The Smart City Active Mobile Phone Intervention (SCAMPI) study to promote physical activity through active transportation in healthy adults: a study protocol for a randomised controlled trial

Anna Ek, Christina Alexandrou, Christine Delisle Nyström, Artur Direito, Ulf Eriksson, Ulf Hammar, Pontus Henriksson, Ralph Maddison, Ylva Trolle Lagerros, Marie Löf, Anna Ek, Christina Alexandrou, Christine Delisle Nyström, Artur Direito, Ulf Eriksson, Ulf Hammar, Pontus Henriksson, Ralph Maddison, Ylva Trolle Lagerros, Marie Löf

Abstract

Background: The global pandemic of physical inactivity represents a considerable public health challenge. Active transportation (i.e., walking or cycling for transport) can contribute to greater total physical activity levels. Mobile phone-based programs can promote behaviour change, but no study has evaluated whether such a program can promote active transportation in adults. This study protocol presents the design and methodology of The Smart City Active Mobile Phone Intervention (SCAMPI), a randomised controlled trial to promote active transportation via a smartphone application (app) with the aim to increase physical activity.

Methods/design: A two-arm parallel randomised controlled trial will be conducted in Stockholm County, Sweden. Two hundred fifty adults aged 20-65 years will be randomised to either monitoring of active transport via the TRavelVU app (control), or to a 3-month evidence-based behaviour change program to promote active transport and monitoring of active travel via the TRavelVU Plus app (intervention). The primary outcome is moderate-to-vigorous intensity physical activity (MVPA in minutes/day) (ActiGraph wGT3x-BT) measured post intervention. Secondary outcomes include: time spent in active transportation measured via the TRavelVU app, perceptions about active transportation (the Transport and Physical Activity Questionnaire (TPAQ)) and health related quality of life (RAND-36). Assessments are conducted at baseline, after the completed intervention (after 3 months) and 6 months post randomisation.

Discussion: SCAMPI will determine the effectiveness of a smartphone app to promote active transportation and physical activity in an adult population. If effective, the app has potential to be a low-cost intervention that can be delivered at scale.

Trial registration: ClinicalTrials.gov NCT03086837 ; 22 March, 2017.

Keywords: Accelerometer; Active transport; Application; Behaviour change; Physical activity; Smartphone; Walkability; mHealth.

Conflict of interest statement

Ethics approval and consent to participate

SCAMPI was approved by the Regional Ethical Board in Stockholm, Sweden January 11th 2017 (dnr: 2016/2403–31) with an amendment on June 30th 2017 (dnr: 2017/1373–32). All participants provide informed consent on the study webpage before entering the study.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Flow-chart of the SCAMPI trial design
Fig. 2
Fig. 2
Screenshot of daily summary of travel behaviours in the TRavelVU app
Fig. 3
Fig. 3
Screenshots of daily and weekly summaries of travel behaviours and achievements related to set goals

