The Effect of a Consumer-Based Activity Tracker Intervention on Accelerometer-Measured Sedentary Time Among Retirees: A Randomized Controlled REACT Trial

Kristin Suorsa, Tuija Leskinen, Anna Pulakka, Jaana Pentti, Eliisa Löyttyniemi, Ilkka Heinonen, Jussi Vahtera, Sari Stenholm, Kristin Suorsa, Tuija Leskinen, Anna Pulakka, Jaana Pentti, Eliisa Löyttyniemi, Ilkka Heinonen, Jussi Vahtera, Sari Stenholm

Abstract

Background: Effective strategies to reverse the increasing trend of sedentary behavior after retirement are needed. The aim of this study was to examine the effect of 12-month activity tracker-based intervention on daily total and prolonged sedentary time (≥60 minutes) among recent retirees.

Methods: Randomization to intervention and control groups was performed to 231 retirees (mean age 65.2 [SD 1.1] years, 83% women). Intervention participants wore a consumer-based wrist-worn activity tracker (Polar Loop 2, Polar, Kempele, Finland), including daily activity goal, every day and night for 12 months. The activity tracker also gave vibrating reminders to break up uninterrupted inactivity periods after 55 minutes. A wrist-worn triaxial ActiGraph wGT3X-BT accelerometer was used to measure sedentary time at baseline and at 3-, 6-, and 12-month time points.

Results: The use of an activity tracker did not reduce daily total or prolonged sedentary time over 12 months (p values for time * group interaction 0.39 and 0.27, respectively). In the post hoc analysis focusing on short- and medium-term effects on prolonged sedentary time, no differences between the intervention and control groups over 3 months were found, but a tendency for a greater decrease in prolonged sedentary time in the intervention group over 6 months was seen (mean difference in changes between the groups 29 minutes, 95% CI -2 to 61).

Conclusions: The activity tracker with inactivity alerts did not elicit changes in sedentary time over 12 months among recent retirees. Alternative approaches may be needed to achieve long-term changes in sedentary time among retirees. Clinical Trials registration Number: NCT03320746.

Keywords: Prolonged sedentary time; Prompt; Retirement; Self-monitoring; Wearable device.

© The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America.

Figures

Figure 1.
Figure 1.
Changes in (A) daily total sedentary time and (B) daily prolonged sedentary time during the follow-up.

