Skip to main content
Top
Published in: BioPsychoSocial Medicine 1/2019

Open Access 01-12-2019 | Care | Review

Potential benefits of integrating ecological momentary assessment data into mHealth care systems

Authors: Jinhyuk Kim, David Marcusson-Clavertz, Kazuhiro Yoshiuchi, Joshua M. Smyth

Published in: BioPsychoSocial Medicine | Issue 1/2019

Login to get access

Abstract

The advancement of wearable/ambulatory technologies has brought a huge change to data collection frameworks in recent decades. Mobile health (mHealth) care platforms, which utilize ambulatory devices to collect naturalistic and often intensively sampled data, produce innovative information of potential clinical relevance. For example, such data can inform clinical study design, recruitment approach, data analysis, and delivery of both “traditional” and novel (e.g., mHealth) interventions. We provide a conceptual overview of how data measured continuously or repeatedly via mobile devices (e.g., smartphone and body sensors) in daily life could be fruitfully used within a mHealth care system. We highlight the potential benefits of integrating ecological momentary assessment (EMA) into mHealth platforms for collecting, processing, and modeling data, and delivering and evaluating novel interventions in everyday life. Although the data obtained from EMA and related approaches may hold great potential benefits for mHealth care system, there are also implementation challenges; we briefly discuss the challenges to integrating EMA into mHealth care system.
Literature
1.
go back to reference Hayes BM, Aspray W. Health informatics : a patient-centered approach to diabetes. Cambridge, Mass.: MIT Press; 2010. xiv, 383 p. p.CrossRef Hayes BM, Aspray W. Health informatics : a patient-centered approach to diabetes. Cambridge, Mass.: MIT Press; 2010. xiv, 383 p. p.CrossRef
2.
go back to reference Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4(1):1–32.CrossRef Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008;4(1):1–32.CrossRef
3.
go back to reference Smyth JM, Juth V, Ma J, Sliwinski MJ. A slice of life: ecologically valid methods for research on social relationships and health across the life span. Soc Personal Psychol Compass. 2017;11(10):e12356.CrossRef Smyth JM, Juth V, Ma J, Sliwinski MJ. A slice of life: ecologically valid methods for research on social relationships and health across the life span. Soc Personal Psychol Compass. 2017;11(10):e12356.CrossRef
4.
go back to reference Kumar S, Nilsen W, Pavel M, Srivastava M. Mobile health: revolutionizing healthcare through transdisciplinary research. IEEE Comput. 2013;46(1):28–35.CrossRef Kumar S, Nilsen W, Pavel M, Srivastava M. Mobile health: revolutionizing healthcare through transdisciplinary research. IEEE Comput. 2013;46(1):28–35.CrossRef
5.
go back to reference Kubiak T, Smyth JM. Connecting domains – ecological momentary assessment in a mobile sensing framework. In: Bachmann H, Montag C, editors. Digital phenotyping and mobile sensing. Berlin: Springer; in press. Kubiak T, Smyth JM. Connecting domains – ecological momentary assessment in a mobile sensing framework. In: Bachmann H, Montag C, editors. Digital phenotyping and mobile sensing. Berlin: Springer; in press.
6.
go back to reference Ben-Zeev D, Schueller SM, Begale M, Duffecy J, Kane JM, Mohr DC. Strategies for mHealth research: lessons from 3 mobile intervention studies. Admin Pol Ment Health. 2015;42(2):157–67.CrossRef Ben-Zeev D, Schueller SM, Begale M, Duffecy J, Kane JM, Mohr DC. Strategies for mHealth research: lessons from 3 mobile intervention studies. Admin Pol Ment Health. 2015;42(2):157–67.CrossRef
7.
go back to reference Ali EE, Chew L, Yap KY-L. Evolution and current status of mhealth research: a systematic review. BMJ Innovations. 2016;2(1):33–40.CrossRef Ali EE, Chew L, Yap KY-L. Evolution and current status of mhealth research: a systematic review. BMJ Innovations. 2016;2(1):33–40.CrossRef
8.
go back to reference Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009;21(4):486–97.CrossRef Shiffman S. Ecological momentary assessment (EMA) in studies of substance use. Psychol Assess. 2009;21(4):486–97.CrossRef
9.
go back to reference Bodin F, McIntyre KM, Schwartz JE, McKinley PS, Cardetti C, Shapiro PA, et al. The Association of Cigarette Smoking with High-Frequency Heart Rate Variability: an ecological momentary assessment study. Psychosom Med. 2017;79(9):1045–50.CrossRef Bodin F, McIntyre KM, Schwartz JE, McKinley PS, Cardetti C, Shapiro PA, et al. The Association of Cigarette Smoking with High-Frequency Heart Rate Variability: an ecological momentary assessment study. Psychosom Med. 2017;79(9):1045–50.CrossRef
10.
