two-way communication between specialists and subjects, significant improvements can be expected in terms of both MetS severity and exercise adherence.

Keywords: Metabolic syndrome, Exercise, Sports medicine

Metabolic syndrome (MetS) is recognized as a cluster of cardiovascular disease risk factors including waist circumference (WC), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, blood pressure (BP), and blood glucose (BG).1 Many healthcare professionals are familiar with these five criteria proposed by the National Cholesterol Education Program-Adult Treatment Panel III (NCEP-ATP 3) in 2005 for diagnosing MetS.2 Reducing the severity of MetS is a significant target to improve global health, as it substantially increases the risk of cardiovascular events.3

For decades, exercise intervention has been utilized for the prevention and treatment of MetS, often in conjunction with other therapies such as medication, diet, and behavior modification. An increase in cardiorespiratory fitness induced by exercise has been well-established as a protective factor against the individual risk factors that constitute MetS.4 Combining exercise with a proper diet is effective in resolving MetS and reducing the severity of related abnormalities.5 Katzmarzyk et al.6 investigated the efficacy of exercise training in the treatment of MetS. They found that the prevalence of MetS, as defined by NCEP-ATP 3 criteria, was 16.9% in a sample of apparently healthy participants (105 out of 621) and that 30.5% of the 105 participants were no longer classified as having MetS after the training. Ostman et al.7 conducted a systematic review and meta-analysis on the effects of exercise training in patients with MetS. They included 16 studies with 23 intervention groups and found that all five risk factors of MetS (WC, TG, HDL, BP, and BG) showed significant improvement.

However, some studies have pointed out that previous research on the effect of exercise on MetS has revealed significant individual variability in responses to specific exercise doses.3,8,9 This variability has been attributed to the absence of a personalized approach to exercise prescription.10 Weatherwax et al.3 compared the effects of individualized and standardized exercise regimens when analyzing changes in MetS severity. They found a significant difference only in systolic BP, with no significant differences observed among other risk factors.

While many studies have explored the effects of exercise on MetS with consideration of individual characteristics, the extent to which this individuality was considered varied across the studies. Various factors were taken into account, including age, sex, body mass index, fitness level, injuries, diseases, and diet. This study aimed to analyze the effectiveness of individualized exercise on the risk factors associated with MetS through a scoping review. This approach allows us to identify a more appropriate method for personalized exercise prescription in the context of MetS.

Through this review, a ‘randomized controlled trial (RCT)’ filter was applied and various databases from inception to March 15, 2023 were explored. The databases include Embase (http://www.embase.com/), PubMed (http://www.ncbi.nlm.nih.gov/pubmed/), EBSCO Academic Search Premier (http://search.ebscohost.com/), and Google Scholar (https://scholar.google.com/). The following keywords were utilized: “individualized exercise and metabolic syndrome,” “personalized exercise and metabolic syndrome,” and “customized exercise and metabolic syndrome.” Following the initial database searches, duplicate studies were eliminated and only English data sources were included.

This review applied Participants, Interventions, Comparisons, Outcomes, Timing of outcome measurement, Settings, and Study Design (PICOTS-SD) frameworks to clarify the inclusion criteria as follows:

Participants: adults with MetS aged 18 to 65 years.

Intervention: individualized, personalized, or customized exercise.

Comparison: standard exercise, routine care, etc.

Outcomes: WC, TG, HDL, BP, and BG.

Time: pre- and post-test.

Study design: RCT or one-group pre- and post-test.

In our initial search, a total of 1,240 studies were identified from various sources: 25 from Embase, 1,192 from PubMed, 13 from EBSCO Academic Search Premier, seven from Google Scholar, and three from other sources. In total, 606 duplicate studies were removed using the EndNote program (Thomson Reuters Co.). Two reviewers (an associate professor with a Ph.D. and an instructor with a Master’s degree) in the field of sports medicine screened the titles and abstracts of 634 studies and selected 17 studies based on the PICOTS-SD criteria. After the detailed screening according to the inclusion criteria, the full-text articles of these 17 studies were assessed for eligibility and eight studies were excluded for the following reasons: no full-text availability (n=1), improper intervention (n=1), non-English paper (n=1), and lack of originality (consensus, oral presentations, abstracts) (n=5). In the end, a total of nine studies were selected for this study (Fig. 1).


