Predicting physical activity among urban adolescent girls: A test of the health promotion model
Corresponding Author
Vicki R. Voskuil PhD, RN, CPNP
Department of Nursing, Hope College, Holland, Michigan
Correspondence Vicki R. Voskuil, A. Paul Schaap Science Center, #1152, Hope College, 35 East 12th Street, P.O. Box 9000, Holland, MI 49422-9000. Email: [email protected]
Search for more papers by this authorLorraine B. Robbins PhD, RN, FAAN, FNP-BC
College of Nursing, Michigan State University, East Lansing, Michigan
Search for more papers by this authorSteven J. Pierce PhD
Center for Statistical Training and Consulting, Michigan State University, East Lansing, Michigan
Search for more papers by this authorCorresponding Author
Vicki R. Voskuil PhD, RN, CPNP
Department of Nursing, Hope College, Holland, Michigan
Correspondence Vicki R. Voskuil, A. Paul Schaap Science Center, #1152, Hope College, 35 East 12th Street, P.O. Box 9000, Holland, MI 49422-9000. Email: [email protected]
Search for more papers by this authorLorraine B. Robbins PhD, RN, FAAN, FNP-BC
College of Nursing, Michigan State University, East Lansing, Michigan
Search for more papers by this authorSteven J. Pierce PhD
Center for Statistical Training and Consulting, Michigan State University, East Lansing, Michigan
Search for more papers by this authorAbstract
The purpose of this study was to test hypothesized relationships of the health promotion model (HPM) as a means of predicting moderate-to-vigorous physical activity (MVPA) among urban, adolescent girls. A secondary analysis of baseline data from a group randomized controlled trial was conducted. The study involved eight urban schools in the Midwestern United States. The sample included girls (N = 517) in the 5th–8th grades. Data were collected on age, body mass index, pubertal status, enjoyment, self-efficacy, social support, options for physical activity (PA), and commitment to PA. MVPA was measured via accelerometers worn by the girls for 7 days. Structural equation modeling was used to analyze study aims. Mean age of the sample was 11.8 years (standard deviation [SD] = 1.0). Girls attained an average of 3.0 (SD = 1.2) minutes per hour of MVPA. Self-efficacy had a positive direct (β = .337; p < .001) and total effect (β = .310; p < .001) on MVPA. Social support and options for PA were not significant predictors of commitment to PA or MVPA. Commitment to PA had a negative but nonsignificant effect (β = −.056; p = .357) on MVPA. The model predicted 10.1% of the variance in MVPA with 9.6% of the variance predicted by self-efficacy. Limitations include lack of longitudinal analysis and inability to generalize the results to other populations such as boys. PA self-efficacy continues to emerge as a significant predictor of MVPA in the HPM. Continued theory testing is needed to better understand the correlates and determinants of PA among adolescent girls before designing theory-based interventions to promote PA.
CONFLICT OF INTERESTS
The authors declare that there is no conflict of interest.
REFERENCES
- Adamo, K. B., Prince, S. A., Tricco, A. C., Connor-Gorber, S., & Tremblay, M. (2009). A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: A systematic review. International Journal of Pediatric Obesity, 4, 2–27. https://doi.org/10.1080/17477160802315010
- American Academy of Pediatrics. (2017). In J. F. Hagan, J. S. Shaw, & P. M. Duncan (Eds.), Bright futures: Guidelines for health supervision of infants, children, and adolescents ( 4th ed.). Elm Grove, IL: American Academy of Pediatrics.
- Ammouri, A. A., Kaur, H., Neuberger, G. B., Gajewski, B., & Choi, W. S. (2007). Correlates of exercise participation in adolescents. Public Health Nursing, 24(2), 111–120. https://doi.org/10.1111/j.1525-1446.2007.00615.x
- Atkin, A. J., VanSluijs, E. M., Dollman, J., Taylor, W. C., & Stanley, R. M. (2016). Identifying correlates and determinants of physical activity in youth: How can we advance the field? Preventive Medicine, 87, 167–169. https://doi.org/10.1016/j.ypmed.2016.02.040
- Bajamal, E., Robbins, L. B., Ling, J., Smith, B., Pfeiffer, K. A., & Sharma, D. (2017). Physical activity among female adolescents in Jeddah, Saudi Arabia: A health promotion model-based path analysis. Nursing Research, 66(6), 473–482. https://doi.org/10.1097/NNR.0000000000000244
- Baker, B. L., Birch, L. L., Trost, S. G., & Davison, K. K. (2007). Advanced pubertal status at age 11 and lower physical activity in adolescent girls. The Journal of Pediatrics, 151(5), 488–493. https://doi.org/10.1016/j.jpeds.2007.04.017
- Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall, Inc.
- Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W.H. Freeman and Company.
- Bauman, A. E., Reis, R. S., Sallis, J. F., Wells, J. C., Loos, R. J. F., & Martin, B. W. (2012). Correlates of physical activity: Why are some people physically active and others not? The Lancet, 380(9838), 258–271. https://doi.org/10.1016/S0140-6736(12)60735-1
- Beets, M. W., Cardinal, B. J., & Alderman, B. L. (2010). Parental social support and the physical activity–related behaviors of youth: A review. Health Education & Behavior, 37(5), 621–644. https://doi.org/10.1177/1090198110363884
- Biddle, S. J. H., Whitehead, S. H., O'Donovan, T. M., & Nevill, M. E. (2005). Correlates of participation in physical activity for adolescent girls: A systematic review of recent literature. Journal of Physical Activity and Health, 2(4), 423–443.
10.1123/jpah.2.4.423 Google Scholar
- Brown, A. S. (2009). Promoting physical activity amongst adolescent girls. Issues in Comprehensive Pediatric Nursing, 32(2), 49–64. https://doi.org/10.1080/01460860902737400
- Brown, T. A. (2015). Confirmatory factor analysis for applied research. New York, NY: Guilford Press.
- Buchan, D. S., Ollis, S., Thomas, N. E., & Baker, J. S. (2012). Physical activity behaviour: An overview of current and emergent theoretical practices. Journal of Obesity, 2(2), 1–11. https://doi.org/10.1155/2012/546459
10.1155/2012/546459 Google Scholar
- Burchinal, M. R. (2008). How measurement error affects the interpretation and understanding of effect sizes. Child Development Perspectives, 2(3), 178–180. https://doi.org/10.1111/j.1750-8606.2008.00062.x
- Camacho-Minano, M. J., LaVoi, N. M., & Barr-Anderson, D. J. (2011). Interventions to promote physical activity among young and adolescent girls: A systematic review. Health Education Research, 26(6), 1025–1049. https://doi.org/10.1093/her/cyr040
- Carskadon, M. A., & Acebo, C. (1993). A self-administered rating scale for pubertal development. Journal of Adolescent Health, 14(3), 190–195. https://doi.org/10.1016/1054-139×(93)90004-9
- Cataldo, R., John, J., Chandran, L., Pati, S., & Shroyer, A. L. W. (2013). Impact of physical activity intervention programs on self-efficacy in youths: A systematic review. ISRN Obesity, 2013, 1–11. https://doi.org/10.1155/2013/586497.
