Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

 

 Buy Article Permissions and Reprints

Summary

Objectives: The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users’ own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual’s stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior.

Methods: This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior.

Results: Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a particular day. The predictors included previous exercise behavior, emotional factors (e.g., midday stress), external factors such as weather (e.g., temperature), and self-determination factors (e.g., expectation of exercise). The nomothetic model yielded an average classification error of 36%. The ideographic N-of-1 models used on average about two predictors for each individual, and had an average classification error of 25%, which represented an improvement of 11 percentage points.

Conclusions: Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.

• References

• 1 Garcia MC, Bastian B, Rossen LM. et al. Potentially preventable deaths among the five leading causes of death—United States, 2010 and 2014. MMWR Morb Mortal Wkly Rep 2016; 65: 1245-1255.
  CrossrefPubMedGoogle Scholar
• 2 World Health Organization. Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva, Switzerland: World Health Organization; 2009
  Google Scholar
• 3 Cheung YK, Moon YP, Kulick ER. et al. Leisuretime physical activity and cardiovascular mortality in an elderly population in northern Manhattan: a prospective cohort study. J Gen Intern Med 2017; 32: 168-174 doi: 10.1007/s11606–016–3884-y.
  CrossrefPubMedGoogle Scholar
• 4 Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, Das SR, de Ferranti S, Després J-P, Fullerton HJ, Howard VJ, Huffman MD, Isasi CR, Jiménez MC, Judd SE, Kissela BM, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Magid DJ, McGuire DK, Mohler III ER, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Rosamond W, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Woo D, Yeh RW, Turner MB. on behalf of the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics – 2016 Update: a report from the American Heart Association. Circulation 2016; 133: 447-454.
  CrossrefPubMedGoogle Scholar
• 5 Marcus BH, Forsyth LH, Stone EJ, Dubbert PM, McKenzie TL, Dunn AL, Blair SN. Physical activity behavior change: Issues in adoption and maintenance. Health Psychology 2000; 19: 32-41.
  CrossrefPubMedGoogle Scholar
• 6 Linke SE, Gallo LC, Norman GJ. Attrition and adherence rates of sustained vs. intermittent exercise interventions. Ann Behav Med 2011; 42: 197-209.
  CrossrefPubMedGoogle Scholar
• 7 Pratt M, Epping J, Dietz W. Putting physical activity into public health: A historical perspective from the CDC. Preventive Medicine 2009; 49 (04) 301-302.
  CrossrefPubMedGoogle Scholar
• 8 Graboys TB, Lown B. Coffee, arrhythmias, and common sense. N Engl J Med 1983; 308: 835-837.
  CrossrefPubMedGoogle Scholar
• 9 Stults-Kolehmainen MA, Sinha R. The effects of stress on physical activity and exericise. Sports Med 2014; 44: 81-121.
  CrossrefPubMedGoogle Scholar
• 10 Oman RF, King AC. The effect of life events and exercise program format on the adoption and maintenance of exercise behavior. Health Psychol 2000; 19: 605-612.
  CrossrefPubMedGoogle Scholar
• 11 Holmes SD, Krantz DS, Rogers H, Gottdiener J, Contrada RJ. Mental stress and coronary artery disease: A multidisciplinary guide. Prog Cardiovasc Dis 2006; 49: 106-122.
  CrossrefPubMedGoogle Scholar
• 12 Selye H. The Stress of Life. New York: McGrawHill; 1956
  Google Scholar
• 13 Lazarus R. Psychological stress and the coping process. New York: McGraw-Hill; 1996
  Google Scholar
• 14 Barlow DH, Hersen M, Hartmann DP, Kazdin AE. Single case experimental designs: strategies for studying behavior change. Allyn and Bacon. 1984
