https://orcid.org/0000-0002-1346-7756
Abstract
The associations between self-control and objective measures of physical activity, physical fitness, and adiposity are yet to be explored in young people; this is a gap in the literature that the present study aimed to address. The study employed a cross-sectional design. A total of 101 young people participated in the study. Participants completed the Brief Self-Control Scale as a measure of trait self-control. Free-living physical activity was assessed using an ActiGraph GT3X + triaxial accelerometer, which was worn for 7 days. Participants also completed the 15-meter version of the multistage fitness test as a measure of physical fitness. For the assessment of adiposity, three criterion measures were used: body mass index (BMI), sum of skinfolds, and waist circumference. Data were analyzed using the glm function in the open access software R. Higher levels of trait self-control were associated with higher levels of physical fitness (measured by distance run on the multistage fitness test) and lower adiposity (BMI, waist circumference, and sum of skinfolds) in adolescents. There was a tendency for a positive association between self-control and time spent in vigorous physical activity, although this did not reach statistical significance. In a combined model, self-control was also associated with both physical fitness and waist circumference, with these effects independent of each other. These findings suggest that self-control is associated with healthy behaviors and characteristics in adolescents and is thus potentially an attractive target for future interventions aimed at increasing physical activity and physical fitness and reducing adiposity in this population.
Introduction
Self-control is defined as the ability to volitionally inhibit predominant response tendencies and to resist immediate undesirable temptations to support the pursuit of long-term goals.Citation1 Self-control helps individuals to exhibit appropriate behavior by helping to control urges, juggle competing goals, and maintain focus on the desired goal.Citation2 The capacity to exert self-control can differ both between individuals (i.e., trait self-control) and across situations within the same individual (i.e., state self-control).Citation3 Specifically, the trait perspective of self-control has been linked to a number of positive outcomes, including enhanced psychological well-being, improved interpersonal relationships, and higher levels of achievement and performance.Citation3,Citation4 With regard to health and well-being, self-control is required to, for example, maintain an exercise training program or adhere to a diet regimen. In both cases, self-control is required to overcome the temptation presented by a proximal goal (e.g., skip an exercise training session or eat an unhealthy food) in favor of pursuing the desired distal goal (e.g., to become fitter or to follow a dietary plan).
Despite the well-documented benefits of physical activity for health and well-being in young people,Citation5–7 more than half of young people aged 5 to 18 years do not meet the recommendations of 60 minutes’ moderate-vigorous physical activity per day.Citation8 Furthermore, it is suggested that there has been a decline in endurance-based measures of physical fitness in young people since 1990,Citation9 alongside an increasing prevalence of overweight and obesity,Citation10 contributing to deleterious health outcomes. Given these concerning patterns of low physical activity, low physical fitness, and high adiposity in young people, developing an understanding of factors that may be associated with these behaviors and characteristics is of great interest and may inform the development and implementation of future interventions.
The associations between self-control and health behaviors in young people have mostly focused on eating habits, with very few studies exploring the associations between trait self-control and physical activity. Adolescents (aged 15–20 years old) with high trait self-control are reported to have healthier eating habits (e.g., higher frequency of breakfast consumption and lower intake of chocolate and snacks) and a lower body mass index (BMI) compared to those individuals with low self-control.Citation11,Citation12 Furthermore, some evidence also suggests that adolescents with higher trait self-control are more physically active than their counterparts with low self-control.Citation11,Citation12 However, it is important to note that within these studies, the physical activity data were based on self-report measures, which is associated with reporting errors, social desirability bias, and recall bias, particularly in young people.Citation13 To our knowledge, no studies to date have examined the association between device-measured physical activity (overcoming the aforementioned limitations associated with self-report measures) and trait self-control.
Research conducted in adults has reported a positive association between trait self-control and both physical activity and (aerobic and muscular) fitness.Citation14 Specifically, those with higher trait self-control spent more leisure time being physically active, covered a greater distance on the 12-minute Cooper run test (aerobic fitness), and performed better on a holistic battery of muscle function tests (e.g., pushups, sit-ups, standing long jump).Citation14 Furthermore, it was also reported that there was a negative association between self-control and BMI, whereby participants with higher trait self-control had a lower BMI.Citation14 However, the study of Kinnunen et al.Citation14 was conducted in adults, While it is possible that high levels of trait self-control may also be associated with objective measures of physical fitness and adiposity in young people, this is yet to be explored.