In the initial stage of study selection, two independent reviewers (HS and HC) screened titles and abstracts for eligibility. The agreement between the reviewers was assessed using the kappa statistic,11 which was applied to evaluate inter-rater agreement while correcting for chance agreement. The kappa statistic (KA_B) for reviewer agreement was calculated and found to be 0.735 (95% confidence interval, 0.657 to 0.813), indicating substantial agreement according to Landis and Koch’s guidelines.12 These values also meet the criteria for good agreement per Fleiss’s guidelines.13

In the full-text screening stage, a different combination of reviewers (HS and HC) yielded a kappa statistic (KA_B) of 0.658 (95% confidence interval, 0.211 to 0.999), again indicating substantial agreement.

This scoping review includes nine articles and their characteristics are presented in Table 1.3,14-21 These studies were published between 2009 and 2020. Among them, six studies had both experimental and control groups, while three studies included only an experimental group. The ages of the participants in the seven studies ranged from the 40s to 50s, whereas two studies involved participants in their 20s and 30s.

In clinical cardiovascular practice, addressing exercise dosage and physical activity habits is essential. This should be done for each patient, aiming for the ultimate goal of providing individualized counseling and exercise prescriptions.22 Gronwald et al.23 conducted a study on the perspective of dosage and response to individualized exercise prescriptions. They highlighted numerous personal factors that underscore the importance of individualization in exercise and training. These personal factors encompassed age, gender, anthropometry, genotype, psycho-physiological capacity level, health and recovery status, diet and hydration, medication and doping, hormone status, circadian rhythm, sleep, stress and coping strategies, motivation and emotions, psycho-social factors (e.g., family, school, finances), and social and cognitive activities, among others.

As is evident in the studies included in this research and numerous others, it is often challenging to simultaneously account for all personal factors. Personal factors can be broadly categorized into non-modifiable factors (e.g., sex or genotype) and modifiable factors (e.g., nutrition, social, or cognitive activities).23 This study proposes an alternative classification of personal factors from a clinical and exercise science perspective.

(1) Fixed factors (e.g., sex or genotype)

(2) Adaptation factors (e.g., anthropometry, physical fitness level, recovery stage from injury, health status of cardiovascular disease risk factors, diet, and sleep habits)

(3) Response factors (recovery status from fatigue, diet, and sleep within 2 or 3 days, instantaneous pain)

Fixed factors align with non-modifiable factors, while adaptation and response factors encompass modifiable aspects. Psycho-cognitive and social factors were intentionally excluded to focus solely on the physical, physiological, and medical aspects. As a dosage is precisely adjusted and controlled in the field of medication, prescription of exercise and training should be carefully specified to achieve individualization.23 Fixed factors are considered during the planning or programming stage of individualized exercise prescription, whereas response factors are adjusted during implementation. Adaptation factors are addressed in both planning and implementation.

As dosage is treated in clinical practice, exercise and physical activity should be evaluated and prescribed considering three primary attributes: intensity, duration, and frequency.22 The American College of Sports Medicine introduced the FITT-VP principle, which comprises Frequency (number of exercise sessions), Intensity (exercise difficulty), Time (duration of intervention), Type (exercise mode), Volume (total exercise amount), and Progression (gradual increase in exercise program difficulty) as key components.24,25

Studies on cardiorespiratory fitness have demonstrated that employing a more individualized approach to exercise prescription enhances the effectiveness of exercise intervention.26 Specifically, prescribing ‘exercise intensity’ based on the individual ventilatory threshold (VT) has proven more effective than using standardized relative exercise intensity metrics (e.g., % heart rate reserve).27 This threshold-based model utilizes VT according to the guidelines outlined by the American Council on Exercise Integrated Fitness Training (ACE-IFT) model.28

In the studies included in this research, three studies3,15,20 applied individualized exercise intensity based on the VT. In the ACE-IFT studies, participants in the personalized group were progressively overloaded in terms of exercise time, frequency, and intensity. Comparing a personalized approach to exercise prescription with a standardized protocol may also lead to greater improvements in the clustering of cardiovascular risk factors, therefore reducing MetS severity.3

In most studies focusing on individualized exercise for MetS, the exercise prescription is typically provided by specialists. These specialists can include doctors, physiotherapists, professional instructors or coaches, certified kinesiologists, and professors specializing in exercise prescription. However, the communication is typically one-way, with the specialist’s recommendations taking precedence over the client’s or patient’s preferences, despite the experts’ efforts to make them personalized and customized.

