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The effect of a one-year vigorous physical activity intervention on fitness, cognitive performance and mental health in young adolescents: the Fit to Study cluster randomised controlled trial



Physical activity (PA) may positively stimulate the brain, cognition and mental health during adolescence, a period of dynamic neurobiological development. High-intensity interval training (HIIT) or vigorous PA interventions are time-efficient, scalable and can be easily implemented in existing school curricula, yet their effects on cognitive, academic and mental health outcomes are unclear. The primary aim of the Fit to Study trial was to investigate whether a pragmatic and scalable HIIT-style VPA intervention delivered during school physical education (PE) could improve attainment in maths. The primary outcome has previously been reported and was null. Here, we report the effect of the intervention on prespecified secondary outcomes, including cardiorespiratory fitness, cognitive performance, and mental health in young adolescents.


The Fit to Study cluster randomised controlled trial included Year 8 pupils (n = 18,261, aged 12–13) from 104 secondary state schools in South/Mid-England. Schools were randomised into an intervention condition (n = 52), in which PE teachers delivered an additional 10 min of VPA per PE lesson for one academic year (2017–2018), or into a “PE as usual” control condition. Secondary outcomes included assessments of cardiorespiratory fitness (20-m shuttle run), cognitive performance (executive functions, relational memory and processing speed) and mental health (Strength and Difficulties Questionnaire and self-esteem measures). The primary intention-to-treat (ITT) analysis used linear models and structural equation models with cluster-robust standard errors to test for intervention effects. A complier-average causal effect (CACE) was estimated using a two-stage least squares procedure.


The HIIT-style VPA intervention did not significantly improve cardiorespiratory fitness, cognitive performance (executive functions, relational memory or processed speed), or mental health (all p > 0.05). Subgroup analyses showed no significant moderation of intervention effects by sex, socioeconomic status or baseline fitness levels. Changes in cardiorespiratory fitness were not significantly related to changes in cognitive or mental health outcomes. The trial was marked by high drop-out and low intervention compliance. Findings from the CACE analysis were in line with those from the ITT analysis.


The one-academic year HIIT-style VPA intervention delivered during regular school PE did not significantly improve fitness, cognitive performance or mental health, but these findings should be interpreted with caution given low implementation fidelity and high drop-out. Well-controlled, large-scale, school-based trials that examine the effectiveness of HIIT-style interventions to enhance cognitive and mental health outcomes are warranted.

Trial registration

ISRCTN registry, 15,730,512. Trial protocol and analysis plan for primary outcome prospectively registered on 30th March 2017., NCT03286725. Secondary measures (focus of current manuscript) retrospectively registered on 18 September 2017.


Adolescence is marked by rapid, dynamic social, psychological and neurocognitive development, during which the brain matures and higher-order cognitive processes, such as executive functions, are refined [1,2,3]. It is also the peak onset time for mental health issues [4, 5], including an increased social and biological vulnerability to mood disorders and low self-esteem. Notably, adolescence has been described as a (neurobiological) critical or sensitive period of development [2, 6], characterised by enhanced plasticity, during which experience or environmental influences strongly affect the brain and may have long-lasting effects on behaviour. This period in life therefore presents an opportunity for interventions to have a positive impact on short- and long-term health outcomes, and has been of increasing interest to researchers and policymakers [1, 7].

Interventions involving regular physical activity (PA) have received much attention given that PA is easily accessible, modifiable, cost-effective, scalable and has well-established health benefits, including improvements in cardiometabolic health [8]. Converging lines of evidence suggest that regular PA changes the brain by stimulating processes such as neurogenesis and angiogenesis [9], and could improve cognitive and mental health in young people [10]. In particular, intervention studies involving primarily pre-adolescent children have found some evidence that PA improves cognition, particularly in the domains of attention and executive functions (working memory, cognitive flexibility and inhibitory control), as well as attainment in maths, but considerable between-study heterogeneity makes firm conclusions difficult to draw [11,12,13,14]. A smaller body of work suggests PA can treat depression, reduce anxiety, and improve self-esteem [10, 15]. There is also emerging evidence that childhood PA is predictive of cardiovascular, mental, cognitive, and brain health later in life [16,17,18,19]. However, approximately 80% of adolescents do not meet the recommended 60 min of moderate-to-vigorous PA (MVPA) per day [20, 21] and PA levels decline dramatically during teenage years [22]. Hence, there is scope for interventions to increase PA levels.

Schools are an ideal setting to promote PA among adolescents, including those from lower socioeconomic backgrounds, because pupils spend a large proportion of their waking hours at school and schools have access to the required facilities [23]. Findings from school-based PA interventions have been inconsistent, with recent reviews reporting no meaningful change in PA across the (school) day [24, 25], but enhanced (moderate-to-vigorous) PA during the actual intervention [25, 26]. More promising results have been reported for school-based interventions aimed to increase cardiorespiratory fitness (CRF) [27,28,29,30], self-esteem [31], and cognitive performance, with meta-analyses showing that interventions delivered during curricular physical education (PE) are particularly effective at improving cognition [13, 14]. The majority of these interventions, however, have involved large doses of PA (e.g. 60 min 4 times per week) that may not be easily implemented in, and scaled up to, all schools, where crowded school curricula require PA interventions to be time-efficient [32, 33].

High-intensity interval training (HIIT) is a time efficient and cost-effective strategy to improve cardiovascular health and CRF in young people [34,35,36,37] that can be implemented easily during the school day [38]. HIIT definitions vary [39], but typically involve short or longer bouts (from < 45 s to 2–4 min) of high-intensity exercise interspersed by periods of rest or light activity [40, 41], leading to physical adaptations that are comparable to endurance training results [42, 43]. While interventions involving working at or near the maximum intensity (e.g. > 90% VO2peak, or max heart rate) may be limited in their scalability and effectiveness [39], there is some promising evidence from less intense HIIT interventions showing improvements in executive functions [44,45,46,47,48,49] and mental well-being [45, 49, 50]. However, most of these interventions were brief (6–14 weeks), relatively well-controlled with small samples (1–6 schools), and did not investigate academic outcomes. The “Burn 2 Learn” cluster randomised trial, which delivered HIIT activity breaks twice a week for six months during curriculum time in 10 intervention schools (compared to 10 control), showed a significant improvement in older adolescents’ (mean age = 16) CRF at six, but not 12 months. In contrast to previous studies, however, it observed no significant improvements in cognitive and mental health outcomes [37, 51]. More pragmatic and large-scale trials, including those involving younger adolescents, are needed to assess the effectiveness and scalability of HIIT.

The Fit to Study cluster randomised trial investigated the effect of a one- academic year HIIT-style intervention delivered by PE teachers during regular PE on maths attainment, mental health, cognitive performance and CRF in young adolescents (12–13 years old) from 104 schools (52 intervention) in South/Mid England [52]. An independent evaluator, NatCen Social Research, analysed the primary outcome, attainment in maths, and found no significant benefit of the HIIT-style intervention [53]. Here, we report the prespecified secondary outcomes of the Fit to Study cluster RCT. We hypothesised that the scalable HIIT-style intervention would improve CRF, cognitive performance and mental health outcomes in this cohort. We also explored whether the effect of the intervention on the prespecified outcomes was moderated by sex, socioeconomic status, or baseline CRF level. We additionally examined whether changes in CRF were related to changes in cognitive and mental health outcomes.


The reporting of this trial followed the Consolidated Standards of Reporting Trial guidelines (CONSORT; checklist is provided in Additional File 1). The template for intervention description and replication (TIDieR) checklist is provided in Additional File 2.


The Fit to Study project was a parallel group, superiority cluster-randomised efficacy trial of a one academic year HIIT-style VPA intervention versus control involving Year 8 pupils (aged 12–13 years) from 104 secondary state schools (52 intervention). The intervention was incorporated into regular PE lessons to minimally disrupt the curriculum and ensure scalability. Assessments took place at baseline (end of Year 7, pupils aged 11–12 years) and after 12 months (end of Year 8, pupils aged 12–13 years).

