Data Coaching: A Strategy to Address Youth Physical Behavior, Motor Competence, and Out-of-School Time Leader Evidence-Based Practices

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Peter Stoepker Department of Kinesiology, College of Health and Human Sciences, Kansas State University, Manhattan, KS, USA

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David A. Dzewaltowski Department of Health Promotion, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA

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Physical inactivity contributes to the development of several chronic diseases including cardiovascular disease, diabetes, cancer, and obesity.1,2 However, <25% of adolescents aged 6–17 years engage in the recommended 60 minutes of physical activity (PA) per day.3 In fact, certain objective measurements calculate rates as low as 10% of youth meeting this recommendation.4 Recent evidence has indicated that meeting PA guidelines is essential since PA behavior tracks into adulthood.5 Therefore, enhancing primary prevention efforts during childhood is an effective way to reduce physical inactivity-related health issues (eg, obesity and diabetes) in adults.6 A common primary prevention strategy in children has been to address PA behavior during the traditional school setting. However, due to schools facing competing demands, it has been found that PA intervention strategies during the school day are not sufficient with <25% of children and adolescents meeting the required recommendations of PA during school.7 Furthermore, it has been found that motor competence (MC) is a key influence on childhood PA. Recent evidence has shown a direct link between MC and lifelong PA behavior8; however, the majority of MC-related interventions occur during the school day,9 where we are seeing limited impact on children’s PA levels and MC. Due to this, there is a need to develop interventions that target children with PA and MC in out-of-school time (OST) settings.

Evidence-Based PA Prevention Practices

Recent evidence has indicated that providing opportunities for PA outside of the school day effectively increased youth PA levels.10 However, few OST programs use evidence-based strategies to build foundational skills in MC that drive interest and engagement in health-enhancing PA. When OST programming has a structured focus on MC development, it can have a positive impact on increasing locomotor and object control skills in children.9 Furthermore, when interventions target the development of MC it can potentially increase daily moderate to vigorous PA.11 There is clear potential for MC-based interventions in youth, but the challenge is in building the capacity for leaders to follow evidence-based strategies in practice.12 Evidence-based strategies are practices that use quality research evidence to meet the needs and preferences of a particular population and setting.13 Specifically, the use of evidence-based strategies should be incorporated by public health-related program leaders (eg, OST program leaders) during program implementation.14 The development of PA- and MC-based training programs for OST leaders offers potential for broader system-level adoption to enhance PA behaviors in youth.

PA Leadership

Systematic training of program leaders offers potential to build capacity for OST program leaders’ ability to enhance PA-related programming.15 It has been recommended that OST programs be led by a trained PA leader.16 However, there are limited guidance, tools, and strategies for OST PA leaders to increase youth PA and MC published. While evidence supports the impact of OST PA programming on youth PA behavior,17 there is little evidence that documents specific strategies OST PA leaders can use to enhance MC. Since MC does not develop naturally over time and requires training and teaching, it is important to ensure PA leaders are properly equipped with a knowledge base in MC development.18

Data Coaching

Data coaching is a capacity-building strategy that offers specific advantages for training OST leaders in youth programming. Data-driven decision making is a widely accepted best practice in a traditional education setting.19 However, it has been found that program leaders (eg, teachers and staff) struggle to transform data into actionable knowledge.20 Minimal research has been conducted to understand how leaders of PA programming use PA-specific data to drive interventions.21 To address issues with data interpretation and translation, the implementation of a data coach has been considered a promising practice.22 A data coach assists in the process of collecting, analyzing, summarizing, and prioritizing data with the goal of increasing student achievement and improving instructional practice.20,22 Primarily used in educational settings, data coaching has yet to be fully implemented in youth PA leadership and practice. Recent studies have found data coaching via sharing PA data with classroom teachers improved student PA and decreased sedentary behavior.23 However, previous studies have examined PA leader workshops and training and were inconclusive on changes in PA leader practice.24,25 Due to this, a more systematic and targeted data coaching approach is needed to address child PA and MC in OST settings.

