Tutorial for Using Control Systems Engineering to Optimize Adaptive Mobile Health Interventions

Eric B Hekler, Daniel E Rivera, Cesar A Martin, Sayali S Phatak, Mohammad T Freigoun, Elizabeth Korinek, Predrag Klasnja, Marc A Adams, Matthew P Buman, Eric B Hekler, Daniel E Rivera, Cesar A Martin, Sayali S Phatak, Mohammad T Freigoun, Elizabeth Korinek, Predrag Klasnja, Marc A Adams, Matthew P Buman

Abstract

Background: Adaptive behavioral interventions are individualized interventions that vary support based on a person's evolving needs. Digital technologies enable these adaptive interventions to function at scale. Adaptive interventions show great promise for producing better results compared with static interventions related to health outcomes. Our central thesis is that adaptive interventions are more likely to succeed at helping individuals meet and maintain behavioral targets if its elements can be iteratively improved via data-driven testing (ie, optimization). Control systems engineering is a discipline focused on decision making in systems that change over time and has a wealth of methods that could be useful for optimizing adaptive interventions.

Objective: The purpose of this paper was to provide an introductory tutorial on when and what to do when using control systems engineering for designing and optimizing adaptive mobile health (mHealth) behavioral interventions.

Overview: We start with a review of the need for optimization, building on the multiphase optimization strategy (MOST). We then provide an overview of control systems engineering, followed by attributes of problems that are well matched to control engineering. Key steps in the development and optimization of an adaptive intervention from a control engineering perspective are then summarized, with a focus on why, what, and when to do subtasks in each step.

Implications: Control engineering offers exciting opportunities for optimizing individualization and adaptation elements of adaptive interventions. Arguably, the time is now for control systems engineers and behavioral and health scientists to partner to advance interventions that can be individualized, adaptive, and scalable. This tutorial should aid in creating the bridge between these communities.

Keywords: adaptive interventions; behavior change; behavioral maintenance; control systems engineering; digital health; eHealth; mHealth; multiphase optimization strategy; optimization; physical activity.

Conflict of interest statement

Conflicts of Interest: None declared.

©Eric B Hekler, Daniel E Rivera, Cesar A Martin, Sayali S Phatak, Mohammad T Freigoun, Elizabeth Korinek, Predrag Klasnja, Marc A Adams, Matthew P Buman. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 28.06.2018.

Figures

Figure 1
Figure 1
Screenshots of the Just Walk App. The image on the left is the view inside the app, which includes the suggested step goal for the day (in the red box), available points (in gold medal in the middle) and current steps (in green box). Below is the person’s step history. The image on the right is the app’s “widget,” which enables a person to receive feedback relative to their goal without opening the app.
Figure 2
Figure 2
Simplified dynamical model version of Social Cognitive Theory.
Figure 3
Figure 3
Dynamic hypothesis.
Figure 4
Figure 4
System identification open loop experiment for Just Walk. These two signals were designed a priori using a pseudorandom signal design strategy. This strategy enabled specification of repeated 16-day cycles (delineated as different colors), which allows for robust data for estimation and validation of dynamical models.
Figure 5
Figure 5
Visualization from one participant from Auto-Regressive Dynamical Modeling.
Figure 6
Figure 6
Model-predictive controller “Receding Horizon” strategy. The model predictive controller visualized here is simplified to include only one controlled variable (desired daily steps), one input (ie, goals), and one disturbance (ie, environmental context). Controller moves (ie, goals) are calculated over a horizon, and only the first control move calculated is implemented. The entire procedure is repeated at the next assessment period and continues until the end of intervention.
Figure 7
Figure 7
Controller simulation.
Figure 8
Figure 8
Control optimization trial for Just Walk.

