Accelerometer-based assessment of physical activity within the Fun For Wellness online behavioral intervention: protocol for a feasibility study

Nicholas D Myers, Seungmin Lee, André G Bateman, Isaac Prilleltensky, Kimberly A Clevenger, Karin A Pfeiffer, Samantha Dietz, Ora Prilleltensky, Adam McMahon, Ahnalee M Brincks, Nicholas D Myers, Seungmin Lee, André G Bateman, Isaac Prilleltensky, Kimberly A Clevenger, Karin A Pfeiffer, Samantha Dietz, Ora Prilleltensky, Adam McMahon, Ahnalee M Brincks

Abstract

Background: Fun For Wellness (FFW) is an online behavioral intervention designed to promote growth in well-being and physical activity by providing capability-enhancing learning opportunities to participants. The conceptual framework for the FFW intervention is guided by self-efficacy theory. Evidence has been provided for the efficacy of FFW to promote self-reported free-living physical well-being actions in adults who comply with the intervention. The objective of this manuscript is to describe the protocol for a feasibility study designed to address uncertainties regarding the inclusion of accelerometer-based assessment of free-living physical activity within the FFW online intervention among adults with obesity in the United States of America (USA).

Method: The study design is a prospective, double-blind, parallel group randomized pilot trial. Thirty participants will be randomly assigned to the FFW or usual care (UC) group to achieve a 1:1 group (i.e., FFW:UC) assignment. Recruitment of participants is scheduled to begin on 29 April 2019 at a local bariatric services center within a major healthcare organization in the Midwest of the USA. There are five eligibility criteria for participation in this study: (1) between 18 and 64 years old, (2) a body mass index ≥ 25.00 kg/m2, (3) ability to access the online intervention, (4) the absence of simultaneous enrollment in another intervention program promoting physical activity, and (5) willingness to comply with instructions for physical activity monitoring. Eligibility verification and data collection will be conducted online. Three waves of data will be collected over a 13-week period. Instruments designed to measure demographic information, anthropometric characteristics, acceptability and feasibility of accelerometer-based assessment of physical activity, self-efficacy, and well-being will be included in the study. Data will be analyzed using descriptive statistics (e.g., recruitment rates), Pearson's correlation coefficient, Bland-Altman analyses, and inferential statistical models under both an intent to treat approach and a complier average causal effect approach.

Discussion: Results are intended to inform the preparation of a future definitive randomized controlled trial.

Trial registration: ClinicalTrials.gov, NCT03906942, registered 8 April 2019.

Trial funding: The Erwin and Barbara Mautner Charitable Foundation and the Michigan State University College of Education.

Keywords: Acceptability; E-health; M-health; Self-efficacy theory; Validity; Well-being.

Conflict of interest statement

Competing interestsTwo co-authors, Adam McMahon and Isaac Prilleltensky, are partners in Wellnuts LLC. Wellnuts LLC may commercialize the FFW intervention in the future.

Figures

Fig. 1
Fig. 1
The conceptual model that guided the 2015 Fun For Wellness efficacy trial [36]
Fig. 2
Fig. 2
The conceptual model that guided the 2018 Fun For Wellness effectiveness trial [37]
Fig. 3
Fig. 3
Flow chart for recruitment of participants throughout data collection

