research paper about physical development pdf

• Open access
• Published: 05 November 2013

Early childhood predictors of toddlers’ physical activity: longitudinal findings from the Melbourne InFANT Program

• Jill Hnatiuk 1 ,
• Jo Salmon 1 ,
• Karen J Campbell 1 ,
• Nicola D Ridgers 1 &
• Kylie D Hesketh 1  

International Journal of Behavioral Nutrition and Physical Activity volume  10 , Article number:  123 ( 2013 ) Cite this article

19k Accesses

41 Citations

13 Altmetric

Metrics details

Young children are at risk of not meeting physical activity recommendations. Identifying factors from the first year of life which influence toddlers’ physical activity levels may help to develop targeted intervention strategies. The purpose of this study was to examine early childhood predictors of toddlers’ physical activity across the domains of maternal beliefs and behaviours, infant behaviours and the home environment.

Data from 206 toddlers (53% male) participating in the Melbourne InFANT Program were collected in 2008–2010 and analysed in 2012. Mothers completed a survey of physical activity predictors when their child was 4- (T1) and 9- months old (T2). Physical activity was assessed by ActiGraph GT1M accelerometers at 19- months (T3) of age.

One infant behaviour at T1 and one maternal belief and two infant behaviours at T2 showed associations with physical activity at T3 and were included in multivariate analyses. After adjusting for the age at which the child started walking and maternal education, the time spent with babies of a similar age at 4-months (β = 0.06, 95% CI [0.02, 0.10]) and the time spent being physically active with their mother at 9-months (β = 0.06, 95% CI [0.01, 0.12]) predicted children’s physical activity at 19-months of age.

Conclusions

Promotion of peer-interactions and maternal-child co-participation in physical activity could serve as a health promotion strategy to increase physical activity in young children. Future research is required to identify other early life predictors not assessed in this study and to examine whether these factors predict physical activity in later life stages.

Regular engagement in physical activity has been associated with a number of positive physical and psychosocial health outcomes in early childhood (birth to five years), including decreased body mass index and fatness [ 1 – 4 ], lower cardiovascular risk factors [ 5 , 6 ], improved bone development [ 7 , 8 ], and improved cognitive, social and emotional development [ 9 , 10 ]. However, currently many preschool-aged children do not meet international physical activity recommendations [ 11 ] of 180 minutes of light, moderate and vigorous physical activity per day [ 12 – 14 ] and a proportion of younger children (<2 years old) also fail to meet recommendations [ 15 , 16 ]. As such, interventions targeting physical activity promotion among young children have emerged in recent years to increase physical activity levels, although these remain scarce and have demonstrated limited success [ 17 ]. Interventions may have greater likelihood of success if they target known predictors of children’s physical activity. While some research exists in preschool-aged children [ 18 , 19 ], no research to date has investigated which factors predict physical activity in younger children.

The Social Cognitive Theory- Family Perspective [ 20 ] (SCT-FP) is an extension of the Social Cognitive Theory [ 21 ] that highlights the importance of the family on the development of children’s physical activity behaviours. It postulates that bi-directional influences of parent and child beliefs and behaviours as well as the home environment interact to affect children’s physical activity. This model is particularly relevant for understanding young children’s physical activity given their unique dependence on and close relationship with parents, often the child’s mother. The SCT-FP model highlights potential constructs (i.e. maternal beliefs, maternal behaviours, infant behaviours, home environment) from which potential early predictor variables can be identified to inform future intervention strategies. Therefore, the aim of this paper was to examine early childhood predictors of young children’s physical activity across the domains of maternal beliefs and behaviours, infant behaviours and the home environment.

Participants

Participant data were obtained from the Melbourne Infant Feeding, Activity & Nutrition Trial (InFANT) Program. Ethics approval for the study was obtained from the Deakin University Ethics Committee and from the Victorian Government’s Office for Children and parents provided informed consent for their own and their child’s participation. The rationale and methods used in the Melbourne InFANT Program have been described in detail previously [ 15 , 22 , 23 ]. Briefly, the Melbourne InFANT Program was a low-dose cluster randomized controlled trial that aimed to provide first-time parents with the skills, knowledge and confidence to reduce obesity-promoting behaviours, including physical inactivity. Parents were recruited through first time parent groups operated by the free, universal maternal and child health centres in randomly selected Local Government Areas. The 15-month intervention was conducted with 542 parent–child pairs from 62 different parent groups between June 2008 and February 2010 when participating children were between 4- and 19-months of age. Data were analysed in 2012.

Questionnaires assessing all predictor variables were self-completed by mothers when the children were aged 4-months (T1- baseline) and 9-months (T2- mid-intervention). Physical activity was assessed every 15 seconds for a minimum of 7 consecutive days using ActiGraph GT1M accelerometers, removed only for sleeping and bathing, when the children were approximately 19-months old (T3- intervention conclusion). As there were no differences in total physical activity between intervention and control group children at intervention conclusion (225.3 ± 41.0 mins vs. 237.4 ± 38.6 mins, p > 0.19) all data were pooled for this study. There were also no intervention group differences for the predictor variables with the exception that mothers’ physical activity at T1 was higher in the control group (removal of this item did not impact results, hence it was retained).

Participant demographics

The child’s sex and date of birth, and maternal education were assessed through the mother’s questionnaire at T1. Maternal education was categorized as low (secondary school or less), middle (trade and certificate qualifications) or high (university degree or higher), consistent with previous research [ 24 ]. Maternal employment status was assessed at each time point and categorized as: on maternity leave/home duties full time, full-time employment, part-time employment or other (consisting of student, retired, unemployed, other). The age that the child started walking was retrospectively asked in the mother’s questionnaire at T3. The time since the child began walking was calculated by subtracting the age when the child began walking from the child’s age when the mother completed the questionnaires.

Predictor variables

Maternal beliefs.

Maternal beliefs were assessed at T1 (child 4-months old) through 36 purpose-designed survey questions covering maternal beliefs, attitudes and intentions regarding their child’s physical activity and television viewing. All questions were scored on a 4-point likert-type scale (0 = strongly disagree/not at all confident to 3 = strongly agree/extremely confident). The questions were based on previous qualitative [ 25 ] and quantitative [ 26 ] work and were tested in a separate sample of parents of infants with moderate to good test-retest reliability (% agreement = 0.56-0.86). Nine factors were generated from the 36 items using exploratory factor analyses with promax rotation in the larger study sample: physical activity knowledge (10 questions examining the importance of physical activity for babies’ and toddlers’ health and development), views of physically active children (4 questions examining maternal perceptions of active children), physical activity optimism (3 questions examining the anticipated ease of engaging children in physical activity), self-efficacy for promoting physical activity (3 questions examining mothers’ confidence for promoting physical activity), future expectations around children’s physical activity and TV viewing (2 questions examining maternal expectations of children’s future physical activity and TV viewing behaviours), floor play concerns (2 questions examining perceptions of safety of floor play), TV knowledge (4 questions examining perceived benefits of TV for young children), TV use (5 questions examining the use of TV for practical reasons) and self-efficacy for limiting TV (3 questions examining mothers’ confidence for limiting TV viewing). Factor scores were generated by averaging the item scores within each factor. All factors at T1 had good internal reliability in the larger sample (Chronbach’s α = 0.58–0.87) and in the subsample for this paper (Cronbach’s α = 0.54-0.88).

At T2, mothers completed a shortened version of the T1 questionnaire focused on main outcomes (to reduce participant burden). The T2 questionnaire contained five of the nine original maternal belief factors: physical activity optimism, self-efficacy for promoting physical activity, future expectations around children’s physical activity and TV viewing, TV use and self-efficacy for limiting TV. Internal reliability for the T2 factors ranged from α = 0.64–0.92 in the subsample for this paper.

Maternal behaviours

Mothers’ physical activity was assessed at T1 and T2 using the validated Active Australia Survey [ 27 ]. Total physical activity (mins/week) was determined by summing the time spent walking (>10 minutes), the time spent in moderate intensity physical activity (MPA) and twice the time spent in vigorous intensity physical activity (VPA) in the past week, since VPA confers greater health benefits [ 27 ]. To avoid errors in over reporting, minutes spent in any given activity intensity were truncated at 840 minutes/week (14 hours) and time spent in all activity intensities was truncated at 1680 minutes/week (28 hours) [ 27 ].

