Carrie M. Peters
David T. Hendry
Nicola J. Hodges- 1Motor Skills Lab, School of Kinesiology, University of British Columbia, Vancouver, BC, Canada
- 2Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
We provide a scoping review of research on athlete development in girls' and women's sports. Our emphasis is on pathways to expertise in the context of deliberate practice theory and associated models, such as the Developmental Model of Sport Participation (DMSP). Despite rationale for sex and gender differences in sport development, there are relatively few studies where the developmental pathways of female elite athletes have been evaluated. We sought to map the scope of the literature on this population over the last 30 years, focusing on measures of practice types and amounts. Following an extensive search of the literature, 32 studies were identified that included all female participants or presented sex/gender disaggregated data. Retrospective methods were commonly used to quantify practice, play and specialization. National-level athletes were the most represented, although there was considerable heterogeneity in sport and expertise-level, making general or comparative judgements challenging. We identified some groups that had accumulated high volumes of practice at a young age, particularly in soccer and gymnastics. Across sports and studies, early majority hours of engagement in the primary sport was the norm. Athletes deviated from predictions in the specialization pathway detailed in the DMSP, by continuing to participate in other sports throughout childhood and adolescence. In addition to highlighting the relative paucity of data pertaining to athlete development pathways in female athletes, we show that the data from these groups deviate from predictions detailed in current models of athlete development.
Introduction
There is a substantial body of literature on the topic of “talent” development in sport and specifically on the question of how sport-specific practice amounts and types, as well as age of engagement, impact on attainment of sport expertise [e.g., [1–3]]. In most of the work on this topic, the developmental experiences of male participants have been the majority focus, or the data has been aggregated across males and females. Because models of expertise development are dependent on these studies, these models are likely to be skewed in their descriptions and hence predictions about pathways to expertise in women's sports. This knowledge gap was highlighted in a recent scoping review of talent development in sports [1], where only ~10% of the included studies focused solely on female athletes (~45% were exclusively on male participants, ~30% included both male and female participants and ~15% of studies did not report the sex/gender of the participants). In this scoping review, we aim to assess and evaluate the current knowledge of the pathways that are associated with expertise in youth and adult high-level female athletes.
Compared to boys, girls mature biologically and reach ages of peak motor skill development earlier [4, 5]. Girls are also less likely to engage in sport and physical activity [6–8] and have fewer opportunities for elite performance [such as paid professional sport leagues [9, 10]]. Some athlete development frameworks include gender specific age ranges for training based on gender differences in peak height velocity [11], where girls reach peak height velocity earlier than boys. Because of these biological differences, interpretations and definitions of early specialization should differ for female and male athletes. Lima et al. suggested that the early specialization of female athletes may be somewhat protective, allowing for better adjustment of athletic performance during pubertal changes [12]. Female athletes also show differences in their response to training and to other psychosocial factors, such as their relationships with parents and peers [13], leading us to expect that the developmental pathways to attain expertise may differ between male and female athletes.
In support of the idea that sex/gender impact developmental pathways, differences have been identified in reviews of the relative age effect [14–17]. Girls and women do not always exhibit the same advantage for birth month as their male counterparts. Differences have been attributed to biological maturation and socialization factors, where post-pubescent female characteristics (such as shorter legs and wider hips) constrain the athletic development of athletes that mature early (or those born earlier in the year) and social values and norms can deter early maturing female athletes from pursuing sport competitively [16]. Moreover, in a review of specialization and diversity in sport, gender was noted as a potential moderator of early specialization with some evidence that girls specialized more than boys [2]. However, in only a third of the studies included in this earlier review [2] was specialization defined, primarily based on a descriptor of intense engagement in year-round practice within one sport at the exclusion of others.
Here we discuss sex/gender as a binary concept, focusing on demographically described female participants in research publications. We acknowledge that gender exists on a spectrum and that the experiences of non-binary athletes have been omitted in the current body of literature. We would like to highlight that the terms sex and gender are also not interchangeable, with the former referring to biological and genetic differences and the latter to the roles and relationships ascribed by society [18]. Both sex and gender factors likely influence the developmental pathways of female athletes, but we are unable to disentangle the two here.
A key facet of the development of sport expertise is the accumulation of deliberate practice activities [19, 20]. According to deliberate practice theory, there is a monotonic relationship between the time engaged in deliberate practice and the level of performance. Deliberate practice is characterized as being effortful, relevant to performance goals, individualized, coach-led, not inherently enjoyable, and has a feedback component [for a recent sport-focused review see [21]]. Originally grounded in music development, there has been some debate as to whether the tenants of deliberate practice, specifically the monotonic benefits assumption, can be generalized to sporting contexts [22–24]. However, studies comparing practice histories of skilled and less skilled athletes have shown that the former accumulate more hours in deliberate practice activities across a range of sports and in a somewhat monotonic fashion [25–27].
Approximately a decade after deliberate practice theory was proposed, Côté et al. published work on the Developmental Model of Sport Participation (DMSP), which had two pathways leading to sports expertise [28–30]. Based on deliberate practice theory, an early specialization pathway was outlined to include high amounts of practice accumulated from a young age in one sport. In contrast, the early sampling pathway was characterized by early multi-sport involvement, high amounts of play and with later specialization in adolescence. In this second pathway, the sampling years (6–12 yr) were characterized by high volumes of play and participation in many sports. Specialization would not begin until the adolescent years (13–15 yr), where athletes focus on one or two sports and engage in equal amounts of practice and play activities. In the investment years (16–18 yr), athletes increase commitment to one sport and engage in a high volume of deliberate practice [30, 31]. The accumulation of diversified sport experiences and play in the early sampling pathway was thought to encourage the broad development of physical and psychosocial skills that benefit future athletic development [32]. In contrast, early specialization was thought to benefit the attainment of sports expertise only when peak performance occurs at a young age, such as in gymnastics and figure skating [33, 34].
Although there has been support for some of the predictions emanating from the DMSP, there have been issues in defining specialization and hence determining pathways based only on two categorically distinct pathways [35, 36]. Single sport participation, high amounts of deliberate practice, year-round training, exclusion of other sports, and intense training have differentially been used as criteria of specialization across studies [37, 38].
In samples of male soccer players, athletes participated in high volumes of sport specific practice and play at relatively young ages, consistent with the early specialization pathway, but diverged from predictions in that pathway by also sampling other sports in childhood [39–42]. Thus, a third pathway has been proposed, characterized by early majority engagement in the primary sport, without limiting participation in other activities [40]. This third pathway may offer some “protection” against proposed negative motivational consequences from high amounts of practice, such as burn-out and low intrinsic motivation [43–45].
A provocative idea is that differences in childhood activities which lead to later success take time to show up and hence distinguish across athletes that attain juvenile vs. adult success [46, 47]. As such, different pathways might characterize the development of adult and adolescent expertise [46]. Indeed, a meta-analysis across multiple sports showed that athletes who attained international success as junior athletes showed a more specialized pathway compared to national or regional athletes, whereas this pattern was reversed for world class adult elite athletes [47]. There was more multi-sport participation during development among adult world class elite athletes compared to lesser skilled peers. Although these data are correlational, based on cross-sectional comparisons and aggregated across various sports with different participation rates and ages of peak performance, success at junior levels is not a good predictor of adult success [48] and ~25% of athletes who attained elite performance as adults in a study of Portuguese athletes did not compete internationally in their youth [49].