References

    1. Sheeran P, Klein WM, Rothman AJ. Health Behavior Change: Moving from Observation to Intervention. Annu Rev Psychol. 2017 Jan 03;68:573–600. doi: 10.1146/annurev-psych-010416-044007.
    1. Rothman AJ, Gollwitzer PM, Grant AM, Neal DT, Sheeran P, Wood W. Hale and hearty policies: how psychological science can create and maintain healthy habits. Perspect Psychol Sci. 2015 Nov;10(6):701–5. doi: 10.1177/1745691615598515. 10.1177/1745691615598515.
    1. Mann T, Tomiyama AJ, Ward A. Promoting public health in the context of the “Obesity Epidemic”: false starts and promising new directions. Perspect Psychol Sci. 2015 Nov;10(6):706–10. doi: 10.1177/1745691615586401.
    1. van der Linden S, Maibach E, Leiserowitz A. Improving public engagement with climate change: five “best practice” insights from psychological science. Perspect Psychol Sci. 2015 Nov;10(6):758–63. doi: 10.1177/1745691615598516.
    1. Chase JA. Interventions to increase physical activity among older adults: a meta-analysis. Gerontologist. 2015 Aug;55(4):706–18. doi: 10.1093/geront/gnu090.
    1. Conn VS, Hafdahl AR, Mehr DR. Interventions to increase physical activity among healthy adults: meta-analysis of outcomes. Am J Public Health. 2011 Apr;101(4):751–8. doi: 10.2105/AJPH.2010.194381.
    1. Conn VS, Hafdahl AR, Moore SM, Nielsen PJ, Brown LM. Meta-analysis of interventions to increase physical activity among cardiac subjects. Int J Cardiol. 2009 Apr 17;133(3):307–20. doi: 10.1016/j.ijcard.2008.03.052.
    1. Payne HE, Lister C, West JH, Bernhardt JM. Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR Mhealth Uhealth. 2015 Feb 26;3(1):e20. doi: 10.2196/mhealth.3335.
    1. Collins LM, Nahum-Shani I, Almirall D. Optimization of behavioral dynamic treatment regimens based on the sequential, multiple assignment, randomized trial (SMART) Clin Trials. 2014 Aug;11(4):426–34. doi: 10.1177/1740774514536795.
    1. Almirall D, Nahum-Shani I, Sherwood NE, Murphy SA. Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Transl Behav Med. 2014 Sep;4(3):260–74. doi: 10.1007/s13142-014-0265-0.
    1. 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 Psychol. 2015 Dec;34S:1209–19. doi: 10.1037/hea0000306.
    1. Nahum-Shani I, Smith SN, Spring BJ, Collins LM, Witkiewitz K, Tewari A, Murphy SA. Just-in-Time Adaptive Interventions (JITAIs) in mobile health: key components and design principles for ongoing health behavior support. Ann Behav Med. 2016 Sep 23;:-. doi: 10.1007/s12160-016-9830-8.
    1. Patrick K, Hekler EB, Estrin D, Mohr DC, Riper H, Crane D, Godino J, Riley WT. The pace of technologic change: implications for digital health behavior intervention research. Am J Prev Med. 2016 Nov;51(5):816–24. doi: 10.1016/j.amepre.2016.05.001.
    1. Riley WT, Glasgow RE, Etheredge L, Abernethy AP. Rapid, responsive, relevant (R3) research: a call for a rapid learning health research enterprise. Clin Transl Med. 2013;2(1):10. doi: 10.1186/2001-1326-2-10.
    1. Riley WT, Martin CA, Rivera DE, Hekler EB, Adams MA, Buman MP, Pavel M, King AC. Development of a dynamic computational model of social cognitive theory. Transl Behav Med. 2016 Dec;6(4):483–95. doi: 10.1007/s13142-015-0356-6.
    1. Riley WT, Nilsen WJ, Manolio TA, Masys DR, Lauer M. News from the NIH: potential contributions of the behavioral and social sciences to the precision medicine initiative. Transl Behav Med. 2015 Sep;5(3):243–6. doi: 10.1007/s13142-015-0320-5.
    1. Riley WT, Rivera DE. Methodologies for optimizing behavioral interventions: introduction to special section. Transl Behav Med. 2014 Sep;4(3):234–7. doi: 10.1007/s13142-014-0281-0.
    1. Riley WT, Rivera DE, Atienza AA, Nilsen W, Allison SM, Mermelstein R. Health behavior models in the age of mobile interventions: are our theories up to the task? Transl Behav Med. 2011 Mar;1(1):53–71. doi: 10.1007/s13142-011-0021-7.
    1. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. Int J Nurs Stud. 2013 May;50(5):587–92. doi: 10.1016/j.ijnurstu.2012.09.010.
