Skip to main content
Sports Medicine - Open
Download PDF

Integrative Proposals of Sports Monitoring: Subjective Outperforms Objective Monitoring

Sports Medicine - Open volume 8, Article number: 41 (2022) Cite this article

A Correction to this article was published on 06 May 2022

This article has been updated

Abstract

Current trends in sports monitoring are characterized by the massive collection of tech-based biomechanical, physiological and performance data, integrated through mathematical algorithms. However, the application of algorithms, predicated on mechanistic assumptions of how athletes operate, cannot capture, assess and adequately promote athletes’ health and performance. The objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as complex adaptive systems (CAS). CAS contain higher-order perceptual units that provide continuous and multilevel integrated information about performer–environment interactions. Such integrative properties offer exceptional possibilities of subjective monitoring for outperforming any objective monitoring system. Future research should investigate how to enhance this human potential to contribute further to athletes’ health and performance. This line of argument is not intended to advocate for the elimination of objective assessments, but to highlight the integrative possibilities of subjective monitoring.

Key Points

  • There is a disconnect between the complexity underpinning human health and performance and the deterministic models through which athletic monitoring and assessment are conceptualized.

  • While focusing on collecting and processing large amounts of data, analysts, scientists and coaches may forget the outstanding potential of the human neurobiological system to dynamically, and rapidly, integrate massive amounts of personal and environmental information.

  • Understanding and valuing subjective-based monitoring are crucial to enhance athletes’ awareness and promote their self-consciousness, autonomy and self-regulation of health and performance.

Introduction

In sporting contexts, the use of monitoring tools and physical activity trackers providing training and general health data has recently expanded dramatically. In 1954, hand-timing regular training runs was considered unusual [1]. However, as technology, beginning in the early 1980s, began to rapidly evolve, new possibilities to assess internal and external training loads during training and competition became available [1]. Today, new wireless technologies are expanding to provide simultaneous data related to biomechanical, physiological and performance variables [2,3,4,5].

Compiling and integrating multiple internal and external variables through a variety of mathematical algorithms (e.g., ACWR) [6,7,8] and making predictions on the basis of artificial intelligence software [9, 10] seem to be leading current, and future, steps in sports monitoring. In effect, there appears to be a tacit assumption that the collection of objective ‘big data’ is the key to provide more pertinent and relevant information to promote athletes’ health and improve athletic performance [11]. Further, moves towards cyborgization—the integration of measurement and computational technologies into human bodies [12, 13]—suggest that current trends of sports monitoring are continuing to evolve towards the tracking of ever-increasing streams of objective data.

Accordingly, coaches and sports scientists are motivated to collect a growing diversity of data, using an array of commercially available assessment technologies [14]. Professional sport organizations are also investing heavily (in terms of time, money and specialized human resources) in new technologies. Providing previously unavailable metrics (e.g., acceleration) without any clear vision of how the new information will be interpreted and actioned appears to be the rule [14]. This approach, however, is not well aligned to the scientific ideal of developing models and hypotheses and then testing deductively. Instead, there is a commercial drive to develop new technologies in advance of hypotheses, to market these technologies based on non-existent or insufficient evidence and to encourage practitioners to embark on little more than 'fishing expeditions' to see what new technologies may offer [15].

The expansive range of emerging technologies may leave analysts and sports science practitioners struggling to deal with collecting, processing and interpreting large quantities of objective data, as occurs, for example, in elite soccer contexts [16, 17]. This, potentially excessive, focuses on objective data may serve to distract practitioners from the outstanding potential of humans to innately integrate and process multiple streams of highly sophisticated information at high speed [18]. Although objective data may be helpful in some contexts (e.g., position data of teams, athletes’ physiological response) [19, 20], it may not inevitably lead to greater insight and better understanding of health and performance [21]. Importantly, there is a clear and problematic disconnect between the neurobiological complexity underpinning human performance and the mechanistic, deterministic, albeit complicated and intricate, biomedical model through which we conventionally conceptualize both athletic training paradigms and athletic assessment protocols [22,23,24].

This apparent disconnect highlights an evident need to evaluate the advantages and costs of technology-enabled objective information against those of athlete-generated subjective information for continuously integrating multilevel, multisource and multimodal information. Accordingly, the objective of this paper is to reorient the current integrative proposals of sports monitoring by re-conceptualizing athletes as CAS.

Monitoring Complex Adaptive Systems

Contemporary athletic monitoring philosophies perpetuate a mechanistic and reductionist philosophical stance in sports training that envisions athletes and teams as linear, deterministic systems composed by multiple components. This view, founded in an historically pervasive, but scientifically inaccurate conception, guides coach decision-making processes in relation to athletes’ health prevention and performance [7, 25].

Neurobiological systems are seen as dominated by components that establish inherently linear, deterministic cause-and-effect relationships among them. Such deterministic systems, when perturbed, respond to imposed stimuli with proportional and predictable adaptive responses as an externally predesigned product of their component behaviours. Yet, across the biological and neurological sciences, this conventional biomedical interpretation has been overthrown by the overwhelming evidence illustrating that human neurobiology is more appropriately conceptualized as a CAS: a nonlinear and dynamic system comprised of multiple embedded complex sub-systems which collaboratively and collectively share co-modulating information both vertically (e.g., genes, cells, tissues, organs, etc.) and horizontally (e.g., among molecules, cells, organs, etc.) to support the continued survival of the unified whole [22, 26].

Sport behaviours, accordingly, are guided and constrained by an embedded and embodied, experience-dependent and goal-directed performer–environment interactions. Hence, athletes’ self-monitoring and self-regulation are important competencies of biological intelligence to perform in cooperative–competitive environments [27].

The failings of applying traditional linear logic to complex sport phenomena have been previously reported, such as in the case of sport injuries [14, 28,29,30,31]. For example, the critical tensile forces that produce muscle rupture in vitro cannot be directly transferred to the complex muscle contraction in vivo [23, 32]. Over the past two decades, the science of complex systems, and particularly the nonlinear dynamic systems theory, has begun to percolate into various branches of sports science [22, 23, 33,34,35]. Recently, the network physiology of exercise—a framework studying the nested dynamics of vertical and horizontal physiological network interactions to understand how physiological states and functions emerge—has been introduced to exercise [22, 26]. This complex system-based approach appears more capable of eloquently capturing the theoretically and methodologically sport-related phenomena such as injuries [29, 30], fatigue [36, 37] or motor control and learning [38,39,40].

Tables 1 and 2 contrast traditional and complex system-based characteristics of athletes and training process, respectively.