Maternal television viewing (TV) time (mins/week) was assessed on weekdays and weekends by the questions, “On a usual weekday (weekend day), about how many hours do you usually spend sitting down and watching television or videos/DVDs?”, shown in previous studies to be valid and reliable [ 28 ]. TV time was determined by calculating a weighted average between the weekday and weekend day responses. To avoid errors in over-reporting, total TV times reported were truncated at 1060 minutes/day (18 hours).

Infant behaviours

Infant behaviours were assessed at T1 and T2 by maternal proxy-report of the amount of time in the last week the infant spent in a variety of physical activity behaviours. Items included: time spent playing games with an adult, time spent being physically active with mum, time spent on the child’s stomach (tummy time), time spent on the floor, time spent with other babies of a similar age, time spent with older toddlers or children and time spent outside. All infant behaviours were presented as mins/week. Test-retest reliability (ICC) for these items, conducted in the separate sample, ranged from 0.25-0.59 at T1 and 0.41–0.86 at T2.

• Home environment

At T1 and T2, mothers were asked, on a 4-point likert-type scale (0 = unlikely to 3 = extremely likely), how likely it is that they will provide a variety of activity equipment (appropriate for young children) in their home (e.g., balls, push-along toy, sand pit). Responses were categorised as 'likely’ (score of 1) if the mother responded she 'already had’ or was 'extremely likely’ or 'very likely’ to provide that piece of equipment in their home for their child. Responses were categorized as 'unlikely’ (score of 0) if the mother responded 'possibly’ or 'unlikely’ to provide that piece of equipment in their home for their child. A total score was then calculated by summing the points for the 12 equipment items. At T1 only, mothers were asked, “How many TVs do you have in your home?” Test-retest reliability in the separate sample was fair to good for provision of activity equipment (ICC = 0.49–0.80 at T1 and ICC = 0.48–0.77 at T2) and excellent for the number of TV’s in the home (% agreement = 0.96).

Accelerometer data reduction

Following current physical activity recommendations, [ 12 – 14 ] the outcome variable was the total time spent in light-to-vigorous intensity physical activity per day. Validated cut-points of 192–1672 and >1672 counts per minute [ 29 ] were applied to the data to determine time spent in light- and moderate- to- vigorous intensity physical activity, respectively and then summed to determine total time (mins/day) spent in physical activity for each child. Twenty minutes of consecutive zero counts were identified as non-wear time and only children with at least 4 days (including at least one weekend day) of valid (≥ 7.4 hours/day) data were included in the analyses [ 15 ]. As higher physical activity levels were observed on weekends compared with weekdays (240.8 ± 50.8 mins vs. 230.6 ± 42.55 mins) a weighted weekly score was calculated. Active time for each valid day (total activity divided by number of days) were separated into weekday (Monday-Friday), weekend (Saturday, Sunday), and weighted whole week data, where the weekdays provided 5/7ths of weekly activity and the weekends provided 2/7ths.

Data analyses

Linear regression analyses were conducted for all predictors (factors and individual items) assessed at T1 (4-months) and T2 (9-months) with child’s physical activity at 19-months as the outcome variable. All models included the following confounder variables: (1) intervention arm (although there was no intervention effect on the outcome variable (physical activity), this was undertaken as a precautionary measure); (2) clustering by the unit of randomization (mother’s group attended; individuals within the same unit are expected to be more alike than if randomly chosen) [ 30 ] and (3) average accelerometer wear minutes/day (physical activity level was positively associated with accelerometer wear time in our sample [r = 0.48]). Any predictor which had a p-value of ≤0.10 in Model A analyses was included in Models B and C. Model C also controlled for maternal education which is a known covariate of older children’s physical activity [ 31 , 32 ] and the age that the child began walking, as children who began walking earlier had higher physical activity levels than those who began walking later in this sample (β = -4.30, 95% CI [-6.69, -1.90]). As few mothers were employed at T1, data were also analysed with maternal employment status at T2 and T3 as a covariate, however no differences in results were observed and thus the results are not presented. Variance Inflation Factors in the regression models were all <10 indicating that multicollinearity was not a concern [ 33 ]. Analyses were conducted in Stata version 12.0.

Participant characteristics

From the 542 parent–child pairs in the Melbourne InFANT Program, 417 children provided accelerometer data at T3, however 130 of these did not have sufficient accelerometer data and a further 81 did not have complete questionnaire data. This resulted in a sample of 206 children (across 60 parent groups) that were included in these analyses. The participant characteristics of this sample are provided in Table  1 . Children included in this study were significantly younger, had been walking for less time and had mothers with higher levels of education than children excluded from the study.

Early predictors of toddlers’ physical activity

Table  2 displays the mean scores for all predictor variables at T1 & T2. Table  3 presents the results of the linear regression models from the T1 predictor items and factors. One infant behaviour item (time spent with other babies of a similar age) had a p-value of <0.10 and was included in Model B and C analyses. In both Models B and C, time spent with other babies of a similar age remained positively associated with toddlers’ PA. In Model B, 22.8% of the variance was accounted for by the included variables (22.5% from covariates). In Model C (which also included maternal education and age at walking), 27.2% of the variance was accounted for by the included variables (26.7% from covariates).

Table  4 presents the results of the linear regression models using the T2 predictor variables. From Model A results, one maternal belief factor (physical activity optimism) and two infant behavioural items (time spent being physically active with mum and time spent with other babies of a similar age) had p-values of <0.10 and were included in Models B and C. In Model B, the time spent with other babies of a similar age was negatively associated with toddlers’ PA and time spent being physically active with mom was positively associated with toddlers’ PA. After controlling for age when the child began walking and maternal education (Model C) only the time spent being physically active with mum remained associated with toddlers’ PA. In Model B, 27.7% of the variance was accounted for by the included variables (22.5% from covariates). In Model C, 30.0% of the variance was accounted for by the included variables (26.7% from covariates).

This study was the first to examine early childhood predictors of toddlers’ physical activity at two different time points (4-months and 9-months old). The results indicated that two of the investigated variables, the time spent with other babies of a similar age at child aged 4-months and the time spent being physically active with the child’s mother at child aged 9-months, significantly predicted toddlers’ objectively assessed physical activity, after adjusting for the age the child started walking and maternal education. These findings potentially highlight the importance of social interaction with peers and maternal-child co-participation in physical activity from a young age for children’s physical activity levels.

Previous cross-sectional research has found that 2-year old children’s outdoor play time (used as a proxy for physical activity) was marginally associated with their mother’s self-reported physical activity level [ 34 ] and that preschool children’s objectively assessed physical activity is related to mothers’ physical activity levels [ 35 , 36 ] and the frequency that she is active with her child [ 19 ]. The findings from the current study suggest that maternal-child co-participation in physical activity early in life may increase children’s physical activity levels as toddlers, although further research is required to better understand this relationship in the early childhood period. The finding that time spent with other children of a similar age at child aged 4-months is more difficult to explain as the positive association between these variables did not persist when assessed at 9-months of age. It is possible that this variable served as a proxy for some other construct at child aged 4-months that was not measured in the study; for example, maternal sociability. While it could be hypothesized that the time children spent with other babies of a similar age was influenced by whether or not they attended a childcare facility, no between-group differences in time spent with other babies of a similar age were observed when assessed via an independent samples t-test (results not shown). However, this insignificant finding may have also occurred due to the fact that very few children (<3%) attended a childcare facility at T1. Future research should further investigate the influence of the time spent with other babies of a similar age and the time spent being physically active with their mother on young children’s physical activity levels before any conclusions can be made.