10.1155/2013/586497 Google Scholar
- Catellier, D. J., Hannan, P. J., Murray, D. M., Addy, C. L., Conway, T. L., Yang, S., & Rice, J. C. (2005). Imputation of missing data when measuring physical activity by accelerometry. Medicine and Science in Sports and Exercise, 37(11 Suppl), S555–S562. https://doi.org/10.1249/01.mss.0000185651.59486.4e
- Centers for Disease Control and Prevention. (2015). A SAS Program for the 2000 CDC Growth Charts (ages 0 to <20 years). Retrieved from http://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm
- Chinapaw, M. J., Lidwine, B., vanPoppel, M. N. M., vanMechelen, W., & Terwee, C. B. (2010). Physical activity questionaires for youth: A systematic review of measurement properties. Sports Medicine, 40(7), 539–563. https://doi.org/10.2165/11531350-000000000-00000
- Chung, A. E., Skinner, A. C., Steiner, M. J., & Perrin, E. M. (2012). Physical activity and BMI in a nationally representative sample of children and adolescents. Clinical Pediatrics, 51(2), 122–129. https://doi.org/10.1177/0009922811417291
- Craggs, C., Corder, K., vanSluijs, E. M. F., & Griffin, S. J. (2011). Determinants of change in physical activity in children and adolescents: A systematic review. American Journal of Preventive Medicine, 40(6), 645–658. https://doi.org/10.1016/j.amepre.2011.02.025
- Davison, K. K., Werder, J. L., Trost, S. G., Baker, B. L., & Birch, L. L. (2007). Why are early maturing girls less active? Links between pubertal development, psychological well-being, and physical activity among girls at ages 11 and 13. Social Science & Medicine, 64(12), 2391–2404. https://doi.org/10.1016/j.socscimed.2007.02.033
- Debate, R. D., Huberty, J., & Pettee, K. (2009). Psychometric properties of the commitment to physical activity scale. American Journal of Health Behavior, 33(4), 425–434. https://doi.org/10.5993/AJHB.33.4.8
- Dewar, D. L., Plotnikoff, R. C., Morgan, P. J., Okely, A. D., Costigan, S. A., & Lubans, D. R. (2013). Testing social-cognitive theory to explain physical activity change in adolescent girls from low-income communities. Research Quarterly for Exercise and Sport, 84(4), 483–491. https://doi.org/10.1080/02701367.2013.842454
- Ding, D., Sallis, J. F., Kerr, J., Lee, S., & Rosenberg, D. E. (2011). Neighborhood environment and physical activity among youth: A review. American Journal of Preventive Medicine, 41(4), 442–455. https://doi.org/10.1016/j.amepre.2011.06.036
- Dishman, R. K., Dunn, A. L., Sallis, J. F., Vandenberg, R. J., & Pratt, C. A. (2010). Social-cognitive correlates of physical activity in a multi-ethnic cohort of middle-school girls: Two-year prospective study. Journal of Pediatric Psychology, 35(2), 188–198. https://doi.org/10.1093/jpepsy/jsp042
- Dishman, R. K., Hales, D. P., Sallis, J. F., Saunders, R., Dunn, A. L., Bedimo-Rung, A. L., & Ring, K. B. (2010). Validity of social-cognitive measures for physical activity in middle-school girls. Journal of Pediatric Psychology, 35(1), 72–88. https://doi.org/10.109/jpepsy/jsp031
- Dishman, R. K., Motl, R. W., Sallis, J. F., Dunn, A. L., Birnbaum, A. S., Welk, G. J., & Jobe, J. B. (2005). Self-management strategies mediate self-efficacy and physical activity. American Journal of Preventive Medicine, 29(1), 10–18. https://doi.org/10.1016/j.amepre.2005.03.012
- Dishman, R. K., Motl, R. W., Saunders, R., Felton, G., Ward, D. S., Dowda, M., & Pate, R. R. (2005). Enjoyment mediates effects of a school-based physical-activity intervention. Medicine & Science in Sports & Exercise, 37(3), 478–487. https://doi.org/10.1249/01.MSS.0000155391.62733.A7
- Dishman, R. K., Motl, R. W., Saunders, R. P., Dowda, M., Felton, G., Ward, D. S., & Pate, R. R. (2002). Factorial invariance and latent mean structure of questionnaires measuring social-cognitive determinants of physical activity among black and white adolescent girls. Preventive Medicine, 34(1), 100–108. https://doi.org/10.1006/pmed.2001.0959
- Dumith, S. C., Gigante, D. P., Domingues, M. R., & Kohl, H. W. (2011). Physical activity change during adolescence: A systematic review and a pooled analysis. International Journal of Epidemiology, 40(3), 685–698. https://doi.org/10.1093/ije/dyq272.
- Duncan, S. C., Strycker, L. A., Chaumeton, N. R., & Cromley, E. K. (2016). Relations of neighborhood environment influences, physical activity, and active transportation to/from school across African American, Latino American, and White girls in the United States. International Journal of Behavioral Medicine, 23(2), 153–161. https://doi.org/10.1007/s12529-015-9508-9
- Durstine, J. L., Gordon, B., Wang, Z., & Luo, X. (2013). Chronic disease and the link to physical activity. Journal of Sport and Health Science, 2(1), 3–11. https://doi.org/10.1016/j.jshs.2012.07.009
- Efrat, M. W. (2017). Exploring strategies that influence children's physical activity self-efficacy. Contemporary Issues in Education Research, 10(2), 87–94.