Table 1 Characteristics of athletes from traditional and complex system-based approaches. Adapted from Pol et al. [23], with permission
Full size table
Table 2 Characteristics of the training process from traditional and complex system-based approaches. Adapted from Balagué et al. [22], with permission
Full size table

In contrast to complicated machines, CAS’ behaviours cannot be accurately predicted from a particular variable or set of variables. While machine behaviours emerge as a product of their individual component behaviours, in CAS, characterized by an interaction-dominant dynamics, behaviour emerges as a product of the nonlinear integration of personal and environmental influences acting across multiple levels and timescales [45, 46]. A fundamental implication of this inherent and undeniable complexity is that, in the context of complex biological and/or medical contexts, fragmented metrics or even the aggregation of multiple metrics, do not appear to provide meaningful predictive validity [47, 48].

Therefore, some authors have questioned the logic underpinning contemporary trends in sports monitoring [21]. They point out that the empirical assessment of surrogate measures of activity intensity, or isolated measures of physical function, even when blended using integrative algorithms or machine learning techniques, does not provide sufficiently reflective snapshots of current health status and/or performance potential. The prevailing fallacy of completing the puzzle through large quantities of multidimensional (e.g., biomechanical, behavioural, morphological, etc.) athlete-centred objective information lacks theoretical support in uncertain and complex scenarios [49, 50]. Collecting isolated snapshots of a limited number of quantifiable measures, in the absence of a clearly defined and appropriately weighted relational hierarchy of performance priorities, promotes a distorted reality [51, 52]. Importantly, these misleading distortions serve to inappropriately prioritize readily accessible, while neglecting seemingly inaccessible, information [6, 7, 53,54,55]. A famous quote says ‘not everything that counts can be counted, and not everything that can be counted counts’ [56].

The perpetuation of protocols and procedures developed under the influence of this conventional biomedical depiction of the brain as a (predominantly) passive, stimulus-driven organ that waits to receive sensory feedback before processing data and generating responses [57] continues to shape coaches–athlete interactions. The conception of athletes’ as mere executors of coaching instructions does not promote athletes’ awareness, such as quality of attention, depth of understanding and the athletes’ perception of the utility and value of subjective monitoring processes [23]. In addition, this perception dramatically oversimplifies the nature, relevance and influence of interpersonal interactions between coaches, athletes and teams [22, 23]. Through this conceptual lens, the properties which enable CAS to spontaneously, dynamically and adaptively customize behavioural responses to continuously changing context-specific demands are typically either ignored or despised.

As shown in Table 1, acknowledging athletes as CAS drives a reframing of the training process: an appreciation of the mutually entwined integration between biopsychosocial influences, training workloads and training outcomes, while also highlighting existing limitations of conventional athletic monitoring.

In Table 2, the traditional role of coaches is re-envisioned. Instead of fixing objectives and task outcomes, and controlling workloads based on personal opinion blended with monitoring information, through the lens of a CAS-informed philosophy the coach is better perceived as a co-learner, a co-designer and a co-adapter of the training process [23]. A mentor, over time, facilitates the athletes’ journey from dependency to autonomy. Athletes in turn, instead of being mere executers of the coaches’ directives, participate actively in the process, cooperating with the coach in the design and adaptation of training workloads. Although conventional monitoring tools can complement co-designed training processes, the key sources of information come from the athletes’ subjective perceptions.

CAS’ approaches have promoted subjective monitoring [22, 29, 58] but also objective monitoring connected to new methods of analysis or old methods applied to new phenomena [22, 59]. Based on coordination dynamics properties, such methods aim to gain new insights into the changing relationships between groups of variables. Uncontrolled manifold method to capture interpersonal synergies in sport [60], squared coherence to capture cardiorespiratory coupling on acute hypoxia [61], network analysis to capture multilevel organs’ interactions [26, 62] or fractal analysis to capture physiological or kinematic variability of athletes in exhausting exercises [36, 37] may stand as examples. The applicability of this approach, together with the development of adequate technology, should enable in the future a more effective, integrative and realistic encapsulation of CAS’ behaviours. For example, cardiorespiratory coordination variables, extracted by a principal component analysis approach, seem to be more sensitive than common measures (e.g., VO2max or ventilatory thresholds) to reveal specific training adaptations [63, 64], testing manipulations [65], workload accumulation [66] and nutritional interventions [67].

However, at this moment the capacity to quantify, process and instantaneously integrate, interpret and deliver multilevel and multidimensional information may not work if attempted. In contrast, subjective monitoring may provide a practical, efficient and effective means of acquiring continuous high-level actionable information [22, 29, 58].

Subjective and Objective Sports Monitoring: Advantages and Limitations

Subjective and objective sports monitoring are distinct in acquiring information and their interpretations do not always agree [18, 68,69,70,71,72]. As illustrated in Fig. 1, objective monitoring commonly fragments the scale of observations to permit the quantitative precision of isolated metrics. Instead of fragmenting, subjective monitoring enables re-composition, integration and dimensional reduction, e.g., compression of psychological, biomechanical and physiological information. Thereby, the generation of subjective perceptions reflects the blended inputs of multiple channels of information, including the organism–environment interaction, highly relevant for health/performance regulation [24, 73]. General health, well-being, recovery, happiness, readiness to train, stress and mood, all stand as examples [74]. Subjective monitoring may also integrate multiple dimensions of awareness, such as proprioceptive [75], kinaesthetic [76], body [77,78,79], somatic [80], interoceptive [81,82,83,84], environmental [85]. Some authors have defined the in situ interactions of these various flavours of awareness, as prospective situational awareness, conceptualizing it under the term of informed awareness [85,86,87]. In that regard, in comparison with conventional objective monitoring, subjective data collection provides opportunities to capture and integrate online multiple streams of relevant and actionable sensory and perceptual information. The potential of such multidimensional subjective information, dwelling at different timescales [88, 89], relates to the sensitivity for capturing the relevant changes of the organism–environment interaction.

Fig. 1
figure 1

Integrative proposals of objective and subjective monitoring. Objective monitoring: "integration achieved by algorithmic treatment of a collection of independent variables". Subjective monitoring: "integration achieved by experiential dimensional reduction (i.e., information compression) of multilevel organism–environment interactions acting at multiple timescales". Adapted from Balagué et al. [22], with permission

Full size image

Saw et al. [18] showed through a systematic review that self-reported measures may report acute and chronic training loads with superior sensitivity and consistency than common objective measures (e.g., blood markers, oxygen consumption, heart rate). In fact, subjective monitoring is sensitive to change, not only related to training loads [90,91,92,93], but also to overtraining status [18, 94, 95], injuries [96,97,98], illness [99, 100] or even the rhythms in the earth’s magnetic field [101]. In addition, it may also provide relevant information for predicting individuals’ health-related behaviour and decision-making [102] or performance status [103, 104]. Therefore, some authors suggest that subjectively acquired information can be more effectively used to enhance performance and/or recovery, than information collated via objective monitoring [105,106,107]. The use of subjective measures is also recommended in bespoke multifactorial models focused on assessing the complex relationship between athlete health, training load, injury and performance [73].