The trial was approved by the Central University Research Ethics Committee of Oxford University (Registration No. R48879). The trial protocol, including analysis plans for the primary outcome measure (attainment in maths), was prospectively registered on the ISRCTN registry (15730512). The protocol of the secondary measures was retrospectively registered at (NCT03286725, 18 September 2017).

Full details of the study design and secondary measures are available elsewhere [52]. The primary outcome was analysed by an independent evaluator, NatCen Social Research, and published by the Education Endowment Foundation [53].


The National Foundation for Educational Research (NFER) led recruitment, supported by Oxford Brookes University. Eligible schools could be either mixed or single sex secondary state or academy schools in South/Mid-England that delivered PE as part of their curriculum, with a proportion of pupils eligible for free school meals (eFSM), preferably more than 15%, which was the average for England at the time of recruitment. Their Year 7 pupils had to move on to Year 8 at the start of the intervention, and they had to be willing to sign an agreement to send opt-out consent forms to parents/carers of Year 7 pupils. A total of 106 eligible schools responded to an invitation to participate in the study. Following the eligibility assessment, two schools declined to participate, and, from those remaining, 81 Year 7 pupils opted out of data storage and were not required to complete the trial’s assessments. Schools that met inclusion criteria and wished to participate provided the sex, age and eFSM status of all Year-7 pupils; they were considered formally recruited upon transfer of the data and memorandum of understanding to NFER. A total of 104 schools (n = 18,261 pupils) were randomised into an intervention and control group.

Measures of self-reported physical activity in the past week (0–7 days) [54] and habitual physical activity over the past week (range 1–7, with 7 the most active) [55] were used to characterise the sample’s baseline PA levels.

Randomisation and blinding

Schools (n = 104) were allocated to an intervention group or “PE as usual” control group (1:1 ratio) using stratified block randomisation. Stratification was arranged according to whether schools were single-sex or co-educational. NatCen Social Research performed the randomisation using a random number generator in Stata (version 12) [56].

Schools were informed of their assigned group following baseline assessments to minimise bias and so that intervention PE teachers could receive training and deliver the intervention. Given the nature of the intervention, it was not feasible for PE teachers or pupils to be blind to group allocation, but neither pupils nor parents were specifically told their assigned group by the research team. Researchers visiting schools for top-up training and collection of fidelity measures were not blinded.


The intervention, co-developed with PE experts and teachers, consisted of a one academic year (10 months: September 2017–June 2018) HIIT-style VPA programme delivered by PE teachers during regular Year 8 PE lessons. Government guidance suggests a minimum of 2 h per week of curricular PE, which is typically scheduled in two, one-hour lessons. Teachers were instructed to incorporate two elements of additional VPA bursts into lessons, consisting of: (1) 4 min of VPA as part of an active 10-min warm-up, and (2) three 2-min (VPA) infusions per hour of PE, where VPA was defined as activity that raises the heart rate to 71–85% of the maximum heart rate [57]. We defined the intervention as HIIT-style because it meets several HIIT criteria [40, 41], yet other HIIT definitions exist that emphasise a higher intensity [58]. To minimise the risk of injuries, the teacher training stressed that pupils should be working vigorously rather than maximally.

The intervention was co-developed with PE experts and teachers. A pilot phase in seven schools explored the feasibility of a multi-component approach to maximising activity during PE, which included a mix of practical lesson organisation strategies and theory-led teaching principles [59]. But feedback from schools indicated a simpler, more structured intervention would be more feasible for teachers to administer while also following the PE curriculum. The efficacy of brief fitness “infusions” to raise heart rate was confirmed in pilot schools. While acknowledging the usefulness of implementation frameworks for optimising study design for disparate settings, we concluded that a simple intervention was best suited to our range of geographical, social and cultural environments.

The warm-up was delivered at the start of each lesson and began with light-intensity movements (e.g. wrist rotations), followed by moderate-intensity activities (e.g. arm rotations) to incorporate two 2-min bursts of VPA (e.g. vigorous arm sprints; running on the spot). The three 2-min infusions (per hour of PE) were incorporated in the main PE lesson. The infusions included, for instance, fast arm rotations, squats and lunges, and sprinting on the spot, interspersed with brief (active) rest periods if needed during each infusion. These were intended to improve CRF, with some incidental benefits for muscular fitness. Teachers were invited to create their own warm-up and/or infusions that met the required intensity and duration of PA.

Schools in the control arm were asked to deliver PE as usual. To minimise drop-out and ensure compliance, school participation was incentivised with each school receiving £500 upon completion of the primary outcome assessment post-intervention.

Teacher training

PE teachers from intervention schools were trained prior to the start of the intervention by attending an online or face-to-face 2-h training session delivered by a team from Oxford Brookes University. Sessions provided an overview of VPA and guideline daily activity levels, how increasing PA is thought to benefit learning and thinking skills, an overview of intervention components and a timetable of assessments in school. Sessions also included a practical exercise on lesson planning and videos of an experienced PE teacher delivering infusions. Following the training session, teachers from intervention schools were given access to the trial’s website containing videos demonstrating the intervention elements. Researchers visited a sample of 30 intervention schools between December 2017 and February 2018 to offer support and answer questions. They offered additional support and top-up training to PE teachers throughout the intervention period.

Intervention fidelity

Intervention fidelity was assessed using a prespecified set of measures, including (1) teacher log books collected throughout the year in intervention schools, (2) an objective measure of class-average minutes of VPA per hour of PE using wrist-worn Axivity AX3 tri-axial accelerometers (Open Lab, Newcastle University, UK) in a convenience sample of participants per school (at least 50% of the Year group, collected once during the intervention period; data processing details are provided in Additional file 3), and (3) pupil-reported compliance with the intervention using a brief three-question survey administered post-intervention in all schools asking whether (i) PE lessons started with a warm-up, (ii) PE lessons included bursts of VPA that raised their heart rate and made them feel out of breath, and (iii) they took part in warm-ups or VPA bursts if asked by PE teachers. Due to low compliance with the log books, NatCen Social Research additionally collected the teacher-reported percentage of PE lessons in which the intervention was delivered as intended (post-intervention, in intervention schools only), as part of the trial evaluation.

Outcome measures

Pupils were assessed before the intervention started (pretest, t0, end of Year 7 prior to the summer break) and immediately following the intervention (posttest, t1). Secondary measures included assessments of CRF, cognitive performance, and mental health.

Cardiorespiratory fitness

CRF was assessed using the 20-m shuttle run (20MSR) test [60]. The outcome measure was the total number of laps completed. Schools with a policy of not using the 20MSR completed the 12 min Cooper Run test instead (n = 4, [61]).

Cognitive performance

Cognitive functioning was assessed with online, computer-based tests of processing speed, visual relational memory and core executive functions (working memory, inhibition, and cognitive flexibility) [62]. The cognitive assessments were programmed in JavaScript using jsPsych [63] and took approximately 50 min to complete. The task order was pseudorandomised across participants within schools. Each participant started with the reaction time task, followed by the remaining tasks in random order. Teachers and pupils were instructed to complete the assessments at home, but a proportion of schools decided to complete the tasks during school time.

Details of the cognitive battery have been provided in the protocol [52]. In brief, the battery consisted of the following tasks:

  • A simple reaction time task was used to assess processing speed. The average reaction time across valid trials was the primary outcome measure.

  • A modified version of a previously described relational memory task [64] was used to assess visual relational memory performance. The proportion of correct responses on valid trials (i.e. accuracy) was the primary outcome measure.

  • A modified Flanker task [65] was used to assess inhibitory control. The congruent and incongruent response accuracies were the primary outcome measures.

  • A visual two-back task [66, 67] was used to assess working memory performance. Response accuracy was the primary outcome measure.

  • A modified colour-shape switching task [65] was used to assess cognitive flexibility. The switch and non-switch response accuracies were the primary outcome measures.

Mental health

Psychosocial problems were measured with the Strength and Difficulties Questionnaire (SDQ) [68], which consists of 25 items measuring five sub-scales: (1) emotional symptoms, (2) conduct problems, (3) hyperactivity / inattention, (4) peer-relationship problems, (5) pro-social behavior. Items are scored from 0 (“not true”) to 2 (“certainly true”). We used combined conduct and hyperactivity scores (externalising, range 0–20) and peer and emotional scores (internalising, range 0–20) as primary outcome measures because there is evidence that in low-risk samples, the more focused subscales might not tap into distinct aspects of mental health [69].