    1. Freigoun MT, Martín CA, Magann AB, Rivera DE, Phatak SS, Korinek E, Hekler EB. System identification of Just Walk: a behavioral mHealth intervention for promoting physical activity. IEEE; American Control Conference; July 3, 2017; Seattle, WA. 2017. pp. 116–21.
    1. Korinek EV, Phatak SS, Martin CA, Freigoun MT, Rivera DE, Adams MA, Klasnja P, Buman MP, Hekler EB. Adaptive step goals and rewards: a longitudinal growth model of daily steps for a smartphone-based walking intervention. J Behav Med. 2018 Feb;41(1):74–86. doi: 10.1007/s10865-017-9878-3.
    1. Phatak SS, Freigoun MT, Martín CA, Rivera DE, Korinek EV, Adams MA, Buman MP, Klasnja P, Hekler EB. Modeling individual differences: a case study of the application of system identification for personalizing a physical activity intervention. J Biomed Inform. 2018;79:82–97. doi: 10.1016/j.jbi.2018.01.010.
    1. Åström KJ. Advances in Control. London: Springer; 1999. Automatic Control - The Hidden Technology; pp. 1–28.
    1. Ashour M, Bekiroglu K, Yang CH, Lagoa C, Conroy D, Smyth J. On the mathematical modeling of the effect of treatment on human physical activity. IEEE; IEEE Conference on Control Applications (CCA); September 19-22, 2016; Buenos Aires. 2016.
    1. Karimi M, Rao RR. Human Body Composition Estimation and Model-Free Control Design for Weight Management. ASME; ASME Proceedings | Biomedical and Bioengineering Applications; October 22-24 2014; San Antonio, TX. 2014.
    1. Karimi M, Rao RR. On the estimation and control of human body composition. Trans Inst Meas Control. 2017 May 30;:-. doi: 10.1177/0142331217705946.
    1. Pirolli P, Mohan S, Venkatakrishnan A, Nelson L, Silva M, Springer A. Implementation intention and reminder effects on behavior change in a mobile health system: a predictive cognitive model. J Med Internet Res. 2017 Nov 30;19(11):e397. doi: 10.2196/jmir.8217.
    1. Collins LM. Optimization of Behavioral, Biobehavioral, and Biomedical Interventions: The Multiphase Optimization Strategy (MOST) New York: Springer; 2018.
    1. Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007 May;32(5 Suppl):S112–8. doi: 10.1016/j.amepre.2007.01.022.
    1. Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, Rivera DE, West R, Wyatt JC. Evaluating digital health interventions: key questions and approaches. Am J Prev Med. 2016 Nov;51(5):843–51. doi: 10.1016/j.amepre.2016.06.008.
    1. Collins LM, Trail JB, Kugler KC, Baker TB, Piper ME, Mermelstein RJ. Evaluating individual intervention components: making decisions based on the results of a factorial screening experiment. Transl Behav Med. 2014 Sep;4(3):238–51. doi: 10.1007/s13142-013-0239-7.
    1. Chakraborty B, Collins LM, Strecher VJ, Murphy SA. Developing multicomponent interventions using fractional factorial designs. Stat Med. 2009 Sep 20;28(21):2687–708. doi: 10.1002/sim.3643.
    1. Collins LM, Dziak JJ, Kugler KC, Trail JB. Factorial experiments: efficient tools for evaluation of intervention components. Am J Prev Med. 2014 Oct;47(4):498–504. doi: 10.1016/j.amepre.2014.06.021.
    1. Lei H, Nahum-Shani I, Lynch K, Oslin D, Murphy SA. A “SMART” design for building individualized treatment sequences. Annu Rev Clin Psychol. 2012;8:21–48. doi: 10.1146/annurev-clinpsy-032511-143152.
    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:1220–8. doi: 10.1037/hea0000305.
    1. Sutton RS, Barto A. Reinforcement Learning: An Introduction. Boston: MIT press; 1998.
    1. Consolvo S, Bentley FR, Hekler EB, Phatak SS. Mobile User Research: A Practical Guide. San Rafael, CA: Morgan & Claypool Publishers; 2017. May, p. -.
    1. Almirall D, Nahum-Shani I, Wang L, Kasari C. Optimization of behavioral, biobehavioral, and biomedical interventions: Advanced topics. New York: Springer; 2018. Experimental designs for research on adaptive interventions: Singly and sequentially randomized trials.
    1. Li Y, Ang KH, Chong GC. PID control system analysis and design. IEEE Control Syst. 2006 Feb;26(1):32–41. doi: 10.1109/MCS.2006.1580152.
    1. Ang KH, Chong G, Li Y. PID control system analysis, design, and technology. IEEE Trans Control Syst Technol. 2005 Jul;13(4):559–76. doi: 10.1109/TCST.2005.847331.