In this scoping review, we detail the research (1990 to May 2021) specifying activities undertaken by female “elite” athletes during childhood. In addition to collecting practice, play, and specialization measures, we present key study characteristics and participant demographics to contextualize the results and to highlight gaps in study populations. We sought and evaluated studies that captured female athletes competing at relatively high levels of performance, in both junior and adult groups. Our main aims were to synthesize the data pertaining to childhood activities and demographics of adult and youth female elite athletes to describe and evaluate pathways to expertise in view of current research and models of athlete development.
Methods
Study search and screen
The protocol was set a priori in accordance with current best practices for scoping reviews [50, 51]. To inform the search strategy, we reviewed known studies examining girls' and women's sports' participation and scoping and systematic reviews in the field. The primary search was conducted by the first author in SPORTDiscus to identify studies published between January 1990 and May 2021. We chose this period as it slightly pre-dates the seminal study on deliberate practice by Ericsson et al. [20], which resulted in a high volume of research in sport related to pathways of skill development and measures of practice. Boolean search terms were used to combine subject terms and synonyms broadly encompassing the population (high level female/women athletes) and the outcomes of interest (practice, participation, specialization/diversification, and developmental activities). The search was limited to scholarly articles and studies where an English abstract was available. Further searching was done in Google Scholar and manual searching of the reference lists of included studies and several key review papers and books. We also conducted a forward search by reviewing studies that cited the included studies and prominent review papers. The primary search uncovered studies that included both male and female athletes. If there was gender disaggregated data, these were included.
Study screening was done through Covidence (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org). The first author conducted an initial title and abstract screen of the identified studies and the full text screen of the studies selected in the title and abstract screen. An independent reviewer (author two) screened 20% of these studies at both stages. Discrepancies in the title and abstract screen were automatically sent to the full text review and discrepancies in the full text screen were resolved through discussion with all three authors.
Empirical studies were included for review if they included measures of practice, play, multisport participation, or specialization. The scope was limited to capture a relatively elite sample of adult and youth female athletes. We acknowledge that there are issues in defining elite status and to maintain transparency in our definitions we keep descriptors in our analysis to allow better inferences as to “elite” status [e.g., [52–54]]. With respect to inclusion based on “elite” status (as detailed in the data extraction section below), we sought studies with adult participants who were competing at the Varsity/University level or higher, including National team athletes and premier/professional league athletes. For youth (U18) elite athletes, we restricted inclusion to athletes that were part of a national training squad or competing internationally. Studies that did not include participant gender in the abstract were sent to full text review, leading to a large number of studies (n = 252) screened at the full-text level. When gender was not reported, these studies were excluded. We included all study design-types, including retrospective, cross-sectional and longitudinal. In some studies, variables of interest, such as practice hours or years of engagement, were reported as demographic measures in the participant's section and not outcome measure. These studies were not included as measures were often poorly defined, leading to ambiguity in how they were obtained.
Data extraction
Results were extracted by the first author using a custom spreadsheet piloted by the first two authors. The second author audited the data extracted from a random sample of 20% of the included studies and no discrepancies were identified. We recorded study details such as the study design, the country in which the data were collected and the sport. We recorded how the authors described the athletes' expertise and categorized them into groups. Adult athletes were categorized by us as “Super Elite” if they were medalists at international level competitions or were ranked highly internationally (i.e., within the top 10). Adult athletes were labeled “National” if they represented their country at an international level of play but did not meet the criteria for Super Elite. Adult athletes that played for a university or college athletic program were classified as “Varsity.” We did not distinguish between programs competing at different levels of university competition. Athletes that played their sport as a career and were paid as full-time players were classified as “Professional” and those that played for a high-level club in their country (e.g., premier league) but did not meet any of the above criteria were classified as “Elite Club” (this could include semi-professional athletes). Because in women's sports there are few opportunities for professional play [55], resulting in a paucity of groups in our sample at this level, we collapsed across the Elite Club and Professional categories and termed all as “Elite Adult” in the reporting of data. Groups that comprised athletes who were under 18 years but were competing at an international level or training with a national development squad were included in our analyses and classified as “Youth Elite.” Therefore, for descriptive comparisons of the groups, we consider the order of Super Elite, National, Elite Adult, Varsity, and Youth Elite as most expert (Super Elite) to least expert (Youth Elite).
Because we were interested in potential factors leading to adult expertise, we did not include youth or adult club-level athletes (including those competing at a provincial/state level). Participants' current age and the age when measures were taken were also recorded as were definitions of all dependent variables related to time spent in practice and play activities and sport specialization/diversification. When data were presented in figures in extracted studies, a plot digitizer tool (WebPlotDigitizer, https://automeris.io/WebPlotDigitizer) was used to extract numeric values.
Data aggregation and transformations
We reported measures of practice, multisport participation, specialization and play as a function of sport in childhood (6–12 yr), early adolescence (13–15 yr) and late adolescence (16–18 yr) years, corresponding to the sampling, specializing and investment years, respectively [29]. In dividing age categories in this way, rather than presenting yearly amounts at each age, we were able to include more studies within an age category and better synthesize the data. Such dividing of data across these age categories also allowed us to compare the data to existing models of athlete development [30, 40]. When data were reported across multiple categories (e.g., from ages 14–18 yr), intermediate categories were added spanning the age periods (such as “sampling and specializing”). When authors reported multiple data points within a category of the DMSP (i.e., reported by age), data were averaged or summed depending on the measure (i.e., averaged for hours/year and summed for aggregated hours). When researchers reported data as a proportion of participants (e.g., 50% of the sample started their sport before the age of 10 years), we calculated and reported the median response. In cases where authors reported total years of involvement in the sport rather than start age of primary sport involvement, we subtracted the years of involvement from the current reported age of participants.
Results
Included study characteristics
From the 1,831 papers identified, 32 studies met the inclusion criteria and were included in our analyses as detailed in the PRISMA flow diagram in Figure 1. Of this final set of studies, group level data were extracted (n = 42 groups). In 11 cases, data from multiple groups representing different levels of expertise and in some cases different sports were extracted from the same study publication. In determining groups, we noted that some independent publications were based off the same group sample, such that there was the possibility of duplicate reporting of dependent variables. Therefore, although we include all studies in our reporting of study characteristics, we removed duplicate dependent variables in our reporting of outcomes1. Table 1 gives details of all included studies including sport, expertise classification, the country where data were collected, group sample size, and outcome measures reported (i.e., practice, play, multisport participation, and specialization).
Figure 1. PRISMA flow diagram for the scoping review process.
Table 1. Included study characteristics.
Practice
Twenty-two studies included broad measures of practice. In Figures 2A–C, respectively, we present practice hours per week, practice hours per year, and accumulated practice hours across what have been termed the sampling years (6–12 yr), the specializing years (13–15 yr) and the investment years (16–18 yr) as a function of sport [30]. Intervening categories are presented when data span across sampling and specializing years (e.g., 11–14 yr). As would be expected, practice hours increased across time. However, the so termed specializing and investment years showed little change across time in terms of average hours/week and hours/year of sport-specific practice. Soccer, volleyball and rhythmic gymnastics were the sports most represented in these figures. A few researchers also reported practice hours as a function of years into career and years of involvement [68, 74, 78], but due to the lack of studies, we have not included these data here.