Self-esteem was assessed with the global and physical self-esteem subscales of the short version of the Physical Self-Description Questionnaire [70] (range: 1–7; higher scores indicate better outcomes).

Data analysis

Sample size

Sample size calculations were performed by NatCen for the primary intention-to-treat (ITT) analysis, which compared maths test performance between intervention and control schools [56]. Details on the sample size calculation, including an a-posteriori computed minimum detectable effect size for CRF, cognitive- and mental health outcomes, are provided in Additional file 3.

Data cleaning

The computer-based questionnaire and cognitive assessments were completed in school or at home, without supervision from researchers or teachers. A conservative approach to data cleaning was used, that focussed on removing careless responders or data collection errors from the dataset. We additionally removed schools (n = 4, 561 pupils) that completed the Cooper Run Test. Details of the cleaning procedures are provided in Additional file 3.

Statistical analysis

The primary analyses were performed on an intention-to-treat basis on multiply imputed data under the assumption data was missing at random (MAR). To assess the effect of the intervention on CRF, cognitive outcomes, and mental health, we followed recommendations for (cluster) randomised trials: models included baseline values of the outcome measure (ANCOVA) and included the stratification variable used at randomization (school gender-type) [71]. Standard errors were corrected for the clustering of pupils within school using robust (sandwich) estimators (type CR2), that have shown good performance even with a small number of clusters [72].

The primary analyses assessed the effect of the intervention on CRF, mental health (self-esteem and internalising-, and externalising symptoms) and cognitive performance. We used confirmatory factor analysis to create an executive function (EF) latent defined by accuracy measures of the colour-shape switching task, Flanker task and two-back task. We used a latent variable model to capture the common variance shared by these different measures. Structural equation models (SEM) were constructed with Full Information Maximum Likelihood estimators to allow for missing data. Standard errors were adjusted for clustering (Huber-White). We subsequently additionally adjusted the primary analysis models for the period (i.e. summer term, holidays or autumn term) and location (i.e. school or home) the tasks were completed.

In line with the analysis of the primary outcome [53], exploratory subgroup analyses were conducted to assess whether any effect of the intervention on secondary outcomes (EF latent variable, relational memory, processing speed, in−/externalising symptoms and global/physical self-esteem) was moderated by sex, socioeconomic status or high or low baseline CRF. Baseline CRF levels were dichotomised into high- and low-fit category using sex-specific normative values (high fit males > 39 laps, high fit females > 28 laps) [73].

We also explored whether a change in CRF was related to a change in cognitive measures or mental health, and whether this relationship was moderated by treatment status, controlling for age, sex, socioeconomic status (eFSM), school gender-type (single sex or co-educational), and location (home or school) and period of completion (summer term, holidays or autumn term) of assessments, using linear models with cluster-robust standard errors.

The primary ITT analysis will provide a conservative (or underestimated) treatment effect when schools do not adhere to the intervention [74]. We therefore estimated a complier average causal effect (CACE) by fitting an instrumental variable model using the two-stage least squares method, to estimate the average treatment effect in the population of compliers (details are provided in Additional File 3). Intervention schools in which the intervention was delivered in > 50% of PE lessons were classified as compliant.

We used multilevel multiple imputation by chained equations (MICE) to impute missing data under the assumption that data was MAR (details in Additional files 3 and 4). We separately imputed the data for the CACE analyses, given the large amount of missingness in the fidelity measure. As a sensitivity analysis, we repeated all the analyses with complete-case data (i.e. available cases per analysis model).

Two-sided inferences with p < 0.05 were considered statistically significant. The adjusted standardized and unstandardized mean differences are provided. The standardized mean difference score was computed using the pooled unconditional standard deviation of the outcome [53, 75]. Bonferroni-correction was applied to correct for multiple comparisons where appropriate. An overview of analysis software is presented in Additional file 3.


One hundred-four schools, totalling 18,261 pupils participated in the study (Fig. 1), and were randomised into an intervention and control group. Prior to baseline assessments but post-randomisation, 11 schools withdrew from the trial. The schools were unaware of their allocated group at the time of drop-out and school or pupil level demographics did not significantly predict drop-out (Additional file 5). Analyses were therefore limited to the schools that were part of the trial at baseline assessments (n = 93, 16,017 pupils). A total of 24 schools (17 intervention) were further lost to follow-up.

Fig. 1
figure 1

CONSORT flow diagram of schools and participants

The trial intended to collect all secondary outcomes in all participants, but for various reasons, including both school drop-out and non-completion at the level of individual pupils, some degree of missing data was present for all secondary measures. Out of 16,017 pupils at baseline, 2182 (~ 14%) completed all secondary outcomes, 8114 (~ 51%) completed the CRF assessment, 6414 (~ 40%) completed the questionnaire and 6174 (~ 39%) completed one or more cognitive assessments, following data cleaning, at post-test. Excluded participants during data cleaning were more likely to be male and from a lower socioeconomic background (see Additional file 6 for an overview). The primary analyses were performed on an intention-to-treat basis on multiply imputed data (n = 16,017). Sensitivity analyses using complete-cases only are reported at the end of the results section.

Sample characteristics

Descriptive statistics for school- and pupil level characteristics at baseline (n = 93) are presented in Table 1 (additional school characteristics are presented in additional file 7). The groups are approximately similar on all measures.

Table 1 School- and pupil level baseline characteristics

Approximately 20% of the sample reported that they were physically active for at least 60 min per day for the entire week (i.e. meeting the PA guidelines), which is comparable between the groups (Additional file 7). Moreover, across schools, on average, 14 min per hour of PE was spent in MVPA, and approximately 4 min in VPA.

School curricula in intervention and control schools included, on average, a similar number of PE lessons (1.61 vs 1.59, range: 1–2), a comparable duration of PE lessons (72.3 vs 72.2 min), and total duration of PE per week (110 vs 107 min; details in Additional file 7).

Effects on cardiorespiratory fitness

CRF levels increased from baseline to post-test across groups (Table 2, and distribution plots of complete-cases in Additional file 8). However, no significant difference in CRF levels were observed between the intervention and control group at posttest (standardized mean difference [SMD] = 0.02, 95% CI: -0.11, 0.16).

Table 2 Mean scores for outcome measures by group, with (un)standardized mean differences

Effects on cognitive function

The primary cognitive outcome measure was an EF latent variable defined by accuracy measures of the colour-shape switching task, Flanker task and two-back task. We used a latent variable model to capture the common variance shared by these different measures. The model used is shown in Fig. 2 and provides an adequate fit to the data (χ2 (54) = 739.431; p < 0.001; RMSEA [Root Mean Square Error of Approximation] = 0.037 [90% CI: 0.035, 0.039]; CFI [Comparative Fit Index] = 0.97; TLI [Tucker-Lewis Index] = 0.96). The EF latent variable shows considerable longitudinal stability (standardized coefficient = 0.82) demonstrating that this construct has good reliability, despite a lack of factorial invariance (i.e. the factor loadings on the EF factor vary across time), possibly because of a differential change in individual assessments due to the intervention. The intervention group showed no significant difference in EF skills compared to the control group at posttest (SMD = 0.017, 95% CI:  -0.14, 0.17).

Fig. 2
figure 2

Path diagram showing the effects of the VPA intervention on an Executive Function (EF) latent variable. All coefficients are standardized except for the path coefficient for Group which is y-standardized (equivalent to Cohen’s d). The 95% robust (Huber-White) confidence interval (CI) is shown for the Group coefficient. The stratification variable (school gender-type, dummy coded) was included as a set of additional covariates but they are not shown in the model. The following correlations between residuals were included in the model (t0 = pretest, t1 = posttest): Flankerincon, t0 with Flankerincon,t1 (0.34), Flankercon,t0 with Flankercon, t1 (0.29), Switchnsw, t0 with Switchnsw, t1 (0.09), Flankercon, t0 with Flankerincon, t0 (0.39) Switchnsw, t0 with Switchsw, t0 (0.074), Flankerincon, t1 with Flankercon, t1 (0.41), 2-backt1 with 2-backt0 (0.38), but are not shown in the diagram. Abbreviations: EF = executive function, incon = incongruent, con = congruent, nsw = non-switch, sw = switch

An improvement in relational memory performance and processing speed (reaction time task) was observed across groups from pre to post-intervention. However, no significant difference was observed in relational memory performance (SMD =  -0.1, 95% CI:  -0.25, 0.02) or processing speed (SMD = 0.04, 95% CI:  -0.07, 0.15) between the intervention and control group at posttest.