References

    1. World Health Organization . Obesity and overweight fact sheet. 2017.
    1. United States Department of Health and Human Services . Managing overweight and obesity in adults: systematic evidence review from the obesity expert panel. 2013.
    1. World Health Organization . Global recommendations on physical activity for health. 2010.
    1. United States Department of Health and Human Services . Physical activity guidelines advisory committee scientific report. 2018.
    1. Tudor-Locke C, Brashear MM, Johnson WD, Katzmarzyk PT. Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women. Int J Behav Nutr Phys Act. 2010;7:60. doi: 10.1186/1479-5868-7-60.
    1. Gourlan MJ, Trouilloud DO, Sarrazin PG. Interventions promoting physical activity among obese populations: a meta-analysis considering global effect, long-term maintenance, physical activity indicators and dose characteristics. Obes Rev. 2011;12:e633–e645. doi: 10.1111/j.1467-789X.2011.00874.x.
    1. Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001;52:1–26. doi: 10.1146/annurev.psych.52.1.1.
    1. Bandura A. Self-efficacy: the exercise of control. New York: Freeman; 1997.
    1. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380:258–271. doi: 10.1016/S0140-6736(12)60735-1.
    1. Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behavior – and are they the same? Health Educ Res. 2011;26:308–322. doi: 10.1093/her/cyr005.
    1. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100:126–131.
    1. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effects of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–229. doi: 10.1016/S0140-6736(12)61031-9.
    1. Kohl HW, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380:294–305. doi: 10.1016/S0140-6736(12)60898-8.
    1. Sallis JF, Bull F, Guthold R, Heath GW, Inoue S, Kelly P, et al. Progress in physical activity over the Olympic quadrennium. Lancet. 2016;388:1325–1336. doi: 10.1016/S0140-6736(16)30581-5.
    1. Heath GW, Parra DC, Sarmiento OL, Anderson LB, Owen N, Goenka S, et al. Evidence-based intervention in physical activity: lessons from around the world. Lancet. 2012;380:272–281. doi: 10.1016/S0140-6736(12)60816-2.
    1. Reis RS, Salvo D, Ogilvie D, Lambert EV, Goenka S, Brownson RC. Scaling up physical activity interventions worldwide: stepping up to larger and smarter approaches to get people moving. Lancet. 2016;388:1337–1348. doi: 10.1016/S0140-6736(16)30728-0.
    1. Vandelonotte C, Spathonis KM, Eakin EG, Owen N. Website-delivered physical activity interventions: a review of the literature. Am J Prev Med. 2007;33:54–64. doi: 10.1016/j.amepre.2007.02.041.
    1. Davies CA, Spence JC, Vandelanotte C, Caperchione CM, Mummery WK. Meta-analysis of internet delivered interventions to increase physical activity levels. Int J Behav Nutr Phys Act. 2012;9:52. doi: 10.1186/1479-5868-9-52.
    1. Bassett DR, Cureton AL, Ainsworth BE. Measurement of daily walking distance-questionnaire versus pedometer. Med Sci Sports Exerc. 2000;32:1018–1023. doi: 10.1097/00005768-200005000-00021.
    1. Freedson PS, Miller K. Objective monitoring of physical activity using motion sensors and heart rate. Res Q Exerc Sport. 2000;71(Suppl 2):21–29. doi: 10.1080/02701367.2000.11082782.
    1. Trost SG, Mciver KL, Pate RP. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(Suppl 11):531–543. doi: 10.1249/01.mss.0000185657.86065.98.
    1. Troiano RPD, Berrigan D, Dodd KD, Mâsse LC, Tilert T, Mcdowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40:181–188. doi: 10.1249/mss.0b013e31815a51b3.
    1. Ainsworth BE, Montoye HJ, Leon AS. Methods of assessing physical activity during leisure and work. In: Bouchard C, Shepard RJ, Stephens T, editors. Physical activity, fitness, and health: international proceedings and consensus statement. Champaign: Human Kinetics; 1994. pp. 146–159.
    1. Baranowski T. Validity and reliability of self-report of physical activity: an information processing perspective. Res Q Exerc Sport. 1988;59:314–327. doi: 10.1080/02701367.1988.10609379.
    1. Washburn RA, Montoye HJ. The assessment of physical activity by questionnaire. Am J Epidemiol. 1986;123:563–575. doi: 10.1093/oxfordjournals.aje.a114277.
    1. Sallis JF, Saelens BE. Assessment of physical activity by self-report: status limitations, and future directions. Res Q Exerc Sport. 2000;71(Suppl 2):1–14. doi: 10.1080/02701367.2000.11082780.
    1. Lee IM, Shiroma EJ. Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sports Med. 2014;48:197–201. doi: 10.1136/bjsports-2013-093154.
    1. Troiano RP, McClain JJ, Brychta RJ, Chen KY. Evolution of accelerometer methods for physical activity research. Br J Sports Med. 2014;48:1019–1023. doi: 10.1136/bjsports-2014-093546.