Despite the increasing availability of high-tech-based objective tools, the use of subjective monitoring has recently increased in specific sport contexts [74, 108]. Typically, the RPE remains one of the most popular objective measures of subjective information in sport (e.g., in professional football, [109]) [94, 110, 111]. Other measures to evaluate constructs such as fatigue, pain or internal training loads have also been validated [73, 92, 112,113,114,115].

As there is no internal absolute scale for measuring subjective variables, and judgments in psychobiological tasks are context dependent, some researchers have proposed the use of ordinal instead of cardinal scales [116,117,118,119,120,121]. Such strategies enable capture of the nonlinear dynamics of integrated variables (e.g., perceived exertion, volition states, attention focus) during exercise and may help to anticipate exhaustion and task failure. Effort phases defined by perceived exertion thresholds, informing about the time and workload intensities that can be sustained over time [118], have been detected and correlated with conventional physiological measures of anaerobic threshold [88]. Such ordinal and continuous recording strategies may solve one of the main limitations of subjective monitoring: to precisely transform the complex information of subjective perception(s) in oversimplified numbers [74]. In particular, during exercise this information emerges from the continuous perception of and acting on affordances dwelling at relatively short timescales [86, 122]. Thus, it is possible to increase the monitored sensitivity to qualitative and non-proportional effects of training and/or competition workloads using ordinal recording strategies [88]. Similarly, verbal feedback with the coach serves to improve performance, to monitor progress and to build common trust and buyin of athletes [123].

In addition, subjective monitoring is cheap and simple to implement compared to objective monitoring, which typically requires an investment in technology and qualified human resources [7, 124]. It can be easily interpreted right there and then by athlete or coach, and it is relatively straigh-forward and self-explanatory [125]. It does not depend on good internet connection or some proprietary software, and it also does not pose data risks in terms of storage, breaches, etc., because it can be processed locally. Further, subjective monitoring is applicable to any athlete, regardless of their performance level, sport or economic possibilities. Consequently, investing in subjective monitoring is not only more sustainable but also an economically valid decision.

Finally, a subtle refocussing on subjective monitoring, through the lens of a CAS-informed approach, may serve to enhance athletes’ autonomy, confidence and promote self-regulation capacities [23, 126]. Furthermore, it also serves to enhance athletes’ and coaches’ education and may drive better comprehension of the training process.

However, several biases may affect the validity and reliability of subjective measures [124, 127, 128], such as the measure’s sensitivity [73, 129] or individual’s subjectivity [11]. In fact, different personal and environmental constraints influence perception [89]. Values and motivation, stable and slow-changing personal constraints, drive faster changing constraints, such as attentional focus and perception [88, 89] (see Fig. 2). The intervention at the level of more stable constraints (e.g., personal values) has a long-lasting effect on the less stable or faster changing constraints (e.g., perceptions). Thus, an effective application of subjective monitoring depends, to a large extent, on the value athletes and coaches place on subjective monitoring. Accordingly, before using subjective monitoring, athletes and coaches should first be educated on its benefits and acquire enough practical experience. Figure 2 hypothesizes that positive changes in beliefs and values towards self-monitoring may enhance athletes’ awareness, increase their autonomy and self-regulation and, thereby, promote their health and performance. In turn, due to circular causality (see Fig. 2), the enhancement of athletes’ awareness and perception will enhance their attention, motivation and value given to subjective monitoring [80, 81]. This may explain why experienced athletes are often more precise in their subjective reports [124].

Fig. 2
figure 2

Relations between slow-changing and fast-changing constraints influencing subjective perception. Adapted from Balagué et al. (89), with permission

Full size image

The pillars of an effective subjective monitoring are athletes’ self-sufficiency, self-consciousness, autonomy and honesty. Athletes who do not fully understand the rationale underpinning subjective monitoring are unlikely to fully and appropriately engage with the monitoring process and may be more open to providing misleading subjective feedback [73].

In structuring an effective monitoring process, it seems of key importance to distinguish when subjective perceptions are sufficient and when complementary objective information is needed [130]. For instance, the objective information about changes occurring at the molecular level can help to detect dysfunctions when the athlete is feeling well [24] or may alert about possible overtraining effects or microscopic injury processes [131]. It can also promote a feeling of security and trust, which has been effective in spontaneous remissions of serious health problems [132].

Importantly, an exclusive focus on objective monitoring may anaesthetize athletes’ sensitivity to internal and external signals. The common overuse of drugs and ergogenic aids (e.g., anti-inflammatory drugs and hormonal treatments) may also contribute to attenuate body signs, producing a loss of sensitivity in athletes [133]. Further, not only athletes but also coaches may be misled when subordinating their decisions to technology [134]. Thus, a shift of focus is reclaimed to avoid replacing the human perception–action cycle by tech-based feedback and expand the already existing human capacities to promote athletes’ health and performance.

Challenges for the Future: Educating Athletes’ Awareness

Pain, injuries or disease may increase athletes’ awareness of previously ignored internal and external information [135,136,137,138]. However, early education, leading to more informed identification and interpretation of relevant information, may serve to enhance this development [29].

Through subjective monitoring, coaches may promote athletes’ awareness and encourage them to focus their attention on health and performance states. A diversity of training challenges may provide opportunities to generate, experience and practice under different physical, cognitive, affective and environmental conditions, thereby providing brain and body with the richness of sensory experience required to adequately stimulate and promote the development of awareness. In this regard, a variety of movement-based contemplative and therapy approaches, often categorized as mind–body (e.g., Yoga, Qigong, Tai Chi), claim to enhance awareness through movement experiences [78]. In fact, bodily sensory systems are the first to develop and play a fundamental role in the formation of the sense of self, which involves a complex interplay of brain, body and environment information [139,140,141,142]. What a living organism senses and perceives is a function of how it moves, and how a living organism moves is a function of what it senses and perceives [143].

Future research should investigate the potential for awareness-promoting strategies and training methods to enhance the value and influence of subjective monitoring systems. Furthermore, the value of educational programmes specifically targeting athletes’ awareness may also provide a productive avenue to promote, in a highly cost-effective manner, self-regulation during training and competition.

Conclusions

Athlete monitoring technologies are pervasive, rapidly evolving and vigorously marketed. As sports organizations become increasingly inundated with ‘big data’, compiling, integrating and distinguishing between worthwhile and unnecessary informational streams has become a formidable challenge, for researchers and practitioners alike. Mathematical algorithms and artificial intelligence techniques clearly do not adequately capture the self-organized and nonlinear dynamic behaviour of CAS. The presumption that empirical data are superior to information generated by a self-aware, educated and informed athlete is, as highlighted here, fundamentally flawed. Subjective monitoring enables integration, blending and dimensional reduction of the multiple information streams that coalesce to shape the performer–environment system.