Implementation of an OST Data Coach Intervention

Consistent with the application of process control theory to inform efforts for health behavior change, we will utilize the investigate (sensor), design (control decision), practice (effector implementation action), and reflect (feedback) process (IDPR) that has been successfully implemented in community child PA promotion efforts.2628 The IDPR cycle will take program leaders through 4 specific steps to improve child PA and MC: Investigate (eg, what are the current practices and PA levels of children in the program?), design (eg, development of a prototype of integration of evidence-based strategies into current routines?), practice (eg, implementation of designed routine PA strategy to increase PA), and reflect (eg, did the prototype for integration of new practices increase child PA?). IDPR departs from the typical rapid improvement plan, do, study, and act29 by beginning with identifying the state of the current system and by allowing for the design of local solutions.

We believe that providing a systematic and targeted learning-by-doing approach informed by data collected in real time will provide PA leaders with knowledge on how to improve practice and create a more active environment. Table 1 provides an applied example of a detailed breakdown of how to integrate the data coaching sessions rooted in the IDPR cycle.

Table 1

Elements of an OST Data Coaching Intervention Using IDPR Principles

Intervention elementConceptIndicatorData sourceAssessment timeline
IDPR: InvestigateSensor:

 • What is the current state of OST programming?

 • What are current inputs, practices, and outputs?
• How is the OST program organized?

• What behaviors do program leaders exhibit?

• Current PA-related behaviors
• Selected site leader PA-promoting behaviors from SOSPAN

• Accelerometer (PA)

• TGMD-3 (MC)
• First data coaching session with site leaders
IDPR: DesignController:

 • What do we want to design?

 • How can we create a more PA environment?
What can be designed to influence site leaders?

• OST program routines and OST standards
• Evidence-based PA/MC strategies (eg, Let Us Play)

• Goal setting to increase PA-promoting practices
• Second data coaching session with site leaders
IDPR: PracticeEffector:

 • What is being implemented
• Evidence-based PA-promoting strategies• Implementation checklist (strategies to increase PA/MC)

• Goal setting adherence
• Third data coaching session
IDPR: ReflectReflect:

 • Do we update our design?

 • Implementation facilitators and barriers
• Feedback from site leaders

• Perceived effectiveness of the implementation of new strategies

• PA/MC behavior

• Observation
• Accelerometer (PA)

• TGMD-3 (MC)

• Evidence-based site leader PA-promoting strategies (SOSPAN)
• Fourth data coaching session

Abbreviations: IDPR, investigate (sensor), design (control decision), practice (effector implementation action), and reflect (feedback) process; MC, motor competence; OST, out-of-school time; PA, physical activity; SOSPAN, System for Observing Staff Promotion of Activity and Nutrition; TGMD-3, Test for Gross Motor Development, third edition. Note: Table shows a hypothetical overview of how to provide data coaching during an academic school year with potential PA, MC, and site leader assessment tools to use.

Conclusions

In summary, we believe that OST settings can be a logical point of intervention to address child PA and MC. Furthermore, data coaching can be viewed as a promising strategy to help address PA leader behavior by helping them understand how to integrate evidence-based PA-promoting practices. Adopting this approach is unique and differs from a traditional one-size-fits-all approach by directly working with PA program leaders to investigate and contextualize factors that influence successful implementation in real-world OST settings.

References

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    Hodgin KL, von Klinggraeff L, Dauenhauer B, et al. Effects of sharing data with teachers on student PA and sedentary behavior in the classroom. J Phys Act Health. 2020;17:585591. PubMed ID: 32335524

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
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    Dunn-Carver M, Pope L, Dana G, et al. Evaluation of a teacher-led physical activity curriculum to increase preschooler physical activity. Open J Prev Med. 2013;3(1):141147. doi:

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    • Export Citation
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    Essay AM, Schenkelberg MA, Seggern MJV, et al. A protocol for a local community monitoring and feedback system for physical activity in organized group settings for children. J Phys Act Health. 2023;20(5):385393. PubMed ID: 36965493

    • Search Google Scholar
    • Export Citation
  • 27.