Although positive associations were observed between children’s physical activity and both the time spent with other peers and being active with their mother, similar findings were not observed in relation to the time spent with older toddlers or children. This is contrary to previous research in school-aged children which suggests that children with an older sibling are more active than children without an older sibling [ 37 ]. One possible explanation for this finding is that the children in this study were first born, and thus many of them did not spend any time with older toddlers or children. Having such a high percentage of children who spent no time with older toddlers or children may have reduced the discriminant ability of this variable, making it difficult to determine its influence on physical activity in this cohort.

While only considered as a covariate, this study found that the age when the child began walking was related to their physical activity levels as toddlers. This information is important, as it may serve as a way to identify children at risk of low levels of physical activity, who may benefit from early support strategies to engage in sufficient physical activity. To put the effect size identified in this study into context, there would be a difference of approximately 40 minutes of physical activity per day at 19-months in favour of early walkers (8-months), compared to those who began walking at the uppermost (18-months) limit of the age range of typical motor development for healthy children [ 38 ]. Given there is evidence that low levels of physical activity can track into childhood [ 39 ], developing strategies from an early age to increase physical activity in children who are at risk may be beneficial. We are aware of only one other study in this area. This work found that certain motor milestones such as age at standing unaided and walking with support were positively associated with a modest increase in sport participation at age 14-years [ 40 ]. It is reasonable to assume that the relationship between children’s age at walking and physical activity level may be explained by the length of time that the children had been walking. In other words those with higher levels of physical activity had simply been walking longer and were therefore more adept at upright movement than those who had more recently mastered the skill. However, when the time since walking commenced was examined as a potential predictor of physical activity, no association was observed (results not shown). Future research should examining physical activity levels in young children should account for the age that children started walking in the analyses. Research is also warranted to specifically investigate how motor development is related to future physical activity levels in children.

Despite assessing a range of variables covering the key aspects of the SCT-FP model, most of the variables examined when the children were 4- and 9-months old were not associated with toddlers’ physical activity, suggesting that it is very challenging to identify key early life predictors of physical activity. This finding is consistent with other recent research whereby no modifiable correlates of toddlers’ physical activity were identified [ 16 ]. It could be hypothesized that physical activity in young children is largely biologically determined, with external influences having a smaller effect on physical activity compared to older children, at least in the short-term. The finding that the age of walking was significantly associated with objectively assessed physical activity at 19-months lends credence to this hypothesis. Given that this was the first study of its kind, and few studies have reported objectively measured physical activity levels of children under 2-years old, these findings provide a platform to inform subsequent research to develop our understanding of young children’s physical activity behaviour and to identify other potential predictors of physical activity. However, limitations of the manuscript must be acknowledged. As the effect sizes of both the significant variables were small, they were single item variables, and multiple statistical tests were performed, it is possible that the findings observed were due to chance. Additionally, attending a child care facility may have been an important correlate at T2, but this information was not collected. However, attempts were made to account for this limitation by running the regression models with maternal employment status included. Finally, our sample was highly educated and had children who were younger and had been walking for less time. This may preclude generalizability to the wider population.

The time spent with other babies of a similar age at 4-months of age and the time spent being physically active with the child's mother at 9-months of age were positively (albeit weakly) associated with physical activity levels at 19-months of age. Thus, promotion of peer-interactions and maternal-child co-participation in physical activity could potentially serve as a health promotion strategy to increase physical activity in young children. However, further investigation is required to determine if the time spent with other babies of a similar age at 4-months is associated with toddlers’ physical activity in separate samples, and whether the time spent with other babies of a similar age and time spent being physically active with the child’s mother predicts physical activity in later life stages (e.g. preschool and primary school years). Furthermore, future research should examine other potential early predictors of physical activity not assessed in this study.

Atkin L-M, Davies PS: Diet composition and body composition in preschool children. Am J Clin Nutr. 2000, 72: 15-21.

CAS   Google Scholar  

Janz KF, Levy SM, Burns TL, Torner JC, Willing MC, Warren JJ: Fatness, Physical Activity, and Television Viewing in Children during the Adiposity Rebound Period: The Iowa Bone Development Study. Prev Med. 2002, 35: 563-571. 10.1006/pmed.2002.1113.

Article   Google Scholar  

Metallinos-Katsaras ES, Freedson PS, Fulton JE, Sherry B: The Association Between an Objective Measure of Physical Activity and Weight Status in Preschoolers. Obesity. 2007, 15: 686-694. 10.1038/oby.2007.571.

Trost SG, Sirard JR, Dowda M, Pfeiffer KA, Pate RR: Physical activity in overweight and nonoverweight preschool children. Int J Obes Relat Metab Disord. 2003, 27: 834-839. 10.1038/sj.ijo.0802311.

Article   CAS   Google Scholar  

Saakslahti A, Numminen P, Niinikoski H, Rask-Nissila L, Viikari J, Tuominen J, Valimaki I: Is physical activity related to body size, fundamental motor skills, and CHD risk factors in early childhood?. Pediatr Exerc Sci. 1999, 11: 327-340.

Google Scholar  

Sääkslahti A, Numminen P, Varstala V, Helenius H, Tammi A, Viikari J, Välimäki I: Physical activity as a preventive measure for coronary heart disease risk factors in early childhood. Scand J Med Sci Sports. 2004, 14: 143-149. 10.1111/j.1600-0838.2004.00347.x.

Janz KF, Burns TL, Torner JC, Levy SM, Paulos R, Willing MC, Warren JJ: Physical Activity and Bone Measures in Young Children: The Iowa Bone Development Study. Pediatrics. 2001, 107: 1387-1393. 10.1542/peds.107.6.1387.

Binkley T, Specker B: Increased periosteal circumference remains present 12 months after an exercise intervention in preschool children. Bone. 2004, 35: 1383-1388. 10.1016/j.bone.2004.08.012.

Colwell M, Lindsey E: Preschool Children’s Pretend and Physical Play and Sex of Play Partner: Connections to Peer Competence. Sex Roles. 2005, 52: 497-509. 10.1007/s11199-005-3716-8.

Lindsey EW, Colwell MJ: Preschoolers' emotional competence: links to pretend and physical play. Child Stud J. 2003, 33: 39-52.

Hinkley T, Salmon J, Okely AD, Crawford D, Hesketh K: Preschoolers’ Physical Activity, Screen Time, and Compliance with Recommendations. Med Sci Sports Exerc. 2012, 44: 458-465. 10.1249/MSS.0b013e318233763b.

UK Department of Health: Making the case for UK physical activity guidelines for early years. 2011, 22-24. https://www.gov.uk/government/publications/uk-physical-activity-guidelines .

Australian Department of Health and Ageing: Discussion paper for the development of physical activity recommendations for children under five years. 2008, Canberra: ACT, 1-191.

Canadian Society for Exercise Physiology: Canadian physical activity guidelines for the early years. 2012, http://www.csep.ca/english/view.asp?x=804 .

Hnatiuk J, Ridgers ND, Salmon J, Campbell K, McCallum Z, Hesketh K: Physical Activity Levels and Patterns of 19-Month-Old Children. Med Sci Sports Exerc. 2012, 44: 1715-1720. 10.1249/MSS.0b013e31825825c4.

Wijtzes AI, Kooijman MN, Kiefte-de Jong JC, de Vries SI, Henrichs J, Jansen W, Jaddoe VWV, Hofman A, Moll HA, Raat H: Correlates of Physical Activity in 2-Year-Old Toddlers: The Generation R Study. The Journal of Pediatrics. 2013, 163 (3): 791-799. 10.1016/j.jpeds.2013.02.029.

Hesketh K, Campbell K: Interventions to prevent obesity in 0–5 year olds: an updated systematic review of the literature. Obesity. 2010, 18: S27-S35. 10.1038/oby.2009.429.

Hinkley T, Crawford D, Salmon J, Okely AD, Hesketh K: Preschool Children and Physical Activity: A Review of Correlates. Am J Prev Med. 2008, 34: 435-441. 10.1016/j.amepre.2008.02.001.

Hinkley T, Salmon J, Okely AD, Hesketh K, Crawford D: Correlates of Preschool Children's Physical Activity. Am J Prev Med. 2012, 43: 159-167. 10.1016/j.amepre.2012.04.020.