The validity and functionality of enhanced subjective monitoring, however, demand that both coaches and athletes are adequately informed and engaged, share a common interpretation of key subjective descriptive terms and share a coherent conceptual understanding of the value of subjective assessments. Positive changes in beliefs about self-monitoring may enhance athletes’ awareness, increase engagement, autonomy and self-regulation, and thereby, promote health and performance. This is not an argument to eliminate objective assessments, but to restore badly needed balance and to realign monitoring convention with theory and evidence. Critically, in the future, there is a need to evolve more discerning methods for creatively capturing, interpreting and presenting subjective athlete-generated information. Similarly, there is a clear need to provide specific education programmes to improve both athletes’ awareness and coaches’ understanding of the potential of subjective monitoring and training prescription to enhance both performance and health outcomes.

Availability of Data and Material

Not applicable.

Change history

Abbreviations

ACWR:

Acute: chronic workload ratio

CAS:

Complex adaptive systems

RPE:

Rating of perceived exertion

VO2max :

Maximal oxygen consumption

References

  1. Foster C, Rodriguez-Marroyo JA, de Koning JJ. Monitoring training loads: the past, the present, and the future. Int J Sports Physiol Perform. 2017;12(2):1–8.

    Google Scholar 

  2. Herold M, Kempe M, Bauer P, Meyer T. Attacking key performance indicators in soccer: current practice and perceptions from the elite to youth academy level. J Sports Sci Med. 2021;20:158–69.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Quintas G, Reche X, Sanjuan-Herráez JD, Martínez H, Herrero M, Valle X, et al. Urine metabolomic analysis for monitoring internal load in professional football players. Metabolomics. 2020;16(4):45.

    Article  CAS  PubMed  Google Scholar 

  4. Moser O, Riddell MC, Eckstein ML, Adolfsson P, Rabasa-Lhoret R, van den Boom L, et al. Glucose management for exercise using continuous glucose monitoring (CGM) and intermittently scanned CGM (isCGM) systems in type 1 diabetes: position statement of the European Association for the Study of Diabetes (EASD) and of the International Society for Pediatric and Adolescent Diabetes (ISPAD) endorsed by JDRF and supported by the American Diabetes Association (ADA). Diabetologia. 2020;63(12):2501–20.

    Article  CAS  PubMed  Google Scholar 

  5. Delves RIM, Aughey RJ, Ball K, Duthie GM. The quantification of acceleration events in elite team sport: a systematic review. Sports Med - Open. 2021;7(1):45.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Halson SL. Monitoring training load to understand fatigue in athletes. Sports Med. 2014;44:139–47.

    Article  PubMed Central  Google Scholar 

  7. Bourdon PC, Cardinale M, Murray A, Gastin P, Kellmann M, Varley MC, et al. Monitoring athlete training loads: consensus statement. Int J Sports Physiol Perform. 2017;12:161–70.

    Article  Google Scholar 

  8. Hulin BT, Gabbett TJ, Lawson DW, Caputi P, Sampson JA. The acute:chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med. 2016;50(4):231–6.

    Article  PubMed  Google Scholar 

  9. Claudino JG, de Capanema D, O, de Souza TV, Serrão JC, Machado Pereira AC, Nassis GP,. Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: a systematic review. Sports Med Open. 2019;5:28.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Seshadri DR, Thom ML, Harlow ER, Gabbett TJ, Geletka BJ, Hsu JJ, et al. Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden. Front Sports Act Living. 2021;2(630576):1–17.

    Google Scholar 

  11. Tempelaar D, Rienties B, Nguyen Q. Subjective data, objective data and the role of bias in predictive modelling: lessons from a dispositional learning analytics application. PLoS ONE. 2020;15(6):1–29.

    Article  CAS  Google Scholar 

  12. Barfield W, Williams A. Cyborgs and enhancement technology. Philosophies. 2017;2(4):1–18.

    Google Scholar 

  13. Harwood S, Eaves S. Conceptualising technology, its development and future: the six genres of technology. Technol Forecast Soc Change. 2020;160:1–15.

    Article  Google Scholar 

  14. West SW, Clubb J, Torres-Ronda L, Howells D, Leng E, Vescovi JD, et al. More than a metric: how training load is used in elite sport for athlete management. Int J Sports Med. 2020;1–7.

  15. Balagué N, Pol R, Guerrero I. Science or pseudoscience of physical activity and sport? Apunts Educ Física Deport. 2019;136:113–28.

    Article  Google Scholar 

  16. Low B, Coutinho D, Gonçalves B, Rein R, Memmert D, Sampaio J. A systematic review of collective tactical behaviours in football using positional data. Sports Med. 2019;50(2):343–85.

    Article  Google Scholar 

  17. Rein R, Memmert D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus. 2016;5(1).

  18. Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281–91.

    Article  PubMed  Google Scholar 

  19. Browne P, Sweeting AJ, Woods CT, Robertson S. Methodological considerations for furthering the understanding of constraints in applied sports. Sports Med - Open. 2021;7(22):1–12.

    Google Scholar 

  20. Wunderlich F, Memmert D. Forecasting the outcomes of sports events: a review. Eur J Sport Sci. 2020;21(7).

  21. Pol R, Balagué N. Always think before computing! In: Ric A, Robertson S, Sumpter D, editors. Football analytics 2021. The role of context in transferring analytics to the pitch. Barcelona: Barça Innovation Hub; 2021. p. 18–27.

  22. Balagué N, Hristovski R, Almarcha M, Garcia-Retortillo S, Ivanov PC. Network physiology of exercise: vision and perspectives. Front Physiol. 2020;11:1–18.

    Article  Google Scholar 

  23. Pol R, Balagué N, Ric A, Torrents C, Kiely J, Hristovski R. Training or synergizing? Complex systems principles change the understanding of sport processes. Sports Med Open. 2020;6(28):1–13.

    Google Scholar 

  24. Sturmberg JP, Picard M, Aron DC, Bennett JM, Bircher J, DeHaven MJ, et al. Health and disease—Emergent states resulting from adaptive social and biological network interactions. Front Med. 2019;6:1–14.

    Article  Google Scholar 

  25. Fiscutean A. Could an algorithm predict an injury? Nature. 2021;592:10–1.

    Article  CAS  Google Scholar 

  26. Ivanov PCH, Liu KKL, Bartsch RP. Focus on the emerging new fields of network physiology and network medicine. New J Phys. 2016;18(100201).

  27. Hristovski R, Balagué N. Theory of cooperative-competitive intelligence: principles, research directions, and applications. Front Psychol. 2020;11(2220):1–16.