  PubMedGoogle Scholar
• 15 Barlow DH, Nock M, Hersen M. Single case experimental designs: strategies for studying behavior for change. Pearson/Allyn and Bacon. 2009
  Google Scholar
• 16 Smith JD. Single-case experimental designs: a systematic review of published research and current standards. Psychological Methods 2012; 17 (04) 510-550.
  CrossrefPubMedGoogle Scholar
• 17 Lillie EO, Pata B, Diamant J, Issell B, Topol EJ, Schork NJ. The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?. Personalized Medicine 2011; 08 (02) 161-173.
  CrossrefPubMedGoogle Scholar
• 18 Kravitz RL, Duan N. eds and the DEcIDE Methods Center N-of-1 Guidance Panel. Design and Implementation of N-of-1 Trials: A User’s Guide.AHRQ Publication. 13(14) Rockville, MD: Agency for Healthcare Research and Quality; 2014
• 19 Shaffer JA, Falzon L, Cheung K, Davidson KW, Gabler N, Duan N, Bennett D. N-of-1 randomized trials for psychological and health behavior outcomes: a systematic review protocol. Systematic Reviews 2015; 04 (01) 87.
  CrossrefPubMedGoogle Scholar
• 20 Cheung YK, Chakraborty B, Davidson KW. Sequential multiple assignment randomized trial (SMART) with adaptive randomization for quality improvement in depression treatment program. Biometrics 2015; 71: 450-459.
  CrossrefPubMedGoogle Scholar
• 21 Bolger N, Davis A, Rafaeli E. Diary methods: capturing life as it is lived. Ann Rev Psychol 2003; 54: 579-616.
  CrossrefPubMedGoogle Scholar
• 22 Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annual Review Clinical Psychology 2008; 04: 1-32.
  PubMedGoogle Scholar
• 23 Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, Riley WT, Shar A, Spring B, Spruijt-Metz D, Hedeker D, Honavar V, Kravitz R, Lefebvre D, Mohr DC, Murphy SA, Quinn C, Shusterman V, Swendeman D. Mobile health technology evaluation. Am J Prev Med 2013; 45: 228-236.
  CrossrefPubMedGoogle Scholar
• 24 Swan M. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data 2013; 01 (02) 85-99.
  CrossrefPubMedGoogle Scholar
• 25 Gray K, Martin-Sanchez FJ, Lopez-Campos GH, Almalki M, Merolli M. (2016). Person-generated Data in Self-quantification. Methods Inf Med 2017; 56 (01) 40-45.
  Article in Thieme ConnectPubMedGoogle Scholar
• 26 Rosenberg M. Society and the adolescent selfimage. Princeton, NJ: Princeton University Publishers; 1965
  Google Scholar
• 27 Pearlin LI, Lieberman MA, Menaghan EG, Mullan JT. The stress process. J Health Soc Behav 1981; 22: 337-356.
  CrossrefPubMedGoogle Scholar
• 28 Burg MM, Schwartz JE, Kronish IM. et al. Does stress result in you exercising less? Or does exercising result in you being less stressed? Or is it both? Testing the bi-directional stress-exercise association at the group and person (n of 1) level. Ann Behav Med. 2017. forthcoming. doi: 10.1007/ s12160–017–9902–4.
  Google Scholar
• 29 Ward DS, Evenson KR, Vaughn A, Rodgers AB, Troiano RP. Accelerometer use in physical activity: best practices and research recommendations. Med Sci Sports Exerc 2005; 37: S582-588.
  CrossrefPubMedGoogle Scholar
• 30 Garber CE, Blissmer B, Deschenes MR. et al. American College of Sports Medicine position stand. Quanity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc 2011; 43: 1334-1359.
  CrossrefPubMedGoogle Scholar
• 31 Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol 2008; 04: 1-32.
  CrossrefPubMedGoogle Scholar
• 32 Smyth JM, Wonderlich SA, Sliwinski MJ, Crosby RD, Engel SG, Mitchell JE, Calogero RM. Ecological momentary assessment of affect, stress, and binge-purge behaviors: day of week and time of day effects in the natural environment. Int J Eat Disord 2009; 42 (05) 429-436.
  CrossrefPubMedGoogle Scholar