Taylor WC, Baranowski T, Sallis JF: Family determinants of childhood physical activity: a social cognitive model. Advances in Exercise Adherence. Edited by: Dishman RK. 1994, Champaign, IL: Human Kinetics, 319-342.

Bandura A: Social foundations of thought and action: a social cognitive theory. 1986, Englewood Cliffs, NJ: Prentice Hall

Campbell K, Hesketh K, Crawford D, Salmon J, Ball K, McCallum Z: The Infant Feeding Activity and Nutrition Trial (INFANT) an early intervention to prevent childhood obesity: Cluster-randomised controlled trial. BMC Public Health. 2008, 8: 103-10.1186/1471-2458-8-103.

Campbell KJ, Lioret S, McNaughton SA, Crawford DA, Salmon J, Ball K, McCallum Z, Gerner BE, Spence AC, Cameron AJ: A Parent-Focused Intervention to Reduce Infant Obesity Risk Behaviors: A Randomized Trial. Pediatrics. 2013, 131: 652-660. 10.1542/peds.2012-2576.

Salmon J, Timperio A, Telford A, Carver A, Crawford D: Association of Family Environment with Children's Television Viewing and with Low Level of Physical Activity. Obes Res. 2005, 13: 1939-1951. 10.1038/oby.2005.239.

Hesketh K, Hinkley T, Campbell K: Children's physical activity and screen time: qualitative comparison of views of parents of infants and preschool children. Int J Behav Nutr Phys Act. 2012, 9: 152-10.1186/1479-5868-9-152.

Campbell K, Hesketh K, Silverii A, Abbott G: Maternal self-efficacy regarding children's eating and sedentary behaviours in the early years: Associations with children's food intake and sedentary behaviours. Int J Pediatr Obes. 2010, 5: 501-508. 10.3109/17477161003777425.

Australian Institute of Health and Welfare (AIHW): Active Australia Survey: a guide and manual for implementation, analysis and reporting. 2003, Canberra: AIHW, 2003.

Salmon J, Owen N, Crawford D, Bauman A, Sallis JF: Physical activity and sedentary behavior: A population-based study of barriers, enjoyment, and preference. Health Psychol. 2003, 22: 178-188.

Trost SG, Fees BS, Haar SJ, Murray AD, Crowe LK: Identification and Validity of Accelerometer Cut-Points for Toddlers. Obesity. 2011, 20 (11): 2317-2319. doi:10.1038/oby.2011.1364

Goldstein H: Multilevel statistical models. 2003, Hodder Arnold: London, Great Britain

Fogelholm M, Nuutinen O, Pasanen M, Myöhänen E, Säätelä T: Parent–child relationship of physical activity patterns and obesity. Int J Obes Relat Metab Disord. 1999, 23: 1262-1268.

Gordon-Larsen P, McMurray RG, Popkin BM: Determinants of Adolescent Physical Activity and Inactivity Patterns. Pediatrics. 2000, 105: e83-10.1542/peds.105.6.e83.

Kutner MH: Applied linear statistical models. 2005, Boston: McGraw-Hill, 5

Xu H, Wen LM: Screen time and physical activity of young children reflects their mother's behaviours: findings from the Healthy Beginnings Trial. Aust N Z J Public Health. 2012, 36: 396-396. 10.1111/j.1753-6405.2012.00902.x.

Oliver M, Schofield GM, Schluter PJ: Parent influences on preschoolers' objectively assessed physical activity. J Sci Med Sport. 2010, 13: 403-409. 10.1016/j.jsams.2009.05.008.

Ruiz R, Gesell SB, Buchowski MS, Lambert W, Barkin SL: The Relationship Between Hispanic Parents and Their Preschool-Aged Children's Physical Activity. Pediatrics. 2011, 127: 888-895. 10.1542/peds.2010-1712.

Bagley S, Salmon J, Crawford D: Family Structure and Children's Television Viewing and Physical Activity. Med Sci Sports Exerc. 2006, 38: 910-918. 10.1249/01.mss.0000218132.68268.f4.

WHO Multicentre Growth Reference Study Group: WHO Motor Development Study: Windows of achievement for six gross motor development milestones. Acta Paediatr. 2006, 95: 86-95.

Janz KF, Burns TL, Levy SM: Tracking of Activity and Sedentary Behaviors in Childhood: The Iowa Bone Development Study. Am J Prev Med. 2005, 29: 171-178. 10.1016/j.amepre.2005.06.001.

Ridgway CL, Ong KK, Tammelin TH, Sharp S, Ekelund U, Jarvelin M-R: Infant Motor Development Predicts Sports Participation at Age 14 Years: Northern Finland Birth Cohort of 1966. PLoS ONE. 2009, 4: e6837-10.1371/journal.pone.0006837.

Download references

Acknowledgements

The authors would like to thank the National Health & Medical Research Council, Heart Foundation Victoria and Deakin University for funding support, the Melbourne InFANT program staff members, the Maternal and Child Health Nurses in each participating community, and the parents and children who graciously participated in the Melbourne InFANT Program. Jill Hnatiuk is supported by a Deakin University International Postgraduate Research Scholarship. Jo Salmon is supported by a NHMRC Principal Research Fellowship APP1026216. Karen Campbell was supported by the Victorian Health Promotion Foundation. Nicola Ridgers is supported by an Australian Research Council Discovery Early Career Researcher Award. Kylie Hesketh is supported by a National Heart Foundation of Australia Career Development Award.

Author information

Authors and affiliations.

Centre for Physical Activity and Nutrition Research, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia

Jill Hnatiuk, Jo Salmon, Karen J Campbell, Nicola D Ridgers & Kylie D Hesketh

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Jill Hnatiuk .

Additional information

Competing interest.

The authors declare that they have no competing interest.

Authors’ contributions

KC, KH and JS designed the Melbourne InFANT Program from which the data were drawn. JH analysed the data and drafted the article. KH, JS, KC and NR assisted with data analysis and interpretation and revised the manuscript for important intellectual content. All authors conceptualized and designed the idea for the paper and gave approval for the final version to be published.

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and permissions

About this article

Cite this article.

Hnatiuk, J., Salmon, J., Campbell, K.J. et al. Early childhood predictors of toddlers’ physical activity: longitudinal findings from the Melbourne InFANT Program. Int J Behav Nutr Phys Act 10 , 123 (2013). https://doi.org/10.1186/1479-5868-10-123

Download citation

Received : 18 April 2013

Accepted : 30 October 2013

Published : 05 November 2013

DOI : https://doi.org/10.1186/1479-5868-10-123

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Early childhood
• Physical activity
• Maternal behaviour

International Journal of Behavioral Nutrition and Physical Activity

ISSN: 1479-5868

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Sports Health
• v.14(1); Jan-Feb 2022

Youth Athlete Development Models: A Narrative Review

Mathew varghese.

† Department of Pediatrics, Weill Cornell Medical College, New York, New York

Sonia Ruparell

‡ Division of Orthopedics & Sports Medicine, Boston Children’s Hospital, Boston, Massachusetts

§ Harvard Medical School/Division of Musculoskeletal Ultrasound, Boston Children’s Hospital, Boston, Massachusetts

Cynthia LaBella

‖ Institute for Sports Medicine, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois

¶ Northwestern University Feinberg School of Medicine, Chicago, Illinois

Physical activity has shown to be beneficial for the overall physical and mental health of youth. There has been an increasing focus on youth sports moving from a recreational activity to becoming a launching pad for participation at elite levels. Several models of athlete development have emerged to guide specialized and nonspecialized athletes at an age-appropriate level, taking into consideration their physical and mental development. The purpose of this review is to summarize the current evidence and theoretical models regarding youth athlete development and discuss broader initiatives for sports participation and future directions for the field.

Evidence Acquisition:

An electronic databases search, including PubMed, Google Scholar, ScienceDirect, National Institutes of Health, UpToDate, and Springer was conducted. Articles from 1993 to 2021 were included. The search terms long term athlete development , LTAD model , youth physical development , youth athlete development , sports specialization , and pediatric athlete , among others, were used.