10.19030/cier.v10i2.9919 Google Scholar
- Ekelund, U., Luan, J., Sherar, L. B., Esliger, D. W., Griew, P., & Cooper, A. (2012). Moderate to vigorous physical activity and sedentary time and cardiac risk factors in children and adolescents. Journal of the American Medical Association, 307(7), 704–712. https://doi.org/10.1001/jama.2012.156
- Evenson, K. R., Birnbaum, A. S., Bedimo-Rung, A. L., Sallis, J. F., Voorhees, C. C., Ring, K., & Elder, J. P. (2006). Girls' perception of physical environmental factors and transportation: Reliability and association with physical activity and active transport to school. International Journal of Behavioral Nutrition and Physical Activity, 3, 28. https://doi.org/10.1186/1479-5868-3-28
- Evenson, K. R., Catellier, D. J., Gill, K., Ondrak, K. S., & McMurray, R. G. (2008). Calibration of two objective measures of physical activity for children. Journal of Sports Sciences, 26(14), 1557–1565. https://doi.org/10.1080/02640410802334196
- Fakhouri, T. H., Hughes, J. P., Burt, V. L., Song, M., Fulton, J. E., & Ogden, C. L. (2014). Physical activity in U.S. youth aged 12-15 years, 2012. National Center for Health Statistics Data Brief, 141, 1–8.
- Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
- Freedman, D. S., Wang, J., Thornton, J. C., Mei, Z., Sopher, A. B., Pierson, R. N., & Horlick, M. (2009). Classification of body fatness by body mass index-for-age categories among children. Archives of Pediatrics & Adolescent Medicine, 163(9), 805–811. https://doi.org/10.1001/archpediatrics.2009.104
- Fritz, M. S., & MacKinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239. https://doi.org/10.1111/j.1467-9280.2007.01882.x
- Garcia, A. W., Broda, M. A., Frenn, M., Coviak, C., Pender, N. J., & Ronis, D. L. (1995). Gender and developmental differences in exercise beliefs among youth and prediction of their exercise behavior. Journal of School Health, 65(6), 213–219. https://doi.org/10.1111/j.1746-1561.1995.tb03365.x
- Garn, A. C., McCaughtry, N., Shen, B., Martin, J. J., & Fahlman, M. (2013). Underserved adolescent girls' physical activity intentions and behaviors: Relationships with the motivational climate and perceived competence in physical education. Advances in Physical Education, 3(2), 103–110. https://doi.org/10.4236/ape.2013.32018
10.4236/ape.2013.32018 Google Scholar
- Goodwin, L. D., & Leech, N. L. (2006). Understanding correlation: Factors that affect the size of r. The Journal of Experimental Education, 74(3), 249–266. https://doi.org/10.3200/JEXE.74.3.249-266
- Grant, E. M., Young, D. R., & Wu, T. T. (2015). Predictors of physical activity in adolescent girls using statistical shrinkage techniques for hierarchical longitudinal mixed effects models. PLoS One, 10(4), 1–15. https://doi.org/10.137/journal.pone.0125431
- Green., S. B., & Yang, Y. (2009). Reliability of summed item scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74(1), 155–167. https://doi.org/10.1007/s11336-008-9099-3
- Hearst, M. O., Patnode, C. D., Sirard, J. R., Farbakhsh, K., & Lytle, L. A. (2012). Multi-level predictors of adolescent physical activity: A longitudinal analysis. International Journal of Behavioral Nutrition and Physical Activity, 9(8), 1–10. https://doi.org/10.1186/1479-5868-9-8
- Hills, A. P., Dengel, D. R., & Lubans, D. R. (2015). Supporting public health priorities: Recommendations for physical education and physical activity promotion in schools. Progress in Cardiovascular Diseases, 57(4), 368–374. https://doi.org/10.1016/j.pcad.2014.09.010
- van DerHorst, K., Paw, M. J., Twisk, J. W. R., & VanMechelen, W. (2007). A brief review on correlates of physical activity and sedentariness in youth. Medicine and Science in Sports and Exercise, 39(8), 1241–1250.