References

    1. Harvey JA, Chastin SF, Skelton DA. How sedentary are older people? A systematic review of the amount of sedentary behavior. J Aging Phys Act. 2015;23:471–487. doi:
    1. Katzmarzyk PT, Powell KE, Jakicic JM, Troiano RP, Piercy K, Tennant B. Sedentary behavior and health: update from the 2018 physical activity guidelines advisory committee. Med Sci Sport Exer. 2019;51(6):1227–1241. doi:
    1. Saunders TJ, McIsaac T, Douillette K, et al. . Sedentary behaviour and health in adults: an overview of systematic reviews. Appl Physiol Nutr Metab. 2020;45(10 (suppl. 2)):S197–S217. doi:
    1. Diaz KM, Goldsmith J, Greenlee H, et al. . Prolonged, uninterrupted sedentary behavior and glycemic biomarkers among US Hispanic/Latino adults: the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Circulation. 2017;136:1362–1373. doi:
    1. Diaz KM, Howard VJ, Hutto B, et al. . Patterns of sedentary behavior in US middle-age and older adults: the REGARDS study. Med Sci Sports Exerc. 2016;48:430–438. doi:
    1. Bellettiere J, LaMonte MJ, Evenson KR, et al. . Sedentary behavior and cardiovascular disease in older women: the objective physical activity and cardiovascular health (OPACH) Study. Circulation. 2019;139:1036–1046. doi:
    1. Bellettiere J, Healy GN, LaMonte MJ, et al. . Sedentary behavior and prevalent diabetes in 6,166 older women: the objective physical activity and cardiovascular health study. J Gerontol A Biol Sci Med Sci. 2019;74:387–395. doi:
    1. Chastin SF, Buck C, Freiberger E, et al. ; DEDIPAC Consortium . Systematic literature review of determinants of sedentary behaviour in older adults: a DEDIPAC study. Int J Behav Nutr Phys Act. 2015;12:127. doi:
    1. Suorsa K, Pulakka A, Leskinen T, et al. . Objectively measured sedentary time before and after transition to retirement: the Finnish Retirement and Aging Study. J Gerontol A Biol Sci Med Sci. 2020;75:1737–1743. doi:
    1. Suorsa K, Pulakka A, Leskinen T, Pentti J, Vahtera J, Stenholm S. Changes in prolonged sedentary behaviour across the transition to retirement. Occup Environ Med. 2020;0:1–4. doi:
    1. Van Dyck D, Cardon G, De Bourdeaudhuij I. Longitudinal changes in physical activity and sedentary time in adults around retirement age: what is the moderating role of retirement status, gender and educational level? BMC Public Health. 2016;16:1125. doi:
    1. Leskinen T, Pulakka A, Heinonen OJ, et al. . Changes in non-occupational sedentary behaviours across the retirement transition: the Finnish Retirement and Aging (FIREA) study. J Epidemiol Community Health. 2018;72:695–701. doi:
    1. Ekelund U, Steene-Johannessen J, Brown WJ, et al. ; Lancet Physical Activity Series 2 Executive Committe; Lancet Sedentary Behaviour Working Group . 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;388:1302–1310. doi:
    1. Baxter S, Johnson M, Payne N, et al. . Promoting and maintaining physical activity in the transition to retirement: a systematic review of interventions for adults around retirement age. Int J Behav Nutr Phys Act. 2016;13:12. doi:
    1. Michie S, Richardson M, Johnston M, 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;46:81–95. doi:
    1. Brickwood KJ, Watson G, O’Brien J, Williams AD. Consumer-based wearable activity trackers increase physical activity participation: systematic review and meta-analysis. JMIR Mhealth Uhealth. 2019;7:e11819. doi:
    1. Li LC, Sayre EC, Xie H, et al. . Efficacy of a community-based technology-enabled physical activity counseling program for people with knee osteoarthritis: proof-of-concept study. J Med Internet Res. 2018;20:e159. doi:
    1. Lynch BM, Nguyen NH, Moore MM, et al. . A randomized controlled trial of a wearable technology-based intervention for increasing moderate to vigorous physical activity and reducing sedentary behavior in breast cancer survivors: the ACTIVATE Trial. Cancer. 2019;125:2846–2855. doi:
    1. Rosenberg DE, Anderson ML, Renz A, et al. . Reducing sitting time in obese older adults: the I-STAND randomized controlled trial. J Aging Phys Act. 2020;28:864–874. doi:
    1. Barwais FA, Cuddihy TF, Tomson LM. Physical activity, sedentary behavior and total wellness changes among sedentary adults: a 4-week randomized controlled trial. Health Qual Life Outcomes. 2013;11:183. doi:
    1. Jauho AM, Pyky R, Ahola R, et al. . Effect of wrist-worn activity monitor feedback on physical activity behavior: a randomized controlled trial in Finnish young men. Prev Med Rep. 2015;2:628–634. doi:
    1. Sloan RA, Kim Y, Sahasranaman A, Müller-Riemenschneider F, Biddle SJH, Finkelstein EA. The influence of a consumer-wearable activity tracker on sedentary time and prolonged sedentary bouts: secondary analysis of a randomized controlled trial. BMC Res Notes. 2018;11:189. doi:
    1. Compernolle S, DeSmet A, Poppe L, et al. . Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2019;16:63. doi:
    1. Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004;31:143–164. doi:
    1. Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health. 2010;31:399–418. doi:
    1. Conroy DE, Maher JP, Elavsky S, Hyde AL, Doerksen SE. Sedentary behavior as a daily process regulated by habits and intentions. Health Psychol. 2013;32:1149–1157. doi:
    1. Rosenstock IM, Strecher VJ, Becker MH. Social learning theory and the Health Belief Model. Health Educ Q. 1988;15:175–183. doi:
    1. Elavsky S, Knapova L, Klocek A, Smahel D. Mobile health interventions for physical activity, sedentary behavior, and sleep in adults aged 50 years and older: a systematic literature review. J Aging Phys Act. 2019;27:565–593. doi:
    1. Stephenson A, McDonough SM, Murphy MH, Nugent CD, Mair JL. Using computer, mobile and wearable technology enhanced interventions to reduce sedentary behaviour: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2017;14:105. doi:
