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Abstract
Objectives Many athletes struggle in managing the end of their career, often gaining weight and adopting unhealthy lifestyles. Lifestyle programmes targeting former athletes who have gained substantial fat mass (FM) postsports career are lacking. We studied the effects of the Champ4Life programme on body composition and other health-related outcomes in former elite athletes with overweight or obesity.
Methods Ninety-four former athletes(42.4±7.3 y, 34.0% female) were recruited and randomly assigned to either an intervention group (IG; n=49) or a control group (CG; n=45). The IG attended 12 educational sessions addressing physical activity, weight management and nutrition. They also had a nutrition appointment aimed to prescribe a moderate caloric deficit(~300–500 kcal/day). Dual-energy X-ray absorptiometry was used to assess body composition. The Short-Form Health Survey-36 questionnaire was used to measure general health-related quality of life. Blood samples were collected to assess cardiometabolic health parameters.
Results At 12 months, the IG lost more weight (estimated difference (ED)=−5.3 kg; −6.9 to −3.8), total FM (ED=−4.1 kg; −5.4 to −2.8) and abdominal FM (ED=−0.49 kg; −0.64 to −0.33) than did the CG (p’s<0.001). Cardiometabolic health markers also improved significantly (p<0.05) more in the IG at 12 months (insulin (ED=−4.9 μU/mL;−8.0 to −1.8); homoeostatic model assessment (ED=−1.2; −2.1 to −0.4); total cholesterol (ED=−21.8 mg/dL; -35.4 to −8.2); low-density lipoprotein (ED=18.2 mg/dL;−29.2 to −7.1)), as did quality-of-life dimensions (physical functioning (ED=11.7; 6.5 to 16.9); physical role (ED=17.6; 2.1 to 33.0); general health (ED=19.4; 11.4 to 27.4); vitality (ED=13.3; 5.3 to 21.3) and mental health (ED=12.3; 4.1 to 20.6)).
Conclusions The Champ4Life programme was effective in substantially reducing total and abdominal FM while preserving fat-free mass and improving health-related markers. These findings will enable evidence-based decisions when implementing lifestyle interventions targeting retired elite athletes.
Trial registeration number NCT03031951.
- athletes
- body composition
- weight loss
- quality of life
- physical fitness
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information. All data relevant to the study are included in the article or uploaded as supplementary information. For more information, please contact the corresponding author.
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Introduction
Athletes must follow nutritional recommendations during their careers to maintain good performances.1 2 When they finish their sports careers, however, their reduced energy expenditure may not lead to an equivalent reduction in energy intake as observed by Stubbs et al3 in lean men after an imposed sedentary routine. Thus, when exposed to new lower energy requirements, it is expected that former athletes face an undesired weight gain.4 Other studies have shown that after they retire from sports careers, not all athletes sustain a regular exercise routine.5 The resulting positive energy balance and subsequent weight gain increase the risk of developing obesity-related adverse health effects,4 particularly in individuals with low levels of physical activity (PA).6 Retired National Football League (NFL) players with a higher body mass index (BMI) have increased odds of developing metabolic syndrome, dyslipidaemia, elevated fasting plasma glucose, blood pressure7 and subclinical atherosclerosis8 compared with their counterparts with a lower BMI.9 Similarly, among Sumo wrestlers, those exposed to higher BMIs showed a higher mortality rate.10 11 In Portugal, as based on self-reported height and weight,~50% of former elite athletes in 2013 are living with overweight or obesity12 whereas the prevalence of preobesity and obesity in the adult Portuguese population was ~58% in 2015.13 Many athletes report difficulties in managing the end of their sports careers mainly due to the lack of plans for career termination.14 After the inevitable end of the athletic career, athletes face challenges in dealing with their new lifestyle routines which may affect well-being and a healthy living, one of the major gaps identified in current athletes’ dual-career research.15 Several studies underscored the relevance of promoting healthier lifestyles behaviours such as the maintenance of sufficient levels of PA in early retirement along with cardiovascular risk factor screening.5–7
Lifestyle interventions are recognised non-pharmacological approaches in several populations for preventing and treating obesity.16 However, tailored healthy lifestyle-promoting programmes targeting former athletes who have gained substantial weight are lacking,4 or their effectiveness has yet to be determined as it has for the obesity prevention programme for retired NFL athletes.17
Additionally, the resulting weight-loss impact of lifestyle programmes relies on measurements of weight or BMI to assess intervention-related changes,18 19 precluding understanding of the distinct contributions of fat mass (FM) and fat-free mass (FFM).20 Loss of FM, mainly at the abdominal area, is associated with improvements in chronic disease risk factors among adults.21–23 Still, measuring FM as part of diet or exercise interventions is usually regarded as a secondary outcome, with weight or BMI as the main outcome.19 24 Former athletes exposed to vigorous elite-class exercise training, mainly those involved in strength or power sports, have higher age-adjusted FFM at older ages than age-matched and area-matched control subjects.25 Hence, lifestyle trials that induce changes in body composition rather than weight should be addressed in this unique and understudied population and should include study of the likely impact of such interventions on health-related markers.