    1. Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Eng Pract. 2003 Jul;11(7):733–64. doi: 10.1016/S0967-0661(02)00186-7.
    1. Eysenbach G. The law of attrition. J Med Internet Res. 2005 Mar;7(1):e11. doi: 10.2196/jmir.7.1.e11.
    1. Morari M, Zafiriou E. Robust Process Control. Englewood Cliffs, NJ: Prentice Hall; 1989.
    1. Floegel TA, Florez-Pregonero A, Hekler EB, Buman MP. Validation of consumer-based hip and wrist activity monitors in older adults with varied ambulatory abilities. J Gerontol A Biol Sci Med Sci. 2017 Feb;72(2):229–36. doi: 10.1093/gerona/glw098.
    1. Hekler EB, Buman MP, Grieco L, Rosenberger M, Winter SJ, Haskell WL, King AC. Validation of physical activity tracking via Android smartphones compared to ActiGraph accelerometer: laboratory-based and free-living validation studies. JMIR Mhealth Uhealth. 2015 Apr 15;3(2):e36. doi: 10.2196/mhealth.3505.
    1. Ljung L. System Identification: Theory for the User (2nd Edition) Upper Saddle River, NJ: Prentice Hall; 1999.
    1. Ljung L. System Identification Toolbox for Use with MATLAB: User's Guide. Natick, MA: The MathWorks Inc; 1995.
    1. Ljung L. Experiments with Identification of Continuous Time Models. IFAC Proc Volumes. 2009;42(10):1175–1180. doi: 10.3182/20090706-3-FR-2004.00195.
    1. Ljung L, Glad T. Prentice Hall. New Jersey: Prentice-Hall; 1994. Modeling of Dynamic Systems.
    1. Martin CA, Rivera DE, Hekler EB. A control engineering approach for optimizing physical activity behavioral interventions. IEEE; Ecuador Technical Chapters Meeting (ETCM); October 12-14, 2016; Guayaquil, Ecuador. 2016. pp. 12–14.
    1. Rivera DE, Martín CA, Timms KP, Deshpande S, Nandola N, Hekler E. Mobile Health: Sensors, Analytic Methods, and Applications. Basel: Springer International Publishing; 2017. Control Systems Engineering for Optimizing Behavioral mHealth Interventions; pp. 455–93.
    1. Clague J, Bernstein L. Physical activity and cancer. Curr Oncol Rep. 2012 Dec;14(6):550–8. doi: 10.1007/s11912-012-0265-5.
    1. Bianchini F, Kaaks R, Vainio H. Weight control and physical activity in cancer prevention. Obes Rev. 2002 Feb;3(1):5–8. doi: 10.1046/j.1467-789X.2002.00046.x.
    1. Murtagh EM, Nichols L, Mohammed MA, Holder R, Nevill AM, Murphy MH. The effect of walking on risk factors for cardiovascular disease: an updated systematic review and meta-analysis of randomised control trials. Prev Med. 2015 Mar;72:34–43. doi: 10.1016/j.ypmed.2014.12.041.
    1. Karstoft K, Winding K, Knudsen SH, Nielsen JS, Thomsen C, Pedersen BK, Solomon TP. The effects of free-living interval-walking training on glycemic control, body composition, and physical fitness in type 2 diabetic patients: a randomized, controlled trial. Diabetes Care. 2013 Feb;36(2):228–36. doi: 10.2337/dc12-0658.
    1. Tudor-Locke C, Hart TL, Washington TL. Expected values for pedometer-determined physical activity in older populations. Int J Behav Nutr Phys Act. 2009 Aug 25;6:59. doi: 10.1186/1479-5868-6-59.
    1. Tudor-Locke C, Craig CL, Aoyagi Y, Bell RC, Croteau KA, De Bourdeaudhuij I, Ewald B, Gardner AW, Hatano Y, Lutes LD, Matsudo SM, Ramirez-Marrero FA, Rogers LQ, Rowe DA, Schmidt MD, Tully MA, Blair SN. How many steps/day are enough? For older adults and special populations. Int J Behav Nutr Phys Act. 2011 Jul 28;8:80. doi: 10.1186/1479-5868-8-80.
    1. Bauer UE, Briss PA, Goodman RA, Bowman BA. Prevention of chronic disease in the 21st century: elimination of the leading preventable causes of premature death and disability in the USA. Lancet. 2014 Jul;384(9937):45–52. doi: 10.1016/s0140-6736(14)60648-6.
    1. Tudor-Locke C, Craig C, Thyfault J, Spence J. A step-defined sedentary lifestyle index: <5000 steps/day. Appl Physiol Nutr Metab. 2013 Feb;38(2):100–14. doi: 10.1139/apnm-2012-0235.
    1. Cdc. [2018-05-14]. BRFSS Prevalence & Trends Data .
    1. Owen N, Bauman A, Brown W. Too much sitting: a novel and important predictor of chronic disease risk? Br J Sports Med. 2009 Feb;43(2):81–3. doi: 10.1136/bjsm.2008.055269.