    1. Evans RE, Fawole HO, Sheriff SA, Dall PM, Grant PM, Ryan CG. Point-of-choice prompts to reduce sitting time at work: a randomized trial. Am J Prev Med. 2012;43:293–297. doi:
    1. Ross R, Chaput J, Giangregorio LM, et al. . Canadian 24-hour movement guidelines for adults aged 18–64 years and adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep1. Appl Physiol Nutrit Metab. 2020;45:S57–S102. doi:
    1. Del Pozo-Cruz J, García-Hermoso A, Alfonso-Rosa RM, et al. . Replacing sedentary time: meta-analysis of objective-assessment studies. Am J Prev Med. 2018;55:395–402. doi:
    1. Prince SA, Saunders TJ, Gresty K, Reid RD. A comparison of the effectiveness of physical activity and sedentary behaviour interventions in reducing sedentary time in adults: a systematic review and meta-analysis of controlled trials. Obes Rev. 2014;15:905–919. doi:
    1. Leskinen T, Suorsa K, Tuominen M, et al. . The effect of consumer-based activity tracker intervention on physical activity among recent retirees—an RCT study. Med Sci Sports Exerc. 2021. doi:
    1. Polar Electro. Polar flow.. Updated 2020. Accessed September 21, 2020.
    1. Migueles JH, Rowlands A, Huber F, Sabia S, van Hees V. GGIR: a research community-driven open source R package for generating physical activity and sleep outcomes from multi-day raw accelerometer data. J Meas Phys Behav. 2019;2(3):188–196. doi:
    1. van Hees VT, Sabia S, Jones SE, et al. . Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. 2018;8:12975. doi:
    1. Sabia S, van Hees VT, Shipley MJ, et al. . Association between questionnaire- and accelerometer-assessed physical activity: the role of sociodemographic factors. Am J Epidemiol. 2014;179:781–790. doi:
    1. van Hees VT, Gorzelniak L, Dean León EC, et al. . Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013;8:e61691. doi:
    1. Rowlands A, Mirkes E, Yates T, et al. . Accelerometer-assessed physical activity in epidemiology: are monitors equivalent? Med Sci Sports Exerc. 2018;50(2):257–265. doi:.
    1. van Hees V. Accelerometer data processing with GGIR.. Updated 2018. Accessed September 5, 2020.
    1. Statistics Finland. Classification of occupations.. Updated 2010. Accessed September 5, 2020.
    1. World Health Organisation. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000:841:1–253. . Accessed September 5, 2020.
    1. Hays RD, Sherbourne CD, Mazel RM. The RAND 36-item health survey 1.0. Health Econ. 1993;2:217–227. doi:
    1. Aalto AM, Aro AR, Mähönen M. RAND 36-item health survey 1.0. Finnish version on the health-related quality of life questionnaire.Helsinki, Finland: Stakes; 1995. . Accessed September 5, 2020.
    1. Physical Activity Guidelines Advisory Committee. Physical activity guidelines advisory committee report.Washington, DC: U.S. Department of Health and Human Services; 2008. . Accessed September 5, 2020.
    1. Wijsman CA, Westendorp RG, Verhagen EA, et al. . Effects of a web-based intervention on physical activity and metabolism in older adults: randomized controlled trial. J Med Internet Res. 2013;15:e233. doi:
    1. Shin G, Feng Y, Jarrahi MH, Gafinowitz N. Beyond novelty effect: a mixed-methods exploration into the motivation for long-term activity tracker use. JAMIA Open. 2019;2:62–72. doi:
    1. Brickwood KJ, Williams AD, Watson G, O’Brien J. Older adults’ experiences of using a wearable activity tracker with health professional feedback over a 12-month randomised controlled trial. Digit Health. 2020;6:2055207620921678. doi:
    1. Kononova A, Li L, Kamp K, et al. . The use of wearable activity trackers among older adults: focus group study of tracker perceptions, motivators, and barriers in the maintenance stage of behavior change. JMIR Mhealth Uhealth. 2019;7:e9832. doi:
    1. Gardner B, Flint S, Rebar AL, et al. . Is sitting invisible? Exploring how people mentally represent sitting. Int J Behav Nutr Phys Act. 2019;16:85. doi:
    1. Stephenson A, Garcia-Constantino M, McDonough SM, Murphy MH, Nugent CD, Mair JL. Iterative four-phase development of a theory-based digital behaviour change intervention to reduce occupational sedentary behaviour. Digit Health. 2020;6:2055207620913410. doi:
    1. Lyons EJ, Swartz MC, Lewis ZH, Martinez E, Jennings K. Feasibility and acceptability of a wearable technology physical activity intervention with telephone counseling for mid-aged and older adults: a randomized controlled pilot trial. JMIR Mhealth Uhealth. 2017;5:e28. doi:
    1. Ashe MC, Winters M, Hoppmann CA, et al. . “Not just another walking program”: everyday activity supports you (EASY) model-a randomized pilot study for a parallel randomized controlled trial. Pilot Feasibility Stud. 2015;1:4. doi:
    1. Lynch B, Nguyen N, Moore M, et al. . Maintenance of physical activity and sedentary behavior change, and physical activity and sedentary behavior change after an abridged intervention: secondary outcomes from the ACTIVATE trial. Cancer. 2019;125(16):2856–2860. doi:
    1. Gardner B, Lally P, Wardle J. Making health habitual: the psychology of ‘habit-formation’ and general practice. Brit J Gen Pract. 2012;62(605):664–666. doi:.
    1. Henriksen A, Grimsgaard S, Horsch A, Hartvigsen G, Hopstock L. Validity of the polar M430 activity monitor in free-living conditions: validation study. JMIR Form Res. 2019;3:e14438. doi:
    1. Suorsa K, Pulakka A, Leskinen T, et al. . Comparison of sedentary time between thigh-worn and wrist-worn accelerometers. J Meas Phys Behav. 2020;3:234–243. doi:
    1. Cabanas-Sánchez V, Esteban-Cornejo I, Migueles JH, et al. . Twenty four-hour activity cycle in older adults using wrist-worn accelerometers: the seniors-ENRICA-2 study. Scand J Med Sci Sports. 2020;30:700–708. doi:
    1. Statistics Finland. The women and men in Finland 2016.. Updated 2016. Accessed September 5, 2020.

Source: PubMed

3
Abonnere