References

    1. Morris JN. Exercise in the prevention of coronary heart disease: today's best buy in public health Med Sci Sports Exerc 1994;26(7):807-814.
    1. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302–1310. doi: 10.1016/S0140-6736(16)30370-1.
    1. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–229. doi: 10.1016/S0140-6736(12)61031-9.
    1. Nocon M, Hiemann T, Muller-Riemenschneider F, Thalau F, Roll S, Willich SN. Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil. 2008;15(3):239–246. doi: 10.1097/HJR.0b013e3282f55e09.
    1. Wen CP, Wai JP, Tsai MK, Yang YC, Cheng TY, Lee MC, et al. Minimum amount of physical activity for reduced mortality and extended life expectancy: a prospective cohort study. Lancet. 2011;378(9798):1244–1253. doi: 10.1016/S0140-6736(11)60749-6.
    1. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–257. doi: 10.1016/S0140-6736(12)60646-1.
    1. Ekblom-Bak E, Olsson G, Ekblom Ö, Ekblom B, Bergström G, Börjesson M. The daily movement pattern and fulfilment of physical activity recommendations in Swedish middle-aged adults: the SCAPIS pilot study. PLoS One. 2015;10(5):e0126336. doi: 10.1371/journal.pone.0126336.
    1. Gerike R, de Nazelle A, Nieuwenhuijsen M, Panis LI, Anaya E, Avila-Palencia I, et al. Physical activity through sustainable transport approaches (PASTA): a study protocol for a multicentre project. BMJ Open. 2016;6(1):e009924. doi: 10.1136/bmjopen-2015-009924.
    1. Costa S, Ogilvie D, Dalton A, Westgate K, Brage S, Panter J. Quantifying the physical activity energy expenditure of commuters using a combination of global positioning system and combined heart rate and movement sensors. Prev Med. 2015;81:339–344. doi: 10.1016/j.ypmed.2015.09.022.
    1. Sahlqvist S, Song Y, Ogilvie D. Is active travel associated with greater physical activity? The contribution of commuting and non-commuting active travel to total physical activity in adults. Prev Med. 2012;55(3):206–211. doi: 10.1016/j.ypmed.2012.06.028.
    1. Reynolds R, McKenzie S, Allender S, Brown K, Foulkes C. Systematic review of incidental physical activity community interventions. Prev Med. 2014;67:46–64. doi: 10.1016/j.ypmed.2014.06.023.
    1. Mueller N, Rojas-Rueda D, Cole-Hunter T, de Nazelle A, Dons E, Gerike R, et al. Health impact assessment of active transportation: a systematic review. Prev Med. 2015;76:103–114. doi: 10.1016/j.ypmed.2015.04.010.
    1. Flint E, Cummins S, Sacker A. Associations between active commuting, body fat, and body mass index: population based, cross sectional study in the United Kingdom. BMJ. 2014;349:g4887. doi: 10.1136/bmj.g4887.
    1. Laverty AA, Mindell JS, Webb EA, Millett C. Active travel to work and cardiovascular risk factors in the United Kingdom. Am J Prev Med. 2013;45(3):282–288. doi: 10.1016/j.amepre.2013.04.012.
    1. Tate EB, Spruijt-Metz D, O'Reilly G, Jordan-Marsh M, Gotsis M, Pentz MA, et al. mHealth approaches to child obesity prevention: successes, unique challenges, and next directions. Transl Behav Med. 2013;3(4):406–415. doi: 10.1007/s13142-013-0222-3.
    1. Flores Mateo G, Granado-Font E, Ferre-Grau C, Montana-Carreras X. Mobile phone apps to promote weight loss and increase physical activity: a systematic review and meta-analysis. J Med Internet Res. 2015;17(11):e253. doi: 10.2196/jmir.4836.
    1. Pfaeffli Dale L, Dobson R, Whittaker R, Maddison R. The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: a systematic review. Eur J Prev Cardiol. 2016;23(8):801–817. doi: 10.1177/2047487315613462.
    1. Schippers M, Adam PC, Smolenski DJ, Wong HT, de Wit JB. A meta-analysis of overall effects of weight loss interventions delivered via mobile phones and effect size differences according to delivery mode, personal contact, and intervention intensity and duration. Obes Rev. 2017;18(4):450–459. doi: 10.1111/obr.12492.