Effects on mental health

Participants reported an increase in psychosocial problems from pre to post-intervention. The primary ITT analysis, however, demonstrated no significant difference in internalising (SMD = 0.05, 95%CI:  -0.05, 0.16) and externalising scores (SMD = 0.06, 95%CI: -0.03, 0.16) between intervention and control groups at posttest. Moreover, across treatment groups, a decrease in global- and physical self-esteem was observed from baseline to posttest. However, pupils in the intervention group did not differ significantly in their global (SMD =  -0.05, 95%CI:  -0.22, 0.12) or physical self-esteem (SMD =  -0.01, 95%CI: − 0.16, 0.14) compared to control at posttest.

Subgroup analysis

The effect of the intervention on cognitive and mental health outcomes was not significantly moderated by participants’ sex, socioeconomic status or baseline CRF levels, following correction for multiple comparisons (alpha = 0.002, with 25 comparisons; details in Additional File 9). Uncorrected analyses showed that two (of 25) comparisons had a p-value just below the conventional significance threshold (at p = 0.04, and p = 0.03).

Relationship of change in CRF with changes in cognition and mental health

One-year changes in CRF were not significantly related to changes in cognitive measures or indicators of mental health (internalising-, externalising symptoms, or self-esteem; all corrected-p > 0.05, details are provided in Additional file 10), and these relationships were not moderated by treatment group (corrected-p > 0.05 for interactions).

Intervention fidelity and complier average causal effect analysis

School-level and pupil level fidelity measures were collected towards the end of the intervention or post-intervention (details in Additional file 11). Of 46 intervention schools at baseline, 15 (33%) received face-to-face training, 26 (57%) attended an online training programme and 5 (11%) did not receive training (and were lost to follow-up). Objective measures of PA collected with accelerometers during a single PE lesson during the intervention indicated that pupils in intervention schools spent, on average, more lesson time in VPA compared to control (full lesson: 3.65 vs 3.09 min/hour, p = 0.23; active lesson: 4.91 vs 4.38 min/hour, p = 0.07), but this difference was not statistically significant. Pupil-reported measures of compliance suggested that PE lessons in both intervention and control schools often started with a warm-up and incorporated infusions (median = 4, with 1 = “never” and 5 = “always”). Moreover, pupils in both treatment and control schools indicated that they would take part in a warm-up and infusions if asked by a PE teacher (median: 4.75 vs 5, respectively).

Of 29 intervention schools that were part of the trial at follow-up, 22 provided information on the percentage of PE lessons in which the intervention was delivered as intended. Of these schools, 17 (77%) reported having delivered the intervention as intended in at least 50% of PE lessons across the year and only two schools (9%) delivered the intervention in over 90% of PE lessons. CACE estimates were similar in directionality to and in line with those obtained from the ITT analysis, and are provided in Additional file 11.

Additional analyses

We inspected whether there were baseline differences in self-reported PE enjoyment and attitudes towards PA between the groups, which could make the groups more or less susceptible to intervention effects. There was no evidence of a difference in PE enjoyment (p = 0.91) or PA attitudes at baseline (p = 0.57, details in Additional file 12). Moreover, no significant differences were observed in PE enjoyment (p = 0.58) or attitudes towards PA (p = 0.1) between the intervention and control group at posttest (details in Additional file 12).

Sensitivity analyses

The primary ITT models, testing for the effect of the intervention on CRF, cognitive and mental health outcomes, were additionally adjusted for where (home or school) and when (summer, holidays or autumn) participants completed the assessments. Adjusting for these confounding variables did not change the conclusions (i.e. all p > 0.05 or 95%CI included zero), but minor changes in the size and directionality of treatment effects were observed (details in Additional file 8).

We additionally repeated the primary ITT (Additional file 8), subgroup (Additional file 9), change-change (additional file 10), CACE (Additional file 11) and additional analysis (Additional file 12) on complete-case data. These analyses did not change the conclusions of the primary analyses (all p > 0.05).


In a large cohort of British adolescents, we assessed the effect of the Fit to Study scalable HIIT-style VPA intervention on secondary outcome measures including CRF, cognitive performance, and mental health. In line with findings for the primary outcome (attainment in maths [53]) but in contrast to our hypotheses, the HIIT-style VPA programme, incorporated into regular PE lessons for one academic year, did not significantly improve CRF, cognitive performance (EF, relational memory and processing speed), or mental health at posttest. Moreover, the effect of the intervention was not significantly moderated by sex, socioeconomic status or baseline CRF levels. Finally, one-year changes in CRF were not significantly related to changes in cognitive or mental health outcomes. Crucially, however, only a small proportion of schools complied with the intervention.

Findings in context

Previous studies that examined the effect of HIIT and VPA interventions have shown robust effects on CRF in adolescents [34, 35, 76]. There is less research examining the effects of such interventions on cognitive- and mental health, but studies to date have shown improvements in EF [44,45,46,47, 77] and psychological health [45, 50], though a recent large-scale study found no support for these effects [37]. Despite the increasing body of evidence indicating the effectiveness of high-intensity exercise to improve health outcomes, we did not observe significant improvements in any of the outcome measures. Our findings that sex, socioeconomic status and baseline CRF did not significantly moderate the effects of the intervention on outcomes contrast with evidence from various meta-analyses showing that males [78] or females [79] and those with lower baseline CRF, may benefit most from high intensity PA interventions [80].

It has been hypothesised that changes in CRF – reflective of physiological changes – may mediate PA-related effects on cognitive- and mental health outcomes [81, 82]. In our study, both groups improved their CRF from pre to post-intervention. However, no group mean difference in CRF was found and, in line with findings from a recent meta-analysis [83], no significant relationships between changes in CRF and changes in cognitive performance or mental health were observed.

Given that existing evidence supports the positive impact of HIIT and VPA on CRF and suggests associated improvements in cognitive function and psychological health, it is important to interpret Fit to Study’s null results in the context of the trial: the absence of evidence is not evidence of absence [84]. Crucially, the Fit to Study intervention was limited by poor intervention fidelity, a common issue in the field of PA research [85], and provides an alternative explanation for the lack of the trial’s effectiveness. Fewer than half the schools in the intervention group managed to deliver the intervention in at least 50% of PE lessons during the year. Objective measures of VPA collected during single PE lessons demonstrated that the levels of VPA achieved were lower than the target level: approximately 4–5 min/hour of the active lesson was spent in VPA. Although we detected a trend towards higher VPA in the intervention schools, this failed to reach significance. Clearly, without a significant change or difference in exposure level (i.e. PA), no changes or differences in outcomes are to be expected. To explore the effect of compliance, we estimated the treatment effect in the population of compliers, which consisted of schools delivering the intervention in > 50% of lessons. These analyses did not change the results of the primary intention-to-treat analysis, although it is possible that a dose greater than 50% is required to observe a complier effect.

One of the main criticisms of HIIT-type interventions is the risk of poor adherence and attrition [39, 86], most likely due to the high intensity (and perceived exertion) inherent to HIIT. However, post-intervention teacher surveys, reported previously [53], suggested that not the intensity, but time constraints, lesson disruption (i.e. flow and objectives), lack of space (in combination with class size), seasonal variation and declining engagement due to lack of perceived improvements and repetitiveness of intervention elements, were important determinants of poor adherence in Fit to Study. Indeed, a recent meta-review reported mean adherence rates to be over 80% for HIIT interventions [58]. It is important to note, however, that fidelity is often poorly reported or not reported at all [58, 87], with possible overestimation of adherence due to publication bias. Despite the implementation challenges, schools reported that they would recommend the Fit to Study intervention to promote PA [53].