If associations do exist between self-control and health behaviors in young people, this has important implications for potential interventions to enhance health and well-being (through physical activity and diet), using self-control training techniques as a potential intervention. However, it is important to fully understand and document the relationships between self-control and physical activity, physical fitness, and adiposity before investing effort in the design and implementation of such interventions. Therefore, the aim of the present study was to explore the associations between self-control and physical activity, physical fitness, and adiposity in adolescents aged 11 to 15 years.
Methods
Study design
The present study employed a cross-sectional design. Following approval from the host institution’s ethical advisory committee, written parental consent and participant assent were obtained and a health screen questionnaire was completed (by parents on behalf of the young people) to ensure that participants had no medical conditions that could affect their participation in the study. Due to the nature of the school-based research and data collection for the present study, participants were recruited via their schools. Five schools in the East Midlands, UK, were contacted using convenience sampling and agreed to participate in the study. The location of the schools varied and included rural urban town and inner city (index of multiple deprivation ranged from 10,586 to 30,035 [32nd to 92nd percentile]).
All measures were undertaken in the participant’s school, as part of a larger study examining cardiometabolic health in young people.Citation15 The present study reports on the associations of physical activity, physical fitness, and adiposity with self-control in young people. Participants completed a familiarization trial during which they self-reported their date of birth (to calculate age) and sex, and they were presented an opportunity to practice each of the measures involved in the study. Following familiarization, participants were provided with an ActiGraph GT3X + accelerometer to wear for 7 days. After the 7 days of physical activity measurement, participants completed the main experimental trial, consisting of the Brief Self-Control Scale, anthropometric measurements, and the multistage fitness test (MSFT; for details of each, see measures section).
Participants
A total of 113 young people were recruited to participate in this study. However, 12 participants withdrew due to being absent from school for one of the experimental sessions (n = 8) or attending a school trip (n = 4). Therefore, 101 young people completed the study: 63 participants (34 girls) in year 7 (11–12 years) and 38 participants (23 girls) in year 10 (14–15 years) (). Exclusion criteria for participation in the study included any health condition that may be exacerbated by completing the exercise aspects of the study, as determined by the parent-completed health screen questionnaire.
Table 1. Descriptive characteristics (mean ± SD) of participants, split by year group and sex.
Measures
Brief Self-Control Scale
Trait self-control was measured using the 13-item Brief Self-Control Scale.Citation17 Participants indicated the extent to which each of the statements reflected how they usually act on a 5-point Likert scale, ranging from 1 (not at all) to 5 (very much). Five items were reverse-scored as they reflected lack of self-control (e.g., “I wish I had more self-discipline”). The items have demonstrated acceptable internal consistency and predictive validity in previous research.Citation17 The Brief Self-Control Scale provides a total score for trait self-control (scores range from 13 to 65), with higher scores indicating higher trait levels of self-control. The Brief Self-Control Scale was preferred in the present study given that the set of 13 items was considered more appropriate for the study population and demonstrates validity with the full Self-Control Scale.Citation17,Citation18
Height and weight
For descriptive purposes, height was measured using a Leicester Height Measure (Seca, Hamburg, Germany), and body mass was measured using digital scales (Seca 770; Seca, Hamburg, Germany). These measures were taken behind a privacy screen in the school setting.
Adiposity
For the assessment of adiposity, three criterion measures were used: BMI, sum of skinfolds, and waist circumference. BMI was calculated using height and body mass, as previously described. Skinfolds were measured at four sites (triceps, subscapular, supraspinale, and front thigh). All measurements were taken in line with the International Society for the Advancement of Kinanthropometry guidelines.Citation19 As per recommendations, two measurements were taken at each site and the mean was used as the criterion measure. However, if the discrepancy between the first two measurements was greater than 10%, a third measurement was taken and the median was used as the criterion measure. The sum of skinfolds was used as the overall measure of adiposity, in line with other work in young people.Citation20 Finally, waist circumference was measured at the end of an exhalation, at the narrowest point between the xiphoid process of the sternum and the iliac crest, to the nearest 0.1 cm.Citation21 Skinfold and waist circumference measures were taken by a trained kinanthropometrist behind a privacy screen in the school setting.