This article describes the results of the 1-year randomised controlled trial Champ4Life in former athletes exposed to a lifestyle intervention vs a waiting-list control group (CG). The primary aim of the trial was to determine whether Champ4Life can help inactive former athletes aged 18–65 years with a BMI ≥25 kg/m2 to reduce total and abdominal FM over 12 months. Secondary outcomes included cardiometabolic blood biomarkers, resting systolic and diastolic blood pressure, free-living PA, physical fitness and quality of life.
Methods
Study design
Champ4life was a 1-year randomised controlled trial targeting former elite athletes living with overweight and obesity. A comprehensive description of the study protocol, including the recruitment procedures, exclusion and inclusion criteria, randomisation process, lifestyle intervention and methods, is available elsewhere (online supplemental file 1).26 27
Supplemental material
Prior to participants’ recruitment, the trial was registered at www.clinicaltrials.gov (clinicaltrials.gov ID: NCT03031951).
Participants
Participants were recruited through media advertisements and direct email, databases, and referral sources provided by the Portuguese Olympic Committee, the Union of Professional Soccer Players and several national sports federations. The following inclusion criteria were adopted: (1) to be a former high-level athlete, aged 18–65 years old; (2) to be inactive (<30 min/day of moderate-intensity PA for at least 5 days per week or <20 min/day of vigorous PA intensity for at least 3 days per week); (3) to have a BMI ≥25 kg/m2 and (4) to be willing to attend the educational sessions at the study site and be ready to modify their diet and their PA habits to lose weight.
To verify eligibility and before the randomisation process, participants performed baseline assessments of body composition, free-living PA, blood pressure, physical fitness, metabolic markers and quality of life.26
Randomisation process
Eligible participants were randomly allocated to an intervention or CG. Randomisation was performed according to an automated computer-generated randomisation scheme managed by the principal investigator. Randomisation was stratified by sex, and random length blocks and sequentially numbered opaque sealed envelopes were used. The study was single-blinded, as the research team who assessed all outcomes were blinded to participant group assignment. Also, all outcome data were kept blinded until the final data entry for the entire study was completed.
Lifestyle intervention programme
The Champ4Life programme aimed to promote sustained lifestyle changes in inactive former athletes with overweight or obesity. Briefly, the IG attended an initial nutrition appointment presented by a certified dietitian to prompt a moderate caloric reduction (~300–500 kcal/day) and to provide a well-balanced personalised diet plan. Follow-up appointments were scheduled to adjust individual energy requirements. Additionally, IG completed 12 educational sessions throughout the 4 months of the intervention. These educational sessions addressed PA, weight management and nutrition; with a detailed description provided elsewhere.26
Waiting list (CG)
Participants from the CG were placed on a waiting list to be offered the Champ4Life programme after they completed all measurements at the three time points: baseline (0 months) and after 4 and 12 months.
Measurements
All the measurements were performed at baseline, postprogramme (4 months) and after follow-up period (12 months), except for lower body strength test and cardiorespiratory fitness test, which were only performed at baseline and after 12 months.
Body composition
Anthropometry
Weight and height were measured with the participants in bathing suits and no shoes to the nearest 0.01 kg and 0.1 cm, using a weight scale and a stadiometer (Seca, Hamburg, Germany), respectively. BMI was calculated as 
and the cutoffs of the WHO were used.28
Dual-energy X-ray absorptiometry
Dual-energy X-ray absorptiometry (DXA; Hologic Explorer-W, Waltham, Massachusetts, USA) was used to determine total FM and FFM as described elsewhere.29 Total abdominal fat, which includes intra-abdominal fat plus subcutaneous fat, was determined at the android region, by identifying a specific region of interest within the analysis programme. Based on a test–retest using 10 participants, the coefficient of variations in our laboratory for FM, FFM and abdominal FM (android region) are 1.7%, 0.8% and 0.01%, respectively.30 31
Resting systolic and diastolic blood pressure
Systolic and diastolic blood pressure were obtained with the participant in the sitting position using a digital sphygmomanometer (HEM-907-E, Omron, Tokyo, Japan).