    1. Bopp M, Sims D, Matthews SA, Rovniak LS, Poole E, Colgan J. There's an app for that: development of a smartphone app to promote active travel to a college campus. Journal of Transport & Health. 2016;3(3):305–314. doi: 10.1016/j.jth.2016.02.007.
    1. Bopp M, Sims D, Matthews SA, Rovniak LS, Poole E, Colgan J. Development, implementation, and evaluation of active lions: a campaign to promote active travel to a university campus. Am J Health Promot. 2018;32(3):536–545. doi: 10.1177/0890117117694287.
    1. Chan AW, Tetzlaff JM, Gotzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586. doi: 10.1136/bmj.e7586.
    1. Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gotzsche PC, Krleza-Jeric K, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013;158(3):200–207. doi: 10.7326/0003-4819-158-3-201302050-00583.
    1. Eysenbach G. CONSORT-EHEALTH: implementation of a checklist for authors and editors to improve reporting of web-based and mobile randomized controlled trials. Stud Health Technol Inform. 2013;192:657–661.
    1. Lee CC. Fuzzy-logic in control-systems - fuzzy-logic controller. 1. IEEE Transactions on SMC. 1990;20(2):404–418.
    1. Lee CC. Fuzzy-logic in control-systems - fuzzy-logic controller. 2. IEEE Transactions on SMC. 1990;20(2):419–435.
    1. Bandura A. Human agency in social cognitive theory. Am Psychol. 1989;44(9):1175–1184. doi: 10.1037/0003-066X.44.9.1175.
    1. Sallis JF, Cervero RB, Ascher W, Henderson KA, Kraft MK, Kerr J. An ecological approach to creating active living communities. Annu Rev Public Health. 2006;27:297–322. doi: 10.1146/annurev.publhealth.27.021405.102100.
    1. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013;46(1):81–95. doi: 10.1007/s12160-013-9486-6.
    1. Abroms LC, Whittaker R, Free C, Mendel Van Alstyne J, Schindler-Ruwisch JM. Developing and pretesting a text messaging program for health behavior change: recommended steps. JMIR mHealth and uHealth. 2015;3(4):e107. doi: 10.2196/mhealth.4917.
    1. Muench F, van Stolk-Cooke K, Morgenstern J, Kuerbis AN, Markle K. Understanding messaging preferences to inform development of mobile goal-directed behavioral interventions. J Med Internet Res. 2014;16(2):e14. doi: 10.2196/jmir.2945.
    1. Sundquist K, Eriksson U, Kawakami N, Skog L, Ohlsson H, Arvidsson D. Neighborhood walkability, physical activity, and walking behavior: the Swedish neighborhood and physical activity (SNAP) study. Soc Sci Med. 2011;72(8):1266–1273. doi: 10.1016/j.socscimed.2011.03.004.
    1. Nystrom CD, Sandin S, Henriksson P, Henriksson H, Trolle-Lagerros Y, Larsson C, et al. Mobile-based intervention intended to stop obesity in preschool-aged children: the MINISTOP randomized controlled trial. Am J Clin Nutr. 2017;105(6):1327–1335.
    1. Lagerros YT, Sandin S, Bexelius C, Litton JE, Lof M. Estimating physical activity using a cell phone questionnaire sent by means of short message service (SMS): a randomized population-based study. Eur J Epidemiol. 2012;27(7):561–566. doi: 10.1007/s10654-012-9708-4.
    1. Bexelius C, Lof M, Sandin S, Trolle Lagerros Y, Forsum E, Litton JE. Measures of physical activity using cell phones: validation using criterion methods. J Med Internet Res. 2010;12(1):e2. doi: 10.2196/jmir.1298.
    1. Leppanen MH, Nystrom CD, Henriksson P, Pomeroy J, Ruiz JR, Ortega FB, et al. Physical activity intensity, sedentary behavior, body composition and physical fitness in 4-year-old children: results from the ministop trial. Int J Obes. 2016;40(7):1126–1133. doi: 10.1038/ijo.2016.54.
    1. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nystrom C, Mora-Gonzalez J, Lof M, et al. Accelerometer data collection and processing criteria to assess physical activity and other outcomes: a systematic review and practical considerations. Sports Med. 2017;47(9):1821–1845. doi: 10.1007/s40279-017-0716-0.
    1. Adams EJ, Goad M, Sahlqvist S, Bull FC, Cooper AR, Ogilvie D, et al. Reliability and validity of the transport and physical activity questionnaire (TPAQ) for assessing physical activity behaviour. PLoS One. 2014;9(9):e107039. doi: 10.1371/journal.pone.0107039.
    1. Sullivan M, Karlsson J. The Swedish SF-36 health survey III. Evaluation of criterion-based validity: results from normative population. J Clin Epidemiol. 1998;51(11):1105–1113. doi: 10.1016/S0895-4356(98)00102-4.
    1. Hays RD, Sherbourne CD, Mazel RM. The RAND 36-item health survey 1.0. Health Econ. 1993;2(3):217–227. doi: 10.1002/hec.4730020305.
    1. Hays RD, Morales LS. The RAND-36 measure of health-related quality of life. Ann Med. 2001;33(5):350–357. doi: 10.3109/07853890109002089.
    1. Saelens BE, Sallis JF, Black JB, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation. Am J Public Health. 2003;93(9):1552–1558. doi: 10.2105/AJPH.93.9.1552.
    1. Arvidsson D, Kawakami N, Ohlsson H, Sundquist K. Physical activity and concordance between objective and perceived walkability. Med Sci Sports Exerc. 2012;44(2):280–287. doi: 10.1249/MSS.0b013e31822a9289.
    1. Cerin E, Conway TL, Cain KL, Kerr J, De Bourdeaudhuij I, Owen N, et al. Sharing good NEWS across the world: developing comparable scores across 12 countries for the neighborhood environment walkability scale (NEWS) BMC Public Health. 2013;13:309. doi: 10.1186/1471-2458-13-309.
    1. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–399. doi: 10.1002/sim.4067.
    1. Ratitch B, O'Kelly M, Tosiello R. Missing data in clinical trials: from clinical assumptions to statistical analysis using pattern mixture models. Pharm Stat. 2013;12(6):337–347. doi: 10.1002/pst.1549.
    1. Djurhuus S, Hansen HS, Aadahl M, Glumer C. The association between access to public transportation and self-reported active commuting. Int J Environ Res Public Health. 2014;11(12):12632–12651. doi: 10.3390/ijerph111212632.
    1. Kinnafick FE, Thogersen-Ntoumani C, Duda J. The effect of need supportive text messages on motivation and physical activity behaviour. J Behav Med. 2016;39(4):574–586. doi: 10.1007/s10865-016-9722-1.
    1. Fjeldsoe BS, Miller YD, O'Brien JL, Marshall AL. Iterative development of MobileMums: a physical activity intervention for women with young children. Int J Behav Nutr Phys Act. 2012;9:151. doi: 10.1186/1479-5868-9-151.
    1. Arnott B, Rehackova L, Errington L, Sniehotta FF, Roberts J, Araujo-Soares V. Efficacy of behavioural interventions for transport behaviour change: systematic review, meta-analysis and intervention coding. Int J Behav Nutr Phys Act. 2014;11:133. doi: 10.1186/s12966-014-0133-9.
    1. Bardus M, van Beurden SB, Smith JR, Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int J Behav Nutr Phys Act. 2016;13:35. doi: 10.1186/s12966-016-0359-9.
    1. Bird EL, Baker G, Mutrie N, Ogilvie D, Sahlqvist S, Powell J. Behavior change techniques used to promote walking and cycling: a systematic review. Health Psychol. 2013;32(8):829–838. doi: 10.1037/a0032078.
    1. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28(6):690–701. doi: 10.1037/a0016136.
    1. Samdal GB, Eide GE, Barth T, Williams G, Meland E. Effective behaviour change techniques for physical activity and healthy eating in overweight and obese adults; systematic review and meta-regression analyses. Int J Behav Nutr Phys Act. 2017;14(1):42. doi: 10.1186/s12966-017-0494-y.
    1. Delisle C, Sandin S, Forsum E, Henriksson H, Trolle-Lagerros Y, Larsson C, et al. A web- and mobile phone-based intervention to prevent obesity in 4-year-olds (MINISTOP): a population-based randomized controlled trial. BMC Public Health. 2015;15:95. doi: 10.1186/s12889-015-1444-8.

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