    1. King AC, Sallis JF. Why and how to improve physical activity promotion: lessons from behavioral science and related fields. Prev Med. 2009 Oct;49(4):286–8. doi: 10.1016/j.ypmed.2009.07.007.
    1. Haskell WL, Blair SN, Hill JO. Physical activity: health outcomes and importance for public health policy. Prev Med. 2009 Oct;49(4):280–2. doi: 10.1016/j.ypmed.2009.05.002.
    1. Bauman A, Bull F, Chey T, Craig CL, Ainsworth BE, Sallis JF, Bowles HR, Hagstromer M, Sjostrom M, Pratt M, IPS Group The international prevalence study on physical activity: results from 20 countries. Int J Behav Nutr Phys Act. 2009 Mar 31;6:21. doi: 10.1186/1479-5868-6-21.
    1. Woolf K, Reese CE, Mason MP, Beaird LC, Tudor-Locke C, Vaughan LA. Physical activity is associated with risk factors for chronic disease across adult women's life cycle. J Am Diet Assoc. 2008 Jun;108(6):948–59. doi: 10.1016/j.jada.2008.03.015.
    1. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008 Jan;40(1):181–8. doi: 10.1249/mss.0b013e31815a51b3.
    1. Sugiyama T, Healy GN, Dunstan DW, Salmon J, Owen N. Joint associations of multiple leisure-time sedentary behaviours and physical activity with obesity in Australian adults. Int J Behav Nutr Phys Act. 2008 Jul 1;5:35. doi: 10.1186/1479-5868-5-35.
    1. Matthews CE. Physical activity in the United States measured by accelerometer: comment. Med Sci Sports Exerc. 2008 Jun;40(6):1188; author reply 1189. doi: 10.1249/MSS.0b013e31817057da.
    1. Warburton DE, Nicol CW, Bredin SS. Health benefits of physical activity: the evidence. CMAJ. 2006 Mar 14;174(6):801–9. doi: 10.1503/cmaj.051351.
    1. Physical Activity Guidelines Advisory Committee Physical Activity Guidelines Advisory Committee report, 2008. Part A: Executive Summary. Nutr Rev. 2009 Feb;67(2):114–20. doi: 10.1111/j.1753-4887.2008.00136.x.
    1. Rauner A, Jekauc D, Mess F, Schmidt S, Woll A. Tracking physical activity in different settings from late childhood to early adulthood in Germany: the MoMo longitudinal study. BMC Public Health. 2015 Apr 17;15:391. doi: 10.1186/s12889-015-1731-4.
    1. Hirvensalo M, Lintunen T. Life-course perspective for physical activity and sports participation. Eur Rev Aging Phys Act. 2011 Jan 11;8(1):13–22. doi: 10.1007/s11556-010-0076-3.
    1. Kern ML, Reynolds CA, Friedman HS. Predictors of physical activity patterns across adulthood: a growth curve analysis. Pers Soc Psychol Bull. 2010 Aug;36(8):1058–72. doi: 10.1177/0146167210374834.
    1. Levin S, Jacobs DR, Ainsworth BE, Richardson MT, Leon AS. Intra-individual variation and estimates of usual physical activity. Ann Epidemiol. 1999 Nov;9(8):481–8.
    1. Bélanger M, Gray-Donald K, O'Loughlin J, Paradis G, Hanley J. Influence of weather conditions and season on physical activity in adolescents. Ann Epidemiol. 2009 Mar;19(3):180–6. doi: 10.1016/j.annepidem.2008.12.008.
    1. Brandon CA, Gill DP, Speechley M, Gilliland J, Jones GR. Physical activity levels of older community-dwelling adults are influenced by summer weather variables. Appl Physiol Nutr Metab. 2009 Apr;34(2):182–90. doi: 10.1139/H09-004.
    1. Centers for Disease ControlPrevention (CDC) Monthly estimates of leisure-time physical inactivity--United States, 1994. MMWR Morb Mortal Wkly Rep. 1997 May 9;46(18):393–7.
    1. Matthews CE, Ainsworth BE, Thompson RW, Bassett DR. Sources of variance in daily physical activity levels as measured by an accelerometer. Med Sci Sports Exerc. 2002 Aug;34(8):1376–81.
    1. Pivarnik JM, Reeves MJ, Rafferty AP. Seasonal variation in adult leisure-time physical activity. Med Sci Sports Exerc. 2003 Jun;35(6):1004–8. doi: 10.1249/01.MSS.0000069747.55950.B1.
    1. Tucker P, Gilliland J. The effect of season and weather on physical activity: a systematic review. Public Health. 2007 Dec;121(12):909–22. doi: 10.1016/j.puhe.2007.04.009.
    1. Small M, Bailey-Davis L, Morgan N, Maggs J. Changes in eating and physical activity behaviors across seven semesters of college: living on or off campus matters. Health Educ Behav. 2013;40(4):435–41.