Physical activity
Free-living physical activity was assessed using an ActiGraph GT3X + triaxial accelerometer (ActiGraph, Pensacola, FL, USA). Participants were provided with the accelerometer at the familiarization session. Participants were instructed to wear the accelerometer for 7 days and to wear the accelerometer at all times (24 h.d−1); with the only exception being to remove the accelerometer for water-based activities such as showering or swimming, based on previous recommendations.Citation22,Citation23 Prior to leaving the familiarization session, participants were fitted with the accelerometer (worn on a waist band above the right hip) by a member of the research team to ensure that the accelerometer was properly fitted. The accelerometers were initialized to capture data at 90 Hz. The accelerometers were subsequently returned to the research team at the main experimental trial.
Data were downloaded using ActiLife software (v6.13.4; ActiGraph, Pensacola, FL, USA) and converted to .csv files (15-second epochs). Non-wear time was classified based on the previous algorithm outlined by Choi et al.Citation24 Participants were removed from subsequent analyses if they did not have four days of valid wear time (≥10 hours per day), including 1 weekend day (n = 17); thus, analysis for physical activity variables was performed on 84 participants. Furthermore, assumed periods of sleep (23:00 to 06:00) were removed prior to analysis, in line with previous work.Citation23 Activity classifications (time spent in each intensity of activity) were based on the count cutoffs of Romanzini et al.Citation25 summarized as follows: sedentary (≤180 counts.15 s−1), light physical activity (181–756 counts.15 s−1), moderate physical activity (757–1,111 counts.15 s−1), and vigorous physical activity (>1,112 counts.15 s−1). A further moderate-vigorous physical activity variable was created, as the sum of the time spent in moderate plus time spent in vigorous activity (i.e., > 757 counts.15 s−1).
Physical fitness
For the assessment of physical fitness, participants completed the 15-meter version of the MSFT. The MSFT is a commonly used field-based measure of physical fitness in young peopleCitation26 and has demonstrated excellent reliability in this population.Citation27 The 15-meter version was used due to practical limitations in that some of the school sports hall facilities were not large enough to allow the full 20-meter versionCitation28 to be used. The 15-meter version of the MSFT starts at a speed of 6 km.h−1 and increases by 0.5 km.h−1 with each stage (each stage is ∼1 minute in duration). Participants were instructed to run to volitional exhaustion or until they were unable to maintain the required pace for three consecutive shuttles. Verbal encouragement was provided by the research team throughout, and an experienced member of the research team ran alongside the participants for pacing purposes. The criterion measure used was the distance covered (meters), in line with previous work.Citation15,Citation20
Statistical analysis
Data were analyzed using the open access software R (v4.0.5; www.r-project.org). All variables displayed a normal distribution, with the exception of the time spent in vigorous physical activity; subsequently, this variable was log-transformed prior to analyses. Initially, regression models were run using the glm function in R to assess the relationship between self-control and each variable of interest (time spent in each activity classification [physical activity]; distance run on the MSFT [physical fitness]; and BMI, sum of skinfolds, and waist circumference [adiposity]). Subsequently, a multiple regression model was built, also using the glm function in R, to find the combination of variables that best explained the variance in self-control (judged by the lowest Akaike information criterion [AIC]). Prior to this model being built, the variables of interest were checked for collinearity (using the cor.test function); any variables with a correlation of r > 0.7 were not included in the same model. For all models, residuals were assessed to ensure they met the underlying assumptions of normality and homoscedasticity. Statistical significance was accepted as p < .05.
Results
For descriptive purposes, mean values for physical activity, physical fitness, adiposity, and self-control, for the whole sample and split by year group and sex, are displayed in . There was no difference in self-control between the year groups (p = .19) or sexes (p = .16), nor did year group and sex interact to affect self-control (p = .18). Therefore, all participants were analyzed together, regardless of year group and sex.