Energy intake
A 3-day food record (including one weekend day) was collected to characterise the macronutrient composition of the diet at the three assessment times using a software package (Food Processor SQL, ESHA Research, Salem, Oregon, USA).
Free-living PA and energy expenditure
Participants were asked to wear an ActiGraph GT3X+ accelerometer (ActiGraph, Pensacola, Florida, USA) to assess their PA, which was expressed as minutes per day spent in different intensities, as described elsewhere.32 The following cutoffs were used: sedentary, <100 counts/min (≤1.5 metabolic equivalent of task (METs)); light 100–2019 counts/min (1.5–2.9 METs); moderate, 2020–5998 counts/min (3–5.9 METs); vigorous, ≥5999 counts/min (≥6 METs).33 A total of at least three valid days (600 or more minutes of monitor wearing) of accelerometer data were required for reporting.34 Participants were asked to record waking and sleeping hours and accelerometer wear time in a logbook, recording the timing and reasons for every time the device was removed.
Physical fitness
Cardiorespiratory fitness test
The Bruce protocol test was used to assess maximal cardiorespiratory capacity.35 36 The test was performed on a variable-speed and inclined treadmill (Quinton Treadmill, Model 640, 90TM Series).
Strength tests
To assess upper-body strength, forearm maximal isometric strength was determined by use of a JAMAR plus digital portable hand dynamometer (Sammons Preston, Bolingbrook, Illinois, USA). The right and left forearms maximal isometric strength of each participant were assessed, alternately, until three attempts per side were reached. In each attempt, the participant applied the maximal grip strength during ~5 s, with a recovery period of ~60 s. The highest value for each hand was used for the analysis.
To assess lower-body strength, a horizontal leg press isometric test was conducted. Participants were asked to bend the leg and the knee joint at an angle of 110° and to place their feet on a force plate (Bertec, Colombia, USA), which was connected to the acquisition system (Biopac, Systems Model MP100). Participants completed five maximal voluntary repetitions lasting ~30 s. Plux software (Biosignalsplux) was used to analyse the highest value among the maximal voluntary repetitions.26
Blood samples
Measurement of glucose and lipid profiles, including total cholesterol, high-density lipoprotein (HDL) and low-density lipoprotein (LDL), was performed in serum samples using coloured enzymatic tests, in an automated analyzer (Cobas Integra 400, Roche Diagnostics, Portugal). Hlycated haemoglobin (HbA1C) was assessed by high-performance liquid chromatography in an autoanalyzer (HA 8160, A.Menarini Diagnostics, Portugal). Insulin was assessed by use of an automated analyzer (Cobas e411, Roche Diagnostics, Portugal) by electrochemiluminescence. The homoeostatic model assessment for insulin resistance (HOMA-IR) was calculated through the following equation37:
Quality of life
The 36-item Short-Form Health Survey (SF-36) (α=0.82) questionnaire was used to measure general health-related quality of life.38 The SF-36 comprises 36 items composed of 8 dimensions: physical functioning (Cronbach’s α=0.83), physical role limitations (Cronbach’s α=0.89), bodily pain (Cronbach’s α=0.88), general health (Cronbach’s α = 0.82), emotional role limitations (Cronbach’s α=0.74), social functioning (Cronbach’s α=0.71), vitality (Cronbach’s α=0.86) and mental health (Cronbach’s α=0.90).38
Statistics
Statistical analysis was performed using IBM SPSS statistics V.27.0 (IBM). Linear mixed models included randomised group and time as fixed effects, with sex and baseline values as covariates, to assess primary and secondary outcomes for the impact of group, time (baseline—0 months, postintervention—4 months and follow-up—12 months) and group-by-time interaction. The covariance matrix for repeated measures within subjects over time was modelled as Compound Symmetry. Model residual distributions were examined graphically, and by using the Kolmogorov-Smirnov test, and no data transformations were necessary. Contrasts were performed to assess difference-in-differences (DiD) between the IG and CG throughout time [postprogramme(4 months)/follow-up(12 months) – T2, vs baseline(0 months) – T1], calculated as the difference between changes for IG (Difference_IG=Time2 -Time1) and changes for CG (Difference_CG=Time2 – Time1): DiD = (Difference_IG) – (Difference_CG).
All analyses were intention to treat including data from all participants who were randomly assigned.
Sensitivity analyses were carried out for analyses of the two primary outcomes (FM and android FM) and also for body weight and waist circumference, by using imputation of missing data based on multivariate linear regression to simultaneously predict missing outcomes data from demographics and baseline measures. Statistical significance was set at p<0.05 (two tailed).
Results
A flow diagram of the study is presented in figure 1. The recruitment, screening and initial evaluations were initiated in March of 2017; the 1-year intervention started in September 2018 with the follow-up assessments occurring until February of 2020. The main reason participants who showed interest in joining this programme were excluded was being physically active (50.8%). Other reasons such as having BMI <25 kg/m2 (29.5%), taking medications that may affect the energy balance homoeostasis (6.6%), being outside the age range (6.6%), reporting previous weight loss (WL) (3.3%), and not having sufficient information (ie, did not complete the first evaluation) (3.3%) also led to exclusion. Regarding participants who were included in the study, overall adherence at 12 months, defined as the percentage of participants who completed the study, was 72.3% (71.4% IG; 73.3% CG)
Champ4life trial flow chart. BMI, body mass index.
The baseline sociodemographic characteristics of participants allocated to the CG and IG are presented in online supplemental table S1. Most participants had at least 12 years of education and worked full time. Participants were former athletes of different sports such as martial arts (25.6%), football (14.9%), athletics (mainly sprinters, middle-distance and long-distance track and field) (14.9%), dancing/gymnastics (10.6%), swimming (8.5%), volleyball (9.6%), handball (5.3%), rugby (3.2%) and others (7.4%).
Supplemental material
Changes in body composition at the postprogramme (4 months) and 12-month time points, adjusted for baseline values and sex, are presented in table 1.
Changes in body composition at the postprogramme time point (4 months) and at follow-up (12 months)*
The effect of the programme favoured the IG in terms of improved body composition compared with the CG. Specifically, weight loss was greater in the IG than in the CG at both the postprogramme, with an estimated difference (ED) of −4.7 kg (95% CI −6.1 to −3.3; p<0.001), and 12 months (ED=−5.3 kg (95% CI −6.9 to −3.7) p<0.001) time points. The IG also showed greater decreases in BMI, waist circumference, FM (kg and %) and android fat (kg) (p<0.001). The increase in relative FFM(%) was greater in the IG postprogramme and after 12 months (p<0.001).
The results of sensitivity analyses using imputation for missing data and mixed models with adjustments for baseline values and sex were similar (online supplemental table S2).
Supplemental material
Values for metabolic biomarkers and the EDs (adjusted for baseline values and sex) are presented in table 2.
Changes in metabolic biomarkers at the postprogramme time point (4 months) and at follow-up (12 months)*
Postprogramme improvements in systolic and diastolic blood pressure were found in the IG (p<0.05). At the end of the programme, the intervention was effective in reducing diastolic blood pressure, insulin, HOMA, total cholesterol and LDL (p<0.05).
Values for sedentary time, light and moderate-to-vigorous PA, strength and maximal oxygen uptake (VO2max) are presented in table 3. At the end of the program, no differences were found between groups.
Changes in free-living physical activity and physical fitness at the postprogramme time point (4 months) and at follow-up (12 months)
Quality of life
Estimated means and SEs adjusted for baseline values and sex for quality of life are presented in table 4.
Changes in quality-of-life (SF-36 domains) at the postprogramme time point (4 months) and at follow-up (12 months)*
At the end of the programme, the IG reported improvements in physical functioning, physical role, general health, vitality and mental health (p<0.01) compared with the CG.
Considering food diaries analysis, protein intake (g/kg) increased at the post-program time point in the IG (ED=0.21g/kg [95% CI: <0.01 to 0.40], p=0.041), while the carbohydrate and sugar intake decreased significantly (ED=−48.1 g/day (95% CI −88.3 to −7.8) p=0.020 and ED=-20.1g/d [95% CI: -39.3 to -0.8], p=0.041, respectively). At 12 months, the IG showed significant decreases in energy intake (ED=−447.1 kcal/day (95% CI −773.4 to −120.9) p=0.008), carbohydrates (ED=-46.9g/d [95% CI: -90.0 to -3.8], p=0.033), fat (ED=−24.2 g/day (95% CI −41.0 to −7.4) p=0.005 and ED=-0.20g/kg (95% CI: -0.37 to -0.02), p=0.005) and saturated fat (ED=-10.0 g/day (95% CI −17.0 to −3.1) p=0.005).
Discussion
The Champ4Life programme improved body composition and health-related markers in sedentary former elite athletes living with overweight or obesity, presumably by promoting the adoption and sustainability of healthy lifestyle behaviours. The first aim of the project was successfully achieved: participants in the IG lost body weight, body fat, and android fat and maintained these changes for 1 year. Secondary outcomes including metabolic biomarkers (lipid profile, insulin, HOMA), and quality of life were also improved at 1 year. An illustration that summarises the programme results is displayed in figure 2.
Graphical summary of the Champ4life intervention’s outcomes. BP, blood pressure; HOMA, homoeostatic model assessment; LDL, low-density lipoprotein.
Maximum WL was achieved at 12 months, meaning that the participants in the IG were able to sustain their lifestyle behaviours during the follow-up period. These findings extend the results of the PREDIMED-Plus programme, a trial designed to evaluate the effectiveness of a 1-year intensive-weight-loss lifestyle intervention on primary cardiovascular prevention in middle-aged and older adults, which improved body composition outcomes and cardiovascular risk factors.39 However, modest WL at 1 year was observed in similar lifestyle interventions targeting weight management.40 41 Our results suggest that the weight lost during the active intervention phase was effectively maintained after 8 months, although a systematic review of weight-loss clinical trials with a minimum of 1 year of follow-up reported that, in those trials, WL was greatest immediately after the intervention, with some weight regain during the maintenance period.19 Therefore, the favourable results for body composition (ie, a successful fat loss after 4 months and its maintenance during the follow-up period), assessed by a valid and precise method, DXA, substantially reinforce the effectiveness of this intervention. This is particularly relevant because former athletes exposed to vigorous elite-class sports as young adults have higher age-adjusted FFM than do controls when older.25 It is likely that our athletes, who were identified as having preobesity or obesity after retirement, may have sustained their FFM over the weight-gain period throughout adulthood. Therefore, we were able to show that the WL observed in this unique population was due to reductions in FM while FFM was preserved. By contrast, most lifestyle interventions rely on body weight measures to determine the effectiveness of such programmes, which rules out determination of the distinct contribution of FM and FFM to weight changes.19 Compared with previous lifestyle interventions targeting adults with overweight and obesity,40–42 in our former athletes the magnitude of the FM reduction after 12 months was substantially higher (about 5% vs 1%–2%). The magnitude of the reductions observed in the abdominal fat region extend the findings of the 1-year Look AHEAD trial, which was directed to patients with type II diabetes, although outcomes were assessed by use of different methods.21 Indeed, the observed FM reduction, in particular at the abdominal region, is related to improvements in the cardiovascular risk profile of male and female adults with overweight and obesity and patients with type II diabetes.21–23
Even modest WL (3%–5% of initial weight) leads to improvements in several metabolic biomarkers, such as blood pressure, the lipid profile and IR.43 The Champ4Life programme reinforces these health-related improvements, particularly in diastolic blood pressure, insulin, HOMA, total cholesterol and LDL. Other studies with modest WL also showed reductions in some health-related variables.40 44 45 Hołowko et al45 analysed the effects of a 6-week lifestyle intervention on body composition and metabolic profiles in former athletes and observed improvements in total cholesterol, LDL, insulin and HOMA. Similar to the aforementioned study,45 we observed no improvements in HbA1C, glucose levels, HDL or triglycerides, likely because of the absence of participants with diabetes (chronic diseases precluded eligibility to participate in the study). Therefore, because our participants started the intervention without abnormal levels for HbA1C or glucose, we did not expect to observe significant improvements in these outcomes. Nevertheless, it is possible that further WL and its maintenance are needed to optimise some metabolic markers, as previous research observed that participants who lost more weight showed the highest improvements in triglycerides.45