- Hunter Smart, J. E., Cumming, S. P., Sherar, L. B., Standage, M., Neville, H., & Malina, R. M. (2012). Maturity associated variance in physical activity and health-related quality of life in adolescent females: A mediated effects model. Journal of Physical Activity & Health, 9(1), 86–95.
- Hänggi, J. M., Phillips, L. R. S., & Rowlands, A. V. (2013). Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph. Journal of Science and Medicine in Sport, 16(1), 40–44.
- Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7(40), 1–16. https://doi.org/10.1186/1479-5868-7-40
- Javed, A., Jumean, M., Murad, M. H., Okorodudu, D., Kumar, S., Somers, V. K., & Lopez-Jimenez, F. (2015). Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: A systematic review and meta-analysis. Pediatric Obesity, 10(3), 234–244. https://doi.org/10.1111/ijpo.242
- Kann, L., McManus, T., Harris, W. A., Shanklin, S. L., Flint, K. H., Hawkins, J., & Zaza, S. (2016). Youth risk behavior surveillance - United States, 2015. Morbidity and Mortality Weekly Report, 65(6), 1–174
- Kelloway, K. (2015). Using Mplus for structural equation modeling: A researcher's guide ( 2nd ed.). Thousand Oaks, CA: Sage Publications Inc.
- Kelly, E. B., Parra-Medina, D., Pfeiffer, K. A., Dowda, M., Conway, T. L., Webber, L. S., & Pate, R. R. (2010). Correlates of physical activity in Black, Hispanic and White middle school girls. Journal of Physical Activity & Health, 7(2), 184–193.
- Kendzierski, D., & DeCarlo, K. J. (1991). Physical activity enjoyment scale: Two validation studies. Journal of Sport & Exercise Psychology, 13(1), 50–64. https://doi.org/10.1123/jsep.13.1.50
- Kline, R. B. (2016). Principles and practice of structural equation modeling ( 4th ed.). New York, NY: The Guilford Press.
- LeBlanc, A. G., & Janssen, I. (2010). Difference between self-reported and accelerometer measured moderate-to-vigorous physical activity in youth. Pediatric Exercise Science, 22(4), 523–534.
- Ling, J., Robbins, L. B., Resnicow, K., & Bakhoya, M. (2014). Social support and peer norms scales for physical activity in adolescents. American Journal of Health Behavior, 38(6), 881–889. https://doi.org/10.5993/ajhb.38.6.10
- Lubans, D. R., Okely, A. D., Morgan, P. J., Cotton, W., Puglisi, L., & Miller, J. (2012). Description and evaluation of a social cognitive model of physical activity behaviour tailored for adolescent girls. Health Education Research, 27(1), 115–128. https://doi.org/10.1093/her/cyr039
- MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130–149. https://doi.org/10.1037/1082-989X.1.2.130
- Macdonald-Wallis, K., Jago, R., & Sterne, J. A. (2012). Social network analysis of childhood and youth physical activity: A systematic review. American Journal of Preventive Medicine, 43(6), 636–642. https://doi.org/10.1016/j.amepre.2012.08.021
- Marsh, H. W., Morin, A. J., Parker, P. D., & Kaur, G. (2014). Exploratory structural equation modeling: An integration of the best features of exploratory and confirmatory factor analysis. Annual Review of Clinical Psychology, 10, 85–110. https://doi.org/10.1146/annurev-clinpsy-032813-153700
- McCullagh, M. C. (2017). Health promotion. In S. J. Peterson, & T. S. Bredow. (Eds.), Middle range theories: Application to nursing research and practice ( 4th ed, pp. 227–237). Philadelphia, PA: Wulters Kluwer/Lippincott, Williams, & Wilkins.