go back to reference Mitchell JT, Schick RS, Hallyburton M, Dennis MF, Kollins SH, Beckham JC, et al. Combined ecological momentary assessment and global positioning system tracking to assess smoking behavior: a proof of concept study. J Dual Diagn. 2014;10(1):19–29.CrossRef Mitchell JT, Schick RS, Hallyburton M, Dennis MF, Kollins SH, Beckham JC, et al. Combined ecological momentary assessment and global positioning system tracking to assess smoking behavior: a proof of concept study. J Dual Diagn. 2014;10(1):19–29.CrossRef
11.
go back to reference van Velthoven MH, Car J, Zhang Y, Marusic A. mHealth series: new ideas for mHealth data collection implementation in low- and middle-income countries. J Glob Health. 2013;3(2):020101.PubMedPubMedCentral van Velthoven MH, Car J, Zhang Y, Marusic A. mHealth series: new ideas for mHealth data collection implementation in low- and middle-income countries. J Glob Health. 2013;3(2):020101.PubMedPubMedCentral
12.
go back to reference Nilsen W, Ertin E, Hekler EB, Kumar S, Lee I, Mangharam R, et al. Modeling Opportunities in mHealth Cyber-Physical Systems. In: Rehg JM, Murphy SA, Kumar S, editors. Mobile Health: Sensors, Analytic Methods, and Applications: Springer, Cham; 2017. p. 443–53.CrossRef Nilsen W, Ertin E, Hekler EB, Kumar S, Lee I, Mangharam R, et al. Modeling Opportunities in mHealth Cyber-Physical Systems. In: Rehg JM, Murphy SA, Kumar S, editors. Mobile Health: Sensors, Analytic Methods, and Applications: Springer, Cham; 2017. p. 443–53.CrossRef
13.
go back to reference Goldstein SP, Evans BC, Flack D, Juarascio A, Manasse S, Zhang F, et al. Return of the JITAI: applying a just-in-time adaptive intervention framework to the development of m-health solutions for addictive behaviors. Int J Behav Med. 2017;24(5):673–82.CrossRef Goldstein SP, Evans BC, Flack D, Juarascio A, Manasse S, Zhang F, et al. Return of the JITAI: applying a just-in-time adaptive intervention framework to the development of m-health solutions for addictive behaviors. Int J Behav Med. 2017;24(5):673–82.CrossRef
14.
go back to reference 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 Psychol. 2015;34S:1220–8.CrossRef 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 Psychol. 2015;34S:1220–8.CrossRef
15.
go back to reference Spruijt-Metz D, Wen CK, O'Reilly G, Li M, Lee S, Emken BA, et al. Innovations in the use of interactive technology to support weight management. Curr Obes Rep. 2015;4(4):510–9.CrossRef Spruijt-Metz D, Wen CK, O'Reilly G, Li M, Lee S, Emken BA, et al. Innovations in the use of interactive technology to support weight management. Curr Obes Rep. 2015;4(4):510–9.CrossRef
16.
go back to reference 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. Ann Behav Med. 2018;52(6):446–62.CrossRef 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. Ann Behav Med. 2018;52(6):446–62.CrossRef
17.
go back to reference Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(Pt 1):1–39.CrossRef Heron KE, Smyth JM. Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. Br J Health Psychol. 2010;15(Pt 1):1–39.CrossRef
18.
go back to reference Smyth JM, Heron KE. Is providing mobile interventions "just-in-time" helpful? an experimental proof of concept study of just-in-time intervention for stress management. 2016 IEEE Wireless Health; 2016. p. 89–95. Smyth JM, Heron KE. Is providing mobile interventions "just-in-time" helpful? an experimental proof of concept study of just-in-time intervention for stress management. 2016 IEEE Wireless Health; 2016. p. 89–95.
19.
go back to reference Smyth JM, Sliwinski MJ, Zawadzki MJ, Scott SB, Conroy DE, Lanza ST, et al. Everyday stress response targets in the science of behavior change. Behav Res Ther. 2018;101:20–9.CrossRef Smyth JM, Sliwinski MJ, Zawadzki MJ, Scott SB, Conroy DE, Lanza ST, et al. Everyday stress response targets in the science of behavior change. Behav Res Ther. 2018;101:20–9.CrossRef
20.
go back to reference O'Driscoll R, Turicchi J, Beaulieu K, Scott S, Matu J, Deighton K, et al. How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies. Br J Sports Med. 2018. O'Driscoll R, Turicchi J, Beaulieu K, Scott S, Matu J, Deighton K, et al. How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies. Br J Sports Med. 2018.
21.
go back to reference Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313(6):625–6.CrossRef Case MA, Burwick HA, Volpp KG, Patel MS. Accuracy of smartphone applications and wearable devices for tracking physical activity data. JAMA. 2015;313(6):625–6.CrossRef
22.
go back to reference Wang R, Blackburn G, Desai M, Phelan D, Gillinov L, Houghtaling P, et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol. 2017;2(1):104–6.CrossRef Wang R, Blackburn G, Desai M, Phelan D, Gillinov L, Houghtaling P, et al. Accuracy of wrist-worn heart rate monitors. JAMA Cardiol. 2017;2(1):104–6.CrossRef