    Google Scholar 

  28. Bittencourt NFN, Meeuwisse WH, Mendonça LD, Nettel-Aguirre A, Ocarino JM, Fonseca ST. Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition - Narrative review and new concept. Br J Sports Med. 2016;50(21):1309–14.

    Article  CAS  PubMed  Google Scholar 

  29. Pol R, Hristovski R, Medina D, Balagué N. From micro- to macroscopic injuries: a multifactorial, multiscale, and nonlinear dynamic approach. Br J Sport Sci. 2019;53(19):1214–20.

    Google Scholar 

  30. Fonseca ST, Souza TR, Verhagen E, van Emmerik R, Bittencourt NFN, Mendonça LDM, et al. Sports injury forecasting and complexity: a synergetic approach. Sports Med. 2020;50(10):1757–70.

    Article  PubMed  Google Scholar 

  31. Stern BD, Hegedus EJ, Lai YC. Injury prediction as a non-linear system. Phys Ther Sport. 2020;41:43–8.

    Article  PubMed  Google Scholar 

  32. Butterfield TA. Eccentric exercise in vivo: strain-induced muscle damage and adaptation in a stable system. Exerc Sport Sci Rev. 2010;38(2):51–60.

    Article  PubMed  Google Scholar 

  33. Hristovski R, Balagué N, Schöllhorn W. Basic notions in the science of complex systems and nonlinear dynamics. In: Davids K, Hristovski R, Araújo D, Balagué N, Button C, Passos P, editors. Complex systems in sport. London: Routledge/Taylor & Francis Group; 2014. p. 3–17.

    Google Scholar 

  34. Davids K, Glazier P, Ara D, Bartlett R. Movement systems as dynamical systems: the functional role of variability and its implications for sports medicine. Sports Med. 2003;33(4):245–60.

    Article  PubMed  Google Scholar 

  35. Duarte R, Araújo D, Correia V, Davids K. Sports teams as superorganisms: implications of sociobiological models of behaviour for research and practice in team sports performance analysis. Sports Med. 2012;42(8):633–42.

    Article  PubMed  Google Scholar 

  36. Gronwald T, Rogers B, Hoos O. Fractal correlation properties of heart rate variability: a new biomarker for intensity distribution in endurance exercise and training prescription? Front Physiol. 2020;11(550572).

  37. Vázquez P, Hristovski R, Balagué N. The path to exhaustion: time-variability properties of coordinative variables during continuous exercise. Front Physiol. 2016;7(37).

  38. Hristovski R, Davids K, Araújo D, Button C. How boxers decide to punch a target: emergent behaviour in nonlinear dynamical movement systems. J Sports Sci Med. 2006;5:60–73.

    PubMed  PubMed Central  Google Scholar 

  39. Latash ML. Motor synergies and the equilibrium-point hypothesis. Mot Control. 2010;14(3):294–322.

    Article  Google Scholar 

  40. Stergiou N, Decker LM. Human movement variability, nonlinear dynamics, and pathology: is there a connection? Hum Mov Sci. 2011;30(5):869–88.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Bernstein NA. Coordination and regulation of movements. New York: Pergamon Press; 1967.

    Google Scholar 

  42. Kugler PN, Turvey MT. Information, natural law, and the self-assembly of rhythmic movement. Hillsdale, NJ: Erlbaum; 1987.

    Google Scholar 

  43. Haken H. Synergetics: an approach to self-organization. In: Yates FE, editor. Self-organizing systems: the emergence of order. New York: Plenum Press; 1987. p. 417–34.

    Chapter  Google Scholar 

  44. Haken H. Synergetics, an introduction: nonequilibrium phase transitions and self-organization in physics, chemistry, and biology. New York: Springer-Verlag; 1983.

    Book  Google Scholar 

  45. Van Orden GC, Holden JG, Turvey MT. Self-organization of cognitive performance. J Exp Psychol Gen. 2003;132(3):331–50.

    Article  PubMed  Google Scholar 

  46. Balagué N, Torrents C, Hristovski R, Kelso JAS. Sport science integration: an evolutionary synthesis. Eur J Sport Sci. 2017;17(1):51–62.

    Article  PubMed  Google Scholar 

  47. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, et al. Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface. 2018;15:1–47.

    Article  Google Scholar 

  48. Hu JX, Thomas CE, Brunak S. Network biology concepts in complex disease comorbidities. Nat Rev Genet. 2016;17(10):615–29.

    Article  CAS  PubMed  Google Scholar 

  49. Sturmberg JP, Martin CM. How to cope with uncertainty? Start by looking for patterns and emergent knowledge. J Eval Clin Pract. 2021;1–4.

  50. Glazier PS. Towards a Grand Unified Theory of sports performance. Hum Mov Sci. 2017;56:139–56.

    Article  PubMed  Google Scholar 

  51. Da Fontoura CL, Silva FN. Hierarchical characterization of complex networks. J Stat Phys. 2006;125(4):845–76.

    Google Scholar 

  52. Gladyshev J. Life - A complex spontaneous process takes place against the background of non-spontaneous processes initiated by the environment. J Thermodyn Catal. 2017;8(2).

  53. Impellizzeri FM, McCall A, Ward P, Bornn L, Coutts AJ. Training load and its role in injury prevention, part 2: conceptual and methodologic pitfalls. J Athl Train. 2020;55(9):893–901.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Impellizzeri FM, Menaspà P, Coutts AJ, Kalkhoven J, Menaspà MJ. Training load and its role in injury prevention, part I: back to the future. J Athl Train. 2020;55(9):885–92.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Impellizzeri FM, Woodcock S, Coutts AJ, Fanchini M, McCall A, Vigotsky AD. What role do chronic workloads play in the acute to chronic workload ratio? Time to dismiss ACWR and its underlying theory. Sports Med. 2021;51(3):581–92.

    Article  PubMed  Google Scholar 

  56. Cameron WB. Informal sociology: a casual introduction to sociological thinking. New York: Random House; 1963.

    Google Scholar 

  57. Friston K. Does predictive coding have a future? Nat Neurosci. 2018;21(8):1019–21.

    Article  CAS  PubMed  Google Scholar 

  58. Sturmberg JP. Embracing complexity in health. The transformation of science, practice, and policy. Springer International Publishing; 2019.

  59. Weaving D, Jones B, Till K, Abt G, Beggs C. The case for adopting a multivariate approach to optimize training load quantification in team sports. Front Physiol. 2017;8(1024).

  60. Passos P, Milho J, Button C. Quantifying synergies in two-versus-one situations in team sports: an example from Rugby Union. Behav Res Methods. 2018;50(2):620–9.

