Exploring Breaks in Sedentary Behavior of Older Adults Immediately After Receiving Personalized Haptic Feedback: Intervention Study

Sofie Compernolle, Delfien Van Dyck, Greet Cardon, Ruben Brondeel, Sofie Compernolle, Delfien Van Dyck, Greet Cardon, Ruben Brondeel

Abstract

Background: "Push" components of mobile health interventions may be promising to create conscious awareness of habitual sedentary behavior; however, the effect of these components on the near-time, proximal outcome, being breaks in sedentary behavior immediately after receiving a push notification, is still unknown, especially in older adults.

Objective: The aims of this study are to examine if older adults break their sedentary behavior immediately after receiving personalized haptic feedback on prolonged sedentary behavior and if the percentage of breaks differs depending on the time of the day when the feedback is provided.

Methods: A total of 26 Flemish older adults (mean age 64.4 years, SD 3.8) wore a triaxial accelerometer (Activator, PAL Technologies Ltd) for 3 weeks. The accelerometer generated personalized haptic feedback by means of vibrations each time a participant sat for 30 uninterrupted minutes. Accelerometer data on sedentary behavior were used to estimate the proximal outcome, which was sedentary behavior breaks immediately (within 1, 3, and 5 minutes) after receiving personalized haptic feedback. Generalized estimating equations were used to investigate whether or not participants broke up their sedentary behavior immediately after receiving haptic feedback. A time-related variable was added to the model to investigate if the sedentary behavior breaks differed depending on the time of day.

Results: A total of 2628 vibrations were provided to the participants during the 3-week intervention period. Of these 2628 vibrations, 379 (14.4%), 570 (21.7%), and 798 (30.4%) resulted in a sedentary behavior break within 1, 3 and 5 minutes, respectively. Although the 1-minute interval did not reveal significant differences in the percentage of breaks depending on the time at which the haptic feedback was provided, the 3- and 5-minute intervals did show significant differences in the percentage of breaks depending on the time at which the haptic feedback was provided. Concretely, the percentage of sedentary behavior breaks was significantly higher if personalized haptic feedback was provided between noon and 3 PM compared to if the feedback was provided between 6 and 9 AM (odds ratio 1.58, 95% CI 1.01-2.47, within 3 minutes; odds ratio 1.78, 95% CI 1.11-2.84, within 5 minutes).

Conclusions: The majority of haptic vibrations, especially those in the morning, did not result in a break in the sedentary behavior of older adults. As such, simply bringing habitual sedentary behavior into conscious awareness seems to be insufficient to target sedentary behavior. More research is needed to optimize push components in interventions aimed at the reduction of the sedentary behavior of older adults.

Trial registration: ClinicalTrials.gov NCT04003324; https://ichgcp.net/clinical-trials-registry/NCT04003324.

Keywords: mHealth intervention; older adults; sedentary behavior; self-monitoring; sitting behavior; tactile feedback.

Conflict of interest statement

Conflicts of Interest: None declared.

©Sofie Compernolle, Delfien Van Dyck, Greet Cardon, Ruben Brondeel. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 10.05.2021.

Figures

Figure 1
Figure 1
Number of haptic vibrations at different time points.
Figure 2
Figure 2
Percentages of vibrations followed by a break in sedentary behavior by time of day.