Moreover, although Fit to Study was evidence-based, its design prioritised the intervention being brief, inexpensive, simple, feasible and scalable. The intervention was therefore implemented in regular PE lessons, delivered by PE teachers, and adapted to suit the school PE curriculum, consisting of a potential total weekly dose of 20 min of HIIT-style VPA. The target duration and/or intensity was therefore low compared to the majority of interventions that have shown HIIT-related improvements in CRF, cognitive-, and mental health. In particular, cognitive- and mental improvements have been observed following 3–5 HIIT sessions per week [44,45,46, 50], while increased CRF levels have been reported following interventions delivered at higher intensities (> 85% max heart rate) [34]. Moreover, unlike previous studies, the Fit to Study HIIT-style infusions may have been interspersed with substantial (i.e. > 30 s - 3 min) recovery periods. Indeed, recent reviews suggest that interventions with intensities > 85% of the maximum heart rate, two-to-three times per week, with longer high intensity intervals (approx. 4 min) and active recovery periods (of approx. 3 min), lasting more than 7 weeks may provide the optimal stimulus for health improvement [36, 58, 80, 88]. While the Fit to Study intervention was delivered for 10 months, it may be that the actual dose of HIIT was too low or fragmented to observe clear effects, though low intervention compliance renders strong statements about dose tentative.

Practical challenges of school-based PA interventions

Large-scale cluster RCTs embedded in school settings, like Fit to Study, have high ecological validity, but face many practical challenges that reduce the methodological quality of the trial and may introduce bias (see e.g. [32, 89] for reviews). Thorough implementation evaluations of Fit to Study [53], as well as a reflection paper [59], including recommendations for future HIIT-based PA trials, have been published elsewhere [53, 59] and various reviews have been published on school-related barriers [32, 33]; here we highlight challenges related to the scale and intervention approach of Fit to Study.

The scale of the Fit to Study trial was required to provide the statistical power to detect small treatment effects (in the primary outcome) and allowed for a representative sample of young adolescents, yet resulted in less control over the intervention (e.g. training, implementation) and poorer measurement of implementation fidelity. It was not possible for all PE teachers to attend the teacher training sessions, primarily due to time constraints. While teacher training videos were provided to help cascade instructions to colleagues, this process may have caused variability in teacher engagement and the quality of intervention delivery within and across schools. To measure such variability in implementation fidelity, objective and self-report measures were put in place. However, self-report measures are subject to response bias (i.e. social desirability [87, 90]), whereas objective measures of adherence, such as actigraphy or heart rate monitors, have major logistical and financial limitations when used at scale [91]. Smartphone-based applications may prove useful for fidelity measurement in future trials [92, 93].

The Fit to Study trial was designed to examine the efficacy of PA to improve maths attainment (secondarily: CRF, cognitive and mental health), and hence, whether HIIT-style VPA is a viable public health strategy for cognitive enhancement. As a consequence, we used a population-based approach [39, 89], rather than a targeted (e.g. sex-specific) approach, and designed the intervention and its outcomes to be scalable and feasible such that all pupils could participate. Such an inclusive approach prevents isolation and stigmatization [94], yet it is unlikely an intervention works for all, despite promising findings from feasibility testing in a small number of schools (unpublished). Indeed, there is evidence that girls may respond better to PA interventions than boys [95], whereas high intensity PA may show greater cognitive benefits in boys [78]. Similarly, the intervention was easily incorporated into lesson plans of some schools, but proved incompatible with lesson objectives and timetables in others [53]. Schools that struggled with delivering all intervention elements were allowed to omit the third infusion occasionally [53], in order to keep schools, teachers and pupils engaged. Such modifications are not desirable, but suggest that flexibility in intervention delivery may be required [51]. Lack of time was the major barrier [53], not only for implementation of the intervention, but also for conducting secondary outcome assessments. Variability in IT-facilities further complicated completion of assessments, resulting in great amounts of missing data. Future trials are encouraged to use implementation frameworks in the design phase to guide implementation and scale-up of PA trials [51].

Strengths and limitations

Strengths of the study include its novelty in implementing a HIIT-style intervention at scale in a real-world setting, it being a highly scalable intervention, which was delivered at low cost [53], its broad range of secondary outcome measures (which we have already shown to be sensitive to other effects, e.g. between fitness and mental health at baseline [96]), its statistical techniques to deal with missing data, and its large cohort of young adolescents, who reported baseline PA levels that were representative of the UK [97] and the world [21], ensuring generalisability.

The study also has various limitations in addition to poor intervention fidelity and large amounts of missing data. Although we collected self-report data on compliance with the intervention, we did not measure whether individuals reached the required intensity because distribution of heart rate monitors to all participants was unfeasible. It is paramount that future studies put in place measures that capture the multidimensional nature of fidelity [98, 99]. No active control group was used in this efficacy trial, and hence, no conclusions regarding the optimal dose can be drawn. Finally, there is some evidence that HIIT interventions may have greater benefits in overweight / obese children and adolescents [37] and in pubertal children compared to pre-pubertal children [36], yet due to the scale of the trial, the use of opt-out consent, and sensitive nature of pubertal questionnaires, no information on weight or pubertal status was collected.


The one-academic-year HIIT-style VPA intervention had no significant effect on CRF, cognitive performance, or mental health in young adolescents. Large amounts of missing data and poor fidelity with the intervention limit the extent to which conclusions can be drawn regarding the causal relationship of physical activity and health outcomes in adolescents. Future well-controlled trials testing for the effects of HIIT-style interventions are warranted.

Availability of data and materials

The datasets generated and analysed during the current study are not publicly available due to the sensitivity of the data, but are available from the corresponding author on reasonable request.



20-m shuttle run


Complier-average causal effect


Consolidated Standards of Reporting Trial guidelines


Cardiorespiratory fitness


Executive functions


Eligible for free school meals


High-intensity interval training




Missing at random


Multiple imputation by chained equations


Moderate-to-vigorous physical activity


National Foundation for Educational Research


Physical activity


Physical education


Randomised controlled trial


Strength and Difficulties Questionnaire


Structural equation model


Standardized mean difference


Template for intervention description and replication


Vigorous physical activity


  1. Dahl RE, Allen NB, Wilbrecht L, Suleiman AB. Importance of investing in adolescence from a developmental science perspective. Nature. 2018;554(7693):441–50.

    Article  PubMed  CAS  Google Scholar 

  2. Larsen B, Luna B. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci Biobehav Rev. 2018;94:179–95.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Sawyer SM, Azzopardi PS, Wickremarathne D, Patton GC. The age of adolescence. Lancet child Adolesc Heal. 2018;2(18):223–8.

    Article  Google Scholar 

  4. Paus T, Keshavan M, Giedd JN. Why do many psychiatric disorders emerge during adolescence? Nat Rev Neurosci. 2008;9(12):947–57.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime prevalence and age-of-onset distributions of. Arch Gen Psychiatry. 2005;62(6):593–602.

    Article  PubMed  Google Scholar 

  6. Fuhrmann D, Knoll LJ, Blakemore SJ. Adolescence as a sensitive period of brain development. Trends Cogn Sci. 2015;19(10):558–66.

    Article  PubMed  Google Scholar 

  7. Patton GC, Sawyer SM, Santelli JS, Ross DA, Afifi R, Allen NB, Arora M, Azzopardi P, Baldwin W, Bonell C, Kakuma R, Kennedy E, Mahon J, McGovern T, Mokdad AH, Patel V, Petroni S, Reavley N, Taiwo K, Waldfogel J, Wickremarathne D, Barroso C, Bhutta Z, Fatusi AO, Mattoo A, Diers J, Fang J, Ferguson J, Ssewamala F, Viner RM, et al. Our future: a lancet commission on adolescent health and wellbeing. Lancet. 2016;387(10036):2423–78.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 2018 Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report [Internet]. Washington, DC; 2018.

  9. Voss MW, Vivar C, Kramer AF, Van Praag H. Bridging animal and human models of exercise-induced brain plasticity. Trends Cogn Sci. 2013;17(10):525–44.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Biddle SJH, Ciaccioni S, Thomas G, Vergeer I. Physical activity and mental health in children and adolescents: an updated review of reviews and an analysis of causality. Psychol Sport Exerc. 2019;42:146–55.