    1. Wijndaele K, Westgate K, Stephens SK, Blair SN, Bull FC, Chastin SFM, et al. Utilization and harmonization of adult accelerometry data: review and expert consensus. Med Sci Sports Exerc. 2015;47:2129–2139. doi: 10.1249/MSS.0000000000000661.
    1. Cerin E, Cain KL, Oyeyemi AL, Owen N, Conway TL, Cochrane T, et al. Correlates of agreement between accelerometry and self-reported physical activity. Med Sci Sports Exerc. 2016;48:1075–1084. doi: 10.1249/MSS.0000000000000870.
    1. Kooiman TJM, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil. 2015;7:24. doi: 10.1186/s13102-015-0018-5.
    1. de Vries HJ, Kooiman TJM, van Ittersum MW, van Brussel M, de Groot M. Do activity monitors increase physical activity in adults with overweight or obesity? A systematic review and meta-analysis. Obesity. 2016;24:2076–2091. doi: 10.1002/oby.21619.
    1. The Community Guide . Physical activity: interventions including activity monitors for adults with overweight or obesity. 2017.
    1. Howard VJ, Rhodes D, Mosher A, Hutto B, Stewart MS, Colabianchi N, et al. Obtaining accelerometer data in a national cohort of black and white adults. Med Sci Sports Exerc. 2015;47:1531–1537. doi: 10.1249/MSS.0000000000000549.
    1. Howard VJ, Cushman M, Pulley L, Gomez CR, Go RC, Prineas RJ, et al. The reasons for geographic and racial differences in stroke study: objectives and design. Neuroepidemiology. 2005;25:135–143. doi: 10.1159/000086678.
    1. Myers ND, Prilleltensky I, Prilleltensky O, McMahon A, Dietz S, Rubenstein CL. Efficacy of the fun for wellness online intervention to promote multidimensional well-being: a randomized controlled trial. Prev Sci. 2017;18:984–994. doi: 10.1007/s11121-017-0779-z.
    1. . Well-being and physical activity study. . Accessed 9 Jan 2019.
    1. Myers ND, Prilleltensky I, Hill CR, Feltz DL. Well-being self-efficacy and complier average causal effect modeling: a substantive-methodological synergy. Psychol Sport Exerc. 2017;30:135–144. doi: 10.1016/j.psychsport.2017.02.010.
    1. Myers ND, Dietz S, Prilleltensky I, Prilleltensky O, McMahon A, Rubenstein CL, et al. Efficacy of the fun for wellness online intervention to promote well-being actions: a secondary data analysis. Games Health J. 2018;7:225–239. doi: 10.1089/g4h.2017.0132.
    1. Ainsworth BE, Bassett DR, Strath SJ, Swartz AM, O’Brien WL, Thompson RW, et al. Comparison of three methods for measuring the time spent in physical activity. Med Sci Sports Exerc. 2000;32(Suppl 9):457–464. doi: 10.1097/00005768-200009001-00004.
    1. Craig CL, Marshall AL, Sjöström M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35:1381–1395. doi: 10.1249/01.MSS.0000078924.61453.FB.
    1. Eldridge SM, Lancaster GA, Campbell MJ, Thabane L, Hopewell S, Coleman CL, et al. Defining feasibility and pilot studies in preparation for randomized controlled trials: development of a conceptual framework. PLoS One. 2016;11:e0150205. doi: 10.1371/journal.pone.0150205.
    1. Eldridge SM, Chan CL, Campbell MJ, Bond CM, Hopewell S, Thabane L, et al. CONSORT 2010 statement: extension to randomized pilot and feasibility trials. BMJ. 2016;355:i5239. doi: 10.1136/bmj.i5239.
    1. Thabane L, Hopewell S, Lancaster GA, Bond CM, Coleman CL, Campbell MJ, et al. Methods and processes for development of a CONSORT extension for reporting pilot randomized controlled trials. Pilot Feasibility Stud. 2016;2:25. doi: 10.1186/s40814-016-0065-z.
    1. Billingham SAM, Whitehead AL, Julious SA. An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database. BMC Med Res Methodol. 2013;13:104. doi: 10.1186/1471-2288-13-104.
    1. Tully MA, McBride C, Heron L, Hunter RF. The validation of Fitbit Zip™ physical activity monitor as a measure of free-living physical activity. BMC Res Notes. 2014;7:952. doi: 10.1186/1756-0500-7-952.
    1. Schneider M, Chau L. Validation of the Fitbit Zip for monitoring physical activity among free-living adolescents. BMC Res Notes. 2016;9:448. doi: 10.1186/s13104-016-2253-6.
    1. Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. Int J Behav Nutr Phys Act. 2015;12:42. doi: 10.1186/s12966-015-0201-9.
    1. Watson DL, Tharp RG. Self-directed behavior: self-modification for personal adjustment. 10. Belmont: Cengage Learning; 2014.
    1. Seligman M. Flourish: a visionary new understanding of happiness and well-being. New York: Simon & Schuster; 2011.
    1. Hays PA. Creating well-being: four steps to a happier, healthier life. Washington DC: American Psychological Association; 2014.
    1. Gander F, Proyer RT, Ruch W, Wyss T. Strength-based positive interventions: further evidence for their potential in enhancing well-being and alleviating depression. J Happiness Stud. 2013;14:1241–1259. doi: 10.1007/s10902-012-9380-0.
    1. Dolan P. Happiness by design. New York: Penguin; 2014.
    1. Brown KW, Ryan RM. The benefits of being present: mindfulness and its role in psychological well-being. J Pers Soc Psychol. 2003;84:822–848. doi: 10.1037/0022-3514.84.4.822.