Study Design:

Narrative review.

Level of Evidence:

Levels 4 and 5.

Several models of youth athlete development are discussed in this article. More recent models have built on previous models to incorporate more age- and development-specific recommendations; however, no singular model could be identified as the gold standard for youth athlete development, especially given the lack of empirical data to support these models.

Conclusion:

Youth athlete development currently consists of several theoretical models, each with their own strengths and weaknesses, that can guide the training of young athletes to maximize their performance. Those involved in this process—physicians, athletic trainers, coaches, physical educators, and parents—should understand these various models and trial their various features to see what works best for their individual athlete with consideration given to factors such as their stage of development. Ultimately, more empirical data are required to definitively state which is the optimal approach.

Physical activity improves overall health in children and young adults by reducing the risk of obesity, cardiovascular disease, diabetes, depression, and suicide, among other chronic medical conditions. 4 Increasing youth physical activity has become a priority for many countries, leading to the development of national policy statements and strategies to promote physical activity in youth. 29 , 32 The US Department of Health and Human Services and the National Physical Activity Alliance recommend that children and adolescents engage in moderate to vigorous physical activity for at least 60 minutes every day. 23 , 33 Prior to the COVID-19 pandemic, only 20% of adolescents met these guidelines, and research shows that this percentage decreased even further during the pandemic. 7 , 11 Although youth may be engaging in more sports and physical activities since the emergence of the COVID-19 vaccine, the trend of decreasing physical activity and increasing sedentary behavior among children and adolescents remains a challenge. This challenge is even greater for girls, racial and ethnic minorities, youth from households of low socioeconomic status, youth living in rural areas, and youth with disabilities, as these population groups have more barriers to accessing sports and physical activities. 32

Youth who participate in organized sports can reap additional benefits beyond those associated with physical activity alone, including “improving confidence, self-esteem, and providing an opportunity to work on social interaction, communication, leadership, and teamwork.” 8 , 24 There has been an increasing focus on youth sports moving from a recreational activity to becoming a launching pad for participation at elite levels. This shift has led more youth to specialize in a single sport at an earlier age. 13 , 15 , 16 Recent studies, however, have shown that early sports specialization may be a risk factor for overuse injury and burnout. 22 Several models for youth athlete development (YAD) provide guidance for specialized athletes aiming to achieve elite performance while minimizing risk for injury. These YAD models also include guidance for the introduction and maintenance of physical activity for nonathletes, while emphasizing the importance of providing opportunities for all children to participate in sports.

The purpose of this narrative review is to identify and summarize YAD models, describe the evidence supporting their efficacy, discuss their limitations, and offer directions for future research.

Search Strategy

We used the following online databases: PubMed, Google Scholar, ScienceDirect, National Institutes of Health, UpToDate, and Springer, as well as online policy statements from the Department of Health and Human Services in the United States and Canada. The search was conducted in the months of March and April 2021. Keywords used to retrieve publications from January 1993 to April 2021 were long term athlete development , LTAD model , LTAD , implementation , youth physical development , randomized control trial , comparison , qualitative study , quantitative study , youth athlete development , youth athlete , adolescent athlete , pediatrics , sports specialization , and pediatric athlete . Only articles that were available as full text, written in English, and published in peer-reviewed journals were included.

Inclusion Criteria

We included articles on athlete development in the pediatric population. We also included all articles on the effects of athlete development models in the pediatric population.

Exclusion Criteria

We excluded articles on athlete development in other populations such as adults greater than 23 years of age, focused on team building rather than individual athlete training, short-term training programs, and training programs unique to only 1 specialized sport ( Figure 1 ).

Exclusion criteria for published works reviewed in this narrative review.

A total of 110 publications (including peer-reviewed journal articles, governmental policies, and books) were identified by the search. Of these 110, 40 met the inclusion criteria and were reviewed. These included 31 peer-reviewed articles, 7 governmental policies, and 2 books. Additional articles on sports specialization, physical activity, and public policy were included for background information as needed.

Developmental Model of Sports Participation

One of the first YAD models was described by Côté 5 in 1999. This model was based on a qualitative study in which the author interviewed athletes and their families about the athlete’s development in sport over time. 5 In his study, Côté 5 built on Ericsson’s research from 1993 describing the constraints associated with becoming an elite athlete including motivation, effort, and resource. 8 Côté examined factors in families that contributed to young athletes achieving elite levels in sport. The study included a total of 15 interviews with 3 elite rowers, 1 elite tennis player, and their families (siblings and parents). All athletes were aged 18 years at the time of the interviews. 5 The participants were asked open-ended questions, including recollections on first sport participation (“Looking back can you remember and tell me how you first got involved in sport?”), effort and concentration (“Can you tell me over the years how you managed to invest such a high level of effort and concentration into learning and practicing?”) and others identifying the 3 main constraints being evaluated. From these data, the researchers identified 3 time periods of athlete development, which they named the “Developmental Model of Sports Participation”: (1) sampling years (ages 6-13), (2) specialization years (ages 13-15), and (3) investment years (ages 15+ years). 5

Sampling years take place between the ages of 6 and 13 years with emphasis on multisport participation (sampling) and parent responsibility for exposing their children to sports. 5 In the study, it was found that children from the same family were given equal opportunities to participate in various activities during the sampling years. Specialization years take place between ages 13 and 15 years. Athletes slowly decrease the number of activities and focus on 1 or 2 specific sports during this time. There is a focus on sport-specific skill development during this period. 5 Children are more likely to pursue one sport over another if they have a positive interaction with a coach, encouragement from an older sibling or friend, success in an activity, or if they find it enjoyable. 5 The investment years are a longer period and occur from age 15 years and older. The emphasis is on commitment to a single sport with the goal of achieving an elite level of play. The age for entering the investment years can vary per sport. In this period, parents provide direction, feedback, and often help fight setbacks (burnout, injury, and fatigue). 5

While this study provided one of the first structured models for YAD and has served as a framework on which subsequent models were built, it has important limitations. The sample consisted of only 4 athletes and 2 sports. Additionally, the 3 time periods were based on chronological age rather than stage of biologic maturation, which may be a better indicator of readiness for each stage.

Long-term Athlete Development Model

In 2004, Balyi et al 1 described the “long-term athlete development (LTAD) model,” which accounts for biological growth and development by using peak height velocity (PHV) to determine readiness for each stage of training. 2 , 26 PHV is used as an estimate of biological maturation, and the chronological age for achievement of PHV varies from child to child. At the time of PHV, muscle mass, aerobic capacity, energy utilization, and central nervous adaptations increase due to increasing levels of sex hormones. 1 , 20 , 25 As such, biologic maturation is likely a more important marker than chronological age for readiness for each training stage. Importantly, this model also includes a pathway for athletes not interested in elite level competition. 2

The LTAD model has 7 stages 20 , 29 and provides a variety of pathways for participation, training, and competition throughout childhood and adolescence. The 2 main pathways are the “podium pathway” for development of the elite athlete and the “active for life” pathway for the recreational athlete ( Figure 2 ).

Periods of training throughout the year shown in different phases. Separation into different training types by the months of the year is shown as an example of periodization.

The LTAD model is based on 10 factors summarized below 2 , 29 :

• Physical literacy: Foundation of participating in sports by developing motivation, ability, and knowledge to understand movement. Skills include locomotor skills (eg, climbing, galloping, hopping), object control skills (eg, catching, kicking, dribbling, striking with different racquets), and balance movements (eg, dodging, floating, ready position). 29
• Specialization: Sports are classified in this model as either early or late specialization sports. Early specialization sports contain skills that typically peak in performance prior to maturation (eg, gymnastics, diving, figure skating).
• Developmental age: Stage of biologic maturation based on PHV.
• Sensitive periods: Also described as “windows of opportunity” in the LTAD model as the stage of biologic maturity during which the ability to learn a specific skill is easiest. 2 Sensitive periods for skill, stamina, and strength are based on biologic maturation (estimated by PHV), while speed and suppleness (flexibility) are based on chronological age. 29
• Mental, cognitive, and emotional development: Factors that are important in addition to physical development that include understanding fair play and ethics within sport, regulating emotion during play, and decision making. Children move from exploring movement to executing movements over the course of their development. 29
• Periodization: The planning and organization of a training schedule with regard to frequency, duration, and intensity that can be divided into different phases and seasons 29 ( Figure 2 ).
• Competition: Development of a competition calendar and the concept of using training-to-competition ratios for each stage of development. 29
• Excellence takes time: This encompasses the understanding that athlete development and achievement of an elite level in sport takes many years. There is emphasis on the 10,000-hour rule, which is a theory that a minimum of 10 years of deliberate practice is needed for individuals in any field to achieve the elite level. 29 While commonly cited, this theory is not well-supported by scientific data.
• System alignment and integration: Refers to the idea that LTAD must be integrated into the public health and education systems. 29
• Continuous improvement: The LTAD model is based on the concept of continuous improvement and evolution of athlete development that requires flexibility, which is credited to the Japanese philosophy, kaizen. 29 Examples include incorporating current scientific evidence into training and ongoing education for everyone involved in training (coaches, athletic trainers, athletes, etc). 29

Granacher and Borde 12 conducted a prospective study of 45 German fourth graders to evaluate the effects of long-term sport-specific training using the LTAD stages on the following outcomes: physical fitness, body composition, cognitive performance, and academic performance. The study compared children who already took part in competitive organized sports (gymnastics, swimming, soccer, and others) (n = 20) with age-matched peers who were recreational athletes (took part in physical education classes only). The researchers hypothesized that sport-specific training with physical education would enhance physical fitness but would potentially have a negative impact on cognitive and academic development of youth athletes compared with their peers. The study took place over 1 year. The experimental group (n = 20) performed sport-specific training and regular physical education classes 3 times a week. This group included gymnasts, trampoline jumpers, swimmers, track and field athletes, soccer athletes, and 1 BMX cyclist. Like the LTAD model, the intervention used a periodized training schedule. The control group (n = 25) participated only in physical education classes 4 times a week.

Pre- and posttests were done for each group evaluating physical fitness, relative body fat mass, skeletal muscle mass, and cognitive and academic performance, including assessments of reading, mathematics, spelling, attention, and concentration. Physical fitness tests included evaluation of speed (20-m sprint), muscle power (1-kg ball push up, standing long jump), agility (star agility run test), flexibility (stand and reach test for back and hamstring flexibility), endurance (6-min run test), and balance (single-leg stand test). Body measurements (sitting, standing height) and body composition was measured using a bioelectrical impedance analysis system. Academic performance included 4 tests in reading, mathematics, spelling, and attention/concentration. Biologic maturity was estimated by evaluating years from PHV, which was attained using sitting and standing body height, body mass, and age using previously defined criteria. 12 Children were categorized into 3 categories: pre-PHV, PHV, and post-PHV.

All students were classified as PHV in this study. At baseline there were significant differences between the groups in body height, body mass, body mass index, and body composition but there were no differences in cognitive and academic performance. The experimental group had significantly more sport-specific training compared with the control group. After the intervention, 6 out of 7 physical fitness test results were better in the experimental group. The additional hours of sport-specific training did not negatively affect cognitive or academic performance compared with the control group. Academic performance was assessed using standardized testing in German, mathematics, and English through the ELFE 1-6 reading test, the DEMAT 4 mathematics test, and the HSP 4-5 spelling test. Additionally, cognition (focusing on attention and concentration) was evaluated through the standardized d2-test. There was no difference after 1 year in measures of body composition or growth. The sport-specific training and physical education volumes were feasible and safe with no injuries over 1 year. This study demonstrates that structured training models are safe and feasible and may have added benefit to physical fitness and that there is no negative impact on cognitive or academic performance, growth, or body composition.

Stages of the LTAD Model 2 , 29

Physical literacy is developed in the first 3 stages of this model. An additional 2 prestages were initially added for athletes with disabilities but are now emphasized for all athletes.

Develop awareness around what activities, sports, and physical activity opportunities exist. 2 , 29

First involvement

First participation in sport. This should be positive, welcoming, and fun, as a negative first experience may lead to long-term disinterest in physical activity. 2 , 29

Common Pathway (Learning Fundamentals)

Active start.

The goal of this stage is to learn fundamental movements and link them in play. 2 , 29 Physical activity during this stage should be fun and a part of everyday life for the child (eg, running around at the playground or at home). 2 , 29 It is important for the parent or other guardian to give access to unstructured play time. The “Active Start Checklist” provides example items for this stage, which include providing physical activity every day regardless of weather, encouraging such basic movement skills as running, jumping, kicking, and throwing and ensuring games focus on participation and not competition. 2 , 29

FUNdamentals

The FUNdamentals stage is focused on skill development, structured play, and an atmosphere of fun. Programs developed for children in this stage should be structured and monitored. The skills learned in this stage help with future sports and recreational activity participation. The “FUNdamentals Checklist” includes examples such as introducing basic flexibility exercises, encouraging participation in a wide range of sports, including strength training using body weight and introduction of the child to simple rules and ethics of sports. 29

Learn to train

This is described as the “most important period before peak height velocity” 2 and occurs generally between the ages of 9 and 12 years. This is thought to be a sensitive period for learning specific skills in the LTAD model. Therefore, the learn to train stage includes general sports skills, learning the concepts of warm-ups, cooldowns, stretching, and mental preparation for sports. 30 The main focus of this stage is development of physical literacy, which is defined as the physical competence, confidence, and motivation of an athlete to enjoy various types of physical activity including sports. 35 The “Learn to Train Checklist” includes examples such as the introduction of hopping and bounding exercises; development of speed with focus on agility, change of direction, and warming up; development of mental skills, including focus and visualization and the commencing of age-appropriate competition. 29

Podium Pathway

Train to train.

The goal during this stage is to introduce aerobic training before PHV, to develop speed and strength while specializing further into 1 or 2 sports of the athlete’s choice. 2 , 29 There is a focus on applying skills learned in previous stages into competition. 2 Athletes in this stage play to win; however, the focus is still on applying skills learned as well as on having fun. 2 The “Train to Train Checklist” includes examples such as considering sensitive periods of accelerated adaptation to strength training (after PHV or onset of menarche for females; 12-18 months after PHV for males), and changing the competition-specific training ratio to 60:40 (devoting 60% of time to development of technical skill and 40% to competition-specific training). 2 , 29

Train to compete

The focus during this stage is to optimize performance in competition. This stage is largely devoted to optimizing skills for specific sports and positions in each sport. 29 It is important that athletes have mastered the goals of previous stages prior to progressing to the “train to compete” stage. 29 These athletes will be highly specialized, which is defined as year-round, high-intensity single-sport training. The “Train to Compete Checklist” includes examples such as placing special emphasis on competition preparation and changing the competition-specific training ratio to 40:60 (devoting 40% of time to development of technical skill and 60% to competition-specific training). 29

Train to win

This is the final stage of the LTAD model. This is focused on athlete preparation for high-level sport-specific training, competition, and recovery. 3 This stage emphasizes the mastery of skills such as decision making, position-specific and sport-specific technical skills, and fitness skills. 2 The goal is to maximize physical and mental fitness as well as recovery. The “Train to Win Checklist” includes examples such as ensuring training is year-round high intensity and high volume, changing the training-competition ratio to 25:75, and allowing breaks to reduce stress and prevent overuse injury. 29

Active for Life Pathway

Three pathways fit under the umbrella of being active for life ( Figure 3 ).

The “sport for life” diagram for long-term athlete development based on the long-term athlete development model by Balyi et al. 2 , 29 The figure highlights 2 pathways from building a foundation and developing physical literacy to the podium pathway (green and blue) and active for life, which divides into competitive for life (pink) or fit for life (red).