    Article  Google Scholar 

  11. Gunnell KE, Poitras VJ, LeBlanc A, Schibli K, Barbeau K, Hedayati N, et al. Physical activity and brain structure, brain function, and cognition in children and youth: a systematic review of randomized controlled trials. Ment Health Phys Act. 2018;16:105–27.

    Article  Google Scholar 

  12. Singh AS, Saliasi E, Van Den Berg V, Uijtdewilligen L, De Groot RHM, Jolles J, et al. Effects of physical activity interventions on cognitive and academic performance in children and adolescents: a novel combination of a systematic review and recommendations from an expert panel. Br J Sports Med. 2019;53(10):640–7.

    Article  PubMed  Google Scholar 

  13. Álvarez-Bueno C, Pesce C, Cavero-Redondo I, Sánchez-López M, Martínez-Hortelano JA, Martínez-Vizcaíno V. The effect of physical activity interventions on children’s cognition and metacognition: a systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry. 2017;56(9):729–38.

  14. Álvarez-Bueno C, Pesce C, Cavero-Redondo III, Sánchez-López M, Garrido-Miguel M, Martínez-Vizcaíno V. Academic achievement and physical activity: a meta-analysis. Pediatrics. 2017;140(6):e20171498.

    Article  PubMed  Google Scholar 

  15. Ekeland E, Heian F, Hagen KB. Can exercise improve self esteem in children and young people? A systematic review of randomised controlled trials. Br J Sports Med. 2005;39(11):792–8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Twisk JWR, Kemper H, Van Mechelen W. Tracking of activity and fitness and the relationship with cardiovascular disease risk factors. Med Sci Sport Exerc. 2000;32(8):1455–61.

    Article  CAS  Google Scholar 

  17. McKercher C, Sanderson K, Schmidt MD, Otahal P, Patton GC, Dwyer T, Venn AJ, et al. Physical activity patterns and risk of depression in young adulthood: a 20-year cohort study since childhood. Soc Psychiatry Psychiatr Epidemiol. 2014;49(11):1823–34.

    Article  PubMed  Google Scholar 

  18. Perez EC, Bravo DR, Rodgers SP, Khan AR, Leasure JL. Shaping the adult brain with exercise during development: emerging evidence and knowledge gaps. Int J Dev Neurosci. 2019;78(1):147–55.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Middleton LE, Barnes DE, Lui LY, Yaffe K. Physical activity over the life course and its association with cognitive performance and impairment in old age. J Am Geriatr Soc. 2010;58(7):1322–6.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57.

    Article  PubMed  Google Scholar 

  21. Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1.6 million participants. Lancet Child Adolesc Heal. 2020;9(4):335–8.

    Google Scholar 

  22. Dumith SC, Gigante DP, Domingues MR, Kohl HW. Physical activity change during adolescence: a systematic review and a pooled analysis. Int J Epidemiol. 2011;40(3):685–98.

    Article  PubMed  Google Scholar 

  23. Booth M, Okely A. Promoting physical activity among children and adolescents: the strengths and limitations of school-based approaches. Heal Promot J Aust. 2005;16(1):52–4.

    Article  Google Scholar 

  24. Love R, Adams J, van Sluijs EMF. Are school-based physical activity interventions effective and equitable? A meta-analysis of cluster randomized controlled trials with accelerometer-assessed activity. Obes Rev. 2019;20(6):859–70.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jones M, Defever E, Letsinger A, Steele J, Mackintosh KA. A mixed-studies systematic review and meta-analysis of school-based interventions to promote physical activity and/or reduce sedentary time in children. J Sport Heal Sci. 2019;9(1):3–17.

    Article  Google Scholar 

  26. Lonsdale C, Rosenkranz RR, Peralta LR, Bennie A, Fahey P, Lubans DR. A systematic review and meta-analysis of interventions designed to increase moderate-to-vigorous physical activity in school physical education lessons. Prev Med. 2013;56(2):152–61.

  27. Kriemler S, Meyer U, Martin E, Van Sluijs EMF, Andersen LB, Martin BW. Effect of school-based interventions on physical activity and fitness in children and adolescents: a review of reviews and systematic update. Br J Sports Med. 2011;45(11):923–30.

    Article  PubMed  CAS  Google Scholar 

  28. Dobbins M, De Corby K, Robeson P, Husson H, Tirilis D. School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6-18. Cochrane Database Syst Rev. 2009;18(1):CD007651.

    Google Scholar 

  29. Dobbins M, Husson H, Decorby K, Rl L. School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6 to 18 (review). Cochrane Database Syst Rev. 2013;18(2):CD007651.

    Google Scholar 

  30. Pozuelo-Carrascosa DP, García-Hermoso A, Álvarez-Bueno C, Sánchez-López M, Martinez-Vizcaino V. Effectiveness of school-based physical activity programmes on cardiorespiratory fitness in children: a meta-analysis of randomised controlled trials. Br J Sports Med. 2018;52(19):1234–40.

    Article  PubMed  Google Scholar 

  31. Liu M, Wu L, Ming Q. How does physical activity intervention improve self-esteem and self-concept in children and adolescents? Evidence from a meta-analysis. PLoS One. 2015;10(8):e0134804.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Naylor P-JJ, Nettlefold L, Race D, Hoy C, Ashe MC, Wharf Higgins J, et al. Implementation of school based physical activity interventions: A systematic review. Prev Med. 2015;72:95–115.

  33. Hills AP, Dengel DR, Lubans DR. Supporting public health priorities: recommendations for physical education and physical activity promotion in schools. Prog Cardiovasc Dis. 2015;57(4):368–74.

    Article  PubMed  Google Scholar 

  34. Costigan SA, Eather N, Plotnikoff RC, Taaffe DR, Lubans DR. High-intensity interval training for improving health-related fitness in adolescents: a systematic review and meta-analysis. Br J Sports Med. 2015;49(19):1253–61.

    Article  PubMed  CAS  Google Scholar 

  35. Logan GRMM, Harris N, Duncan S, Schofield G. A review of adolescent high-intensity interval training. Sport Med. 2014;44(8):1071–85.

    Article  Google Scholar 

  36. Eddolls WTB, McNarry MA, Stratton G, Winn CON, Mackintosh KA. High-intensity interval training interventions in children and adolescents: a systematic review. Sport Med. 2017;47(11):2363–74.

    Article  Google Scholar 

  37. Lubans DR, Smith JJ, Eather N, Leahy AA, Morgan PJ, Lonsdale C, et al. Time-efficient intervention to improve older adolescents’ cardiorespiratory fitness: findings from the “burn 2 learn” cluster randomised controlled trial. Br J Sports Med. 2020:1–9.

  38. Costigan SA, Eather N, Plotnikoff RC, Taaffe DR, Pollock E, Kennedy SG, Lubans DR, et al. Preliminary efficacy and feasibility of embedding high intensity interval training into the school day: a pilot randomized controlled trial. Prev Med Rep. 2015;2:973–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Biddle SJH, Batterham AM. 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):1–8.

    Article  Google Scholar 

  40. Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle: part I: cardiopulmonary emphasis. Sport Med. 2013;43(5):313–38.

    Article  Google Scholar 

  41. Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle: part II: anaerobic energy, neuromuscular load and practical applications. Sport Med. 2013;43(10):927–54.

    Article  Google Scholar 

  42. Gibala MJ, Little JP, Van Essen M, Wilkin GP, Burgomaster KA, Safdar A, et al. Short-term sprint interval versus traditional endurance training: similar initial adaptations in human skeletal muscle and exercise performance. J Physiol. 2006;575(3):901–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Benda NMM, Seeger JPH, Stevens GGCF, Hijmans-Kersten BTP, Van Dijk APJ, Bellersen L, et al. Effects of high-intensity interval training versus continuous training on physical fitness, cardiovascular function and quality of life in heart failure patients. PLoS One. 2015;10(10):e0141256.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Moreau D, Kirk IJ, Waldie KE. High-intensity training enhances executive function in children in a randomized , placebo-controlled trial. Elife. 2017;6:e25062, High-intensity training enhances executive function in children in a randomized, placebo-controlled trial, DOI:

  45. Costigan SA, Eather N, Plotnikoff RC, Hillman CH, Lubans DR. High-intensity interval training for cognitive and mental health in adolescents. Med Sci Sports Exerc. 2016;48(10):1985–93.