Table 2. Physical activity, physical fitness, adiposity, and self-control values, split by year group and sex (data are presented as mean ± standard deviation).
Individual models
A summary of the findings of the regression models assessing the relationships between self-control and each variable of interest are displayed in . In summary, there were no associations between self-control and physical activity (time spent in each activity classification, all p > .05). However, there was a tendency for a positive association between self-control and time spent in vigorous physical activity (p = .10), whereby higher self-control was associated with spending more time in vigorous physical activity.
Table 3. Regression models assessing the associations between self-control and physical activity, physical fitness, and adiposity.
There was a significant positive association between self-control and physical fitness (distance run on the MSFT), whereby higher levels of physical fitness (measured by distance run on the MSFT) were associated with higher self-control (p = .01). Furthermore, there were significant negative associations between self-control and each measure of adiposity (BMI, waist circumference, and sum of skinfolds; all p < .001), whereby higher self-control was associated with lower measures of adiposity (lower BMI, lower waist circumference, and lower sum of skinfolds).
Combined models
In order to assess which variables could be included in the same model, the degree of collinearity between the significant variables in was checked. The adiposity variables (BMI, waist circumference, and sum of skinfolds) were collinear with each other (BMI and waist circumference, r = .85; BMI and sum of skinfolds, r = .79; waist circumference and sum of skinfolds, r = .71) and thus could not be included in the same model. However, physical fitness (distance run on the MSFT) was not collinear with any of the adiposity measures (BMI, r = −.37; waist circumference, r = −.19; sum of skinfolds, r = −.56); thus, these variables could be combined in the same model.
When combining variables into the same model, the best model (with the lowest AIC) included physical fitness (distance run on the MSFT) and waist circumference (Model 3, ). Specifically, higher self-control was associated with both higher levels of physical fitness (greater distance run on the MSFT) and a lower waist circumference, with these effects statistically significant and independent of each other (). When combining physical fitness (distance run on the MSFT) with BMI (Model 1) and sum of skinfolds (Model 2), the models had a higher AIC (indicating a poorer model fit) and physical fitness was not statistically significant (). This suggests that the model that explained the greatest variance in trait self-control included physical fitness (distance run on the MSFT) and waist circumference and that these effects were independent of each other.
Table 4. Combined regression model that explains the greatest variance in self-control.
Discussion
The main findings of the present study are that higher levels of trait self-control were associated with higher physical fitness (measured by distance run on the MSFT) and lower adiposity (BMI, waist circumference, and sum of skinfolds) in adolescents. In addition, there was a tendency for a positive association between self-control and time spent in vigorous physical activity, although this did not reach statistical significance. Furthermore, self-control was associated with both physical fitness and adiposity (combined model of physical fitness and waist circumference explained the greatest variance in trait self-control), with these effects independent of each other. Overall, these findings suggest that self-control is associated with healthy behaviors and characteristics in adolescents and is thus potentially an attractive target for future interventions aimed at increasing physical activity and physical fitness and reducing adiposity in this population.
One of the main findings of the present study was that higher self-control was associated with higher levels of physical fitness (distance run on the MSFT) in an adolescent population. To our knowledge, the present study is the first to examine the relationship between self-control and fitness in adolescents. Thus, the present study provides novel evidence in adolescents, which is in accordance with evidence in adults where higher self-control has been reported to be positively associated with a greater distance covered on the 12-minute Cooper run test,Citation14 higher V ̇O2 max,Citation29 and greater performance on a battery of muscle function tests.Citation14
The findings of the present study suggest that there are no associations between self-control and time spent in each of the physical activity classifications (sedentary, light physical activity, and moderate physical activity). However, higher trait self-control tended to be associated with more time spent in vigorous physical activity, although this did not reach statistical significance. To our knowledge, the present study is the first to examine the associations between self-control and device-measured physical activity, with the previous studies that have reported a positive association between self-control and physical activity having used self-report–based measures of physical activity.Citation11,Citation12 Given that self-report measures of physical activity are associated with many limitations, particularly in young people,Citation13 this may explain the variance between the findings of the present study and those previously reported. Nonetheless, the findings of the present study suggest that self-control may be particularly important for adolescents’ participation in higher-intensity, vigorous physical activity.