    1. King AC, Hekler EB, Castro CM, Buman MP, Marcus BH, Friedman RH, Napolitano MA. Exercise advice by humans versus computers: maintenance effects at 18 months. Health Psychol. 2014 Feb;33(2):192–6. doi: 10.1037/a0030646.
    1. Hekler EB, Buman MP, Poothakandiyil N, Rivera DE, Dzierzewski JM, Morgan AA, McCrae CS, Roberts BL, Marsiske M, Giacobbi Jr PR. Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification modeling within a behavioral intervention. Health Educ Behav. 2013 Oct;40(1 Suppl):51S–62S. doi: 10.1177/1090198113496787.
    1. Fjeldsoe B, Neuhaus M, Winkler E, Eakin E. Systematic review of maintenance of behavior change following physical activity and dietary interventions. Health Psychol. 2011 Jan;30(1):99–109. doi: 10.1037/a0021974.
    1. Eakin E, Reeves M, Winkler E, Lawler S, Owen N. Maintenance of physical activity and dietary change following a telephone-delivered intervention. Health Psychol. 2010 Nov;29(6):566–73. doi: 10.1037/a0021359.
    1. D'Alonzo KT, Cortese LB. An investigation of habitual and incidental physical activity among Costa Rican and Costa Rican American teenage girls. J Transcult Nurs. 2007 Jul;18(3):201–7. doi: 10.1177/1043659607301296.
    1. Marshall AL, Bauman AE, Patch C, Wilson J, Chen J. Can motivational signs prompt increases in incidental physical activity in an Australian health-care facility? Health Educ Res. 2002 Dec;17(6):743–9.
    1. Hekler EB, Castro CM, Buman MP, King AC. The CHOICE study: a “taste-test” of utilitarian vs. leisure walking among older adults. Health Psychol. 2012 Jan;31(1):126–9. doi: 10.1037/a0025567.
    1. Freigoun MT, Martin CA, Magann AB, Rivera DE, Phatak SS, Korinek EV. System identification of Just Walk: A behavioral mHealth intervention for promoting physical activity. IEEE; American Control Conference (ACC); May 24-26, 2017; Seattle, WA. 2017.
    1. Mantzari E, Vogt F, Shemilt I, Wei Y, Higgins J, Marteau TM. Personal financial incentives for changing habitual health-related behaviors: a systematic review and meta-analysis. Prev Med. 2015 Jun;75:75–85. doi: 10.1016/j.ypmed.2015.03.001.
    1. Mitchell MS, Goodman JM, Alter DA, John LK, Oh PI, Pakosh MT, Faulkner GE. Financial incentives for exercise adherence in adults: systematic review and meta-analysis. Am J Prev Med. 2013 Nov;45(5):658–67. doi: 10.1016/j.amepre.2013.06.017.
    1. Skinner BF. Science and Human Behavior. New York: Simon and Schuster; 1953.
    1. Ferster CB. Schedules of reinforcement with Skinner. New York: Appleton-Century-Crofts; 1970. Century psychology series; pp. 37–46.
    1. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986.
    1. Adams MA, Hurley JC, Todd M, Bhuiyan N, Jarrett CL, Tucker WJ, Hollingshead KE, Angadi SS. Adaptive goal setting and financial incentives: a 2 × 2 factorial randomized controlled trial to increase adults' physical activity. BMC Public Health. 2017 Dec 29;17(1):286. doi: 10.1186/s12889-017-4197-8.
    1. Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E. An adaptive physical activity intervention for overweight adults: a randomized controlled trial. PLoS One. 2013 Dec;8(12):e82901. doi: 10.1371/journal.pone.0082901.
    1. Hekler EB, Michie S, Pavel M, Rivera DE, Collins LM, Jimison HB, Garnett C, Parral S, Spruijt-Metz D. Advancing models and theories for digital behavior change interventions. Am J Prev Med. 2016 Nov;51(5):825–32. doi: 10.1016/j.amepre.2016.06.013.
    1. Martin CA, Desphande S, Hekler EB, Rivera DE. A system identification approach for improving behavioral interventions based on social cognitive theory. American Control Conference (ACC); July 1-3, 2015; Chicago, IL USA. 2015.
    1. Guo P, Rivera DE, Downs DS, Savage JS. Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. American Control Conference (ACC); August 1, 2016; Boston, MA, USA. 2016.
    1. Martin CA, Rivera DE, Riley WT, Hekler EB, Buman MP, Adams MA, King AC. A Dynamical Systems Model of Social Cognitive Theory. IEEE; American Control Conference (ACC); July 21, 2014; Portland, OR USA. 2014.
    1. Riley WT, Martin CA, Rivera DE, Hekler EB, Adams MA, Buman MP, Pavel M, King AC. Development of a dynamic computational model of social cognitive theory. Transl Behav Med. 2016 Dec;6(4):483–95. doi: 10.1007/s13142-015-0356-6.
    1. Hekler EB, Michie S, Pavel M, Rivera DE, Collins LM, Jimison HB, Garnett C, Parral S, Spruijt-Metz D. Advancing models and theories for digital behavior change interventions. Am J Prev Med. 2016 Nov;51(5):825–32. doi: 10.1016/j.amepre.2016.06.013.
    1. Hekler EB, Klasnja P, Riley WT, Buman MP, Huberty J, Rivera DE, Martin CA. Agile science: creating useful products for behavior change in the real world. Transl Behav Med. 2016 Jun;6(2):317–28. doi: 10.1007/s13142-016-0395-7.
    1. Ogunnaike B, Ray WH. Process Dynamics, Modeling, and Control. Oxford: Oxford University Press; 1994.
    1. Dunton GF. Ecological momentary assessment in physical activity research. Exerc Sport Sci Rev. 2017 Dec;45(1):48–54. doi: 10.1249/JES.0000000000000092.
    1. Klasnja P, Hekler EB, Korinek EV, Harlow J, Mishra SR. Toward Usable Evidence: Optimizing Knowledge Accumulation in HCI Research on Health Behavior Change. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems; May 6-11, 2011; Denver, Colorado, USA. New York, NY, USA: ACM; 2017.
    1. Nilsen W, Ertin E, Hekler E, Kumar S, Lee I, Mangharam R. Mobile Health: Sensors, Analytic Methods, and Applications. Basel: Springer International Publishing; 2017. Modeling Opportunities in mHealth Cyber-Physical Systems.
    1. Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, Rivera DE, Spring B, Michie S, Asch DA, Sanna A, Salcedo VT, Kukakfa R, Pavel M. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med. 2015 Sep;5(3):335–46. doi: 10.1007/s13142-015-0324-1.
    1. Young M, Plotnikoff R, Collins C, Callister R, Morgan P. Social cognitive theory and physical activity: a systematic review and meta-analysis. Obes Rev. 2014 Dec;15(12):983–95. doi: 10.1111/obr.12225.
    1. King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL, Buman MP, Banerjee B, Robinson TN, Cirimele J. Harnessing different motivational frames via mobile phones to promote daily physical activity and reduce sedentary behavior in aging adults. PLoS One. 2013 Apr;8(4):e62613. doi: 10.1371/journal.pone.0062613.
    1. King AC, Hekler EB, Grieco LA, Winter SJ, Sheats JL, Buman MP, Banerjee B, Robinson TN, Cirimele J. 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. doi: 10.1371/journal.pone.0156370.
    1. Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983 Jun;51(3):390–5.
    1. Prochaska JO, Velicer WF, Rossi JS, Goldstein MG, Marcus BH, Rakowski W, Fiore C, Harlow LL, Redding CA, Rosenbloom D, Rossi SR. Stages of change and decisional balance for 12 problem behaviors. Health Psychol. 1994;13(1):39–46. doi: 10.1037/0278-6133.13.1.39.
    1. Dong Y, Rivera DE, Thomas DM, Navarro-Barrientos JE, Downs DS, Savage JS, Collins LM. A dynamical systems model for improving gestational weight gain behavioral interventions. Proc Am Control Conf. 2012:4059–4064.
    1. Dong Y, Rivera DE, Downs DS, Savage JS, Thomas DM, Collins LM. Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. Proc Am Control Conf. 2013:1970–1975.
    1. Savage JS, Downs DS, Dong Y, Rivera DE. Control systems engineering for optimizing a prenatal weight gain intervention to regulate infant birth weight. Am J Public Health. 2014 Jul;104(7):1247–54. doi: 10.2105/AJPH.2014.301959.
    1. Locke EA, Latham GP. Building a practically useful theory of goal setting and task motivation: a 35-year odyssey. Am Psychol. 2002;57(9):705–17. doi: 10.1037/0003-066X.57.9.705.
    1. Designkit. [2018-05-15]. The Fieldguide to Human-Centered Design .
    1. Bowen DJ, Kreuter M, Spring B, Cofta-Woerpel L, Linnan L, Weiner D, Bakken S, Kaplan CP, Squiers L, Fabrizio C, Fernandez M. How we design feasibility studies. Am J Prev Med. 2009 May;36(5):452–7. doi: 10.1016/j.amepre.2009.02.002.
    1. Godfrey K. Perturbation Signals for System Identification. London, UK: Prentice Hall; 1993.
    1. Martin CA, Rivera DE, Hekler EB. An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. IEEE; Conference on Decision and Control (CDC); December 15-18, 2015; Osaka, Japan. 2015.