Figure 2. Sample (bar) and individual study means (colored circles) across different age categories defined in past literature as the sampling, specializing and investment years, for practice hours per week (A), practice hours per year (B), and accumulated practice hours (C). Error bars represent sample standard deviation, the intermediate category (Sampling and Specializing) contains data that were reported across ages spanning the sampling and specializing years.
In Figure 3, start age in the main sport (panel A), in main sport practice (panel B), and of specialization (panel C), as a function of sport and expertise, are shown. Data are shown as a function of sport and across the different skill groups given the range of start ages, which was sport and skill dependent. As can be seen in Figure 3A where the data are plotted in order of start age; alpine ski, soccer and gymnastics had early start ages before age 6 yr, whereas rhythmic gymnastics and volleyball had later start ages after age 10 yr. There were only a few sports that had multiple levels of expertise represented, but in general there were no skill-based trends across sports. In gymnastics, National athletes had slightly earlier start ages than Youth-Elite athletes, but this was reversed in rhythmic gymnastics. In soccer, youth athletes were not represented, but among adults, higher level athletes started soccer at an earlier age than the less elite groups. This earlier start age trend was also true for swimming, but here Youth-Elite athletes were represented and they had a slightly later start age than Adult Elite. Few studies reported start age in practice of the primary sport, but consistent with the overall start ages, alpine ski and soccer groups also began practice at a relatively young age (Figure 3B).
Figure 3. Sample (bar) and individual study means (colored circles) across different sports showing start age in the main sport (A), start age in practice (B) and start age of specialization (C). The sports are ordered in terms of start age and expertise category is shown in terms of a color coded grouping variable. Error bars show sample standard deviations.
Specialization and other sport participation
Eighteen studies included measures of practice and other sport participation and out of these, eleven studies also included reports regarding age of specialization. Specialization was defined as either exclusive engagement in one sport [33, 56, 66, 68, 69, 75, 76, 87] or age of investment without exclusive specialization [85]. As shown in Figure 3C, age of specialization was generally around 14 yr, with the exception of swimming and rhythmic gymnastics (~10–11 yr). There were a few sports that had data across multiple levels of expertise. In netball and soccer, both the Elite Adult (soccer) and Youth-Elite (netball) athletes specialized earlier than the National-level athletes, but there were no skill-group differences for rhythmic gymnastics.
The average number of other sports played within each age grouping is reported in Figure 4. Although there was a general drop off in sports from age 6–12 yr to 13–15 yr, this number increased for some sports (i.e., alpine ski and soccer) during the transition to the so termed “investment” years, what we have labeled specializing and investing (~15–16 yr). However, after the age of 16 yr the number of other sports was at its lowest. The number of hours per week and accumulated hours in other sports is presented in Figures 5A,B, respectively2. Again, there was a trend for increasing hours in other sports with age, rather than a decrease, especially in the so termed specializing/investment years (13–18 yr) as compared to “sampling” years (6–12 yr). Soccer and netball were the sports most represented showing these trends.
Figure 4. Sample (bar) and individual study means (colored circles) across different age categories defined in past literature as the sampling, specializing and investment years, for the average number of other sports participated by athletes. Error bars represent sample SDs and individual colored data points represent different sports and studies.
Figure 5. Sample (bar) and individual study means (colored circles) across different age categories defined in past literature as the sampling, specializing and investment years, for average hours per week in other sports (A) and average accumulated hours in other sports (B). Error bars show standard deviation and colored circles represent individual groups for each sport/study. The “Sampling and Specializing” category represents data that span the ages in those categories.
Play amounts
Eight studies (representing two sports) included measures of play in the primary sport, or what were alternatively termed “unstructured activities” [59, 74, 75, 77, 86], as reported in Figure 6. Perhaps somewhat surprisingly, play activities continued to accumulate across development. Data from Youth Elite alpine skiers (age 15.7 yr) were not included [59], because hours/ year were only reported at one time point (~77 h).
Figure 6. Sample (bar) and individual study means (colored circles) across different age categories defined in past literature as the sampling, specializing and investment years, for average accumulated hours of play. Error bars represent sample standard deviation and colored circles represent individual sport groups. The “Specializing and Investment” category represents data that span the ages in those categories.
Discussion
Included study characteristics
After an extensive literature search, we retrieved 32 studies representing 13 sports that quantified the practice, play, and specialization pathways of Adult Super Elite, National, Elite-Adult, Varsity, and Youth-Elite female athletes. Only swimming, soccer, volleyball, and rhythmic gymnastics had three or more levels of expertise represented, allowing some commentary on how these pathways differ across groups of elite athletes within a particular sport. In our sample, athletes competing at the adult National and Youth-Elite (national) levels were the most represented in terms of overall proportion of athletes and these athletes were distributed across a range of sports. Relatively few studies included Super Elite athletes (i.e., Olympic medalists), Professional (paid to play), Varsity, or Elite Club-level female athletes. This paucity in studies is somewhat expected for Super Elite athletes, as there are relatively few athletes competing at this high level, posing challenges for recruitment. In the case of female Professional athletes, fewer opportunities for paid play and the presence of a gender pay gap in sport [9] may contribute to the lack of research on these athletes. The lack of research on women University level athletes is more surprising. The sample heterogeneity present for sport and level of expertise makes it challenging to provide either general or comparative judgements about pathways to expertise for female athletes but we have nonetheless attempted to make some general conclusions. Identification of gaps in research with respect to sports, expertise, and outcome measures also allows some statement about directions for future research on women athletes.
Of the included studies, many (81%), reported measures of practice, although less than half (44%) reported measures of practice at multiple time points. In 59% of studies, multisport participation was quantified in some way, with sport specialization (31%) and play (28%) experiences detailed in about a third of the studies, potentially reflecting testable postulates of the DMSP [29]. All the included studies originated from Western countries, with large proportions originating from the USA, Portugal, Canada, and Australia. This geographical limitation is likely related to our search being restricted to those published with an English abstract. Environmental constraints, such as socio-cultural factors, have been proposed to interact with the development of expertise in sport [88, 89]. Because gender reflects how cultures and societies ascribe roles, characteristics and values to the sexes [18], gender constructs vary cross-culturally [90] and thus our conclusions about developmental pathways are unlikely to generalize outside of “westernized” contexts.
Measures of practice were often framed in the context of the DMSP or deliberate practice theory. There are numerous issues and debates surrounding the definition and subsequent ways of quantifying deliberate practice and potential (mis) interpretations of the definition of deliberate practice [19, 22–24]. Ericsson cautioned against conflating general measures of practice, play and competition with deliberate practice [19, 22]. However, this has been the norm in many sport-related studies, particularly in team sports, where the criteria for deliberate practice is unlikely to be met at the individual player level in group rather than individual practice settings. In our sample, researchers differed in how they qualified and quantified practice, with some distinguishing team and individual “deliberate practice” and others reporting total hours of general training. The majority of reports of practice were based on retrospective recall methods (78%; 30% retrospective interviews and 48% retrospective questionnaires), such that there may be tendencies for athletes to overestimate practice hours [91]. Over half of the studies included had measures of multisport participation. Some researchers defined other sport participation as regular participation for more than 1 month, while others did not define what constituted other sport participation. As also noted by Mosher et al. [36] there was considerable variability in how researchers defined and reported the number of other sport activities. In only a few studies were measures of play included and there was variability in how this was measured, with some including all sport-related play and others focusing specifically on play in the primary sport.