One study attempted to address exercise adherence through self-regulatory behavior change techniques. However, this approach introduced a multitude of factors, including goal-setting, self-monitoring, action planning, and review of behavioral goals, along with problem-solving.29 Furthermore, another study highlighted that the lack of clarity regarding effective communication behaviors in chronic pain management serves as a barrier to implementing psychologically informed physical therapy approaches, which depend on competent communication by physical therapist providers.30

Given the articles mentioned above, this review suggests that applying a two-way communication approach between specialists and individuals can be more effective for individualized exercise prescriptions for MetS than the traditional one-way method. For the two-way communication approach, specialists do not specify a particular value but instead suggest an appropriate range for the FITT-VP components of exercise (Frequency, Intensity, Time, Type, Volume, and Progression). Subsequently, individuals can choose exercises within this predefined range based on their preferences.31 To illustrate this point, this review compared exercise prescriptions using the ACE-IFT model of the conventional one-way communication approach with a new sample model employing a two-way communication approach (Fig. 2).32 In the new sample model, specialists defined a range of exercise time during the first 4 weeks, allowing individuals to choose one of three exercise times each week. This flexibility enables individuals to select an exercise time based on their preference, considering both their physical and psychological conditioning, as specialists prescribed an appropriate range of exercise time tailored to personal factors. The subject first chooses the exercise intensity and then they decide the exercise time after the 5th week. In this two-way communication approach, individuals can adjust a wider combination of exercise intensity and time in contrast to the one-way approach. Ultimately, individuals can customize their exercise routines by adding or subtracting exercise intensity and/or time as needed. This two-way communication method can strengthen the personalized approach to exercise prescription on MetS, by reducing the impact of individual variability on the response to specific exercise prescriptions.

One of the principles in exercise prescription is individualization, which involves tailoring the exercise program to result in a customized or personalized intervention with an appropriate level of overload for a client. To be specific, factors such as the client’s health condition, physical fitness level, age, sex, and other relevant factors are taken into account when designing the frequency, intensity, time, and type of exercise.

However, often, little attention is given to the client’s preferences as an individual factor. In exercise prescription, the predominant communication method is typically one-way, with the specialist providing recommendations to the client without engaging in a two-way dialogue. The specialist makes unilateral decisions about the exercise volume, intensity, and mode, which are then communicated to the client.

Every patient has his or her unique lifestyle and preferences. Although an exercise specialist may consider these, the communication remains one-way, driven solely by the expert’s decisions. Consequently, when the individual selection of the FITT-VP components is integrated through two-way communication between the specialist and the individual, there is the potential for more effective improvements in both the severity of MetS and exercise adherence.

Study concept and design: KBK; analysis and interpretation of data: all authors; drafting of the manuscript: KBK; critical revision of the manuscript: KBK and HS; administrative, technical, or material support: HC and HS; and study supervision: KBK and HS.

Fig. 1. Flow chart for searching studies.
Fig. 2. A comparison of exercise prescription with one-way and two-way communications. ACE-IFT, American Council on Exercise Integrated Fitness Training; HR, heart rate; VT1, first ventilatory threshold; RT, resistance training; VT2, second ventilatory threshold.

The characteristics of the studies included in the scoping review

Author (year) Group Number Age (yr) Exercise type Frequency (day/wk) Time (min) Duration (wk) Intensity
Garcin et al. (2019)14 EXP 19 54.8 ± 8.1 Cycling 3 45 12 RPE of COP
Byrd et al. (2019)15 EXP1 16 34.2 ± 9.8 Standardized MICT 3–5 30–50 13 40%–65% HRR
EXP2 16 32.1 ± 6.9 Personalized MICT+HIIT 3–5 30–50 13 More or less than VT+100% VO2max
CON 15 33.9 ± 6.9
Aizawa et al. (2009)16 EXP 34* 53.9 ± 8.7 Aerobic exercise Most days 30 24 Moderate
CON 29 Aerobic exercise Most days 30 24 Moderate
Stuckey et al. (2013)17 EXP 12 56.9 ± 7.0 Step exercise Everyday 10,000 steps 8 70%–85% MHR
Lundqvist et al. (2020)18 EXP 98 56.4 ± 10.2 PT Physical activity level was measured at baseline and at 1- and 2-year follow-ups
CON 92 57.5 ± 11.3 HCC
Cho et al. (2020)19 EXP1 43 48.9 ± 7.8 App+personalized coaching Group education and exercise self-logging, at baseline and at 6, 12, 24 weeks follow-ups
EXP2 45 49.2 ± 7.5 App only
CON 41 49.5 ± 7.9 Group education only
Seward et al. (2019)20 EXP 70 46.6 ± 16.7 Cardiorespiratory fitness, resistance training 3 45–90 12 More or less than VT
CON 72 45.6 ± 12.5 Inactivity 12
Avram et al. (2011)21 EXP 28 21.3 ± 3.1 Interval training 3 50 9 mo More or less than AT
Weatherwax et al. (2018)3 EXP1 19 44.9 ± 11.4 Cardiorespiratory fitness training 3 Isocaloric volume 12 More or less than VT
EXP2 19 51.2 ± 12.5 3 12 40%–65% HRR
CON 8 45.6 ± 7.9 Maintain lifestyle 12