Another key finding of the present study was the negative association between self-control and all measures of adiposity (BMI, waist circumference, and sum of skinfolds). These findings are in accordance with previous studies that also report a negative association between self-control and BMI in young peopleCitation11,Citation12 and adults.Citation14 However, the present study extends these findings by going beyond using BMI as the sole measure of adiposity and also examining waist circumference and sum of skinfolds as additional measures of adiposity. This could be of particular importance given that waist circumference and sum of skinfolds have been reported to be of greater importance for cardiometabolic health than BMI in adolescents.Citation20 It is also important to note that the association of self-control with physical fitness and adiposity were independent of each other, as evidenced by the model that explained the greatest variance in self-control including both physical fitness and waist circumference. This suggests that self-control has the potential to influence physical fitness and adiposity independently and is thus important for a range of healthy behaviors and characteristics in this population.
Therefore, it is pertinent to suggest that self-control could be an appropriate intervention target for future interventions aimed at increasing physical fitness and reducing adiposity in this population. Given the well-documented positive effects of higher physical fitness levels and lower adiposity on health and well-being,Citation20 it is thus possible that interventions aimed at enhancing self-control in young people could, through increased physical activity, increased physical fitness, and reduced adiposity, enhance health and well-being. Indeed, it has previously been reported in a large sample (∼12,000) of older adolescents (mean age, 16 years old) that lower levels of self-control were associated with a greater risk of being diagnosed with a range of both physical and mental health conditions.Citation30 However, we hypothesize that it is unlikely that self-control directly impacts health and well-being and, rather, that it has an indirect effect through variables such as physical activity, physical fitness, and adiposity, with preliminary evidence of these relationships in adolescents provided in the present study. For example, adolescents with higher self-control are more likely to select healthy behaviors (e.g., to complete a bout of physical activity) and resist unhealthy alternatives (e.g., watching television), ultimately increasing physical fitness, reducing adiposity, and enhancing health and well-being. Furthermore, it has previously been suggested that self-control can be enhanced through specific self-control training interventions in young people.Citation31 Based upon the associations of self-control with healthy behaviors demonstrated in the present study, the potential for interventions to enhance self-control (and subsequently increasing physical activity and physical fitness) to ultimately enhance health and well-being warrant further investigation.
However, while the present study is the first to examine the important associations between self-control and physical activity, physical fitness, and adiposity in adolescents, it is not without limitation. Importantly, the present study is cross-sectional in nature, and thus while the findings demonstrate associations between self-control and healthy behaviors and characteristics in adolescents, it does not demonstrate causality (i.e., the direction of the effect). Indeed, it has previously been suggested that the relationship between self-control and exercise/physical activity is bidirectional in nature.Citation32 Therefore, future intervention-based work should consider the direction of this relationship. In particular, the associations between self-control and healthy behaviors and characteristics in the present study should be extended by implementing a self-control–based intervention and measuring the effect on physical activity, physical fitness, and adiposity in adolescents. Importantly, previous research has suggested that an individual’s self-control capacity can be trained by employing specific training techniques,Citation33 including squeezing a handgrip twice per day for two weeksCitation34 and use of the nondominant hand daily for everyday tasks for two weeks.Citation35 Therefore, future research should aim to develop self-control training interventions suitable for an adolescent population. Given the associations demonstrated in the present study, such interventions would have the potential to increase (vigorous) physical activity, enhance fitness, and reduce adiposity, thus leading to enhanced health and well-being.
Furthermore, while this study provides novel findings regarding the importance of self-control for physical activity, physical fitness, and adiposity in adolescents, it does not consider the importance of self-control on diet and healthy eating in this population. Future work should extend the findings of the present study by also including measures of diet to consider how self-control also impacts diet and the subsequent effects on health and well-being and whether the relationships of self-control with physical activity and diet are potentially related. In addition, future work could also consider the associations between self-control and other aspects of health and well-being, such as mental well-being.