Competitive for life

Individuals in this group have a minimum of 60 minutes of moderate daily activity or 30 minutes of vigorous daily activity. 29 They may participate in multiple sports and transition from a highly competitive level (eg, collegiate) to lifelong competitive sport (eg, high-level age group competitions). 29

Fit for life

Individuals in this group are physically active for a similar amount of time to the competitive for life group. They move from competitive sports to recreational activities and may be involved in sports careers or volunteering. 29 They also may participate in recreational sports and/or explore new physical activities (eg, a hockey player trying rock climbing for the first time). 29

Sport and physical activity leaders

Individuals in this category move from competitive athletics to volunteering or working in athletic leadership or support roles (eg, coaches, officials, administrators). 29 They use their own previous experiences to ensure sports are a positive experience for athletes.

Sport for life

The Sport for Life organization in Canada is a movement to improve the quality of sport and physical activity. Sport for Life uses the LTAD model while emphasizing systems implementation within the community to promote lifelong participation in sports and physical activity. It recommends the integration of competitive sports, recreational activity, and physical education in school and school-based sports within the same system. 2 , 29 Each stage has an accompanying checklist to ensure children are meeting the goals of each stage. Like the LTAD model, the Sport for Life model has 2 separate pathways: one for athletes with the goal of achieving elite-level competition and another for recreational athletes whose goal is to participate in sports and physical activity to maintain an active and healthy lifestyle without entering high-level competition. Sport for Life also provides specific examples of the LTAD model applied to different sports (eg, hockey, baseball, soccer). 29

Indigenous sport for life

The Sport for Life organization in Canada partnered with Aboriginal Sport Circle to adapt their framework to “define a pathway for Indigenous athletes into high performance sport and increase the number of Indigenous peoples who are active for life.” 14 This adaptation considers cultural and societal norms within the community and was created in collaboration with local Indigenous organizations. This adaptation serves as a model for other youth athlete development model modifications for various groups including LBGTQ youth, youth of different ethnicities and race, and different community groups.

Youth Physical Development Model

In 2012, the youth physical development (YPD) model proposed by Lloyd and Oliver 20 also emphasized a development-based over aged-based approach and the importance of starting with fundamental movement skills in training young athletes. However, compared with the LTAD model, YPD provides more detail regarding which type of training should be emphasized during each developmental stage and accounts for additional gender differences.

YPD stages are early childhood (ages 2-4 years), middle childhood (ages 5-9 years), adolescence (ages 10-19 years), and adulthood (ages 20-21 years). 20 Growth rates are divided into rapid growth, steady growth, adolescent growth spurt, and decline in growth rate, which correspond with the developmental stages described. The YPD model also takes into account maturation status (pre-PHV and post-PHV), training adaptation (neural or neural + hormone phases), training structure (structured vs unstructured), and physical qualities of training (fundamental movement skills, sport-specific skills, mobility, agility, speed, power, strength, hypertrophy, endurance, and metabolic conditioning) 20 ( Figure 4 ). YPD emphasizes that it is possible to train an athlete in any of these physical qualities at any stage throughout childhood and adolescence, in contrast to the “windows of opportunity” described by Balyi et al. 1 , 2 However, the YPD model does recognize that there may be optimal times to train each physical quality ( Figure 4 and Table 1 ). For example, for an adolescent female (ages 10-19 years), at PHV and in a phase of neural and hormonal maturity, training should focus on agility, speed, power, strength, hypertrophy, and endurance and be moderately to highly structured. 20

Youth physical development (YPD) model for females (pink) and males (blue). 14 The physical qualities emphasized at different stages of development are shown in both charts. The size of the text correlates with the physical quality that should be emphasized (eg, larger text should be more emphasized, smaller text is less emphasized). FMS, fundamental movement skills; MC, metabolic conditioning; PHV, peak height velocity; SSS, sport-specific skills.

The Physical Qualities of the Youth Physical Development (YPD) Model

The YPD model is easy to understand and encourages participation in sports for all youth. 21 The YPD model is not geared solely toward reaching a certain level of competition but focuses on the holistic development of young athletes at all levels and therefore may be more generalizable than the LTAD model.

Limitations

Scarce data.

During our search, we were unable to find large studies comparing these various models. It is important to recognize that the description of athlete development stages by Côté 5 was the foundation for the future models we discuss in this review. However, Côté’s model has significant limitations, including the small number of athlete participants 4 and inclusion of only 3 sports, as well as recall bias because of retrospective study design. The more recent models, including LTAD and YPD, have been included in very few prospective or retrospective studies to provide objective data on their effectiveness. It may be more appropriate to label them “frameworks’’ until they can be rigorously tested and supported by empirical data. 6 A great deal of data are derived from subjective observations lacking in empirical data. Additionally, there is no evidence that these models raise the maximal athletic potential of those participating. These models could simply be bringing youth to their maximal athletic potential at a different time course than other training regimens. 10

Consideration of Recreational and High School–Level Sports Participation

These models are geared toward training and developing youth athletes with the goal of achieving elite levels in sport. Although some of the new models provide alternative training stages for youth interested in high school sports, recreational sports, and lifelong physical activity, there is still a larger emphasis on the elite-level athlete.

Rethinking “Windows of Opportunity” and Different Stages of Training

It is unknown whether the “windows of opportunity” in the LTAD model are generalizable or whether there are individual differences. Van Hooren and De Ste Croix 34 suggested that breaking down athletic training into manageable constructs is reductionist logic that discredits the overall complexity of the field. The LTAD models tend to focus on a singular attribute to train during a specific “window of opportunity,” but often attributes, such as flexibility, speed, strength, and so on, are all interconnected in their training. They question whether it is possible to increase overall speed without concurrently giving attention during training to other necessary attributes, such as coordination and balance. Focusing on specific windows of opportunity may be discouraging to young athletes, sports providers, coaches, and educators if missed and ultimately result in a negative experience or discouragement of achieving a certain level in sport. Van Hooren and De Ste Croix go on to note that these models point out what should be trained at each developmental stage, but they do not provide frameworks for how best to train these qualities—that is, resistance, plyometric, or other forms of training—as certain motor skills may respond better to specific training techniques. More specific questions regarding skill-specific training, such as timing (during or after warm-ups), number of days per week, repetitions, and duration of rest period, are also important to answer to fully understand the practical application of LTAD models. 34

Examples of elite athletes who were late specializers may be a proof of concept that these windows of opportunities are not as rigid as presented in current models. Falk et al 9 studied female rugby players (ages 18-23 years) that were considered at the “training to perform stage” (eg, training to win stage). They studied anthropometric and fitness characteristics, including height, body mass index, grip strength, flexibility, endurance, and others. Overall, they found little to no improvement in these qualities when tracking individual performances across multiple seasons while implementing the appropriate LTAD steps at the training to win stage. This study demonstrated that despite their expected developmental stage, participants were already considered “elite athletes” and the use of the LTAD model was only effective in maintenance of current fitness level and did not increase athletic ability. 9 Finally, the YPD model does not explicitly differentiate the stages of training that are mentioned in the LTAD model.

Impact of Extrinsic Factors

The LTAD and YPD models place a large focus on classifying children’s overall developmental/athletic stage to provide the most effective training possible. Although this is critical, it may not be the only factor worth considering. There are nutritional, environmental, and psychological factors that also influence an athlete’s readiness for a certain stage. 10

Notably, race, ethnicity, socioeconomic status, and youth who identify as LGBTQ (lesbian, gay, bisexual, transgender, queer or questioning) were not accounted for within any of the models discussed. 32 Limited data exist on the generalizability of these models across diverse populations of athletes. The LTAD model does offer a systems approach to integrating the physical education system, school sports, elite-level sports and recreational sports, which provides a framework for other public health systems and equitably encourages lifelong participation in sport. The LTAD model also has been adapted to become a holistic model specific to Indigenous peoples across Canada and has been adapted to various specific sports. However, in general, these models do not directly account for financial constraints of different components (for example, public school education system vs elite competitive team) or that not all athletes have access to the same resources (for example, a young athlete from a household of low socioeconomic status that does not have access to sports leagues or other physical activity programs for the development of physical literacy skills).