    Article  CAS  PubMed  Google Scholar 

  61. Uryumtsev DY, Gultyaeva VV, Zinchenko MI, Baranov VI, Melnikov VN, Balioz NV, et al. Effect of acute hypoxia on cardiorespiratory coherence in male runners. Front Physiol. 2020;11(630).

  62. Ivanov PCh. The new field of network physiology: building the human physiolome. Front Netw Physiol. 2021;1(711778).

  63. Balagué N, González J, Javierre C, Hristovski R, Aragonés D, Álamo J, et al. Cardiorespiratory coordination after training and detraining. A principal component analysis approach. Front Physiol. 2016;7(35).

  64. Garcia-Retortillo S, Gacto M, O’Leary TJ, Noon M, Hristovski R, Balagué N, et al. Cardiorespiratory coordination reveals training-specific physiological adaptations. Eur J Appl Physiol. 2019;119(8):1701–9.

    Article  CAS  PubMed  Google Scholar 

  65. Garcia-Retortillo S, Javierre C, Hristovski R, Ventura JL, Balagué N. Cardiorespiratory coordination in repeated maximal exercise. Front Physiol. 2017;8(387).

  66. Garcia-Retortillo S, Javierre C, Hristovski R, Ventura JL, Balagué N. Principal component analysis as a novel approach for cardiorespiratory exercise testing evaluation. Physiol Meas. 2019;40(8).

  67. Esquius L, Garcia-Retortillo S, Balagué N, Hristovski R, Javierre C. Physiological- and performance-related effects of acute olive oil supplementation at moderate exercise intensity. J Int Soc Sports Nutr. 2019;16(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Duignan C, Doherty C, Caulfield B, Blake C. Single-item self-report measures of team-sport athlete wellbeing and their relationship with training load: a systematic review. J Athl Train. 2020;55(9):944–53.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Eston R. Use of ratings of perceived exertion in sports. Int J Sports Physiol Perform. 2012;7(2):175–82.

    Article  PubMed  Google Scholar 

  70. Jahedi S, Méndez F. On the advantages and disadvantages of subjective measures. J Econ Behav Organ. 2014;98:97–114.

    Article  Google Scholar 

  71. Montull L, Vázquez P, Hristovski R, Balagué N. Hysteresis behaviour of psychobiological variables during exercise. Psychol Sport Exerc. 2020;48(101647):1–9.

    Google Scholar 

  72. Steele J. What is (perception of) effort? Objective and subjective effort during task performance. PsyArXiv. 2020.

  73. Coyne JOC, Gregory Haff G, Coutts AJ, Newton RU, Nimphius S. The current state of subjective training load monitoring—A practical perspective and call to action. Sports Med Open. 2018;4:58.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Jeffries AC, Wallace L, Coutts AJ, McLaren SJ, McCall A, Impellizzeri FM. Athlete-reported outcome measures for monitoring training responses: a systematic review of risk of bias and measurement property quality according to the COSMIN guidelines. Int J Sports Physiol Perform. 2020;15(9):1203–15.

    Article  Google Scholar 

  75. Suetterlin KJ, Sayer AA. Proprioception: where are we now? A commentary on clinical assessment, changes across the life course, functional implications and future interventions. Age Ageing. 2014;43(3):313–8.

    Article  PubMed  Google Scholar 

  76. Garn SN, Newton RA. Kinesthetic awareness in subjects with multiple ankle sprains. Phys Ther. 1988;68(11):1667–71.

    Article  CAS  PubMed  Google Scholar 

  77. Morganti F, Rezzonico R, Cheng SC, Price CJ. Italian version of the scale of body connection: validation and correlations with the interpersonal reactivity index. Complement Ther Med. 2020;51.

  78. Mehling WE, Wrubel J, Daubenmier JJ, Price CJ, Kerr CE, Silow T, et al. Body awareness: a phenomenological inquiry into the common ground of mind-body therapies. Philos Ethics Humanit Med. 2011;6(1):6.

    Article  PubMed  PubMed Central  Google Scholar 

  79. Mehling WE, Gopisetty V, Daubenmier J, Price CJ, Hecht FM, Stewart A. Body awareness: construct and self-report measures. PLoS ONE. 2009;4(5):e5614.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Bakal DA. Minding the body: clinical uses of somatic awareness. New York: Guilford Press; 2001.

    Google Scholar 

  81. Sherrington CS. The integrative action of the nervous system. Cambridge: Cambridge University Press; 1947.

    Google Scholar 

  82. Mehling WE, Price C, Daubenmier JJ, Acree M, Bartmess E, Stewart A. The multidimensional assessment of interoceptive awareness (MAIA). PLoS ONE. 2012;7(11):e48230.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Garfinkel SN, Critchley HD. Interoception, emotion and brain: new insights link internal physiology to social behaviour. Commentary on: “Anterior insular cortex mediates bodily sensibility and social anxiety” by Terasawa et al (2012). Soc Cogn Affect Neurosci. 2013;8(3):231–4.

  84. Garfinkel SN, Seth AK, Barrett AB, Suzuki K, Critchley HD. Knowing your own heart: distinguishing interoceptive accuracy from interoceptive awareness. Biol Psychol. 2015;104:65–74.

    Article  PubMed  Google Scholar 

  85. Shaw R, Kinsella-Shaw J. The survival value of informed awareness. J Conscious Stud. 2007;14(1–2):137–54.

    Google Scholar 

  86. Araújo D, Hristovski R, Seifert L, Carvalho J, Davids K. Ecological cognition: expert decision-making behaviour in sport. Int Rev Sport Exerc Psychol. 2017;12(1):1–25.

    Article  Google Scholar 

  87. Montull L, Vázquez P, Rocas L, Hristovski R, Balagué N. Flow as an embodied state. Informed awareness of slackline walking. Front Psychol. 2020;10(2993):1–11.

    Google Scholar 

  88. Balagué N, Hristovski R, Garcia-Retortillo S. Perceived Exertion –Dynamic psychobiological model of exercise-induced fatigue. In: Tenenbaum G, Eklund R, editors. Handbook of sport psychology. 4th ed. New York: Wiley; 2019.

  89. Balagué N, Pol R, Torrents C, Ric A, Hristovski R. On the relatedness and nestedness of constraints. Sports Med Open. 2019;5(1).

  90. Drew MK, Finch CF. The relationship between training load and injury, illness and soreness: a systematic and literature review. Sports Med. 2016;46(6):861–83.

    Article  PubMed  Google Scholar 

  91. Gaudino P, Iaia FM, Strudwick AJ, Hawkins RD, Alberti G, Atkinson G, et al. Factors influencing perception of effort (session rating of perceived exertion) during elite soccer training. Int J Sports Physiol Perform. 2015;10(7):860–4.

    Article  PubMed  Google Scholar 

  92. Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36(6):1042–7.