    Article  PubMed  Google Scholar 

  46. Jeon YK, Ha CH. The effect of exercise intensity on brain derived neurotrophic factor and memory in adolescents. Environ Health Prev Med. 2017;22(1):1–6.

    Article  CAS  Google Scholar 

  47. Leahy AA, Michels MFI, Eather N, Hillman CH, Shigeta TT, Lubans DR, Smith JJ, et al. Feasibility of test administration and preliminary findings for cognitive control in the burn 2 learn pilot randomised controlled trial. J Sports Sci. 2020;38(15):1708–16.

    Article  PubMed  Google Scholar 

  48. Venckunas T, Snieckus A, Trinkunas E, Baranauskiene N, Solianik R, Juodsnukis A, Streckis V, Kamandulis S, et al. Interval running training improves cognitive flexibility and aerobic power of young healthy adults. J Strength Cond Res. 2016;30(8):2114–21.

    Article  PubMed  Google Scholar 

  49. Leahy AA, Mavilidi MF, Smith JJ, Hillman CH, Eather N, Barker D, et al. Review of High-Intensity Interval Training for Cognitive and Mental Health in Youth. Med Sci Sport Exerc. 2020;52:10.

    Google Scholar 

  50. Leahy AA, Eather N, Smith JJ, Hillman CH, Morgan PJ, Plotnikoff RC, Nilsson M, Costigan SA, Noetel M, Lubans DR, et al. Feasibility and preliminary efficacy of a teacher-facilitated high-intensity interval training intervention for older adolescents. Pediatr Exerc Sci. 2019;31(1):107–17.

    Article  PubMed  Google Scholar 

  51. Kennedy SG, Leahy AA, Smith JJ, Eather N, Hillman CH, Morgan PJ, Plotnikoff RC, Boyer J, Lubans DR, et al. Process evaluation of a school-based high-intensity interval training program for older adolescents: the burn 2 learn cluster randomised controlled trial. Children. 2020;7(12):299.

    Article  PubMed Central  Google Scholar 

  52. Wassenaar TM, Wheatley CM, Beale N, Salvan P, Meaney A, Possee JB, Atherton KE, Duda JL, Dawes H, Johansen-Berg H, et al. Effects of a programme of vigorous physical activity during secondary school physical education on academic performance, fitness, cognition, mental health and the brain of adolescents (fit to study): study protocol for a cluster-randomised trial. Trials. 2019;20(1):189.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Husain F, Bartasevicius V, Marshall L, Chidley S, Forsyth E. Fit to study. Eval Rep. 2019.

  54. Scott JJ, Morgan PJ, Plotnikoff RC, Lubans DR. Reliability and validity of a single-item physical activity measure for adolescents. J Paediatr Child Health. 2015;51(8):787–93.

    Article  PubMed  Google Scholar 

  55. Hagger MS, Chatzisarantis N, Biddle SJH, Orbell S. Antecedents of children’s physical activity intentions and behaviour: predictive validity and longitudinal effects. Psychol Health. 2001;16(4):391–407.

    Article  Google Scholar 

  56. Husain F. Fit to Study. Evaluation protocol [Internet]. 2017.

  57. Norton K, Norton L, Sadgrove D. Position statement on physical activity and exercise intensity terminology. J Sci Med Sport. 2010;13(5):496–502.

  58. Martland R, Mondelli V, Gaughran F, Stubbs B. Can high-intensity interval training improve physical and mental health outcomes? A meta-review of 33 systematic reviews across the lifespan. J Sports Sci. 2020;38(4):430–69.

    Article  PubMed  Google Scholar 

  59. Wheatley C, Beale N, Wassenaar T, Graham M, Eldridge E, Dawes H, Johansen-Berg H, et al. Fit to study: reflections on designing and implementing a large-scale randomized controlled trial in secondary schools. Trends Neurosci Educ. 2020;20:100134.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Léger 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.

    Article  PubMed  Google Scholar 

  61. Cooper KH. A means of assessing maximal oxygen intake. JAMA. 1968;203(3):201–4.

    Article  PubMed  CAS  Google Scholar 

  62. Diamond A. Executive functions. Annu Rev Psychol. 2013;64(1):135–68.

    Article  PubMed  Google Scholar 

  63. De Leeuw JR. jsPsych: a JavaScript library for creating behavioral experiments in a web browser. Behav Res Methods. 2015;47(1):1–12.

    Article  PubMed  Google Scholar 

  64. Chaddock L, Erickson KI, Prakash RS, Kim JS, Voss MW, Vanpatter M, et al. A neuroimaging investigation of the association between aerobic fitness, hippocampal volume, and memory performance in preadolescent children. Brain Res. 2010;1358:172–83.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Hillman CH, Pontifex MB, Castelli DM, Khan NA, Raine LB, Scudder MR, Drollette ES, Moore RD, Wu CT, Kamijo K, et al. Effects of the FITKids randomized controlled trial on executive control and brain function. Pediatrics. 2014;134(4):e1063–71.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Chen A-G, Yan J, Yin H-C, Pan C-Y, Chang Y-K. Effects of acute aerobic exercise on multiple aspects of executive function in preadolescent children. Psychol Sport Exerc. 2014;15(6):627–36.

    Article  CAS  Google Scholar 

  67. Guiney H, Machado L. Benefits of regular aerobic exercise for executive functioning in healthy populations. Psychon Bull Rev. 2013;20(1):73–86.

    Article  PubMed  Google Scholar 

  68. Goodman R. The strengths and difficulties questionnaire: a research note. J Child Psychol Psychiatry. 1997;38(5):581–6.

    Article  PubMed  CAS  Google Scholar 

  69. Goodman A, Lamping DL, Ploubidis GB. When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the strengths and difficulties questionnaire (SDQ): data from british parents, teachers and children. J Abnorm Child Psychol. 2010;38(8):1179–91.

    Article  PubMed  Google Scholar 

  70. Marsh HW, Martin AJ, Jackson S. Introducing a short version of the physical self description questionnaire: new strategies, short-form evaluative criteria, and applications of factor analyses. J Sport Exerc Psychol. 2010;32(4):438–82.

    Article  PubMed  Google Scholar 

  71. Kahan BC, Morris TP. Reporting and analysis of trials using stratified randomisation in leading medical journals: review and reanalysis. BMJ. 2012;345(7879):1–8.

    Google Scholar 

  72. Pustejovsky JE, Tipton E. Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models. J Bus Econ Stat. 2018;36(4):672–83.

    Article  Google Scholar 

  73. Tomkinson GR, Lang JJ, Tremblay MS, Dale M, Leblanc AG, Belanger K, et al. International normative 20 m shuttle run values from 1142026 children and youth representing 50 countries. Br J Sports Med. 2017;51(21):1545–54.

    Article  PubMed  Google Scholar 

  74. Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials. 2012;9(1):48–55.

    Article  PubMed  Google Scholar 

  75. White IR, Thomas J. Standardized mean differences in individually-randomized and cluster-randomized trials, with applications to meta-analysis. Clin Trials. 2005;2(2):141–51.

    Article  PubMed  Google Scholar 

  76. Owens S, Galloway R, Gutin B. The case for vigorous physical activity in youth. Am J Lifestyle Med. 2017;11(2):96–115.

    Article  PubMed  Google Scholar 

  77. Davis CL, Tomporowski PD, McDowell JE, Austin BP, Miller PH, Allison JD, et al. Exercises improves executive function and achievement and alters brain activation in overweight children a randomized controlled trial. Health Psychol. 2011;30(1):91–8.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Ludyga S, Gerber M, Pühse U, Looser VN, Kamijo K. Systematic review and meta-analysis investigating moderators of long-term effects of exercise on cognition in healthy individuals. Nat Hum Behav. 2020;4(6):603–12.

    Article  PubMed  Google Scholar 

  79. Barha CK, Davis JC, Falck RS, Nagamatsu LS, Liu-Ambrose T. Sex differences in exercise efficacy to improve cognition: a systematic review and meta-analysis of randomized controlled trials in older humans. Front Neuroendocrinol. 2017;46:71–85.

