Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study


Autoři: Shivayogi V. Hiremath aff001;  Amir Mohammad Amiri aff001;  Binod Thapa-Chhetry aff004;  Gretchen Snethen aff001;  Mary Schmidt-Read aff006;  Marlyn Ramos-Lamboy aff007;  Donna L. Coffman aff008;  Stephen S. Intille aff004
Působiště autorů: Department of Health and Rehabilitation Sciences, Temple University, Philadelphia, Pennsylvania, United States of America aff001;  Department of Electrical and Computer Engineering, Temple University, Philadelphia, Pennsylvania, United States of America aff002;  Department of Biomedical Engineering, Widener University, Chester, Pennsylvania, United States of America aff003;  Khoury College of Computer Sciences, Northeastern University, Boston, Massachusetts, United States of America aff004;  Department of Health Sciences, Bouvé College of Health Sciences, Northeastern University, Boston, Massachusetts, United States of America aff005;  Magee Rehabilitation Hospital, Jefferson Health, Philadelphia, Pennsylvania, United States of America aff006;  MossRehab, Einstein Healthcare Network, Philadelphia, Pennsylvania, United States of America aff007;  Department of Epidemiology and Biostatistics, Temple University, Philadelphia, Pennsylvania, United States of America aff008
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223762

Souhrn

Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels. The first, second, and third phases of the study, each with a duration of one month, involved collecting baseline PA levels, providing near-real-time feedback on PA level (PA Feedback), and providing PA Feedback with JITAI, respectively. PA levels in terms of energy expenditure in kilocalories, and minutes of light- and moderate- or vigorous-intensity PA were assessed by an activity monitor during the study. Twenty participants with SCI took part in this research study with a mean (SD) age of 39.4 (12.8) years and 12.4 (12.5) years since injury. Sixteen participants completed the study. Sixteen were male, 16 had paraplegia, and 12 had complete injury. Within-participant comparisons indicated that only two participants had higher energy expenditure (>10%) or lower energy expenditure (<-10%) during PA Feedback with JITAI compared to the baseline. However, eleven participants (69.0%) had higher light- and/or moderate-intensity PA during PA Feedback with JITAI compared to the baseline. To our knowledge, this is the first study to test a PA JITAI for individuals with SCI that responds automatically to monitored PA levels. The results of this pilot study suggest that a sensor-enabled mobile JITAI has potential to improve PA levels of individuals with SCI. Future research should investigate the efficacy of JITAI through a clinical trial.

Klíčová slova:

Behavior – Cell phones – Fatigue – Physical activity – Pilot studies – Spinal cord injury – Wheelchairs – Apps


Zdroje

1. Tawashy A, Eng J, Lin K, Tang P, Hung C. Physical activity is related to lower levels of pain, fatigue, and depression in individuals with spinal cord injury: A correlational study. Spinal Cord. 2009;47(4):301. doi: 10.1038/sc.2008.120 18936771

2. Rimmer JH, Schiller W, Chen M-D. Effects of disability-associated low energy expenditure deconditioning syndrome. Exercise and Sport Sciences Reviews. 2012;40(1):22–9. doi: 10.1097/JES.0b013e31823b8b82 22016146

3. Ginis KM, Hicks A, Latimer A, Warburton D, Bourne C, Ditor D, et al. The development of evidence-informed physical activity guidelines for adults with spinal cord injury. Spinal Cord. 2011;49(11):1088. doi: 10.1038/sc.2011.63 21647164

4. Williams TL, Smith B, Papathomas A. The barriers, benefits and facilitators of leisure time physical activity among people with spinal cord injury: A meta-synthesis of qualitative findings. Health Psychology Review. 2014;8(4):404–25. doi: 10.1080/17437199.2014.898406 25211208

5. Fernhall B, Heffernan K, Jae SY, Hedrick B. Health implications of physical activity in individuals with spinal cord injury: A literature review. Journal of Health and Human Services Administration. 2008;30(4):468–502. 18236700

6. Tsang K, Hiremath SV, Crytzer TM, Dicianno BE, Ding D. Validity of activity monitors in wheelchair users: A systematic review. Journal of Rehabilitation Research and Development. 2016;53(6):641. doi: 10.1682/JRRD.2016.01.0006 27997674

7. Routhier F, Lettre J, Miller WC, Borisoff JF, Keetch K, Mitchell IM, et al. Data logger technologies for manual wheelchairs: A scoping review. Assistive Technology. 2018;30(2):51–8. doi: 10.1080/10400435.2016.1242516 27846371

8. García-Massó X, Serra-Añó P, García-Raffi L, Sánchez-Pérez E, López-Pascual J, Gonzalez L. Validation of the use of Actigraph GT3X accelerometers to estimate energy expenditure in full time manual wheelchair users with spinal cord injury. Spinal Cord. 2013;51(12):898–903. doi: 10.1038/sc.2013.85 23999111

9. Kiuchi K, Inayama T, Muraoka Y, Ikemoto S, Uemura O, Mizuno K. Preliminary study for the assessment of physical activity using a triaxial accelerometer with a gyro sensor on the upper limbs of subjects with paraplegia driving a wheelchair on a treadmill. Spinal Cord. 2014;52:556–63. doi: 10.1038/sc.2014.70 24819509

10. Hiremath SV, Intille SS, Kelleher A, Cooper RA, Ding D. Estimation of energy expenditure for wheelchair users using a physical activity monitoring system. Archives of Physical Medicine and Rehabilitation. 2016;97(7):1146–53. e1. doi: 10.1016/j.apmr.2016.02.016 26976800

11. Spruijt-Metz D, Nilsen W. Dynamic models of behavior for just-in-time adaptive interventions. IEEE Pervasive Computing. 2014 (3):13–7.

12. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, et al. Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine. 2017;52(6):446–62.

13. Nahum-Shani I, Hekler EB, Spruijt-Metz D. Building health behavior models to guide the development of just-in-time adaptive interventions: A pragmatic framework. Health Psychology. 2015;34(S):1209.

14. Klasnja P, Smith S, Seewald NJ, Lee A, Hall K, Luers B, et al. Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps. Annals of Behavioral Medicine. 2018:kay067-kay.

15. Ginis KAM, Latimer AE, Arbour-Nicitopoulos KP, Bassett RL, Wolfe DL, Hanna SE. Determinants of physical activity among people with spinal cord injury: a test of social cognitive theory. Annals of Behavioral Medicine. 2011;42(1):127–33. doi: 10.1007/s12160-011-9278-9 21544701

16. Reynolds GS. A primer of operant conditioning.(Rev ed). 1975.

17. Nooijen CF, Stam HJ, Bergen MP, Bongers-Janssen HM, Valent L, van Langeveld S, et al. A behavioural intervention increases physical activity in people with subacute spinal cord injury: A randomised trial. Journal of Physiotherapy. 2016;62(1):35–41. doi: 10.1016/j.jphys.2015.11.003 26701155

18. Pelletier C, De Zepetnek JT, MacDonald M, Hicks A. A 16-week randomized controlled trial evaluating the physical activity guidelines for adults with spinal cord injury. Spinal Cord. 2015;53(5):363. doi: 10.1038/sc.2014.167 25266695

19. Intille S, Haynes C, Maniar D, Ponnada A, Manjourides J, editors. μEMA: Microinteraction-based ecological momentary assessment (EMA) using a smartwatch. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing; 2016: ACM.

20. Ponnada A, Haynes C, Maniar D, Manjourides J, Intille S. Microinteraction Ecological Momentary Assessment Response Rates: Effect of Microinteractions or the Smartwatch? Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies. 2017;1(3):92. doi: 10.1145/3130957 30198012

21. Quinlan JR. C4.5: programs for machine learning: Morgan Kaufmann Publishers Inc.; 1993. 302 p.

22. Collins EG, Gater D, Kiratli J, Butler J, Hanson K, Langbein WE. Energy cost of physical activities in persons with spinal cord injury. Medicine and Science in Sports and Exercise. 2010;42(4):691–700. doi: 10.1249/MSS.0b013e3181bb902f 19952846

23. Ginis KAM, Phang SH, Latimer AE, Arbour-Nicitopoulos KP. Reliability and validity tests of the leisure time physical activity questionnaire for people with spinal cord injury. Archives of Physical Medicine and Rehabilitation. 2012;93(4):677–82. doi: 10.1016/j.apmr.2011.11.005 22336103

24. Krupp LB, LaRocca NG, Muir-Nash J, Steinberg AD. The fatigue severity scale: Application to patients with multiple sclerosis and systemic lupus erythematosus. Archives of Neurology. 1989;46(10):1121–3. doi: 10.1001/archneur.1989.00520460115022 2803071

25. Curtis K, Roach K, Applegate EB, Amar T, Benbow C, Genecco T, et al. Development of the wheelchair user’s shoulder pain index (WUSPI). Spinal Cord. 1995;33(5):290.

26. Anton HA, Miller WC, Townson AF. Measuring fatigue in persons with spinal cord injury. Archives of Physical Medicine and Rehabilitation. 2008;89(3):538–42. doi: 10.1016/j.apmr.2007.11.009 18295634

27. Salisbury S, Nitz J, Souvlis T. Shoulder pain following tetraplegia: a follow-up study 2–4 years after injury. Spinal Cord. 2006;44(12):723. doi: 10.1038/sj.sc.3101908 16505828

28. Brose SW, Boninger ML, Fullerton B, McCann T, Collinger JL, Impink BG, et al. Shoulder ultrasound abnormalities, physical examination findings, and pain in manual wheelchair users with spinal cord injury. Archives of physical medicine and rehabilitation. 2008;89(11):2086–93. doi: 10.1016/j.apmr.2008.05.015 18996236

29. Curtis K, Roach K, Applegate E, Amar T, Benbow C, Genecco T, et al. Reliability and validity of the wheelchair user’s shoulder pain index (WUSPI). Spinal Cord. 1995;33(10):595.

30. Bond DS, Thomas JG, Raynor HA, Moon J, Sieling J, Trautvetter J, et al. B-MOBILE-A smartphone-based intervention to reduce sedentary time in overweight/obese individuals: a within-subjects experimental trial. PloS one. 2014;9(6):e100821. doi: 10.1371/journal.pone.0100821 24964010

31. Spring B, Pellegrini CA, Pfammatter A, Duncan JM, Pictor A, McFadden H, et al. Effects of an abbreviated obesity intervention supported by mobile technology: The ENGAGED randomized clinical trial. Obesity. 2017;25(7):1191–8. doi: 10.1002/oby.21842 28494136

32. Ginis KAM, van der Scheer JW, Latimer-Cheung AE, Barrow A, Bourne C, Carruthers P, et al. Evidence-based scientific exercise guidelines for adults with spinal cord injury: An update and a new guideline. Spinal Cord. 2018;56(4):308. doi: 10.1038/s41393-017-0017-3 29070812

33. Klasnja P, Hekler EB, Shiffman S, Boruvka A, Almirall D, Tewari A, et al. Microrandomized trials: An experimental design for developing just-in-time adaptive interventions. Health Psychology. 2015;34(S):1220.

34. Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro‐randomized trials in mHealth. Statistics in Medicine. 2016;35(12):1944–71. doi: 10.1002/sim.6847 26707831


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2019 Číslo 10