    Article  PubMed  Google Scholar 

  93. Thorpe RT, Atkinson G, Drust B, Gregson W. Monitoring fatigue status in elite team-sport athletes: implications for practice. Int J Sports Physiol Perform. 2017;12(2):27–34.

    Google Scholar 

  94. Foster C, Boullosa D, McGuigan M, Fusco A, Cortis C, Arney BE, et al. 25 years of session rating of perceived exertion: historical perspective and development. Int J Sports Physiol Perform. 2021;28:1–10.

    Google Scholar 

  95. Meeusen R, Duclos M, Foster C, Fry A, Gleeson M, Nieman D, et al. Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the european college of sport science and the American College of Sports Medicine. Med Sci Sports Exerc. 2013;45(1):186–205.

    Article  PubMed  Google Scholar 

  96. Galambos SA, Terry PC, Moyle GM, Locke SA. Psychological predictors of injury among elite athletes. Br J Sports Med. 2005;39(6):351–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Johnson U, Ivarsson A. Psychological predictors of sport injuries among junior soccer players. Scand J Med Sci Sports. 2011;21(1):129–36.

    Article  CAS  PubMed  Google Scholar 

  98. Junge A. The influence of psychological factors on sports injuries: review of the literature. Am J Sports Med. 2000;28(5):10–5.

    Article  Google Scholar 

  99. Anglem N, Lucas SJE, Rose EA, Cotter JD. Mood, illness and injury responses and recovery with adventure racing. Wilderness Environ Med. 2008;19(1):30–8.

    Article  PubMed  Google Scholar 

  100. Zorrilla EP, Luborsky L, McKay JR, Rosenthal R, Houldin A, Tax A, et al. The relationship of depression and stressors to immunological assays: a meta-analytic review. Brain Behav Immun. 2001;15(3):199–226.

    Article  CAS  PubMed  Google Scholar 

  101. Timofejeva I, McCraty R, Atkinson M, Joffe R, Vainoras A, Alabdulgader AA, et al. Identification of a group’s physiological synchronization with earth’s magnetic field. Int J Environ Res Public Health. 2017;14(998):13–9.

    Google Scholar 

  102. Volz-Sidiropoulou E, Gauggel S. Do subjective measures of attention and memory predict actual performance? Metacognition in older couples. Psychol Aging. 2012;27(2):440–50.

    Article  PubMed  Google Scholar 

  103. Graham SR, Cormack S, Parfitt G, Eston R. Relationships between model estimates and actual match-performance indices in professional Australian footballers during an in-season macrocycle. Int J Sports Physiol Perform. 2018;13(3):339–46.

    Article  PubMed  Google Scholar 

  104. Ten Haaf T, Van Staveren S, Oudenhoven E, Piacentini MF, Meeusen R, Roelands B, et al. Prediction of functional overreaching from subjective fatigue and readiness to train after only 3 days of cycling. Int J Sports Physiol Perform. 2017;12(2):87–94.

    Google Scholar 

  105. Koltyn KF, Morgan WP. Efficacy of perceptual versus heart rate monitoring in the development of endurance. Br J Sports Med. 1992;26(2):132–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Beckmann J, Kellmann M. Self-regulation and recovery: approaching an understanding of the process of recovery from stress. Psychol Rep. 2004;95:1135–53.

    Article  PubMed  Google Scholar 

  107. Schaffran P, Kleinert J, Altfeld S, Zepp C, Kallus KW, Kellmann M. Early risk detection of burnout: development of the burnout prevention questionnaire for coaches. Front Psychol. 2019;10(714).

  108. Cormack S, Coutts A. Monitoring training load. In: Joyce D, Lewindon D, editors. Sports injury prevention and rehabilitation: integrating medicine and science for performance solutions. New York: Routledge; 2016.

  109. Rago V, Brito J, Figueiredo P, Costa J, Barreira D, Krustrup P, et al. Methods to collect and interpret external training load using microtechnology incorporating GPS in professional football: a systematic review. Res Sports Med. 2020;28(3):437–58.

    Article  PubMed  Google Scholar 

  110. Desgorces FD, Hourcade JC, Dubois R, Toussaint JF, Noirez P. Training load quantification of high intensity exercises: discrepancies between original and alternative methods. PLoS ONE. 2020;15(8):1–13.

    Article  CAS  Google Scholar 

  111. Haddad M, Stylianides G, Djaoui L, Dellal A, Chamari K. Session-RPE method for training load monitoring: validity, ecological usefulness, and influencing factors. Front Neurosci. 2017;11(612):1–14.

    Google Scholar 

  112. Abbiss CR, Peiffer JJ, Meeusen R, Skorski S. Role of ratings of perceived exertion during self-paced exercise: what are we actually measuring? Sports Med. 2015;45(9):1235–43.

    Article  PubMed  Google Scholar 

  113. Borresen J, Lambert MI. The quantification of training load, effect on performance. Sports Med. 2009;39(9):779–95.

    Article  PubMed  Google Scholar 

  114. Micklewright D, St Clair Gibson A, Gladwell V, Al SA. Development and validity of the rating-of-fatigue scale. Sports Med. 2017;47(11):2375–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Pageaux B. Perception of effort in exercise science: definition, measurement and perspectives. Eur J Sport Sci. 2016;16(8):885–94.

    Article  PubMed  Google Scholar 

  116. Balagué N, Hristovski R, Vainoras A, Vázquez P, Aragonés D. Psychobiological integration during exercise. In: Davids K, Hristovski R, Araújo D, Balagué N, Button C, Passos P, editors. Complex systems in sport. London: Routledge; 2014. p. 82–102.

    Google Scholar 

  117. Balagué N, Hristovski R, Garcia S, Aragonés D, Razon S, Tenenbaum G. Intentional thought dynamics during exercise performed until volitional exhaustion. J Sports Sci. 2015;33(1).

  118. Balagué N, Hristovski R, Garcia S, Aguirre C, Vázquez P, Razon S, et al. Dynamics of perceived exertion in constant-power cycling: time- and workload-dependent thresholds. Res Q Exerc Sport. 2015;86(4):371–8.

    Article  PubMed  Google Scholar 

  119. Aragonés D, Balagué N, Hristovski R, Pol R, Tenenbaum G. Fluctuating dynamics of perceived exertion in constant-power exercise. Psychol Sport Exerc. 2013;14(6):796–803.

    Article  Google Scholar 

  120. Garcia S, Razon S, Hristovski R, Balagué N, Tenenbaum G. Dynamic stability of task-related thoughts in trained runners. Sport Psychol. 2015;29(4):302–9.

    Article  Google Scholar 

  121. Slapsinskaite A, Garcia S, Razon S, Balagué N, Hristovski R, Tenenbaum G. Cycling outdoors facilitates external thoughts and endurance. Psychol Sport Exerc. 2016;27:78–84.