Health-related benefits from lifestyle interventions are often related to improvements in quality of life.46 In the Champ4Life programme, participants increased their scores for physical functioning, general health and vitality. Increased physical functioning has been related with the ability to perform all types of PAs without health limitations.47 Higher vitality and health perception are related with increased perceived energy and a belief in strong personal health.47 Results are in line with the literature, pointing to the important benefits of WL on quality-of-life, especially on physical health dimensions such as physical functioning and general health.48 Furthermore, the literature suggests that WL benefits on health-related quality of life become more pronounced over time, a proposition that also fits the Champ4Life long-term results for physical functioning and general health.49 Besides WL, it is also important to consider the therapeutic effect of participating in the programme per se (eg, group dynamic effects, social support).50
At the end of the programme, sedentary time and moderate-to-vigorous PA did not improve significantly (ED=8.9 min/day (95% CI −19.8 to 37.6), p=0.540; ED=3.6 min/day (95% CI −8.2 to 15.5), p=0.546, respectively) in the IG, corroborating the findings of a recent systematic review on the effects of several lifestyle interventions on sedentary time.51 A considerable number of athletes decrease their PA and adopt sedentary behaviours after retiring from their sports careers.52 During the educational sessions, participants learnt about the benefits of increasing their PA and reducing sedentary behaviour and were challenged to make small changes in their daily life to become more active, although no individual workout plans or in-person exercise sessions were provided. It is likely that former elite athletes struggle with different PA routines after their past involvement in highly demanding sport-related activities. The adjustment to a different type of PA (usually lighter and not as challenging) may lead to some resistance, which should be addressed in future interventions. Previous findings underscore that participation in regular PA may provide positive outcomes to former athletes’ health53; thus, small increases in former athletes’ PA should be promoted by sports-related professionals. Nevertheless, it should be underscored that the reductions observed in body weight and total and abdominal FM may have compensated for the insufficient increase in PA considering the achieved health-related benefits. A recent nationwide population-based cross-sectional study concluded that although PA levels appear to provide benefits in an overall dose-response manner on the risk of developing hypertension or diabetes across BMI categories, people with a higher BMI have a higher odds of developing cardiovascular disease risk factors than do their healthy-BMI counterparts, independently of PA levels.54 Our findings may suggest that weight and body composition management should remain a relevant target for health policies aimed at reducing cardiovascular disease risk in people with overweight or obesity.
The Champ4Life programme was not effective in improving upper-body or lower-body muscular strength or cardiorespiratory fitness, likely as a result of the lack of adoption and sustainability of healthy PA behaviours. However, it should be underscored that the IG increased VO2max by 1 MET, an improvement of 10% relative to baseline, which corresponds to a meaningful clinical outcome. Even smaller increments in cardiorespiratory fitness promote large improvements in health-related outcomes.55 56 Indeed, Lee et al56 observed that a 1-MET increase in cardiorespiratory fitness over time was associated with reductions in all-cause and cardiovascular mortality by 15% and 19%, respectively, in 14 345 men.
Future lifestyle intervention studies will benefit from including exercise sessions along with educational sessions to ensure that participants increase their PA and decrease their sedentary time.
Limitations
Despite the effectiveness of the Champ4Life programme on health-related markers, some limitations need to be addressed. First, abdominal FM was determined by DXA by using the android region of interest, which includes both subcutaneous and visceral adipose tissue (VAT). MRI or CT are the reference methods for determining VAT, although DXA-derived abdominal FM is highly associated with VAT measures.57 Second, this study did not ensure that participants were in neutral energy balance before the intervention. Because we did not establish a strict energy deficit (although the participants received a personalised nutrition plan), the participants may have been exposed to different magnitudes of energy restriction that we did not account for. In addition, because participants were stimulated to increase their PA without any specifications about the amount of time or the intensity, a large discrepancy in energy balance likely occurred. Indeed, it was not possible to clearly separate the impact of moderate-to-vigorous PA from that of exercise because some participants did not appropriately report this information in a logbook. Last, final cardiorespiratory fitness assessments were limited as 22 examinations were suspended owing to the COVID-19 outbreak. Although a trend toward a VO2max improvement was observed, the insufficient number of participants may have contributed to the lack of significance regarding the effectiveness of the Champ4Life intervention on this relevant health-related marker.