23.
go back to reference Gilgen-Ammann R, Schweizer T, Wyss T. RR interval signal quality of a heart rate monitor and an ECG Holter at rest and during exercise. Eur J Appl Physiol. 2019;119(7):1525–32.CrossRef Gilgen-Ammann R, Schweizer T, Wyss T. RR interval signal quality of a heart rate monitor and an ECG Holter at rest and during exercise. Eur J Appl Physiol. 2019;119(7):1525–32.CrossRef
24.
go back to reference Ramkumar PN, Haeberle HS, Ramanathan D, Cantrell WA, Navarro SM, Mont MA, et al. Remote patient monitoring using Mobile health for Total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplast. 2019. Ramkumar PN, Haeberle HS, Ramanathan D, Cantrell WA, Navarro SM, Mont MA, et al. Remote patient monitoring using Mobile health for Total knee arthroplasty: validation of a wearable and machine learning-based surveillance platform. J Arthroplast. 2019.
25.
go back to reference Kim J, Nakamura T, Kikuchi H, Sasaki T, Yamamoto Y. Co-variation of depressive mood and locomotor dynamics evaluated by ecological momentary assessment in healthy humans. PLoS One. 2013;8(9):e74979.CrossRef Kim J, Nakamura T, Kikuchi H, Sasaki T, Yamamoto Y. Co-variation of depressive mood and locomotor dynamics evaluated by ecological momentary assessment in healthy humans. PLoS One. 2013;8(9):e74979.CrossRef
26.
go back to reference Blaauw FJ, Schenk HM, Jeronimus BF, van der Krieke L, de Jonge P, Aiello M, et al. Let's get Physiqual - an intuitive and generic method to combine sensor technology with ecological momentary assessments. J Biomed Inform. 2016;63:141–9.CrossRef Blaauw FJ, Schenk HM, Jeronimus BF, van der Krieke L, de Jonge P, Aiello M, et al. Let's get Physiqual - an intuitive and generic method to combine sensor technology with ecological momentary assessments. J Biomed Inform. 2016;63:141–9.CrossRef
27.
go back to reference Szlezak N, Evers M, Wang J, Perez L. The role of big data and advanced analytics in drug discovery, development, and commercialization. Clin Pharmacol Ther. 2014;95(5):492–5.CrossRef Szlezak N, Evers M, Wang J, Perez L. The role of big data and advanced analytics in drug discovery, development, and commercialization. Clin Pharmacol Ther. 2014;95(5):492–5.CrossRef
28.
go back to reference Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, et al. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med. 2015;5(3):335–46.CrossRef Spruijt-Metz D, Hekler E, Saranummi N, Intille S, Korhonen I, Nilsen W, et al. Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research. Transl Behav Med. 2015;5(3):335–46.CrossRef
29.
go back to reference 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;1(1):53–71.CrossRef 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;1(1):53–71.CrossRef
30.
go back to reference Luers B, Klasnja P, Murphy S. Standardized effect sizes for preventive Mobile health interventions in micro-randomized trials. Prev Sci. 2019;20(1):100–9.CrossRef Luers B, Klasnja P, Murphy S. Standardized effect sizes for preventive Mobile health interventions in micro-randomized trials. Prev Sci. 2019;20(1):100–9.CrossRef
31.
go back to reference Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro-randomized trials in mHealth. Stat Med. 2016;35(12):1944–71.CrossRef Liao P, Klasnja P, Tewari A, Murphy SA. Sample size calculations for micro-randomized trials in mHealth. Stat Med. 2016;35(12):1944–71.CrossRef
32.
go back to reference Pavel M, Jimison HB, Korhonen I, Gordon CM, Saranummi N. Behavioral informatics and computational modeling in support of proactive health management and care. IEEE Trans Biomed Eng. 2015;62(12):2763–75.CrossRef Pavel M, Jimison HB, Korhonen I, Gordon CM, Saranummi N. Behavioral informatics and computational modeling in support of proactive health management and care. IEEE Trans Biomed Eng. 2015;62(12):2763–75.CrossRef
33.
go back to reference Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228–36.CrossRef Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228–36.CrossRef
34.
go back to reference Chen C-H, Smyth JM. Special issue on health behavior in the information age. Journal of Healthcare Informatics Research. in press. Chen C-H, Smyth JM. Special issue on health behavior in the information age. Journal of Healthcare Informatics Research. in press.
Metadata
Title
Potential benefits of integrating ecological momentary assessment data into mHealth care systems
Authors
Jinhyuk Kim
David Marcusson-Clavertz
Kazuhiro Yoshiuchi
Joshua M. Smyth
Publication date
01-12-2019
Publisher
BioMed Central
Keyword
Care
Published in
BioPsychoSocial Medicine / Issue 1/2019
Electronic ISSN: 1751-0759
DOI
https://doi.org/10.1186/s13030-019-0160-5

Other articles of this Issue 1/2019

BioPsychoSocial Medicine 1/2019 Go to the issue