- Metcalf, B., Henley, W., & Wilkin, T. (2012). Effectiveness of intervention on physical activity of children: Systematic review and meta-analysis of controlled trials with objectively measured outcomes (EarlyBird 54). BMJ, 345, e5888. https://doi.org/10.1136/bmj.e5888
- Mohamadian, H., & Arani, M. (2014). Factors predicting the physical activity behavior of female adolescents: A test of the Health Promotion Model. Journal of Preventive Medicine and Public Health, 47(1), 64–71. https://doi.org/10.3961/jpmph.2014.47.1.64
- Mota, J., Almeida, M., Santos, P., & Ribeiro, J. C. (2005). Perceived neighborhood environments and physical activity in adolescents. Preventive Medicine, 41(5), 834–836. https://doi.org/10.1016/j.ypmed.2005.07.012
- Motl, R. W., Dishman, R. K., Saunders, R., Dowda, M., Felton, G., & Pate, R. R. (2001). Measuring enjoyment of physical activity in adolescent girls. American Journal of Preventive Medicine, 21(2), 110–117. https://doi.org/10.1016/S0749-3797(01)00326-9
- Motl, R. W., Dishman, R. K., Trost, S. G., Saunders, R. P., Dowda, M., Felton, G., & Pate, R. R. (2000). Factorial validity and invariance of questionnaires measuring social-cognitive determinants of physical activity among adolescent girls. Preventive Medicine, 31(5), 584–594. https://doi.org/10.1006/pmed.2000.0735
- Muthén, L. K., & Muthén, B. O. (2015a). Mplus statistical analysis with latent variables (Version 7.4) [Statistical software] ( 7th ed.). Los Angeles, CA: Muthén & Muthén.
- Muthén, L. K., & Muthén, B. O. (2015b). Mplus user's guide ( 7th.). Los Angeles, CA: Muthén & Muthén.
- Ogden, C. L., Carroll, M. D., Lawman, H. G., Fryar, C. D., Kruszon-Moran, D., Kit, B. K., & Flegal, K. M. (2016). Trends in obesity prevalence among children and adolescents in the United States, 1988–1994 through 2013–2014. Journal of the American Medical Association, 315(21), 2292–2299. https://doi.org/10.1001/jama.2016.6361
- Page, A., Cooper, A. R., Stamatakis, E., Foster, L. J., Crowne, E. C., Sabin, M., & Shield, J. P. H. (2005). Physical activity patterns in nonobese and obese children assessed using minute-by-minute accelerometry. International Journal of Obesity, 29(9), 1070–1076. https://doi.org/10.1038/sj.ijo.0802993
- Pakarinen, A., Parisod, H., Smed, J., & Salanterä, S. (2017). Health game interventions to enhance physical activity self-efficacy of children: A quantitative systematic review. Journal of Advanced Nursing, 73(4), 794–811.
- Pandey, S., & Elliott, W. (2010). Suppressor variables in social work research: Ways to identify in multiple regression models. Journal of the Society for Social Work and Research, 1(1), 28–40. https://doi.org/10.5243/jsswr.2010.2
10.5243/jsswr.2010.2 Google Scholar
- Pate, R. R., Stevens, J., Pratt, C., Sallis, J. F., Schmitz, K. H., Webber, L. S., & Young, D. R. (2006). Objectively measured physical activity in sixth-grade girls. Archives of Pediatrics & Adolescent Medicine, 160(12), 1262–1268. https://doi.org/10.1001/archpedi.160.12.1262
- Patnode, C. D., Lytle, L. A., Erickson, D. J., Sirard, J. R., Barr-Anderson, D., & Story, M. (2010). The relative influence of demographic, individual, social, and environmental factors on physical activity among boys and girls. International Journal of Behavioral Nutrition and Physical Activity, 7(1), 1–10. https://doi.org/10.1186/1479-5868-7-79
- Paxton, R. J., Nigg, C., Motl, R. W., Yamashita, M., Chung, R., Battista, J., & Change, J. (2008). Physical activity enjoyment scale short form – does it fit for children? Research Quarterly for Exercise and Sport, 79(3), 423–427. https://doi.org/10.1080/02701367.2008.10599508
- Pekmezi, D., Jennings, E., & Marcus, B. H. (2009). Evaluating and enhancing self-efficacy for physical activity. American College of Sports Medicine's Health & Fitness Journal, 13(2), 16–21. https://doi.org/10.1249/FIT.0b013e3181996571
- Pender, N., Murdaugh, C., & Parsons, M. (2015). Health promotion in nursing practice ( 7th ed.). Upper Saddle River, NJ: Pearson Education.