    1. Lee H, Rivera DE, Mittelmann HD. Constrained minimum crest factor multisine signals for “Plant-Friendly” identification of highly interactive systems. IFAC Proc Volumes. 2003;36(16):915–20. doi: 10.1016/S1474-6670(17)34877-2.
    1. Rivera DE, Lee H, Mittelmann HD, Braun MW. Constrained multisine input signals for plant-friendly identification of chemical process systems. J Process Control. 2009 Apr;19(4):623–35. doi: 10.1016/j.jprocont.2008.08.006.
    1. Hekler EB, Klasnja P, Traver V, Hendriks M. Realizing effective behavioral management of health: the metamorphosis of behavioral science methods. IEEE Pulse. 2013 Sep;4(5):29–34. doi: 10.1109/MPUL.2013.2271681.
    1. Martin CA, Deshpande S, Hekler EB, Rivera DE. A system identification approach for improving behavioral interventions based on Social Cognitive Theory. American Control Conference (ACC); July 1-3, 2015; Chicago, IL, USA. 2015.
    1. Bao L, Intille SS. Activity recognition from user-annotated acceleration data. In: Ferscha A, Mattern F, editors. Pervasive Computing. Pervasive 2004. Lecture Notes in Computer Science, vol 3001. Berlin, Heidelberg: Springer; 2004. pp. 1–17.
    1. Cohen J. A power primer. Psychol Bull. 1992 Jul;112(1):155–9.
    1. Lindskog P, Ljung L. Tools for semiphysical modelling. Int J Adapt Control Signal Process. 1995 Nov;9(6):509–23. doi: 10.1002/acs.4480090605.
    1. Martin CA, Rivera DE, Hekler EB. A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control. American Control Conference (ACC); July 6-8, 2016; Boston, MA, USA. 2016.
    1. Martin CA, Rivera DE, Hekler EB. A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control. American Control Conference (ACC); July 6-8, 2016; Boston, MA, USA. 2016.
    1. García CE, Prett D, Morari M. Model predictive control: theory and practice—a survey. Automatica. 1989 May;25(3):335–48. doi: 10.1016/0005-1098(89)90002-2.
    1. Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E. An adaptive physical activity intervention for overweight adults: a randomized controlled trial. PLoS One. 2013 Dec;8(12):e82901. doi: 10.1371/journal.pone.0082901.
    1. Riddle M, Science of Behavior Change Working Group News from the NIH: using an experimental medicine approach to facilitate translational research. Transl Behav Med. 2015 Dec;5(4):486–8. doi: 10.1007/s13142-015-0333-0.
    1. Vandenbroucke JP, Broadbent A, Pearce N. Causality and causal inference in epidemiology: the need for a pluralistic approach. Int J Epidemiol. 2016 Dec 1;45(6):1776–86. doi: 10.1093/ije/dyv341.
    1. Wilson E. Consilience: The Unity of Knowledge. New York: Vintage; 1999.
    1. Mohr DC, Ho J, Hart TL, Baron KG, Berendsen M, Beckner V, Cai X, Cuijpers P, Spring B, Kinsinger SW, Schroder KE, Duffecy Jenna. Control condition design and implementation features in controlled trials: a meta-analysis of trials evaluating psychotherapy for depression. Transl Behav Med. 2014 Dec;4(4):407–23. doi: 10.1007/s13142-014-0262-3.
    1. Shadish WR, Cook TD, Campbell DT. Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Independence KY USA: Wadsworth Publishing; 2002.
    1. Miller G. The smartphone psychology manifesto. Perspect Psychol Sci. 2012 May;7(3):221–37. doi: 10.1177/1745691612441215.
    1. Carver C, Scheier MF. Control theory: a useful conceptual framework for personality-social, clinical, and health psychology. Psychol Bull. 1982;92(1):111–35. doi: 10.1037/0033-2909.92.1.111.
    1. Rioch DM. Plans and the structure of behavior. Arch Gen Psychiatry. 1961 Dec 1;5(6):620–22. doi: 10.1001/archpsyc.1961.01710180104014.
    1. Flach JM, Bennett KB, Jagacinski RJ, Mulder M, van Paassen M. The Oxford Handbook of Cognitive Engineering. Oxford: Oxford Press; 2013. The closed-loop dynamics of cognitive work.
    1. Norman DA, Stappers PJ. DesignX: complex sociotechnical systems. She Ji: J Des Econ Innov. 2015;1(2):83–106. doi: 10.1016/j.sheji.2016.01.002.

Source: PubMed

Sponsorer og samarbeidspartnere

Medisinsk tilstand

Legemiddelintervensjoner

3
Abonnere