    Article  Google Scholar 

  122. St Clair Gibson A, Baden DA, Lambert MI, Lambert EV, Harley YXR, Hampson D, et al. The conscious perception of the sensation of fatigue. Sports Med. 2003;33(3):167–76.

    Article  PubMed  Google Scholar 

  123. Mason RJ, Farrow D, Hattie JAC. Sports coaches’ knowledge and beliefs about the provision, reception, and evaluation of verbal feedback. Front Psychol. 2020;11(571552):1–10.

    Google Scholar 

  124. Saw AE, Main LC, Gastin PB. Monitoring athletes through self-report: factors influencing implementation. J Sports Sci Med. 2015;14(1):137–46.

    PubMed  PubMed Central  Google Scholar 

  125. Chow GM, Luzzeri M. Post-event reflection: a tool to facilitate self-awareness, self-monitoring, and self-regulation in athletes. J Sport Psychol Action. 2019;10(2):106–18.

    Article  Google Scholar 

  126. Almarcha M, Balagué N, Torrents C. Healthy teleworking: towards personalized exercise recommendations. Sustainability. 2021;13(3192):1–12.

    Google Scholar 

  127. Brener ND, Billy JO, Grady WR. Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: evidence from the scientific literature. J Adolesc Health. 2003;33:436–57.

    Article  PubMed  Google Scholar 

  128. Bollen KA, Paxton P. Detection and determinants of bias in subjective measures. Am Sociol Rev. 1998;63(3):465–78.

    Article  Google Scholar 

  129. Baldwin W. Information no one else knows: the value of self-report. In: Stone AA, Turkkan JS, Bachrach CA, Jobe JB, Kurtzman HS, Cain VS, editors. The science of self-report: implications for research and practice. Lawrence Erlbaum Associates Publishers; 2000. p. 3–7.

    Google Scholar 

  130. Anzanpour A, Azimi I, Gotzinger M, Rahmani AM, TaheriNejad N, Liljeberg P, et al. Self-awareness in remote health monitoring systems using wearable electronics. In: Design, Automation & Test in Europe Conference & Exhibition. Lausanne, Switzerland: IEEE; 2017. p. 1056–61.

  131. Hoffman NJ. Omics and exercise: global approaches for mapping exercise biological networks. Cold Spring Harb Perspect Med. 2017;7(10).

  132. Schilder JN, de Vries MJ, Goodkin K, Antoni M. Psychological changes preceding spontaneous remission of cancer. Clin Case Stud. 2004;3(4):288–312.

    Article  Google Scholar 

  133. Moeller SJ, Goldstein RZ. Impaired self-awareness in human addiction: deficient attribution of personal relevance. Trends Cogn Sci. 2014;18(12):635–41.

    Article  PubMed  PubMed Central  Google Scholar 

  134. Woods CT, Araújo D, Davids K, Rudd J. From a technology that replaces human perception–action to one that expands it: some critiques of current technology use in sport. Sports Med Open. 2021;7(1):76.

    Article  PubMed  PubMed Central  Google Scholar 

  135. Gray R. Differences in attentional focus associated with recovery from sports injury: Does injury induce an internal focus? J Sport Exerc Psychol. 2015;37(6):607–16.

    Article  PubMed  Google Scholar 

  136. Slapšinskaite A, Hristovski R, Razon S, Balagué N, Tenenbaum G. Metastable pain-attention dynamics during incremental exhaustive exercise. Front Psychol. 2017;7(2054).

  137. Slapšinskaite A, Razon S, Serre NB, Hristovski R, Tenenbaum G, Reddy H. Local pain dynamics during constant exhaustive exercise. PLoS ONE. 2015;10(9).

  138. Venhorst A, Micklewright D, Noakes TD. Perceived fatigability: utility of a three-dimensional dynamical systems framework to better understand the psychophysiological regulation of goal-directed exercise behaviour. Sports Med. 2018;48(11):2479–95.

    Article  PubMed  Google Scholar 

  139. Ehrsson HH. The experimental induction of out-of-body experiences. Science. 2007;317(5841):1048–1048.

    Article  CAS  PubMed  Google Scholar 

  140. Haselager WFG, Broens M, Gonzalez MEQ. The importance of sensing one’s movements in the world for the sense of personal identity. Riv Internazionale Filos E Psicol. 2012;3:1–11.

    Google Scholar 

  141. Ionta, S, Gassert, R, Blanke, O. Multi-sensory and sensorimotor foundation of bodily self-consciousness- an interdisciplinary approach. Front Psychol. 2011;2(383).

  142. Leyva A. Embodied movement consciousness. Phenomenol Cogn Sci. 2022.

  143. Maturana HR, Varela FJ. The tree of knowledge: the biological roots of human understanding. Boston, MA: New Science Library; 1987.

    Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This article was supported by the Institut Nacional d’Educació Física de Catalunya (INEFC) and the Generalitat de Catalunya. LM is the recipient of a predoctoral fellowship from INEFC.

Author information

Authors and Affiliations

  1. Complex Systems in Sport Research Group, Institut Nacional d’Educació Física de Catalunya (INEFC), Universitat de Barcelona, Barcelona, Spain

    Lluc Montull, Agne Slapšinskaitė-Dackevičienė & Natàlia Balagué

  2. University School of Health and Sport, University of Girona, Girona, Spain

    Lluc Montull

  3. Department of Sports Medicine, Faculty of Nursing and Faculty of Public Health, Health Research Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania

    Agne Slapšinskaitė-Dackevičienė

  4. Institute of Coaching and Performance, School of Sport and Wellbeing, University of Central Lancashire, Preston, PR1 2HE, UK

    John Kiely

  5. Complex Systems in Sport Research Group, Faculty of Physical Education, Sport and Health, Ss. Cyril and Methodius University in Skopje, Skopje, Macedonia

    Robert Hristovski

Authors
  1. Lluc Montull
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Agne Slapšinskaitė-Dackevičienė
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. John Kiely
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert Hristovski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Natàlia Balagué
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LM, AS and NB developed the initial research idea of the manuscript. All authors contributed to organizing and writing the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Natàlia Balagué.

Ethics declarations

Ethics Approvals and Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing interests

Lluc Montull, Agne Slapšinskaitė-Dackevičienė, John Kiely, Robert Hristovski and Natàlia Balagué declare that they have no conflicts of interest relevant to the content of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Montull, L., Slapšinskaitė-Dackevičienė, A., Kiely, J. et al. Integrative Proposals of Sports Monitoring: Subjective Outperforms Objective Monitoring. Sports Med - Open 8, 41 (2022). https://doi.org/10.1186/s40798-022-00432-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s40798-022-00432-z

Keywords