- Pender, N. J, Garcia, A. W, & Ronis, D. L. (2014). Health Promotion Model instruments to measure HPM behavioral determinants: Planning for exercise [Commitment] (Adolescent Version) University of Michigan School of Nursing. http://deepblue.lib.umich.edu/handle/2027.42/85347
- Perry, C. K., Garside, H., Morones, S., & Hayman, L. L. (2012). Physical activity interventions for adolescents: An ecological perspective. The Journal of Primary Prevention, 33(2-3), 111–135. https://doi.org/10.1007/s10935-012-0270-3
- Petersen, A. C., Crockett, L., Richards, M., & Boxer, A. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17(2), 117–133. https://doi.org/10.1007/bf01537962
- Plotnikoff, R. C., Costigan, S. A., Karunamuni, N., & Lubans, D. R. (2013). Social cognitive theories used to explain physical activity behavior in adolescents: A systematic review and meta-analysis. Preventive Medicine, 56(5), 245–253. https://doi.org/10.1016/j.ypmed.2013.01.013
- Poitras, V. J., Gray, C. E., Borghese, M. M., Carson, V., Chaput, J. P., Janssen, I., & Sampson, M. (2016). Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Applied Physiology, Nutrition, and Metabolism, 41(6), S197–S239. https://doi.org/10.1139/apnm-2015-0663
- Reimers, A. K., Jekauc, D., Mess, F., Mewes, N., & Woll, A. (2012). Validity and reliability of a self-report instrument to assess social support and physical environmental correlates of physical activity in adolescents. BMC Public Health, 12(1), 1–10. https://doi.org/10.1186/1471-2458-12-705
- Rhodes, R. E., & Nigg, C. R. (2011). Advancing physical activity theory: A review and future directions. Exercise and Sport Sciences Reviews, 39(3), 113–119. https://doi.org/10.1097/JES.0b013e31821b94c8
- Robbins, L. B., Ling, J., Sharma, D. B., Dalimonte-Merckling, D. M., Voskuil, V. R., Resnicow, K., & Pfeiffer, K. A. (2018). Intervention effects of “Girls on the Move” on increasing physical activity: A group randomized trial. Annals of Behavioral Medicine, 53, 1–8. https://doi.org/10.1093/abm/kay054
- Robbins, L. B., Ling, J., Wesolek, S. M., Kazanis, A. S., Bourne, K. A., & Resnicow, K. (2016). Reliability and validity of the commitment to physical activity scale for adolescents. American Journal of Health Promotion, 31(4), 343–352. https://doi.org/10.4278/ajhp.150114-QUAN-665
- Robbins, L. B., Pfeiffer, K. A., Vermeesch, A., Resnicow, K., You, Z., An, L., & Wesolek, S. M. (2013). “Girls on the Move” intervention protocol for increasing physical activity among low-active underserved urban girls: A group randomized trial. BMC Public Health, 13(474), 1–12. https://doi.org/10.1186/1471-2458-13-474
- Robusto, K. M., & Trost, S. G. (2012). Comparison of three generations of ActiGraph activity monitors in children and adolescents. Journal of Sports Sciences, 30(13), 1429–1435. https://doi.org/10.1080/02640414.2012.710761
- SAS Institute Inc (2011). SAS (Version 9.3) [Statistical software]. Cary, NC: SAS Institute Inc.
- Sallis, J. F., Prochaska, J.J., & Taylor, W.C. (2000). A review of correlates of physical activity of children and adolescents. Medicine and Science in Sports and Exercise, 32(5), 963–975.
- Slootmaker, S. M., Schuit, A. J., Chinapaw, M. J., Seidell, J. C., & vanMechelen, W. (2009). Disagreement in physical activity assessed by accelerometer and self-report in subgroups of age, gender, education and weight status. International Journal of Behavior Nutrition and Physical Activity, 6(17), 1–10. https://doi.org/10.1186/1479-5868-6-17
- StataCorp. (2015). Stata statistical software (Version 14) [Statistical software]. College Station, TX: StataCorp LP.