In the following paragraphs, we outline the general patterns of sport participation pertaining to the age categories detailed in the DMSP; that is ages 6–12 yr (so termed sampling), ages 13–15 yr (so termed specializing) and ages 16–18 yr (so termed investment years) [29, 30]. We consider the data with respect to evidence supporting a more diversified or specialized route for female athletes and relate these considerations to the early specialization pathway (which is more in alignment with deliberate practice theory), early majority engagement pathway [40], and the early diversification pathway of the DMSP. Where possible, we consider age and skill-based comparisons in view of a suggested dissociation in patterns of sport involvement that define junior and adult elite athletes [47].
Early childhood (6–12 yr)
In general, the early childhood years were characterized by moderate engagement in practice activities with diversified sport participation. Athletes participated in an average of 2.7 other sports for 1.7 h/week, accumulating 788 h of practice in other sports during the sampling years. Although this is in line with the early diversification pathway of the DMSP [29, 30], there was also significant investment in sport-specific practice activities at a young age; with an average of 3.5 h of practice/week, 260 h of practice/year, and 927 accumulated hours of practice in the primary sport. Notably, the majority of time was spent practicing in the primary sport for these future elite athletes, even in these early childhood years, in line with the early majority engagement pathway [40, 41]. Although there were few studies where play amounts were reported, athletes reported relatively high amounts of play during early childhood (M = 390 h accumulated), which translated to ~33% of their sport time spent in play activities relative to practice.
The ages in which athletes began participating in their primary sport was highly varied in our sample, although this variability was mostly between sports rather than between categories of expertise. Within sports there were small differences in the start age in the primary sport across different categories of expertise. In gymnastics and swimming, the adult National and Super Elite athletes, respectively, had earlier start ages than the Youth-Elite groups. In soccer, although youth athletes were not represented, the higher-level adult athletes started earlier than the less elite. Although the data is lacking in women's and girls' sports, the current data does not show evidence supporting a delayed start age for adult elite athletes [47].
In sports such as gymnastics and figure skating, where peak success is often attained in late adolescence/early adulthood, our data were consistent with past research, where early specialized training is shown [10]. We also saw data consistent with ideas of late specialization sports, where coordination or physical requirements can hinder early engagement [10]. In our sample, several sports, including volleyball, field hockey, handball, netball, and rhythmic gymnastics, had quite late start ages compared to artistic gymnastics, soccer, swimming and alpine skiing (see Figure 3A). It is likely that these first mentioned ball control sports require developed motor skills and physical maturation for successful competition, leading to later sport starting ages. In the case of rhythmic gymnastics, there are coordination requirements that would need to be developed, in addition to fundamental gymnastic skills before athletes can start to use props and engage in this sport. Hence, our data on women and girls serve to further highlight the sport-dependent nature of pathways to expertise, necessitating sport specific recommendations in models of athlete development. These sport-specific data are consistent across male and female contexts because at young ages (before physical maturation), many sports offer mixed-sex/mixed-gender participation and sex differences in biological maturation do not emerge until adolescence.
In summary, elite female athletes engaged early in childhood in high amounts of sport-specific practice, whilst also engaging in approximately three other sports and high play amounts (where detailed). Their early childhood involvement would best be described as one of early majority engagement, rather than either early specialization or diversification.
Early adolescence (13–15 yr)
In what has been termed the specializing years (i.e., 13–15 yr) [30], athletes devoted more time to their primary sport than in the sampling years, but without exclusive specialization. Practice hours were on average 14.8 h/week or 717 h/year. By the age of 15 yr, 2,023 h of practice had been accumulated on average. In these early adolescent years, there was increased variability in practice amounts both within and between sports, reflecting the unique training demands and constraints of the different sports. For the start age of specialization, there was again some consistency across sports and expertise categories, with the majority of sports showing specialization in this time period ~14 yr (with the exception of swimming and rhythmic gymnastics at ~11–12 yr). Soccer showed the largest range across skill groups (with specialization being reported as earlier for Elite Adult vs. Adult National athlete groups). These ages reported in this time period are mostly consistent with what would be expected based on the diversification pathway of the DMSP. Congruent with all developmental pathways, there was a decrease in the amount of time spent in play activities relative to practice during this period (21% of overall sport time).
Deviating from predictions of the early diversification pathway, the female athlete groups from our sample showed only a small reduction in the number of other sports in these so termed “specializing” years (M = 1.6), compared to the sampling years (M = 2.7) and actually increased the hours spent in these other sports by ~2 h from childhood (M = 3.8 h/week). Corroborating these data, 63% of a sample of National level soccer athletes participated in other sports in early adolescence [75]. However, not all researchers reported other sport participation in the early childhood years and as such the trends in the figures may be misleading [59, 80]. Youth-Elite alpine skiers only had data in late adolescence [59, 80] and National level rhythmic gymnasts did not have data represented during childhood [80]. For both these athlete groups, there was higher than average (>1 SD above the sample mean) other sport participation in adolescence [59, 80]. We do not know why other sport involvement would be so high for these groups, beyond the seasonal nature of skiing or potentially relatively lax definitions of diversified sport participation in these studies. Youth-Elite handball athletes participated in fewer (>1 SD below the mean) additional sports in the childhood and early adolescence years compared to the group mean [64]. Because these athletes specialized relatively late at a mean age of 12.7 yr (perhaps reflecting transitions to secondary school or high-school where sport-team practice is regulated by the schools on an almost daily basis), this may partially explain lack of involvement in other sports.
Supporting deliberate practice theory predictions, in both the childhood and early adolescent years, Super-Elite level (international medalist) rhythmic gymnasts accumulated more practice hours than both National-level rhythmic gymnasts and other sport groups (>1 SD above the sample mean) [33]. Similarly, groups of Youth-Elite and National-level gymnasts participated in more practice hours per week in gymnastics (>1 SD above the sample mean) than other sports in early and late adolescence, respectively [80]. Corroborating these data, a sample of figure skaters started practice at a younger age, but increased practice hours per week at a similar rate as team sport athletes and musicians, indicating that figure skaters were investing in their sport earlier than other groups [92] (note that these data were not included in our sample as the data were not disaggregated for gender).
Late adolescence (15-18 yr)
Late adolescence, termed the “investment” years in the DMSP [30], was characterized by high amounts of practice (M = 13.9 h/week, 790 h/year, 4,508 h accumulated), but with continued participation in diversified sport activities (M = 1.2 other sports, 3.0 h/week). Following the predicted decline in play activities, these athletes spent 12% of sport time in play activities compared to practice in their primary sport.