References

    1. Leask CF, Harvey JA, Skelton DA, Chastin SF. Exploring the context of sedentary behaviour in older adults (what, where, why, when and with whom) Eur Rev Aging Phys Act. 2015 Oct 7;12(1):4–8. doi: 10.1186/s11556-015-0146-7.
    1. Matthews CE, Chen KY, Freedson PS, Buchowski MS, Beech BM, Pate RR, Troiano RP. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol. 2008 Apr 01;167(7):875–81. doi: 10.1093/aje/kwm390.
    1. de Rezende Leandro Fornias Machado, Rodrigues Lopes M, Rey-López Juan Pablo, Matsudo VKR, Luiz ODC. Sedentary behavior and health outcomes: an overview of systematic reviews. PLoS One. 2014 Aug 21;9(8):e105620. doi: 10.1371/journal.pone.0105620.
    1. Chastin S, Gardiner P, Ashe M, Harvey J, Leask C, Balogun S, Helbostad J, Skelton D. Interventions for reducing sedentary behaviour in community-dwelling older adults. Cochrane Database Syst Rev. 2017:9. doi: 10.1002/14651858.cd012784.
    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 Mar 06;5(3):e28. doi: 10.2196/mhealth.6967.
    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 Aug 01;27(4):565–593. doi: 10.1123/japa.2017-0410.
    1. Direito A, Carraça Eliana, Rawstorn J, Whittaker R, Maddison R. mHealth technologies to influence physical activity and sedentary behaviors: behavior change techniques, systematic review and meta-analysis of randomized controlled trials. Ann Behav Med. 2017 Apr 18;51(2):226–239. doi: 10.1007/s12160-016-9846-0.
    1. Maher JP, Conroy DE. A dual-process model of older adults' sedentary behavior. Health Psychol. 2016 Mar;35(3):262–72. doi: 10.1037/hea0000300.
    1. Hermsen S, Frost J, Renes RJ, Kerkhof P. Using feedback through digital technology to disrupt and change habitual behavior: a critical review of current literature. Computers in Human Behavior. 2016 Apr;57:61–74. doi: 10.1016/j.chb.2015.12.023.
    1. Luers B, Klasnja P, Murphy S. Standardized effect sizes for preventive mobile health interventions in micro-randomized trials. Prev Sci. 2019 Jan 9;20(1):100–109. doi: 10.1007/s11121-017-0862-5.
    1. Compernolle S, DeSmet A, Poppe L, Crombez G, De Bourdeaudhuij I, Cardon G, van der Ploeg HP, Van Dyck D. 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 Aug 13;16(1):63–16. doi: 10.1186/s12966-019-0824-3.
    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 Nov;32(11):1149–57. doi: 10.1037/a0031629.
    1. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, Murphy SA. Microrandomized trials: an experimental design for developing just-in-time adaptive interventions. Health Psychol. 2015 Dec;34S(Suppl):1220–8. doi: 10.1037/hea0000305.
    1. van Dantzig S, Geleijnse G, van Halteren AT. Toward a persuasive mobile application to reduce sedentary behavior. Pers Ubiquit Comput. 2012 Jul 12;17(6):1237–1246. doi: 10.1007/s00779-012-0588-0.
    1. Walton A, Nahum-Shani I, Crosby L, Klasnja P, Murphy S. Optimizing digital integrated care via micro-randomized trials. Clin Pharmacol Ther. 2018 Jul 19;104(1):53–58. doi: 10.1002/cpt.1079.
    1. Compernolle S, Cardon G, van der Ploeg HP, Van Nassau F, De Bourdeaudhuij I, Jelsma JJ, Brondeel R, Van Dyck D. Engagement, acceptability, usability, and preliminary efficacy of a self-monitoring mobile health intervention to reduce sedentary behavior in Belgian older adults: mixed methods study. JMIR mHealth uHealth. 2020 Oct 29;8(10):e18653. doi: 10.2196/18653.
    1. Lakerveld J, Ben Rebah M, Mackenbach JD, Charreire H, Compernolle S, Glonti K, Bardos H, Rutter H, De Bourdeaudhuij I, Brug J, Oppert J. Obesity-related behaviours and BMI in five urban regions across Europe: sampling design and results from the SPOTLIGHT cross-sectional survey. BMJ Open. 2015 Oct 27;5(10):e008505. doi: 10.1136/bmjopen-2015-008505.
    1. Gill JMR, Hawari NSA, Maxwell DJ, Louden D, Mourselas N, Bunn C, Gray CM, van der Ploeg HP, Hunt K, Martin A, Wyke S, Mutrie N. Validation of a Novel Device to Measure and Provide Feedback on Sedentary Behavior. Med Sci Sports Exerc. 2018 Mar;50(3):525–532. doi: 10.1249/MSS.0000000000001458.
    1. Jelsma JGM, Renaud LR, Huysmans MA, Coffeng JK, Loyen A, van Nassau F, Bosmans JE, Speklé EM, van der Beek AJ, van der Ploeg HP. The Dynamic Work study: study protocol of a cluster randomized controlled trial of an occupational health intervention aimed at reducing sitting time in office workers. BMC Public Health. 2019 Feb 13;19(1):188. doi: 10.1186/s12889-019-6467-0.
    1. Kehler DS, Clara I, Hiebert B, Stammers AN, Hay J, Schultz A, Arora RC, Tangri N, Duhamel TA. The association between patterns of physical activity and sedentary time with frailty in relation to cardiovascular disease. Aging Med (Milton) 2019 Mar 19;2(1):18–26. doi: 10.1002/agm2.12059.
    1. Compernolle S, De Cocker K, Cardon G, De Bourdeaudhuij I, Van Dyck D. Older adults' perceptions of sedentary behavior: a systematic review and thematic synthesis of qualitative studies. Gerontologist. 2020 Nov 23;60(8):572–582. doi: 10.1093/geront/gnz127.
    1. Dunton GF, Atienza AA, Castro CM, King AC. Using ecological momentary assessment to examine antecedents and correlates of physical activity bouts in adults age 50+ years: a pilot study. Ann Behav Med. 2009 Dec 6;38(3):249–55. doi: 10.1007/s12160-009-9141-4.
    1. Curran SL, Beacham AO, Andrykowski MA. Ecological momentary assessment of fatigue following breast cancer treatment. J Behav Med. 2004 Oct;27(5):425–444. doi: 10.1023/b:jobm.0000047608.03692.0c.

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