    Schenkelberg MA, Essay AM, Rosen MS, et al. A protocol for coordinating rural community stakeholders to implement whole-of-community youth physical activity surveillance through school systems. Prev Med Rep. 2021;24:101536. PubMed ID: 34976611

    • Search Google Scholar
    • Export Citation
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    Essay AM, Schlechter CR, Mershon CA, et al. A scoping review of whole-of-community interventions on six modifiable cancer prevention risk factors in youth: a systems typology. Prev Med. 2021;153:106769. PubMed ID: 34416222

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Data coaching is a promising intervention strategy to address youth physical activity behavior, motor competence, and out-of-school time leader evidence-based practices.

Systematic training of out-of-school time program leaders offers the potential to build capacity to enhance physical activity-related programming.

Out-of-school time settings can be a logical point of intervention to address both child’s physical activity and motor competence.

  • Collapse
  • Expand
  • 1.

    Kriska A, Delahanty L, Edelstein S, et al. Sedentary behavior and physical activity in youth with recent onset of type 2 diabetes. Pediatrics. 2013;131(3):e850e856. PubMed ID: 23400602

    • Search Google Scholar
    • Export Citation
  • 2.

    Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. US Dept of Health and Human Services; 2018.

    • Search Google Scholar
    • Export Citation
  • 3.

    Abbott RA, Straker LM, Mathiassen SE. Patterning of children’s sedentary time at an away from school. Obesity. 2012;21(1):E131E133. PubMed ID: 23505193

    • Search Google Scholar
    • Export Citation
  • 4.

    Cooper AR, Goodman A, Page, AS et al. Objectively measured PA and sedentary time in youth: the International Children’s Accelerometry Database (ICAD). Int J Behav Nutr Phys Act. 2015;12:113. PubMed ID: 26377803

    • Search Google Scholar
    • Export Citation
  • 5.

    Telama R. Tracking of physical activity from childhood to adulthood: a review. Obes Facts. 2009;2(3):187195. PubMed ID: 20054224

  • 6.

    Utesch T, Bardid F, Büsch D, Strauss B. The relationship between motor competence and physical fitness from early childhood to early adulthood: a meta-analysis. Sports Med. 2019;49(4):541551. PubMed ID: 30747376

    • Search Google Scholar
    • Export Citation
  • 7.

    Grao-Cruces A, Velázquez-Romero MJ, Rodríguez-Rodríguez F. Levels of physical activity during school hours in children and adolescents: a systematic review. Int J Environ Res Public Health. 2020;17(13):4773. PubMed ID: 32630760

    • Search Google Scholar
    • Export Citation
  • 8.

    Morgan PJ, Barnett LM, Cliff DP, et al. Fundamental movement skill interventions in youth: a systematic review and meta-analysis. Pediatrics. 2013;132(5):e1361e1383. PubMed ID: 24167179

    • Search Google Scholar
    • Export Citation
  • 9.

    Lee J, Zhang T, Chu TL, Gu X, Zhu P. Effects of a fundamental motor skill-based afterschool program on children’s physical and cognitive health outcomes. Int J Environ Res Public Health. 2020;17(3):733. PubMed ID: 31979255

    • Search Google Scholar
    • Export Citation
  • 10.

    Dauenhauer B, Kulinna P, Marttinen R, Stellino MB. Before- and after-school physical activity: programs and best practices. J Phys Educ Recreat Dance. 2022;93(5):2026. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Graham M, Azevedo L, Wright M, Innerd AL. The effectiveness of fundamental movement skill interventions on moderate to vigorous physical activity levels in 5- to 11-year-old children: a systematic review and meta-analysis. Sports Med. 2022;52(5):10671090. PubMed ID: 34881411

    • Search Google Scholar
    • Export Citation
  • 12.

    Fielding JE, Briss PA. Promoting evidence-based public health policy: can we have better evidence and more action? Health Aff. 2006;25(4):969978. PubMed ID: 16835176

    • Search Google Scholar
    • Export Citation
  • 13.