National and Varsity adult soccer players and Youth-Elite alpine skiers participated in a relatively high number of other sports in the combined specializing and investment years (14–18 yr, M = 4.1 sports). In this category, groups maintained (Varsity soccer players and Youth-Elite alpine skiers) or slightly increased (National soccer players) the number of other sports in comparison to the sampling years [59, 77]. In male soccer athletes, diversified sport involvement has also been reported to be relatively high and maintained throughout development [42–45]. Moreover, the National soccer players represented in our sample had accumulated more hours of play in early and late adolescence compared to the group mean (>1 SD above the mean), although there were few sports represented with play amounts. National level volleyball players also continued to accumulate high amounts of play activities throughout the so termed specializing and investment years [57, 71]. This might reflect the cultural context for these athletes and the capacity for informal play activities in Portuguese volleyball.
Limitations and recommendations
Although we were able to gather developmental data from over 40 groups of elite athletes, the interpretation and generalizability of our findings are limited in several ways. First, there was considerable heterogeneity across sports, across categories of expertise and concerning definitions and types of measures reported. As such, broad statements are difficult to make that represent pathways descriptive of girls and women athletes generally. On the positive, the diversity in sports and categories of expertise captured in this work does allow us to describe a broad range of athlete experiences, providing a strong base for future work. Although others have tried to aggregate across sports based on whether they are team or individual [e.g., [93]], or game sports vs. CGS (centremetre, grams or seconds) sports [e.g., [47]], this variation noted across sports in our review, even within those that might be considered to be of the same category (i.e., soccer and volleyball), illustrates what gets lost or misinterpreted through such aggregation. What we would like to see are more systematic investigations within specific sports amongst girls and women athletes, including longitudinal follow up studies, especially following those athletes who achieved success at the youth/junior levels of sport. This sport-focused investigation coupled with increased specificity in measurement and definitions, will allow for better recommendations about pathways which best engender success and allow for later aggregation of data once such sport-specific nuances are known [91].
In many of the studies in our sample, research questions were posed in the context of the DMSP to discern between groups following what is considered an early specialization or an early sampling pathway. As such, the way data are collected may be biased by the model (i.e., where dichotomous categories are searched for, such as “specializers” or “non-specializers,” or data are collected within specific age bands that correspond to an already assumed period of specialization). There have been suggestions to consider practice amount and issues concerning specialization in the context of biological maturation, such that key age ranges for determining practice hours within the primary and in other sports (or non-sport activities) would be different across the sexes [11]. Such considerations are consistent with the long-term athlete development model [10] where sex based physical maturation characteristics impact generally on “advice” to progress from a training for fun to training to win level of engagement within a sport. Ideally we would be collecting data from individual athletes concerning their age of pubertal onset, but such individual-based data does raise concern for data collection methods and perhaps underscores a need for physiologists to team up with skill acquisition specialists to best collect data on developmental progressions.
Conclusions
In this scoping review of developmental pathways of elite female athletes, we show some differences in how expertise has been attained in comparison to general pathways proposed in the literature, based predominantly on male athletes. In general, women elite athletes reported increasing practice amounts as they continued in their sport throughout the childhood years but deviated from predicted pathways by continuing participation in other sports throughout adolescence, in what have been proposed to be the specializing and investment years [30, 32]. In addition to highlighting differences in pathways, we also highlight a gender gap in our knowledge of developmental pathways leading to expertise in girls' and women's sports. Although the current literature spans a range of sports, the relative paucity of research on female athletes means that there are still not enough data within specific sports and categories of expertise to draw conclusions regarding sport-specific pathways and differences between elite groups. In addition to further study of pathways toward expert performance of elite female athletes, we also recommend collection of data from non-western socio-cultural contexts, longitudinal data throughout the development years (particularly through the adolescence transition) and well-defined definitions of practice, play and specialization to allow better comparisons across studies. It may be that pathways and hence models of elite sport development need to be different for males and females, particularly when opportunities for professional careers are currently limited and where biological and psychosocial differences in maturation exist across sexes and genders. In future, researchers may wish to move beyond testing current dichotomous models of athlete development to explore the upper and lower limits of early engagement (i.e., a continuum of specialization), as well as discerning differential consequences for early patterns of engagement for long-term success and other measures of continued sport participation (such as injury, and psychological wellbeing). Exploring these patterns across defined male and female samples would give researchers and practitioners an evidence-base to create more nuanced athlete development models and programs that offer the next generation of female athletes the opportunity to safely grow, develop and flourish in their future sporting endeavors.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.
Author contributions
CP performed the study search and data extraction which was audited by DH. All authors contributed to conception and design of the study. All authors contributed to writing and revisions of the submitted manuscript.
Funding
This work was supported by a SSHRC (Social Sciences and Humanities Research Council of Canada) SPRI (Sport Participation Research Initiative) grant awarded to NH (grant # 003385-2017).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Footnotes
1. ^Coutinho et al. noted that they used the same participants for their 2016 and 2021 studies. In the 2016 study, age ranges of 8–12, 13–16, and 17–20 yr were reported whereas yearly measures were given from ages 6–12 yr in the 2021 study. Because of the duplicate reporting in the 8–12 yr age range, we only included measures from the 6–12 yr category from the 2021 study, representing a larger age range. We did not include data from Coutinho et al. [56] as the sample characteristics and our measures of interest were identical to those in Coutinho et al. [57]. Similarly, Fawver et al. [58], and Cowan et al. [59] used the same sample of participants as reported in De Couto et al. [[60]; confirmed via personal communication], such that any duplicate outcomes of interest reported in this work were not included in our final analyses. There was also replication of participants across studies by Johnson et al. [61] and Johnson et al. [62]. We have included both Johnson et al. [61] and Johnson et al. [62] in the characteristics of included studies but have omitted duplicated data.
2. ^Bruce at al. [68] and Cowan et al. [59] reported hours of other sport participation per year. Values were divided by 52 to convert to a weekly value.
References
1. Baker J, Wilson S, Johnston K, Dehghansai N, Koenigsberg A, de Vegt S, et al. Talent research in sport 1990–2018: a scoping review. Front Psychol. (2020) 11:607710. doi: 10.3389/fpsyg.2020.607710
2. Disanti JS, and Erickson K. Youth sport specialization: a multidisciplinary scoping systematic review. J Sports Sci. (2019) 37:2094–105. doi: 10.1080/02640414.2019.1621476
3. Turnnidge J, Allan V, and Côté J. The development of skill and interest in sport. In: Hodges NJ, Williams AM, editors. Skill Acquisition in Sport: Research, Theory and Practice. 3rd ed. London: Routledge (2019). doi: 10.4324/9781351189750-19
4. Viru A, Loko J, Volver A, Laaneots L, Karelson K, and Viru M (1998). Age periods of accelerated improvement of muscle strength, power, speed and endurance in the age interval 6–18 years. Biol. Sport. 15:211–27. Available online at: https://www.researchgate.net/publication/282723834.