*With metabolic syndrome (MetS); †Without MetS.

EXP, experimental group; RPE, ratings of perceived exertion; COP, crossover point of substrate utilization; MICT, moderate intensity, continuous training; HRR, heart rate reserve; HIIT, high-intensity interval training; VT, ventilatory threshold; VO2max, maximal oxygen uptake; CON, control group; MHR, maximum heart rate; PT, individualized physical activity by physiotherapist; HCC, ordinary physical activity at health care center; AT, anaerobic threshold.

  1. Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep 2018;20:12.
    Pubmed KoreaMed CrossRef
  2. Lemieux I, Després JP. Metabolic syndrome: past, present and future. Nutrients 2020;12:3501.
    Pubmed KoreaMed CrossRef
  3. Weatherwax RM, Ramos JS, Harris NK, Kilding AE, Dalleck LC. Changes in metabolic syndrome severity following individualized versus standardized exercise prescription: a feasibility study. Int J Environ Res Public Health 2018;15:2594.
    Pubmed KoreaMed CrossRef
  4. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ 2006;174:801-9.
    Pubmed KoreaMed CrossRef
  5. Yamaoka K, Tango T. Effects of lifestyle modification on metabolic syndrome: a systematic review and meta-analysis. BMC Med 2012;10:138.
    Pubmed KoreaMed CrossRef
  6. Katzmarzyk PT, Leon AS, Wilmore JH, Skinner JS, Rao DC, Rankinen T, et al. Targeting the metabolic syndrome with exercise: evidence from the HERITAGE Family Study. Med Sci Sports Exerc 2003;35:1703-9.
    Pubmed CrossRef
  7. Ostman C, Smart NA, Morcos D, Duller A, Ridley W, Jewiss D. The effect of exercise training on clinical outcomes in patients with the metabolic syndrome: a systematic review and meta-analysis. Cardiovasc Diabetol 2017;16:110.
    Pubmed KoreaMed CrossRef
  8. Williamson PJ, Atkinson G, Batterham AM. Inter-individual responses of maximal oxygen uptake to exercise training: a critical review. Sports Med 2017;47:1501-13.
    Pubmed CrossRef
  9. Bouchard C, Blair SN, Church TS, Earnest CP, Hagberg JM, Häkkinen K, et al. Adverse metabolic response to regular exercise: is it a rare or common occurrence? PLoS One 2012;7:e37887.
    Pubmed KoreaMed CrossRef
  10. Mann TN, Lamberts RP, Lambert MI. High responders and low responders: factors associated with individual variation in response to standardized training. Sports Med 2014;44:1113-24.
    Pubmed CrossRef
  11. Donner A, Klar N. The statistical analysis of kappa statistics in multiple samples. J Clin Epidemiol 1996;49:1053-8.
    Pubmed CrossRef
  12. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-74.
    CrossRef
  13. Fleiss JL. Statistical methods for rates and proportions. 2nd ed. John Wiley; 1981.
  14. Garcin M, Coquart JB, Duhamel A, Borel B, Boitel G, Delsart P, et al. Effects of an individualized rehabilitation program prescribed by perceived exertion in women with metabolic syndrome. Gazz Med Ital 2019;178:118-25.
    CrossRef
  15. Byrd BR, Keith J, Keeling SM, Weatherwax RM, Nolan PB, Ramos JS, et al. Personalized moderate-intensity exercise training combined with high-intensity interval training enhances training responsiveness. Int J Environ Res Public Health 2019;16:2088.
    Pubmed KoreaMed CrossRef
  16. Aizawa K, Shoemaker JK, Overend TJ, Petrella RJ. Effects of lifestyle modification on central artery stiffness in metabolic syndrome subjects with pre-hypertension and/or pre-diabetes. Diabetes Res Clin Pract 2009;83:249-56.
    Pubmed CrossRef