- Taymoori, P., Lubans, D., & Berry, T. R. (2010). Evaluation of the Health Promotion Model to predict physical activity in Iranian adolescent boys. Health Education & Behavior, 37(1), 84–96. https://doi.org/10.1177/1090198109356407
- Taymoori, P., Rhodes, R. E., & Berry, T. R. (2010). Application of a social cognitive model in explaining physical activity in Iranian female adolescents. Health Education Research, 25(2), 257–267. https://doi.org/10.1093/her/cyn051
- Timperio, A., Crawford, D., Telford, A., & Salmon, J. (2004). Perceptions about the local neighborhood and walking and cycling among children. Preventive Medicine, 38(1), 39–47. https://doi.org/10.1016/j.ypmed.2003.09.026
- Troiano, R. P., Berrigan, D., Dodd, K. W., Masse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise, 40(1), 181–188. https://doi.org/10.1249/mss.0b013e31815a51b3
- Trost, S. G. (2007). State of the art reviews: Measurement of physical activity in children and adolescents. American Journal of Lifestyle Medicine, 1(4), 299–314. https://doi.org/10.1177/1559827607301686
10.1177/1559827607301686 Google Scholar
- United States Department of Health and Human Services (2018). 2018 Physical activity guidelines for Americans ( 2nd ed.). Washingtion D.C.: U.S. Department of Health and Human Services. https://health.gov/paguidelines/second-edition/pdf/Physical_Activity_Guidelines_2nd_edition.pdf
- vanBuuren, S., Brand, J., Groothuis-Oudshoorn, K., & Rubin, D. B. (2006). Fully conditional specification in multivariate imputation. Journal of Statistical Computation and Simulation, 76(12), 1049–1064. https://doi.org/10.1080/10629360600810434
- vanBuuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.
- Voskuil, V. R., Frambes, D. A., & Robbins, L. B. (2017). Effect of physical activity interventions for girls on objectively measured outcomes: A systematic review of randomized controlled trials. Journal of Pediatric Health Care, 31(1), 75–87. https://doi.org/10.1016/j.pedhc.2016.03.003
- Voskuil, V. R., Pierce, S. J., & Robbins, L. B. (2017). Comparing the psychometric properties of two physical activity self-efficacy instruments in urban, adolescent girls: Validity, measurement invariance, and reliability. Frontiers in Psychology, 8, 1301. https://doi.org/10.3389/fpsyg.2017.01301
- Warburton, D. E. R., & Bredin, S. S. D. (2017). Health benefits of physical activity: A systematic review of current systematic reviews. Current Opinion in Cardiology, 32(5), 541–556. https://doi.org/10.1097/HCO.0000000000000437
- Warren, J. M., Ekelund, U., Besson, H., Mezzani, A., Geladas, N., & Vanhees, L. (2010). Assessment of physical activity - a review of methodologies with reference to epidemiological research: A report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. European Journal of Cardiovascular Prevention and Rehabilitation, 17(2), 127–139. https://doi.org/10.1097/HJR.0b013e32832ed875
- White, I. R., Royston, P., & Wood, A. M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine, 30(4), 377–399. https://doi.org/10.1002/sim.4067
- Wolf, E. J., Harrington, K. M., Clark, S. L., & Miller, M. W. (2013). Sample size requirements for structural equation models: An evaluation of power, bias, and solution propriety. Educational and Psychological Measurement, 73(6), 913–934. https://doi.org/10.1177/0013164413495237
- World Health Organziation. (2019). Adolescent health. Retrieved from https://www.who.int/maternal_child_adolescent/adolescence/en/
- Wu, T. Y., & Pender, N. (2002). Determinants of physical activity among Taiwanese adolescents: An application of the health promotion model. Research in Nursing & Health, 25(1), 25–36. https://doi.org/10.1002/nur.10021
- Wu, T. Y., & Pender, N. (2005). A panel study of physical activity in Taiwanese youth: Testing the revised health-promotion model. Family & Community Health, 28(2), 113–124.
- Wu, T. Y., Pender, N., & Noureddine, S. (2003). Gender differences in the psychosocial and cognitive correlates of physical activity among Taiwanese adolescents: A structural equation modeling approach. International Journal of Behavioral Medicine, 10(2), 93–105. https://doi.org/10.1207/S15327558IJBM1002_01
- Yang, Y., & Green, S. B. (2014). Evaluation of structural equation modeling estimates of reliability for scales with ordered categorical items. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 11(1), 23–34. https://doi.org/10.1027/1614-2241/a000087