5. Malina RM, Bouchard C, and Bar-Or O. Growth, Maturation and Physical Activity. Champaign, IL: Human Kinetics (2004). p. 307–33. doi: 10.5040/9781492596837
6. Craggs C, Corder K, van Sluijs EMF, and Griffin SJ. Determinants of change in physical activity in children and adolescents: a systematic review. Am J Prev Med. (2011) 40:645. doi: 10.1016/j.amepre.2011.02.025
7. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. (2012) 380:247–57. doi: 10.1016/S0140-6736(12)60646-1
8. Berger IE, O'Reilly N, Parent MM, Séguin B, and Hernandez T. Determinants of sport participation among Canadian adolescents. Sport Manag Rev. (2008) 11:277–307. doi: 10.1016/S1441-3523(08)70113-X
9. Cepeda I. Wage Inequality of women in professional tennis of the leading international tournaments: gender equality vs market discrimination? J Int Womens Stud. (2021) 22:407–26. Available online at: https://vc.bridgew.edu/jiws/vol22/iss5/25
10. Wicker P, Breuer C, and Dallmeyer S. The gender earnings gap among elite athletes in semi-professional sports. Manag Sports Leisure. (2021). doi: 10.1080/23750472.2021.1944819
11. Balyi I, and Hamilton A. Long-Term Athlete Development: Trainability in Childhood and Adolescence (2004). Victoria, BC: National Coaching Institute British Columbia & Advanced Training and Performance Ltd.
12. Lima AB, Nascimento J v, Leonardi TJ, Soares AL, Paes RR, Gonçalves CE, et al. Deliberate practice, functional performance and psychological characteristics in young basketball players: a Bayesian multilevel analysis. Int J Environ Res Public Health. (2020) 17:1–14. doi: 10.3390/ijerph17114078
13. Gledhill A, and Harwood C. Developmental experiences of elite female youth soccer players. Int J Sport Exerc Psychol. (2014) 12:150–65. doi: 10.1080/1612197X.2014.880259
14. Smith KL, Weir PL, Till K, Romann M, and Cobley S. Relative age effects across and within female sport contexts: a systematic review and meta-analysis. Sports Med. (2018) 48:1451–78. doi: 10.1007/s40279-018-0890-8
15. Schorer J, Cobley S, Büsch D, Bräutigam H, and Baker J. Influences of competition level, gender, player nationality, career stage and playing position on relative age effects. Scand J Med Sci Sports. (2009) 19:720–30. doi: 10.1111/j.1600-0838.2008.00838.x
16. Vincent J, and Glamser FD. Gender differences in the relative age effect among US Olympic Development Program youth soccer players. J Sports Sci. (2006) 24:405–413. doi: 10.1080/02640410500244655
17. Albuquerque MR, Fukuda DH, da Costa VT, Lopes MC, and Franchini E. Do weight categories prevent athletes from the relative age effect? A meta-analysis of combat sports. Sport Sci Health. (2016) 12:133–9. doi: 10.1007/s11332-016-0282-0
18. Welch V, Puil L, Shea B, Runnels V, Doull M, Tudiver S, et al. Addressing Sex/Gender in Systematic Reviews: Cochrane Musculoskeletal Group Briefing Note. Retrieved from: http://equity.cochrane.org/sex-and-gender-analysis (accessed November 15, 2020).
19. Ericsson KA. Towards a science of the acquisition of expert performance in sports: clarifying the differences between deliberate practice and other types of practice. J Sports Sci. (2020) 38:159–76. doi: 10.1080/02640414.2019.1688618
20. Ericsson KA, Krampe TT, and Tesch-Romer C. The role of deliberate practice in the acquisition of expert performance. Psychol Rev. (1993) 100:363–406. doi: 10.1037/0033-295X.100.3.363
21. Ford PR, and Coughlan EK. Operationalising deliberate practice for performance improvement in sport. In: Hodges NJ, Williams AM, editors. Skill Acquisition in Sport: Research, Theory and Practice. London: Routledge (2020). p. 183–200. doi: 10.4324/9781351189750-10
22. Ericsson KA. Summing up hours of any type of practice versus identifying optimal practice activities: commentary on Macnamara, Moreau, & Hambrick 2016. Perspect Psychol Sci. (2016) 11:351–4. doi: 10.1177/1745691616635600
23. Macnamara BN, Hambrick DZ, and Moreau D. How important is deliberate practice? Reply to Ericsson (2016). Perspect Psychol Sci. (2016) 11:355–8. doi: 10.1177/1745691616635614
24. Macnamara BN, Moreau D, and Hambrick DZ. The relationship between deliberate practice and performance in sports: a meta-analysis. Perspect Psychol Sci. (2016) 11:333–50. doi: 10.1177/1745691616635591
25. Baker J, and Young B. 20 Years later: deliberate practice and the development of expertise in sport. Int Rev Sport Exerc Psychol. (2014) 7:135–57. doi: 10.1080/1750984X.2014.896024
26. Ward P, Hodges NJ, and Williams AM. Deliberate practice and expert performance: defining the path to excellence. In: Hodges NJ, Williams AM, editors. Skill Acquisition in Sport: Research, Theory and Practice. Routledge (2004). p. 255–82. doi: 10.4324/9781351189750
27. Ford PR, Coughlan EK, Hodges NJ, and Williams AM. Deliberate practice in sport. In: Routledge Handbook of Sport Expertise. London: Routledge (2015). p. 347–62. doi: 10.4324/9781315776675-30
28. Côté J, Baker J, and Abernethy B. From play to practice: A developmental framework for the acquisition of expertise in team sport. In Starkes J, Ericsson KA, editors. Expert Performance in Sports: Advances in Research on Sport Expertise. Champaign, IL: Human Kinetics (2003) p. 89–113. Available online at: https://canada.humankinetics.com/collections/motor-behavior/products/expert-performance-in-sports
29. Côté J, and Vierimaa M. The developmental model of sport participation: 15 years after its first conceptualization. Sci Sports. (2014) 29:S63–9. doi: 10.1016/j.scispo.2014.08.133
30. Côté J. The influence of the family in the development of talent in sport. Sport Psychol. (1999) 13:395–417. doi: 10.1123/tsp.13.4.395
31. Côté J, and Hay J. Children's involvement in sport: A developmental perspective, in Silva JM, Stevens DE, editors. Psychological Foundations of Sport. Boston, MA: Benjamin Cummings (2002). p. 484–502. Available online at: https://www.pearson.ch/HigherEducation/Pearson/EAN/9780205331444/Psychological-Foundations-of-Sport
32. Côté J, Baker J, and Abernethy B. Practice and play in the development of sport expertise. In: Tenenbaum G, Eklund RC, editors. Handbook of Sport Psychology. (2012). p. 184–202. doi: 10.1002/9781118270011.ch8
33. Law MP, Côté J, and Ericsson KA. Characteristics of expert development in rhythmic gymnastics: a retrospective study. Int J Sport Exerc Psychol. (2007) 5:82–103. doi: 10.1080/1612197X.2008.9671814
34. Deakin JM, and Cobley S. A search for deliberate practice: a developmental framework for the acquisition of expertise in team sports. In: Starkes JL, Ericsson KA, editors. Expert Performance in Sports: Advances in Research on Sport Expertise. Champaign, IL: Human Kinetics (2003) p. 115–35.