  17. Stuckey MI, Kiviniemi AM, Petrella RJ. Diabetes and technology for increased activity study: the effects of exercise and technology on heart rate variability and metabolic syndrome risk factors. Front Endocrinol (Lausanne) 2013;4:121.
    Pubmed KoreaMed CrossRef
  18. Lundqvist S, Börjesson M, Cider Å, Hagberg L, Ottehall CB, Sjöström J, et al. Long-term physical activity on prescription intervention for patients with insufficient physical activity level: a randomized controlled trial. Trials 2020;21:793.
    Pubmed KoreaMed CrossRef
  19. Cho SMJ, Lee JH, Shim JS, Yeom H, Lee SJ, Jeon YW, et al. Effect of smartphone-based lifestyle coaching app on community-dwelling population with moderate metabolic abnormalities: randomized controlled trial. J Med Internet Res 2020;22:e17435.
    Pubmed KoreaMed CrossRef
  20. Seward S, Ramos J, Drummond C, Dalleck A, Byrd B, Kehmeier M, et al. Inter-individual variability in metabolic syndrome severity score and VO2max changes following personalized, community-based exercise programming. Int J Environ Res Public Health 2019;16:4855.
    Pubmed KoreaMed CrossRef
  21. Avram C, Oravitan M, Hoble LD, Almajan-Guta B. Cardiopulmonary exercise testing-effective method for evaluation and recommendation of individualized exercise training in patients with metabolic syndrome. Timiş Phys Educ Rehabil J 2011;4:23-8.
  22. Wasfy MM, Baggish AL. Exercise dose in clinical practice. Circulation 2016;133:2297-313.
    Pubmed KoreaMed CrossRef
  23. Gronwald T, Törpel A, Herold F, Budde H. Perspective of dose and response for individualized physical exercise and training prescription. J Funct Morphol Kinesiol 2020;5:48.
    Pubmed KoreaMed CrossRef
  24. Chen FT, Hopman RJ, Huang CJ, Chu CH, Hillman CH, Hung TM, et al. The effect of exercise training on brain structure and function in older adults: a systematic review based on evidence from randomized control trials. J Clin Med 2020;9:914.
    Pubmed KoreaMed CrossRef
  25. American College of Sports Medicine. ACSM's guidelines for exercise testing and prescription. 10th ed. Lippincott Williams and Wilkins; 2017.
  26. Mann S, Beedie C, Jimenez A. Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. Sports Med 2014;44:211-21.
    Pubmed KoreaMed CrossRef
  27. Wolpern AE, Burgos DJ, Janot JM, Dalleck LC. Is a threshold-based model a superior method to the relative percent concept for establishing individual exercise intensity?: a randomized controlled trial. BMC Sports Sci Med Rehabil 2015;7:16.
    Pubmed KoreaMed CrossRef
  28. Bryant CX, Green DJ. ACE personal trainer manual. 4th ed. American Council on Exercise; 2010.
  29. Mazzoni AS, Brooke HL, Berntsen S, Nordin K, Demmelmaier I. Exercise adherence and effect of self-regulatory behavior change techniques in patients undergoing curative cancer treatment: secondary analysis from the phys: can randomized controlled trial. Integr Cancer Ther 2020;19:1534735420946834.
    Pubmed KoreaMed CrossRef
  30. Chapman CR, Woo NT, Maluf KS. Preferred communication strategies used by physical therapists in chronic pain rehabilitation: a qualitative systematic review and meta-synthesis. Phys Ther 2022;102:pzac081.
    Pubmed KoreaMed CrossRef
  31. Shin YA, Kim KB. The importance of individual choice and intention in exercise adherence and weight management. J Obes Metab Syndr 2021;30:317-9.
    Pubmed KoreaMed CrossRef
  32. Dalleck LC, Haney DE, Buchanan CA, Weatherwax RM. Does a personalised exercise prescription enhance training efficacy and limit training unresponsiveness?: a randomised controlled trial. J Fit Res 2016;5:15-27.