Conclusions
The Champ4Life programme was effective in improving body composition and health-related markers in former athletes. The observed improvements in cardiovascular risk markers and quality-of-life dimensions imply that lifestyle interventions may reduce the risk of chronic diseases if participants sustain their improved health behaviours. The findings of the Champ4Life programme will support investigators and stakeholders in the sports-related field in making evidence-based decisions when implementing lifestyle interventions to ensure healthy living of athletes after their sports careers.
Key messages
Why was this study done?
Athletes may find it difficult to manage lifestyle changes after retiring from a sports career because of a lack of support in this transition period.
Studies have shown that after athletes retire from sports careers, they do not maintain regular exercise, likely resulting in positive energy balance and subsequent weight gain.
Former athletes with a higher body mass are highly susceptible to developing metabolic syndrome, dyslipidaemia, elevated fasting plasma glucose and elevated blood pressure.
Tailored lifestyle-promotion programmes targeting former athletes who have gained substantial weight are lacking or have yet to be proven effective.
What are the new findings?
The programme was effective in reducing weight, reducing total and abdominal fat mass, and preserving fat-free mass 12 months after baseline.
The Champ4Life participants also showed improvements in cardiovascular risk markers, quality-of-life dimensions and other secondary outcomes.
The findings of the Champ4Life programme will support evidence-based implementation of lifestyle interventions to ensure healthy living of athletes after their sports careers.
Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information. All data relevant to the study are included in the article or uploaded as supplementary information. For more information, please contact the corresponding author.
Ethics statements
Patient consent for publication
Ethics approval
The Ethics Committee of the Faculty of Human Kinetics, University of Lisbon (Lisbon, Portugal), approved the study (CEFMH Approval Number: 16/2016). The trial was conducted in accordance with the Declaration of Helsinki for human studies from the World Medical Association.
Acknowledgments
Acknowledgments:The authors express their gratitude to all the participants involved in this study. The authors also thank to Jennifer Holmes, ELS, for language and medical editing.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors The Champ4Life programme led by Primary Investigator AMS obtained funding for the research. All authors contributed to the design of the study. The first draft of this manuscript was produced by AMS and CLN. All authors have read and agreed to the published version of the manuscript.
Funding Financial support was provided by the Portuguese Institute of Sports and Youth and by the International Olympic Committee, under the Olympic Solidarity Promotion of the Olympic Values Unit (Sports Medicine and Protection of Clean Athletes Programme). The current work was also supported by national funding from the Portuguese Foundation for Science and Technology within the R&D units UIDB/00447/2020. CLN and RF were supported with a PhD scholarship from the Portuguese Foundation for Science and Technology (SFRH/BD/143725/2019 and 2020.05397.BD, respectively).
Disclaimer The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests In the last 36 months, DA has received personal payments or promises for same from: American Society for Nutrition; Alkermes; American Statistical Association; Big Sky Health; Biofortis; California Walnut Commission; Clark Hill PLC; Columbia University; Dynamic AQS; Fish & Richardson, P.C.; Frontiers Publishing; Gelesis; Henry Stewart Talks; IKEA; Indiana University; Arnold Ventures (formerly the Laura and John Arnold Foundation); Johns Hopkins University; Kaleido Biosciences; Law Offices of Ronald Marron; MD Anderson Cancer Center; Medical College of Wisconsin; National Institutes of Health (NIH); Medpace; National Academies of Science; Sage Publishing; The Obesity Society; Sports Research; The Elements Agency; Tomasik, Kotin & Kasserman; University of Alabama at Birmingham; University of Miami; Nestle; WW (formerly Weight Watchers International); Whistle Labs. Donations to a foundation have been made on his behalf by the Northarvest Bean Growers Association. DA was previously an unpaid member of the International Life Sciences Institute North America Board of Trustees. DA’s institution, Indiana University, and the Indiana University Foundation have received funds or donations to support his research or educational activities from: NIH; USDA; Soleno Therapeutics; American Egg Board; California Walnut Commission, Almond Board; Peanut Institute; Mondelez; National Cattlemen’s Beef Association; Eli Lilly and Co.; Reckitt Benckiser Group; Alliance for Potato Research and Education; American Federation for Aging Research; Dairy Management; Arnold Ventures; the Gordon and Betty Moore Foundation; the Alfred P. Sloan Foundation; and numerous other for-profit and non-profit organisations to support the work of the School of Public Health and the university more broadly.The remaining authors reported no conflicts of interest.
Provenance and peer review Not commissioned; externally peer reviewed.
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