35. Baker J, Mosher A, and Fraser-Thomas J. Is it too early to condemn early sport specialisation? Br J Sports Med. (2021) 55:179–80. doi: 10.1136/bjsports-2020-102053
36. Mosher A, Fraser-Thomas J, and Baker J. What defines early specialization: a systematic review of literature. Front Sports Act Living. (2020) 2:596229. doi: 10.3389/fspor.2020.596229
37. Wiersma LD. Risks and benefits of youth sport specialization: perspectives and recommendations. Pediatr Exerc Sci. (2000) 12:13–22. doi: 10.1123/pes.12.1.13
38. Hill GM, and Simons J. A study of the sport specialization on high school athletics. J Sport Soc Issues. (1989) 13:1–13. doi: 10.1177/019372358901300101
39. Sieghartsleitner R, Zuber C, Zibung M, and Conzelmann A. “The early specialised bird catches the worm!” - a specialised sampling model in the development of football talents. Front Psychol. (2018) 9:188. doi: 10.3389/fpsyg.2018.00188
40. Ford PR, Ward P, Hodges NJ, and Williams AM. The role of deliberate practice and play in career progression in sport: the early engagement hypothesis. High Ability Stud. (2009) 20:65–75. doi: 10.1080/13598130902860721
41. Ford PR, and Williams AM. The developmental activities engaged in by elite youth soccer players who progressed to professional status compared to those who did not. Psychol Sport Exerc. (2012) 13:349–52. doi: 10.1016/j.psychsport.2011.09.004
42. Hendry DT, and Hodges NJ. Early majority engagement pathway best defines transitions from youth to adult elite men's soccer in the UK: a three time-point retrospective and prospective study. Psychol Sport Exerc. (2018) 36:81–9. doi: 10.1016/j.psychsport.2018.01.009
43. Hendry DT, and Hodges NJ. Pathways to expert performance in soccer. J Expertise. (2019) 2:1–13. Available online at: https://www.journalofexpertise.org/articles/volume2_issue1/index.html
44. Hendry DT, Crocker PRE, Mark Williams A, and Hodges NJ. Tracking and comparing self-determined motivation in elite youth soccer: influence of developmental activities, age, and skill. Front Psychol. (2019) 10:1–20. doi: 10.3389/fpsyg.2019.00304
45. Hendry DT, Crocker PRE, and Hodges NJ. Practice and play as determinants of self-determined motivation in youth soccer players. J Sports Sci. (2014) 32:1091–9. doi: 10.1080/02640414.2014.880792
46. Güllich A, and Emrich E. Considering long-term sustainability in the development of world class success. Eur J Sport Sci. (2014) 14:383–97. doi: 10.1080/17461391.2012.706320
47. Güllich A, Macnamara BN, and Hambrick DZ. What makes a champion? Early Multidisciplinary practice, not early specialization, predicts world-class performance. Perspect Psychol Sci. (2022) 17:6–29. doi: 10.1177/1745691620974772
48. Gullich A, and Cobley S. On the efficacy of talent identification and talent development programmes. In: Baker J, Cobley S, Schorer J, Wattie N, editors. The Routledge Handbook of Talent Identification and Development in Sport. London: Routledge (2017). p. 80–98. doi: 10.4324/9781315668017-7
49. Barreiros AN, and Fonseca AM. A retrospective analysis of portuguese elite athletes' involvement in international competitions. Int J Sports Sci Coach. (2012) 7:593–600. doi: 10.1260/1747-9541.7.3.593
50. Moher D, Liberati A, Tetzlaff J, and Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. (2009) 6:e1000097. doi: 10.1371/journal.pmed.1000097
51. Peters MDJ, Marnie C, Tricco AC, Pollock D, Munn Z, Alexander L, et al. Updated methodological guidance for the conduct of scoping reviews. JBI Evid Implement. (2021) 19:3–10. doi: 10.1097/XEB.0000000000000277
52. McAuley ABT, Baker J, and Kelly AL. Defining “elite” status in sport: from chaos to clarity. German J Exerc Sport Res. (2021) 52:193–7. doi: 10.1007/s12662-021-00737-3
53. Williams A, Day S, Stebbings G, and Erskine R (2017). What does ‘elite' mean in sport and why does it matter. Sport Exerc. Scient. 51:6. Available online at: https://www.iat.uni-leipzig.de/datenbanken/iks/sponet/Record/4056787.
54. Baker J, Wattie N, and Schorer J. Defining Expertise: a taxonomy for researchers in skill acquisition and expertise. In: Baker J, Farrow D, editors. Routledge Handbook of Sport Expertise. London: Routledge (2015). p. 145–55. doi: 10.4324/9781315776675-13
55. Zerunyan N. Time's up: addressing gender-based wage discrimination in professional sports. Loyola of Los Angeles Entert Law Rev. (2018) 38:229–59. Available online at: https://digitalcommons.lmu.edu/elr/vol38/iss3/2
56. Coutinho P, Mesquita I, Fonseca AM, and Côté J. Expertise development in volleyball: the role of early sport activities and players' age and height. Kinesiology. (2015) 47:215–25. Retrieved from: https://hrcak.srce.hr/150549
57. Coutinho P, Mesquita I, Davids K, Fonseca AM, and Côté J. How structured and unstructured sport activities aid the development of expertise in volleyball players. Psychol Sport Exerc. (2016) 25:51–9. doi: 10.1016/j.psychsport.2016.04.004
58. Fawver B, Cowan RL, DeCouto BS, Lohse KR, Podlog L, and Williams AM. Psychological characteristics, sport engagement, and performance in alpine skiers. Psychol Sport Exerc. (2020) 47:101616. doi: 10.1016/j.psychsport.2019.101616
59. Cowan RL, Fawver B, Lohse KR, Taylor T, Ford PR, and Williams AM. Modeling talent development pathways in alpine ski racers. Psychol Sport Exerc. (2021) 55:101942. doi: 10.1016/j.psychsport.2021.101942
60. DeCouto BS, Fawver B, Taylor T, and Williams AM. Physical fitness is associated with better technical performance in adolescent alpine ski racers after controlling for practice time: a retrospective regression analysis. J Sports Sci. (2021) 39:380–7. doi: 10.1080/02640414.2020.1823088
61. Johnson MB, Tenenbaum G, and Edmonds WA. Adaptation to physically and emotionally demanding conditions: the role of deliberate practice. High Ability Stud. (2006) 17:117–36. doi: 10.1080/13598130600947184
62. Johnson MB, Tenenbaum G, Edmonds WA, and Castillo Y. A comparison of the developmental experiences of elite and sub-elite swimmers: similar developmental histories can lead to differences in performance level. Sport Educ Soc. (2008) 13:453–75. doi: 10.1080/13573320802445108
63. Baker J, Côté J, and Abernethy B. Sport-specific practice and the development of expert decision-making in team ball sports. J Appl Sport Psychol. (2003) 15:12–25. doi: 10.1080/10413200305400
64. Schorer J, Baker J, Bagats S, and Strauss B (2012). Training differences and selection in a talent identification system. Talent Dev Excell. 4:23–32. Available online at: https://www.researchgate.net/publication/259638507
65. Barynina II, and Vaitsekhovskii SM. The aftermath of early sports specialization for highly qualified swimmers. Fitness Sports Rev Int. (1992) 27:132.