Conclusions
Overall, the findings of the present study suggest that trait self-control is associated with a range of healthy behaviors in adolescents. Specifically, higher self-control was associated with higher physical fitness levels and lower adiposity in adolescents. These findings provide important preliminary evidence that self-control is a potential target for interventions aimed at increasing physical activity, increasing physical fitness, and reducing adiposity in adolescents, ultimately enhancing health and well-being in this population. It is important for future work to extend upon this proof of concept and begin to develop and evaluate such interventions in adolescent populations.
Declaration of interest
The authors report there are no competing interests to declare.
Data availability statement
Data are available from corresponding author upon reasonable request.
Funding
The author(s) reported there is no funding associated with the work featured in this article.
References
- Baumeister RF, Bratslavsky E, Muraven M, Tice DM. Ego depletion: is the active self a limited resource? J Pers Soc Psychol. 1998;74(5):1252–1265. doi:10.1037/0022-3514.74.5.1252.
- Baumeister RF, Vohs KD, Tice DM. The strength model of self-control. Curr Dir Psychol Sci. 2007;16(6):351–355. doi:10.1111/j.1467-8721.2007.00534.
- Tangney JP, Baumeister RF, Boone AL. High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers. 2004;72(2):271–324. doi:10.1111/j.0022-3506.2004.00263.x.
- De Ridder DTD, Lensvelt-Mulders G, Finkenauer C, Stok FM, Baumeister RF. Taking stock of self-control: a meta-analysis of how trait self-control relates to a wide range of behaviors. Pers Soc Psychol Rev. 2012;16(1):76–99. doi:10.1177/1088868311418749.
- Biddle SJH, Asare M. Physical activity and mental health in children and adolescents: a review of reviews. Br J Sports Med. 2011;45(11):886–895. doi:10.1136/bjsports-2011-090185.
- Eime RM, Young JA, Harvey JT, Charity MJ, Payne WR. A systematic review of the psychological and social benefits of participation in sport for children and adolescents: informing development of a conceptual model of health throughout sport. Int J Behav Nutr Phys Act. 2013;10:98. doi:10.1186/1479-5868-10-98.
- Warburton DER, Nicol CW, Bredin SSD. Health benefits of physical activity: the evidence. CMAJ. 2006;174(6):801–809. doi:10.1503/cmaj.051351.
- Public Health England. Physical activity data tool: statistical commentary. https://www.gov.uk/government/statistics/physical-activity-data-tool-march-2021-update/physical-activity-data-tool-statistical-commentary-march-2021. Published March 2021. Accessed December 2021.
- Fuhner T, Kliegl R, Arntz F, Kriemler S, Granacher U. An update on secular trends in physical fitness of children and adolescents from 1972-2015: a systematic review key points. Sports Med. 2021;51(2):303–320. doi:10.1007/s40279-020-01373.
- UK Government. Childhood obesity: a plan for action. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/546588/Childhood_obesity_2016__2__acc.pdf. Published August 2016. Accessed December 2021.
- Junger M, Van Kampen M. Cognitive ability and self-control in relation to dietary habits, physical activity and bodyweight in adolescents. Int J Behav Nutr. 2010;7(2):22. doi:10.1186/1479-5868-7-22.
- Wills TA, Isasi CR, Mendoza D, Ainette MG. Self-control constructs related to measures of dietary intake and physical activity in adolescents. J Adolesc Health. 2007;41:51–58. doi:10.1016/j.adohealth.2007.06.013.
- Trost SG. Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time? Int J Behav Nutr. 2020;17:1–4. doi:10.1186/s12966-020-00929-4.
- Kinnunen MI, Suihko J, Hankonen N, Absetz P, Jallinoja P. Self-control is associated with physical activity and fitness among young males. Behav Med. 2012;38(3):83–89. doi:10.1080/08964289.2012.693975.
- Williams RA, Cooper SB, Dring KJ, et al. Physical fitness, physical activity and adiposity: associations with risk factors for cardiometabolic disease and cognitive function across adolescence. BMC Pediatr. 2022;22(1):75. doi:10.1186/s12887-022-03118-3.
- Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240–1240. doi:10.1136/bmj.320.7244.1240.
