Can the use of a single integrated unitary autonomic index provide early clues for eventual eligibility for olympic games?
Optimal autonomic regulation and stress resilience might be considered critical elements of athletic performance. We hypothesize that a novel unitary autonomic index for sports (ANSIs), together with a somatic stress related symptom score (4SQ) might help characterize athletes who were eventually selected for the Rio 2016 Olympic Games Italian team (Rio +).
In this retrospective study we examined 778 athletes (age 24.4 ± 6.7 yrs) who underwent a planned yearly pre-participation screening. All athletes underwent clinical, autonomic and exercise ECG evaluation. The combination of vagal and sympathetic indices from RR variability into ANSIs was performed by radar plot and percent ranking of index variables. We assessed (Rio +) versus (Rio −) athletes also after subdivision into three sport intensity groups (low, mid and high intensity).
Overall there were no significant differences between (Rio +) and (Rio −) athletes when considering individual spectral derived variables. Conversely, the unitary Index ANSIs was significantly higher in (Rio +) compared to (Rio −) athletes (respectively 54.5 ± 29.5 and 47.9 ± 28.4 p = 0.014). This difference was particularly evident (p = 0.017) in the group of athletes characterized by both high static and dynamic components. 4SQ was smaller in the (Rio +) group, particularly in the groups of athletes characterized by both low-medium static and dynamic components.
ANSIs, a proxy of integrated cardiac autonomic regulation and simple assessment of resilience to stress, may differentiate Italian athletes who were eventually selected for participation in the 2016 Rio Olympic Games from those who were not, suggesting the possibility of a “winning functional phenotype”.
KeywordsOlympic Games Elite athletes Autonomic nervous system Stress resilience
Somatic Stress Related Symptom Score
Autonomic nervous system
Unitary multivariate percent ranked ANS index
Unitary autonomic index for sports
Body mass index
ECG chest lead
Italian national olympic committee
Heart rate variability
RR Interval variability
Statistical package for the social sciences
We would like to thank Dana Alon Shiffer (LA, CA, USA) for mother tongue language and English style assistance.
Compliance with ethical standards
Conflict of interest
Authors declare that they have no conflict of interest.
- Badilini F, Pagani M, Porta A (2005) Heartscope: a software tool adressing autonomic nervous system regulation. Comput Cardiol 32:259–262Google Scholar
- Buchheit M (2014) Monitoring training status with HR measures: do all roads lead to Rome?. Front Physiol 5Google Scholar
- D’Souza A, Bucchi A, Johnsen AB, Logantha SJ, Monfredi O, Yanni J, Prehar S, Hart G, Cartwright E, Wisloff U, Dobryznski H, DiFrancesco D, Morris GM, Boyett MR (2014) Exercise training reduces resting heart rate via downregulation of the funny channel HCN4. Nat Commun 5:3775PubMedPubMedCentralGoogle Scholar
- Hess WR. Nobel lecture: the central control of the activity of internal organs”.Nobelprize.org.Nobel Media AB 2014. Web. 29 Sep 2016. http://www.nobelprize.org/nobel_prizes/medicine/laureates/1949/hess-lecture.html
- Iellamo D, Pigozzi F, Spataro A, Di S, Fagnani V, Roselli F, Rizzo A, Malacarne M, Pagani M, Lucini M D (2006) Autonomic and psychological adaptations in Olympic rowers. J Sports Med Phys Fitness 46:598–604Google Scholar
- Katona PG, Jih F (1975) Respiratory sinus arrhythmia: noninvasive measure of parasympathetic cardiac control. JApplPhysiol 39:801–805Google Scholar
- La Rovere MT, Bigger JT Jr, Marcus FI, Mortara A, Schwartz PJ (1998) Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators. Lancet 351:478–484CrossRefPubMedGoogle Scholar
- Lucini D, Milani RV, Costantino G, Lavie CJ, Porta A, Pagani M (2002) Effects of cardiac rehabilitation and exercise training on autonomic regulation in patients with coronary artery disease. AmHeart J 143:977–983Google Scholar
- Pagani M, Lombardi F, Guzzetti S, Rimoldi O, Furlan R, Pizzinelli P, Sandrone G, Malfatto G, Dell’Orto S, Piccaluga E (1986) Power spectral analysis of heart rate and arterial pressure variabilities as a marker of sympatho-vagal interaction in man and conscious dog. CircRes 59:178–193Google Scholar
- Sala R, Malacarne M, Tosi F, Benzi M, Solaro N, Tamorri S, Spataro A, Pagani M, Lucini D (2017b) May a unitary autonomic index help assess autonomic cardiac regulation in elite athletes? Preliminary observations on the national Italian Olympic committee team. J Sports Med.Phys.FitnessGoogle Scholar
- Sassi R, Cerutti S, Lombardi F, Malik M, Huikuri HV, Peng CK, Schmidt G, Yamamoto Y (2015) Advances in heart rate variability signal analysis: joint position statement by the e-cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society. Europace 17:1341–1353CrossRefPubMedGoogle Scholar
- Significance of differences between pairs of subgroups ((Rio +) vs (Rio -)) is indicated in the bottom left tableGoogle Scholar
- Toninelli G, Vigo C, Vaglio M, Porta A, Lucini D, Badilini F, Pagani M (2012) DynaScope: a software tool for the analysis of heart rate variability during exercise. Comput Cardiol 39:181–184Google Scholar
- White DW, Raven PB (2014) Autonomic neural control of heart rate during dynamic exercise: revisited. JPhysiol 592:2491–2500Google Scholar