66. Bjørndal CT, Ronglan LT, and Andersen SS. The diversity of developmental paths among youth athletes: a 3-year longitudinal study of Norwegian handball players. Talent Dev Excell. (2016) 8:20–32. Available online at: https://www.researchgate.net/publication/315383333
67. Blijlevens SJE, Wylleman P, Bool K, Elferink-Gemser MT, and Visscher C. Acquisition and maintenance of excellence: the challenges faced by Dutch top-level gymnasts throughout different stages of athletic development. Sport Soc. (2020) 23:577–94. doi: 10.1080/17430437.2018.1524106
68. Bruce L, Farrow D, and Raynor A. Performance milestones in the development of expertise: are they critical? J Appl Sport Psychol. (2013) 25:281–97. doi: 10.1080/10413200.2012.725704
69. Buckley PS, Bishop M, Kane P, Ciccotti MC, Selverian S, Exume D, et al. Early single-sport specialization a survey of 3090 high school, collegiate, and professional athletes. Orthop J Sports Med. (2017) 5:1–7. doi: 10.1177/2325967117703944
70. Coutinho P, Mesquita I, Fonseca AM, and de Martin-Silva L. Patterns of sport participation in Portuguese volleyball players according to expertise level and gender. Int J Sports Sci Coach. (2014) 9:579–92. doi: 10.1260/1747-9541.9.4.579
71. Coutinho P, Ramos A, Fonseca AM, Davids K, and Mesquita I. The nature of formative physical activities and sports in the development of senior volleyball players. Int J Sports Sci Coach. (2021) 16:731–40. doi: 10.1177/1747954121992040
72. Da Matta G.B. (2004). The influence of deliberate practice and social support systems on the development of expert and intermediate women volleyball players in Brazil (Doctoral thesis). University of South Carolina, Columbia, SC, United States.
73. de Bosscher V, and de Rycke J. Talent development programmes: a retrospective analysis of the age and support services for talented athletes in 15 nations. Eur Sport Manag Quart. (2017) 17:590–609. doi: 10.1080/16184742.2017.1324503
74. Duffy LJ, Baluch B, and Ericsson KA. Dart performance as a function of facets of practice amongst professional and amateur men and women players. Int J Sport Psychol. (2004) 35:232–345. Available online at: https://www.researchgate.net/publication/228794232
75. Ford PR, Hodges NJ, Broadbent D, O'Connor D, Scott D, Datson N, et al. The developmental and professional activities of female international soccer players from five high-performing nations. J Sports Sci. (2020) 38:1–9. doi: 10.1080/02640414.2020.1789384
76. Güllich A. “Macro-structure” of developmental participation histories and “micro-structure” of practice of German female world-class and national-class football players. J Sports Sci. (2019) 37:1347–55. doi: 10.1080/02640414.2018.1558744
77. Hendry DT, Williams AM, Ford PR, and Hodges NJ. Developmental activities and perceptions of challenge for National and Varsity women soccer players in Canada. Psychol Sport Exerc. (2019) 43:210–8. doi: 10.1016/j.psychsport.2019.02.008
78. Hodges NJ, Kerr T, Starkes JL, Weir PL, and Nananidou A. Predicting performance times from deliberate practice hours for triathletes and swimmers: what, when, and where is practice important? J Exp Psychol Appl. (2004) 10:219–37. doi: 10.1037/1076-898X.10.4.219
79. Hodges NJ, Augaitis L, and Crocker PRE. Sport commitment and deliberate practice among male and female triathletes. Int J Sport Psychol. (2015) 46:652–65. doi: 10.7352/IJSP2015.46
80. Hume PA, Hopkins WG, Robinson DM, Robinson SM, and Hollings SC. Predictors of attainment in rhythmic sportive gymnastics. J Sports Med Phys Fitness. (1993) 33:367–77. Available online at: https://www.researchgate.net/publication/283517553
81. Leite N, and Sampaio JE. Long-term athletic development across different age groups and gender from Portuguese basketball players. Int J Sports Sci Coach. (2012) 7:285–300. doi: 10.1260/1747-9541.7.2.285
82. Naisidou S, Kepesidou M, Kontostergiou M, and Zapartidis I. Differences of physical abilities between successful and less successful young female athletes. J Phys Educ Sport. (2017) 17:294–9. doi: 10.7752/jpes.2017.01044
83. Post EG, Trigsted SM, Riekena JW, Hetzel S, McGuine TA, Brooks MA, et al. The association of sport specialization and training volume with injury history in youth athletes. Am J Sports Med. (2017) 45:1405–12. doi: 10.1177/0363546517690848
84. Staff T, Gobet F, and Parton A. Investigating the period of practice needed to acquire expertise in Great Britain 2012 track and field olympic athletes. J Expert. (2019) 2:148–63. Available online at: https://www.researchgate.net/publication/338544819
85. Storm LK, Kristoffer H, and Krogh CM. Specialization pathways among elite Danish athletes: a look at the Developmental Model of Sport Participation from a cultural perspective. Int J Sport Psychol. (2012) 43:199–222. doi: 10.7352/IJSP.2012.43.199
86. Timmerman EA, Savelsbergh GJP, and Farrow D. Examining the influence of multiple performance characteristics on selection into a representative team in field hockey. High Ability Stud. (2021) 1–21. doi: 10.1080/13598139.2021.1885353
87. Post EG, Thein-Nissenbaum JM, Stiffler MR, Brooks MA, Bell DR, Sanfilippo JL, et al. High school sport specialization patterns of current division I athletes. Sports Health. (2017) 9:148–53. doi: 10.1177/1941738116675455
88. Baker J, and Horton S. A review of primary and secondary influences on sport expertise. High Ability Stud. (2004) 15:211–28. doi: 10.1080/1359813042000314781
89. Araujo D, Fonseca C, and Davids K. The role of ecological constraints on expertise development. Talent Dev Excell. (2010) 2:165–79. Available online at: https://www.researchgate.net/publication/288750954
90. Blackstone AM. Gender roles and society. In: Miller JR, Lerner RM, Schiamberg LB, editors. Human Ecology: An Encyclopedia of Children, Families, Communities, and Environments. (2003). p. 335–8. Santa Barbara, CA: ABC-CLIO.
91. Baker J, Hodges NJ, and Wilson MJ. Collecting and assessing practice activity data: concurrent, retrospective, and longitudinal approaches. In: The Cambridge Handbook of Expertise and Expert Performance. Cambridge: Cambridge University Press (2018). p. 257–70. doi: 10.1017/9781316480748.015
92. Starkes J. (2000). The road to expertise: Is practice the only determinant? Int J Sport Psychol. 31:431–51.
93. Starkes JL, Deakin JM, Allard F, and Hodges NJ. Deliberate practice in sports: what is it anyway? In: Ericsson KA, editor. The Road to Excellence: The Acquisition of Expert Performance in the Arts and Sciences, Sports, and Games. Mahwah, NJ: Lawrence Erlbaum Associates (1996). p. 81–106. Available online at: https://www.routledge.com/The-Road-To-Excellence-the-Acquisition-of-Expert-Performance-in-the-Arts/Ericsson/p/book/9780805822328
Keywords: expertise, deliberate practice, talent, play, specialization
Citation: Peters CM, Hendry DT and Hodges NJ (2022) A scoping review on developmental activities of girls' and women's sports. Front. Sports Act. Living 4:903886. doi: 10.3389/fspor.2022.903886
Received: 24 March 2022; Accepted: 24 August 2022;
Published: 23 September 2022.
Edited by:
Arne Güllich, University of Kaiserslautern, GermanyReviewed by:
Michael Romann, Swiss Federal Institute of Sport Magglingen SFISM, SwitzerlandHumberto M. Carvalho, Federal University of Santa Catarina, Brazil
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*Correspondence: Nicola J. Hodges, nicola.hodges@ubc.ca