- Maloney PW, Grawitch MJ, Barber LK. The multi-factor structure of the Brief Self-Control Scale: discriminant validity of restraint and impulsivity. J Res Pers. 2012;46(1):111–115. doi:10.1016/j.jrp.2011.10.001.
- Pechorro P, DeLisi M, Goncalves RA, Quintas J, Palma VH. The brief self-control scale and its refined version among incarcerated and community youths: psychometrics and measurement invariance. Dev Behav. 2021;42(3):425–442. doi:10.1080/01639625.2019.1684942.
- The International Society for the Advancement of Kinanthropometry. International Standards for Anthropometric Assessment. Lower Hutt (New Zealand): National Library of Australia; 1991.
- Dring KJ, Cooper SB, Morris JG, Sunderland C, Foulds GA, Pckley AG. Multi-stage fitness test performance, V ̇O2 peak and adiposity; effect on risk factors for cardio-metabolic disease min adolescents. Front Physiol. 2019;10:629. doi:10.3389/fphys.2019.00629.
- World Health Organisation. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation. Geneva (Switzerland): WHO; 2008.
- Tudor-Locke C, Barreir TV, Schuna JM, Jr, et al. Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International study of childhood obesity, lifestyle and the environment (ISCOLE). Methodology. 2015;12:11. doi:10.1186/s12966-015-0172-x.
- Migueles JH, Cadenas-Sanchez C, Ekelund U, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 2017;47(9):1821–1845. doi:10.1007/s41279-017-0176-0.
- Choi L, Ward SC, Schnelle JF, Buchowski MS. Assessment of wear/nonwear time classification algorithms for triaxial accelerometer. Med Sci Sports Exerc. 2012;44(10):2009–2016. doi:10.1249/MSS.0b013e318258cb36.
- Romanzini M, Petroski E, Ohara D, Dourado A, Reichert F. Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. Eur J Sport Sci. 2014;14(1):91–99. doi:10.1080/17461391.2012.732614.
- Tomkinson GR, Lang JL, Blanchard J, Leger LA, Tremblay MS. The 20-m shuttle run: assessment and interpretation of data in relation to youth aerobic fitness and health. Pediatr Exerc Sci. 2019;31(2):152–163. doi:10.1123/pes.2018-0179.
- Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci. 1988;6(2):93–101. doi:10.1080/02640418808729800.
- Ramsbottom R, Brewer J, Williams C. A progressive shuttle run test to estimate maximal oxygen uptake. Br J Sports Med. 1988;22(4):141–144. doi:10.1136/bjsm.22.4.141.
- May RW, Seibert GS, Sanchez-Gonzalez MA, Fitzgerald MC, Fincham FD. Dispositional self-control: Relationships with aerobic capacity and morning surge in blood pressure. Stress. 2017;20(1):29–52. doi:10.1080/10253890.2016.1260543.
- Miller HV, Barnes JC, Beaver KM. Self-control and health outcomes in a nationally representative sample. Am J Health Behav. 2011;35(1):15–27. doi:10.5993/ajhb.35.1.2.
- Duckworth AL, Taxer JL, Eskreis-Winkler L, Galla BM, Gross JJ. Self-control and academic achievement. Annu Rev Psychol. 2019;70:373–399. doi:10.1146/annurev-psych-010418-103230.
- Boat R, Cooper SB. Self-control and exercise: a review of the bi-directional relationship. Brain Plast. 2019;5(1):97–104. doi:10.2322/BPL-190082.
- Baumeister RF, Gailliot M, DeWall CN, Oaten M. Self-regulation and personality: how interventions increase regulatory success, and how depletion moderates the effects of traits on behavior. J Pers. 2006;74(6):1773–1801. doi:10.1111/j.1467-6494.2006.99428.x.
- Bray SR, Graham J, Saville P. Self-control training leads to enhanced cardiovascular exercise performance. J Sports Sci. 2014;33:1–10. doi:10.1080/02640414.2014.949830.
- Denson TF, Capper MM, Oaten M, Friese M, Schofield TP. Self-control training decreases aggression in response to provocation in aggressive individuals. J Res Pers. 2011;45(2):252–256. doi:10.1016/j.jrp.2011.02.001.