    Brownson RC, Fielding JE, Maylahn CM. Evidence-based public health: a fundamental concept for public health practice. Annu Rev Public Health. 2009;30(1):175201. PubMed ID: 19296775

    • Search Google Scholar
    • Export Citation
  • 14.

    Brownson RC, Gurney JG, Land GH. Evidence-based decision making in public health. J Public Health Manag Pract. 1999;5(5):8697. PubMed ID: 10558389

    • Search Google Scholar
    • Export Citation
  • 15.

    Stevens M, Rees T, Coffee P, Steffens NK, Haslam SA, Polman R. A social identity approach to understanding and promoting physical activity. Sports Med. 2017;47(10):19111918. PubMed ID: 28349449

    • Search Google Scholar
    • Export Citation
  • 16.

    Stoepker P, Dauenhauer B, Carson RL, et al. Becoming a PA leader (PAL): skills, responsibilities, and training. Strategies. 2020;34(1):2328.

    • Search Google Scholar
    • Export Citation
  • 17.

    Beets MW, Weaver RG, Moore JB, et al. From policy to practice: strategies to meet physical activity standards in YMCA afterschool programs. Am J Prev Med. 2014;46(3):281288. PubMed ID: 24512867

    • Search Google Scholar
    • Export Citation
  • 18.

    Burrows E, Keats M, Kolen A. Contributions of after school programs to the development of fundamental movement skills in children. Int J Exerc Sci. 2014;7(3):236249. PubMed ID: 27293501

    • Search Google Scholar
    • Export Citation
  • 19.

    Mandinach EB. A perfect time for data use: using data-driven decision making to inform practice. Educ Psychol. 2012;47(2):7185. doi:

  • 20.

    Marsh JA, Farrell CC. How leaders can support teachers with data-driven decision making: a framework for understanding capacity building. Educ Manage Adm Lead. 2015;43(2):269289. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Dauenhauer BD, Keating XD, Lambdin D. An examination of physical education data sources and collection procedures during a federally funded grant. J Teach Phys Educ. 37(1):4658. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 22.

    Marsh JA, McCombs J, Martorell F. How instructional coaches support data-driven decision making: policy implementation and effects in Florida middle schools. Educ Policy. 2009;24(6):872907. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 23.

    Hodgin KL, von Klinggraeff L, Dauenhauer B, et al. Effects of sharing data with teachers on student PA and sedentary behavior in the classroom. J Phys Act Health. 2020;17:585591. PubMed ID: 32335524

    • Search Google Scholar
    • Export Citation
  • 24.

    Carson RL, Castelli DM, Kuhn ACP, et al. Impact of trained champions of comprehensive school physical activity programs on school physical activity offerings, youth physical activity and sedentary behaviors. Prev Med. 2014;69:S12S19. PubMed ID: 25158209

    • Search Google Scholar
    • Export Citation
  • 25.

    Dunn-Carver M, Pope L, Dana G, et al. Evaluation of a teacher-led physical activity curriculum to increase preschooler physical activity. Open J Prev Med. 2013;3(1):141147. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26.

    Essay AM, Schenkelberg MA, Seggern MJV, et al. A protocol for a local community monitoring and feedback system for physical activity in organized group settings for children. J Phys Act Health. 2023;20(5):385393. PubMed ID: 36965493

    • Search Google Scholar
    • Export Citation
  • 27.

    Schenkelberg MA, Essay AM, Rosen MS, et al. A protocol for coordinating rural community stakeholders to implement whole-of-community youth physical activity surveillance through school systems. Prev Med Rep. 2021;24:101536. PubMed ID: 34976611

    • Search Google Scholar
    • Export Citation
  • 28.

    Essay AM, Schlechter CR, Mershon CA, et al. A scoping review of whole-of-community interventions on six modifiable cancer prevention risk factors in youth: a systems typology. Prev Med. 2021;153:106769. PubMed ID: 34416222

    • Search Google Scholar
    • Export Citation
  • 29.

    Agency for Healthcare Research and Quality. Plan-Do-Study-Act (PDSA) Directions and Examples. Agency for Healthcare Research and Quality.

    • Search Google Scholar
    • Export Citation
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