The development of broader policy initiatives is important to solve inequity in accessing sports. The National Youth Sports Strategy (NYSS) was developed by multiple experts in the area of sports medicine and provides a framework for holistically understanding youth sports participation in the United States. 32 At the base of this framework, there is emphasis on addressing equity and inclusion for accessing sports, developing physical literacy and creating opportunities for sports sampling. 32 A 2017 study quoted in the NYSS revealed that only 58% of youth ages 6 to 17 years participated in sports after school or on weekends in that year. 32 , 33 “The rates of participation were lower in racial and ethnic minorities, youth from lower income households, youth with a disability and those who identify as gay, lesbian, bisexual or not sure.” 17 , 31 , 32 From a public health perspective, designing easily accessible parks and recreational facilities for community engagement in athletics may reduce this participation gap. It may also increase awareness and exposure to youth sports to allow positive first and ongoing experiences in sports, which have been emphasized in the LTAD model as key drivers to lifelong participation.

Providing a framework for youth with disabilities is another area where these models can be expanded. The additional steps identified by Balyi et al 1 , 2 in the LTAD for youth with disabilities require system-wide changes to be successful. 29 Without additional resources, even with the additional prestages of awareness and first involvement, athletes with disabilities may have a difficult time progressing through the various stages of athlete development because of lack of programming, equipment, transportation, adapted facilities, and coaching.

The Canadian Sport for Life model recognizes the importance of integrating all levels of recreation (physical education in schools, school sports, recreational, and elite-level sports) to equalize opportunities for all youth. 29

Future Directions and Research

Research is needed to measure the effectiveness of these youth athlete development models and whether they achieve their stated goals of improving physical literacy, promoting long-term success in sport, and promoting lifelong physical activity. Further study must also be carried out to assess the applicability of each model to specific sports or types of sport (eg, early vs late specialization sports, team vs individual sports) and diverse populations (all levels of socioeconomic status, various races/ethnicities, youth with disabilities, and LGBTQ youth). It may be the case that not 1 singular model is ideal for all youth, and that a combination of models and/or an individualized approach may be the most effective for maximizing athletic potential and promoting lifelong participation in sports and physical activity. 27

The models of youth athlete development discussed in this review can be used to guide the development and training of youth athletes. Although there are limitations to each of these models, they are an important resource for physicians, athletic trainers, coaches, physical educators, parents, and others interested in the development of youth athletes. An individualized approach is important to consider when implementing the models discussed to ensure inclusion and applicability for all athletes.

The following author declared potential conflicts of interest: C.L. has received grants from NOCSAE, payments from PEDS365, and royalties from the American Academy of Pediatrics.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Physical Development in Adolescence

Related Papers

KAMAL R NAIR

Caesar Alex-Daras

Horizons of Holistic Education

Dr. Shikha Kapur , Dr. Shikha Kapur

ABSTRACT This article examines the important transitory stage in human life i.e. adolescence- a life stage, that lies between childhood and adulthood. Beginning around 10, 11 or 12 years, adolescence concludes between 18-21 years of age. It is a stage, when complete metamorphosis takes place and is akin to a butterfly emerging from its cocoon which in its former stage is a caterpillar! The speed of adolescent changes varies among cultures and societies since they are contingent on the processes of socialization, training and education. Key developmental milestones are achieved during adolescence and in order to understand adolescents it is important to understand their physical, cognitive, psychological, socialemotional, moral, educational and vocational development and the ensuing changes. Sex differences exist between boys and girls when we examine all these factors. The end of this tumultuous period entails physical and sexual maturation, social and economic independence, development of identity, acquisition of skills needed to carry out adult relationships and roles, and the capacity for abstract reasoning. Parents, teachers, peers and the significant others in the adolescent’s environment have to be supportive and empathetic of the turmoil that they go through to enable a smooth well adjusted transition into adulthood. Keywords: adolescence, adolescents, human development, child development, child, childhood, adulthood, cognitive, social, emotional, moral, development, vocational, late maturer, early maturer, transition. Link: http://www.hhecu.org/Home/Issuedetails?t=ReL4FRYircqCfLTnsEw%252fpw%253d%253d

Pediatric Clinics of North America

Paula Duncan

K M Ziyauddin

RCH and related conceptual discussion.

International Journal of Zambrut

Carlos Yeboah

This article is written with our Owen convictions as competitive students in the academic jamboree and due to the pro-active and unabated quantum of youngster’s physiological transition and contextual influences during adolescence development. This paper shows the environmental cues and detailed description of topics that have dominated recent research, including the meaning of adolescence, preadolescence, social, the mental and emotional development of the adolescent child, adolescent problem-solving behaviour (skills), parent-adolescent relations, puberty, the development of the self, and peer relations. We then identify and elaborate on what seem to us to be the most important new directions that have come to the fore in the last decade, including research on the strange and significant interests of adolescents (be it recreational, religious or influential interests), their developmental tasks and contextual influences on development, genetic behaviours that passed on through their family lineage, and some intellectual developments, they undergo (together with the time span at which this intelligence level (IQ) of every single adolescent can be developed). We go further to briefly explain the problems that result from some of the physical deviations that occur during the adolescence period (changes) and how we can help to make these deviations suit us or our children properly. We also expound on the need for adolescents to take up leadership positions or roles and the merits and demerits of associating with friends (and also the type of friends to keep). We draw the curtains down with a well knowledgeable summary that briefly outlines everything discussed in the chapters plus some solutions to help curb some of the problems adolescents are challenged with.

ABLE-18, ICLHESS-18 & MLEIS-18

didik priyandoko

Hari Bhattarai

This study is entitled as "Menstruation Cycle: A Cause of Psychological Changes on Adolescents" It has included some major problems related to the title for the research and specific objectives as well. Background of menstruation cycle, rational of the study and limitation of the study has been included for the completeness of the study in this thesis. According to the data analysis, major psychological changes were found as mental disturbances, abnormal feeling feeling of oneself as women, appearance of secondary maturity. They were mentally disturbed due to menstruation cycle. When this period becomes nearer they had to make plans for the solution of their personal problem. They could not share it anybody too. Thus, these changes made adolescents attentive and active about their personal health and care.

Pediatric and …

Charles Sultan

The Prepubertal Girl Sultan C (ed): Pediatric and Adolescent Gynecology. Evidence-Based Clinical Practice. Endocr Dev. Basel, Karger, 2004, vol 7, pp 23–38 Ambiguous Genitalia in the Newborn: Diagnosis, Etiology and Sex Assignment Charles Sultana, b, Françoise Parisa, ...

Journal of Adolescent Health

James Roemmich

RELATED PAPERS

Emmanuel Nelly Akamagune

Archives of Sexual Behavior

Martha K McClintock

The American Journal of Clinical Nutrition

Pamela Nyirenda

Arun. manic

Siniša Franjić

Bhert Espino

Richard Sharpe

European Journal of Endocrinology

Dieter Wolke

James Cantor

Barry Bogin

Antonianum LXXXV (2010): 451-177.

Jay Feierman

Revista Cientifica General Jose Maria Cordova

Revista Científica General José María Córdova

MuslimMatters

Mobeen Vaid

Eduardo Borba Neves

John Whitehall

Joyeuse Augy

Early Puberty

Malik Adeel Umer

The Journal of Clinical Endocrinology Metabolism

Norman Spack , L. Gooren

Nella Greggio

Adele Briege

Voices of the Women’s Health Movement, Vol. Two. edited by Barbara Seaman and Laura Eldridge.

Rebecca Plante

Environ Health Perspect

Germaine Buck

Indian Journal of Endocrinology and Metabolism

Ashraf Soliman

Shivani Dave

International Journal Of Scientific Advances

Nur Rochmah

Ray Blanchard

Mary Coleen Diango

Annelou de Vries

International Journal of Transgenderism

The Journal of Clinical Endocrinology & Metabolism

Walter Meyer

Developmental Review

Valeria Manera

Indian Journal of Youth and Adolescent Health 

Kaushalendra Kumar Singh , UJJAVAL SRIVASTAVA

Richard McAnulty

Lieb J Van Rooyen

Journal of Obstetric, Gynecologic, & Neonatal Nursing

Lisa Hughes

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024