    Article  PubMed  Google Scholar 

  80. Weston M, Taylor KL, Batterham AM, Hopkins WG. Effects of low-volume high-intensity interval training (HIT) on fitness in adults: a meta-analysis of controlled and non-controlled trials. Sport Med. 2014;44(7):1005–17.

    Article  Google Scholar 

  81. North CT, McCullagh P, Tran ZV. Effect of exercise on depression. Exerc Sport Sci Rev. 1990;18(1):379–416.

    Article  CAS  PubMed  Google Scholar 

  82. Aberg MAI, Pedersen NL, Torén K, Svartengren M, Bäckstrand B, Johnsson T, et al. Cardiovascular fitness is associated with cognition in young adulthood. Proc Natl Acad Sci. 2009;106(49):20906–11.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Tuvey S, Steele J, Horton E, Mayo X, Liguori G, Mann S, et al. Do changes in cardiorespiratory fitness resulting from physical activity interventions impact academic performance and executive function in children and adolescents? A systematic review, meta-analysis, and meta-regression. OSF. 2019;

  84. Alderson P. Absence of evidence is not evidence of absence. BMJ. 2004;328:477–9.

    Article  Google Scholar 

  85. Tarp J, Domazet SL, Froberg K, Hillman CH, Andersen LB, Bugge A. Effectiveness of a School-Based Physical Activity Intervention on Cognitive Performance in Danish Adolescents: LCoMotion — Learning, Cognition and Motion – A Cluster Randomized Controlled Trial. PLoS One. 2016;11(6):1–19.

  86. Perri MG, Anton SD, Durning PE, Ketterson TU, Sydeman SJ, Berlant NE, Kanasky WF, Newton RL, Limacher MC, Martin AD, et al. Adherence to exercise prescriptions: effects of prescribing moderate versus higher levels of intensity and frequency. Health Psychol. 2002;21(5):452–8.

    Article  PubMed  Google Scholar 

  87. Lambert JD, Greaves CJ, Farrand P, Cross R, Haase AM, Taylor AH. Assessment of fidelity in individual level behaviour change interventions promoting physical activity among adults: a systematic review. BMC Public Health. 2017;17(1):765.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Calverley TA, Ogoh S, Marley CJ, Steggall M, Marchi N, Brassard P, Lucas SJE, Cotter JD, Roig M, Ainslie PN, Wisløff U, Bailey DM, et al. HIITing the brain with exercise; mechanisms, consequences and practical recommendations. J Physiol. 2020;598(13):2513–30.

    Article  PubMed  CAS  Google Scholar 

  89. Van Sluijs EMF, Kriemler S. Reflections on physical activity intervention research in young people - dos, don’ts, and critical thoughts. Int J Behav Nutr Phys Act. 2016;13(1):25.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Breitenstein SM, Gross D, Garvey CA, Hill C, Fogg L, Resnick B. Implementation fidelity in community-based interventions. Res Nurs Health. 2010;33(2):164–73.

    Article  PubMed  PubMed Central  Google Scholar 

  91. Lee I-MM, Shiroma EJ. Using accelerometers to measure physical activity in large- scale epidemiologic studies: issues and challenges. Br J Sports Med. 2014;48(3):197–201.

    Article  PubMed  Google Scholar 

  92. Smith JJ, Morgan PJ, Plotnikoff RC, Dally KA, Salmon J, Okely AD, Finn TL, Lubans DR, et al. Smart-phone obesity prevention trial for adolescent boys in low-income communities: the ATLAS RCT. Pediatrics. 2014;134(3):e723–31.

    Article  PubMed  Google Scholar 

  93. Savage N. Made to measure. Nature. 2015;527(7576):S12–3.

    Article  PubMed  CAS  Google Scholar 

  94. Pössel P, Smith E, Alexander O. LARS&LISA: a universal school-based cognitive-behavioral program to prevent adolescent depression. Psicol Reflex Crit. 2018;31(1):23.

    Article  PubMed  PubMed Central  Google Scholar 

  95. Yildirim M, Van Stralen MM, Chinapaw MJM, Brug J, Van Mechelen W, Twisk JWR, et al. For whom and under what circumstances do school-based energy balance behavior interventions work? Systematic review on moderators. Int J Pediatr Obes. 2011;6(2–2):46–57.

    Article  Google Scholar 

  96. Wheatley C, Wassenaar T, Salvan P, Beale N, Nichols T, Dawes H, Johansen-Berg H, et al. Associations between fitness, physical activity and mental health in a community sample of young British adolescents: baseline data from the Fit to Study trial. BMJ Open Sport Exerc Med. 2020;6:e000819.

  97. Sport England. Active lives children and young people survey attitudes towards sport and physical activity (academic year 2017/18) [internet]. Sport England 2018.

  98. Mars T, Ellard D, Carnes D, Homer K, Underwood M, Taylor SJC. Fidelity in complex behaviour change interventions: A standardised approach to evaluate intervention integrity. BMJ Open. 2013;3:11.

    Article  Google Scholar 

  99. Taylor KL, Weston M, Batterham AM. Evaluating intervention fidelity: an example from a high-intensity interval training study. PLoS One. 2015;10(4):e0125166.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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We thank the Fit to Study investigators ( for their help with planning and delivering the Fit to Study trial. Thanks to Josh de Leeuw for assistance with programming in jsPsych and to Richard Neil and the late Kate Curtis for their advice on intervention development and testing in schools. Thanks to Charles Hulme for conducting the latent variable analysis. Finally, we would like to thank all the schools, teachers and pupils who took part in Fit to Study.


The research was funded by the Education Endowment Foundation (EEF) and the Wellcome Trust under their Education and Neuroscience Programme (Grant Reference 2681). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z). HJB is a Wellcome Trust Principal Research Fellow (110027/Z/15/Z) and is supported by the NIHR Oxford Biomedical Research Centre. TEN is partially supported by the Wellcome Trust (100309/Z/12/Z). HD is supported by the Elizabeth Casson Trust and the NIHR Oxford Health Biomedical Research Centre. KDO was supported by UK Wellcome Trust Institutional Strategic Support Fund- LSHTM Fellowship (204928/Z/16/Z) and Wellcome Trust - Royal Society Sir Henry Dale Fellowship (218554/Z/19/Z). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

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Authors and Affiliations



HJB and HD are the principal investigators and conceived the study. NB is the trial coordinator. TW, CW, PS, NB, AM, KA, HD and HJB contributed to the design of the study. TW, CW and NB were responsible for data collection. TW analysed the data. TN and KDO provided statistical advice and guidance. TW drafted the manuscript. All authors were given the opportunity to comment on the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to H. Johansen-Berg.

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Ethics approval and consent to participate

All schools were required to read and sign a memorandum of understanding. Participants and their parent(s)/guardian(s) were asked to read the information sheet and provide opt-out consent. Schools consented for their anonymised data to be published. Individuals who did not opt out of data collection and storage agreed for their anonymised data to be published. The trial has been granted ethical approval by the Central University Research Ethics Committee of Oxford University (Registration No. R48879/RE001). The trial’s secondary measures were retrospectively registered at on 18 September 2017 (NCT03286725).

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:.

CONSORT checklist

Additional file 2:.

TIDieR checklist

Additional file 3:.

Supplementary methods

Additional file 4:.

Missing data overview

Additional file 5:.

Comparison of school drop-outs pre-baseline assessments

Additional file 6:.

Data cleaning

Additional file 7:.

Baseline school, PA and PE characteristics

Additional file 8:.

Additional information for primary outcome analyses

Additional file 9:.

Additional information for subgroup analyses

Additional file 10:.

Additional information for change-change analyses

Additional file 11:.

Intervention fidelity and complier-average causal effect (CACE) analysis

Additional file 12:.

Additional analyses of PE enjoyment and attitudes towards PA

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Wassenaar, T.M., Wheatley, C.M., Beale, N. et al. The effect of a one-year vigorous physical activity intervention on fitness, cognitive performance and mental health in young adolescents: the Fit to Study cluster randomised controlled trial. Int J Behav Nutr Phys Act 18, 47 (2021).

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