    1. Norcross JC. Changeology: 5 steps to realizing your goals and resolutions. New York: Simon & Schuster; 2012.
    1. Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313:625–626. doi: 10.1001/jama.2014.17841.
    1. Tudor-Locke C, Craig CL, Aoyagi Y, Bell RC, Croteau KA, Bourdeaudhuji ID, et al. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011;8:80. doi: 10.1186/1479-5868-8-80.
    1. Lyons EJ, Lewis ZH, Mayrsohn BG, Rowland JL. Behavior change techniques implemented in electronic lifestyle activity monitors: a systematic content analysis. J Med Internet Res. 2014;16:e192. doi: 10.2196/jmir.3469.
    1. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43:357–364. doi: 10.1249/MSS.0b013e3181ed61a3.
    1. Freedson PS, Melanson E, Sirard J. Calibration of the computer science and applications, Inc. accelerometer. Med Sci Sports Exerc. 1998;30:777–781. doi: 10.1097/00005768-199805000-00021.
    1. Hale LA, Pal J, Becker I. Measuring free-living physical activity in adults with and without neurologic dysfunction with a triaxial accelerometer. Arch Phys Rehabil. 2008;89:1765–1771. doi: 10.1016/j.apmr.2008.02.027.
    1. International Physical Activity Questionnaire Research Committee . Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ) – short and long forms. 2005.
    1. Rubenstein CL, Duff J, Prilleltensky I, Jin Y, Dietz S, Myers ND, et al. Demographic group differences in domain-specific well-being. J Community Psychol. 2016;44:499–515. doi: 10.1002/jcop.21784.
    1. Myers ND, Feltz DL, Wolfe EW. A confirmatory study of rating scale category effectiveness for the coaching efficacy scale. Res Q Exerc Sport. 2008;79:300–311. doi: 10.1080/02701367.2008.10599493.
    1. Jo B. Model misspecification sensitivity analysis in estimating causal effects of interventions with non-compliance. Stat Med. 2002;21:3161–3181. doi: 10.1002/sim.1267.
    1. McAuley E. Self-efficacy and the maintenance of exercise participation in older adults. J Behav Med. 1993;16:103–113. doi: 10.1007/BF00844757.
    1. McAuley E. The role of efficacy cognitions in the prediction of exercise behavior in middle-aged adults. J Behav Med. 1992;15:65–88. doi: 10.1007/BF00848378.
    1. Prilleltensky I, Dietz S, Prilleltensky O, Myers ND, Rubenstein CL, Jin Y, et al. Assessing multidimensional well-being: development and validation of the I COPPE scale. J Community Psychol. 2015;43:199–226. doi: 10.1002/jcop.21674.
    1. Myers ND, Prilleltensky I, Jin Y, Dietz S, Rubenstein CL, Prilleltensky O, et al. Empirical contributions of the past in assessing multidimensional well-being. J Community Psychol. 2014;42:789–798. doi: 10.1002/jcop.21653.
    1. Myers ND, Park SE, Lefevor GT, Dietz S, Prilleltensky I, Prado GJ. Measuring multidimensional well-being with the I COPPE scale in a Hispanic sample. Meas Phys Educ Exerc Sci. 2016;20:230–243. doi: 10.1080/1091367X.2016.1226836.
    1. Rubenstein CL. Assessing actions and feelings related to multidimensional well-being: validation of the I COPPE actions and feelings scales. 2017.
    1. Muthén LK, Muthén BO. Mplus user’s guide. 8. Los Angeles: Muthén & Muthén; 1998.
    1. Jo B, Ginexi EM, Ialongo NS. Handling missing data in randomized experiments with noncompliance. Prev Sci. 2010;11:384–396. doi: 10.1007/s11121-010-0175-4.
    1. Pearson K. Mathematical contributions to the theory of evolution. III. Regression, heredity, and panmixia. Phil Trans R Soc Lond A. 1896;187:253–318. doi: 10.1098/rsta.1896.0007.
    1. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8:135–160. doi: 10.1177/096228029900800204.
    1. Cohen J. Statistical power analysis for the behavioral sciences. 2. Hillsdale: Lawrence Erlbaum Associates; 1998.
    1. Hollis S, Campbell F. What is meant by intention to treat analysis? Survey of published randomised controlled trials. BMJ. 1999;319:670–674. doi: 10.1136/bmj.319.7211.670.
    1. Angrist J, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91:444–472. doi: 10.1080/01621459.1996.10476902.
    1. Bloom HS. Accounting for no-shows in experimental evaluation designs. Eval Rev. 1984;8:225–246. doi: 10.1177/0193841X8400800205.
    1. Frangakis CE, Rubin DB. Principal stratification in causal inference. Biometrics. 2002;58:21–29. doi: 10.1111/j.0006-341X.2002.00021.x.
    1. Rubin DB. Bayesian inference for causal effect: the role of randomization. Ann Stat. 1978;6:34–58. doi: 10.1214/aos/1176344064.
    1. MacKinnon D. Introduction to statistical mediation analysis. New York: Lawrence Erlbaum Associates; 2008.
    1. Singal AG, Higgins PDR, Waljee AK. A primer on effectiveness and efficacy trials. Clin Transl Gastroenterol. 2014;5:e45. doi: 10.1038/ctg.2013.13.
    1. Pew Research Center . Mobile fact sheet. 2018.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir