The Physical Activity Wearables in the Police Force (PAW-Force) study: acceptability and impact

Sarah Ann Buckingham, Karyn Morrissey, Andrew James Williams, Lisa Price, John Harrison, Sarah Ann Buckingham, Karyn Morrissey, Andrew James Williams, Lisa Price, John Harrison

Abstract

Background: Policing is a highly stressful and increasingly sedentary occupation. The study aim was to assess the acceptability and impact of a mobile health (mHealth) technology intervention (Fitbit® activity monitor and 'Bupa Boost' smartphone app) to promote physical activity (PA) and reduce sedentary time in the police force.

Methods: Single-group, pre-post, mixed methods pilot study. Police officers and staff (n = 180) were recruited from two police forces in South West England. Participants used the technology for 12 weeks (an 'individual' then 'social' phase) followed by 5 months of optional use. Data sources included Fitbit®-recorded objective step count, questionnaire surveys and semi-structured interviews (n = 32). Outcome assessment points were baseline (week 0), mid-intervention (week 6), post-intervention (week 12) and follow-up (month 8). Paired t-tests were used to investigate changes in quantitative outcomes. Qualitative analysis involved framework and thematic analysis.

Results: Changes in mean daily step count were non-significant (p > 0.05), but self-reported PA increased in the short term (e.g. + 465.4 MET-minutes/week total PA baseline to week 12, p = 0.011) and longer term (e.g. + 420.5 MET-minutes/week moderate-to-vigorous PA baseline to month 8, p = 0.024). The greatest impact on behaviour was perceived by less active officers and staff. There were no significant changes in sedentary time; the qualitative findings highlighted the importance of context and external influences on behaviour. There were no statistically significant changes (all p-values > 0.05) in any secondary outcomes (physical and mental health-related quality of life, perceived stress and perceived productivity), with the exception of an improvement in mental health-related quality of life (SF-12 mental component score + 1.75 points, p = 0.020) from baseline to month 8. Engagement with and perceived acceptability of the intervention was high overall, but a small number of participants reported negative physical (skin irritation) and psychological (feelings of guilt and anxiety) consequences of technology use. Individual app features (such as goal-setting and self-monitoring) were generally preferred to social components (social comparison, competitions and support).

Conclusions: mHealth technology is an acceptable and potentially impactful intervention for increasing PA in the police force. The intervention was less useful for reducing sedentary time and the impact on secondary outcomes is unclear.

Trial registration: NCT03169179 (registered 30th May 2017).

Keywords: Behaviour change; Mobile health; Physical activity; Police force; Sedentary behaviour.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Overview of the study process. Note: All images are the authors’ own
Fig. 2
Fig. 2
Participant enrolment and retention

References

    1. Ramey SL, Perkhounkova Y, Moon M, Tseng HC, Wilson A, Hein M, et al. Physical activity in police beyond self-report. J Occup Environ Med. 2014;56(3):338–343.
    1. Caneppele S, Aebi MF. Crime drop or police recording flop? On the relationship between the decrease of offline crime and the increase of online and hybrid crimes. Policing. 2017;13(1):66–79.
    1. Ramey SL, Perkhounkova Y, Hein M, Chung S, Franke WD, Anderson AA. Building resilience in an urban police department. J Occup Environ Med. 2016;58(8):796–804.
    1. Ruiz J, Morrow E. Retiring the old centurion: life after a career in policing—an exploratory study. Int J Publ Admin. 2005;28(13–14):1151–1186.
    1. Soroka A, Sawicki B. Physical activity levels as a quantifier in police officers and cadets. Int J Occup Med Environ Health. 2014;27(3):498–505.
    1. Can SH, Hendy HM. Behavioral variables associated with obesity in police officers. Ind Health. 2014;52(3):240–247.
    1. Leischik R, Foshag P, Strauß M, Littwitz H, Garg P, Dworrak B, et al. Aerobic capacity, physical activity and metabolic risk factors in firefighters compared with police officers and sedentary clerks. PLoS One. 2015;10(7):e0133113.
    1. Anderson AA, Yoo H, Franke WD. Associations of physical activity and obesity with the risk of developing the metabolic syndrome in law enforcement officers. J Occup Environ Med. 2016;58(9):946–951.
    1. Franke WD, Ramey SL, Shelley MC. Relationship between cardiovascular disease morbidity, risk factors, and stress in a law enforcement cohort. J Occup Environ Med. 2002;44(12):1182–1189.
    1. Hartley TA, Burchfiel CM, Fekedulegn D, Andrew ME, Violanti JM. Health disparities in police officers: comparisons to the U.S. general population. Int J Emerg Ment Health. 2011;13(4):211–220.
    1. Zimmerman FH. Cardiovascular disease and risk factors in law enforcement personnel: a comprehensive review. Cardiol Rev. 2012;20(4):159–166.
    1. Violanti JM, Fekedulegn D, Hartley TA, Andrew ME, Gu JK, Burchfiel CM. Life expectancy in police officers: a comparison with the U.S. general population. Int J Emerg Ment Health. 2013;15(4):217–228.
    1. Guo X, Coberley C, Pope JE, Wells A. The value of a well-being improvement strategy: longitudinal success across subjective and objective measures observed in a firm adopting a consumer-driven health plan. J Occup Environ Med. 2015;57(10):1055–1062.
    1. Baicker K, Cutler D, Song Z. Workplace wellness programs can generate savings. Health Aff (Millwood) 2010;29(2):304–311.
    1. Lagestad P, Van Den Tillaar R. Longitudinal changes in the physical activity patterns of police officers. Int J Police Sci Manag. 2014;16(1):76–86.
    1. Sullivan AN, Lachman ME. Behavior change with fitness Technology in Sedentary Adults: a review of the evidence for increasing physical activity. Front Public Health. 2016;4:289.
    1. Teyhen DS, Aldag M, Edinborough E, Ghannadian JD, Haught A, Kinn J, et al. Leveraging technology: creating and sustaining changes for health. Telemed J E Health. 2014;20(9):835–849.
    1. Fanning J, Mullen SP, McAuley E. Increasing physical activity with mobile devices: a meta-analysis. J Med Internet Res. 2012;14(6):e161.
    1. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and influencing physical activity with smartphone technology: a systematic review. Sports med (Auckland, NZ) 2014;44(5):671–686.
    1. Afshin A, Babalola D, McLean M, Yu Z, Ma W, Chen CY, et al. Information technology and lifestyle: a systematic evaluation of internet and Mobile interventions for improving diet, physical activity, obesity, tobacco, and alcohol use. J Am Heart Assoc. 2016;5(9):e003058.
    1. Buckingham SA, Williams AJ, Morrissey K, Price L, Harrison J. Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: a systematic review. Digit Health. 2019;5:2055207619839883.
    1. Ganesan AN, Louise J, Horsfall M, Bilsborough SA, Hendriks J, McGavigan AD, et al. International Mobile-health intervention on physical activity, sitting, and weight: the Stepathlon cardiovascular health study. J Am Coll Cardiol. 2016;67(21):2453–2463.
    1. Neil-Sztramko SE, Gotay CC, Sabiston CM, Demers PA, Campbell KC. Feasibility of a telephone and web-based physical activity intervention for women shift workers. Transl Behav Med. 2017;7(2):268–276.
    1. Patel MS, Volpp KG, Rosin R, Bellamy SL, Small DS, Heuer J, et al. A randomized, controlled trial of lottery-based financial incentives to increase physical activity among overweight and obese adults. Am J Health Promot. 2018;32(7):1568–1575.
    1. Simons D, De Bourdeaudhuij I, Clarys P, De Cocker K, Vandelanotte C, Deforche B. Effect and process evaluation of a smartphone app to promote an active lifestyle in lower educated working young adults: cluster randomized controlled trial. JMIR mHealth uHealth. 2018;6(8):e10003.
    1. Olsen HM, Brown WJ, Kolbe-Alexander T, Burton NW. A brief self-directed intervention to reduce office Employees' sedentary behavior in a flexible workplace. J Occup Environ Med. 2018;60(10):954–959.
    1. Poirier J, Bennett WL, Jerome GJ, Shah NG, Lazo M, Yeh HC, et al. Effectiveness of an Activity Tracker- and Internet-Based Adaptive Walking Program for Adults: A Randomized Controlled Trial. J Med Internet Res. 2016;18:2.
    1. Reed JL, Cole CA, Ziss MC, Tulloch HE, Brunet J, Sherrard H, et al. The impact of web-based feedback on physical activity and cardiovascular health of nurses working in a cardiovascular setting: a randomized trial. Front Physiol. 2018;9:142.
    1. Skogstad M, Lunde LK, Skare O, Mamen A, Alfonso JH, Ovstebo R, et al. Physical activity initiated by employer and its health effects; an eight week follow-up study. BMC Public Health. 2016;16:377.
    1. van Dantzig S, Geleijnse G, van Halteren AT. Toward a persuasive mobile application to reduce sedentary behavior. Pers Ubiquitous Comput. 2013;17(6):1237–1246.
    1. McCallum C, Rooksby J, Gray CM. Evaluating the impact of physical activity apps and Wearables: interdisciplinary review. JMIR mHealth uHealth. 2018;6(3):e58.
    1. Buijink AW, Visser BJ, Marshall L. Medical apps for smartphones: lack of evidence undermines quality and safety. Evid Based Med. 2013;18(3):90–92.
    1. Knight E, Stuckey MI, Prapavessis H, Petrella RJ. Public health guidelines for physical activity: is there an app for that? A review of android and apple app stores. JMIR mHealth uHealth. 2015;3(2):e43.
    1. King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL, Buman MP, et al. Effects of three motivationally targeted Mobile device applications on initial physical activity and sedentary behavior change in midlife and older adults: a randomized trial. PLoS One. 2016;11(6):e0156370.
    1. Harries T, Eslambolchilar P, Rettie R, Stride C, Walton S, van Woerden HC. Effectiveness of a smartphone app in increasing physical activity amongst male adults: a randomised controlled trial. BMC Public Health. 2016;16:925.
    1. Michie S, Ashford S, Sniehotta FF, Dombrowski SU, Bishop A, French DP. A refined taxonomy of behaviour change techniques to help people change their physical activity and healthy eating behaviours: the CALO-RE taxonomy. Psychol Health. 2011;26(11):1479–1498.
    1. Evenson KR, Goto MM, Furberg RD. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12:159.
    1. Feehan LM, Geldman J, Sayre EC, Park C, Ezzat AM, Yoo JY, et al. Accuracy of Fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR mHealth uHealth. 2018;6(8):e10527.
    1. Tedesco S, Sica M, Ancillao A, Timmons S, Barton J, O'Flynn B. Validity evaluation of the Fitbit Charge2 and the Garmin vivosmart HR+ in free-living environments in an older adult cohort. JMIR mHealth uHealth. 2019;7(6):e13084.
    1. Wang JB, Cataldo JK, Ayala GX, Natarajan L, Cadmus-Bertram LA, White MM, et al. Mobile and wearable device features that matter in promoting physical activity. J Mob Technol Med. 2016;5(2):2–11.
    1. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–1395.
    1. Ware J, Jr, Kosinski M, Keller SD. A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220–233.
    1. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385–396.
    1. Kessler RC, Barber C, Beck A, Berglund P, Cleary PD, McKenas D, et al. The World Health Organization health and work performance questionnaire (HPQ) J Occup Environ Med. 2003;45(2):156–174.
    1. Patton MQ. Qualitative research & evaluation methods. 3. Thousand Oaks, CA: Sage Publications; 2001.
    1. StataCorp . Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC; 2017.
    1. Ritchie J, Spencer L. Qualitative data analysis for applied policy research. In: Bryman A, Burgess RG, editors. Analysing qualitative data. London: Routledge; 1994.
    1. QSR International. NVivo qualitative data analysis software: version 11: QSR International Pty Ltd.; 2015.
    1. Schrager JD, Shayne P, Wolf S, Das S, Patzer RE, White M, et al. Assessing the influence of a Fitbit physical activity monitor on the exercise practices of emergency medicine residents: a pilot study. JMIR mHealth uHealth. 2017;5(1):e2.
    1. Xian Y, Xu H, Xu H, Liang L, Hernandez AF, Wang TY, et al. An Initial Evaluation of the Impact of Pokemon GO on Physical Activity. J Am Heart Assoc. 2017;6:5.
    1. Blair SN, Connelly JC. How much physical activity should we do? The case for moderate amounts and intensities of physical activity. Res Q Exercise Sport. 1996;67(2):193–205.
    1. Cole JA, Tully MA, Cupples ME. "they should stay at their desk until the work's done": a qualitative study examining perceptions of sedentary behaviour in a desk-based occupational setting. BMC Res Notes. 2015;8:683.
    1. Hamari J, Koivisto J. "working out for likes": an empirical study on social influence in exercise gamification. Comput Hum Behav. 2015;50:333–347.
    1. Huang Y, Benford S, Blake H. Digital interventions to reduce sedentary behaviors of office workers: scoping review. J Med Internet Res. 2019;21(2):e11079.
    1. Gilson ND, Pavey TG, Wright OR, Vandelanotte C, Duncan MJ, Gomersall S, et al. The impact of an m-health financial incentives program on the physical activity and diet of Australian truck drivers. BMC Public Health. 2017;17(1):467.
    1. Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, et al. Understanding and promoting effective engagement with digital behavior change interventions. Am J Prev Med. 2016;51(5):833–842.
    1. Kanstrup AM, Bertelsen P, Jensen MB. Contradictions in digital health engagement: an activity tracker's ambiguous influence on vulnerable young adults' engagement in own health. Digit Health. 2018;4:2055207618775192.
    1. Gowin M, Cheney M, Gwin S, Wann TF. Health and fitness app use in college students: a qualitative study. Am J Health Educ. 2015;46(4):223–230.
    1. Howarth A, Quesada J, Silva J, Judycki S, Mills PR. The impact of digital health interventions on health-related outcomes in the workplace: a systematic review. Digit Health. 2018;4:2055207618770861.
    1. O'Connell SE, Griffiths PL, Clemes SA. Seasonal variation in physical activity, sedentary behaviour and sleep in a sample of UK adults. Ann Hum Biol. 2014;41(1):1–8.
    1. Craig P, Cooper C, Gunnell D, Haw S, Lawson K, Macintyre S, et al. Using natural experiments to evaluate population health interventions: new Medical Research Council guidance. J Epidemiol Community Health. 2012;66(12):1182–1186.
    1. Moore G, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, et al. Process evaluation of complex interventions: UK Medical Research Council (MRC) guidance. London: MRC Population Health Science Research Network; 2014.

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