• 33 Moore RC, Depp CA, Wetherell JL, Lene EJ. Ecolgoical momentary assessment versus standard assessment instruments for measuring mindfulness, depressed mood, and anxiety among older adults. J Psychiatr Res 2016; 75: 116-123.
  CrossrefPubMedGoogle Scholar
• 34 Breiman L. Random forests. Machine Learning 2001; 45 (01) 5-32.
  CrossrefPubMedGoogle Scholar
• 35 Cheung K, Duan N. Design of implementation studies for quality improvement programs: an effectiveness-cost-effectiveness framework. Am J Public Health 2014; 104 (01) e23-e30.
  PubMedGoogle Scholar
• 36 Jakicic JM, Davis KK, Rogers RJ, King WC, Marcus MD, Helsel D, Belle SH. Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss. JAMA 2016; 316 (11) 1161-1171.
  CrossrefPubMedGoogle Scholar
• 37 Bloss CS, Wineinger NE, Peters M, Boeldt DL, Ariniello L, Kim JY, Topol EJ. A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors. Peerb J 2016; 04: e1554.
  PubMedGoogle Scholar
• 38 King AC, Sallis JF. Why and how to improve physical activity promotion: Lessons from behavioral science and related fields. Preventive Medicine 2009; 49 (04) 286-288.
  CrossrefPubMedGoogle Scholar
• 39 Hsueh PS, Chang H, Ramakrishnan S. Next Generation Wellness: A Technology Model for Personalizing Healthcare. In Weaver CA, Ball M, Kim G, Kiel JM. editors. Healthcare information Management Systems. 4th ed.. Springer: International Publishing; 2016: 356-374.
  Google Scholar
• 40 Lin M, Mahmooth Z, Dedhia N, Frutchey R, Mercado CE, Epstein DH, O’Brien MS. Tailored, interactive text messages for enhancing weight loss among African American adults: the TRIMM randomized controlled trial. The American Journal of Medicine 2015; 128 (08) 896-904.
  CrossrefPubMedGoogle Scholar
• 41 Mohr DC, Burns MN, Schueller SM, Clarke G, Klinkman M. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. Gen Hosp Psychiatry 2013; 35 (04) 332-338.
  CrossrefPubMedGoogle Scholar
• 42 Mohr DC, Cheung K, Schueller SM, Brown CH, Duan N. Continuous evaluation of evolving behavioral intervention technologies. Am J Prev Med 2013; 45 (04) 517-523.
  CrossrefPubMedGoogle Scholar
• 43 Cheung YK. Dose Finding by the Continual Reassessment Method. Chapman Hall/CRC Press; 2012
  Google Scholar
• 44 Cheung YK, Yu G, Wall MM, Sacco RL, Elkind MSV, Willey JZ. Patterns of leisure-time physical activity using multivariate finite mixture modeling and cardiovascular risk factors in the Northern Manhattan Study. Ann Epidemiol 2015; 25 (07) 469-474.
  CrossrefPubMedGoogle Scholar
• 45 Hsueh PS, Zhu X, Deng V, Ramarishnan S, Ball M. 2014 Dynamic and accretive composition of patient engagement instruments for personalized plan generation. Studies in Health Technology and Informatics. 2014.
  PubMedGoogle Scholar
• 46 Mamykina L, Smaldone AM, Bakken SR. Adopting the sensemaking perspective for chronic disease self-management. Journal of Biomedical Informatics 2015; 56: 406-417.
  CrossrefPubMedGoogle Scholar
• 47 McGillicuddy JW, Gregoski MJ, Weiland AK, Rock RA, Brunner-Jackson BM, Patel SK, Treiber FA. (2013). Mobile Health Medication Adherence and Blood Pressure Control in Renal Transplant Recipients: A Proof-of-Concept Randomized Controlled Trial. JMIR Res Protoc 2013; 02 (02) e32.
  CrossrefPubMedGoogle Scholar
• 48 Karkar R, Zia J, Vilardaga R, Mishra SR, Fogarty J, Munson SA, Kientz JA. A framework for self-experimentation in personalized health. J Am Med Inform Assoc 2016; 23 (03) 440-448.
  CrossrefPubMedGoogle Scholar
• 49 Diaz KM, Krupka DJ, Chang MJ, Peacock J, Ma Y, Goldsmith J, Schwartz JE, Davidson KW. (2015). Fitbit®: An accurate and reliable device for wireless physical activity tracking. Int J Cardiol 2015; 185: 138-140.
  CrossrefPubMedGoogle Scholar
• 50 Evenson KR, Goto MM, Furberg RD. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act 2015; 12: 159.
  CrossrefPubMedGoogle Scholar