Healthc Inform Res.  2018 Oct;24(4):381-386. 10.4258/hir.2018.24.4.381.

Ecological Momentary Assessment Using Smartphone-Based Mobile Application for Affect and Stress Assessment

• Yang YS
• Ryu GW
• Han I
• Oh S
• Choi M

• KMID: 2434554
• DOI: http://doi.org/10.4258/hir.2018.24.4.381

Abstract

OBJECTIVES
This study aimed to describe the process of utilizing a mobile application for ecological momentary assessment (EMA) to collect data on stress and mood in daily life setting.
METHODS
A mobile application for the Android operating system was developed and installed with a set of questions regarding momentary mood and stress into a smartphone of a participant. The application sets alarms at semi-random intervals in 60-minute blocks, four times a day for 7 days. After obtaining all momentary affect and stress, the questions to assess the usability of the mobile EMA application were also administered.
RESULTS
The data were collected from 97 police officers working in Gyeonggi Province of South Korea. The mean completion rate was 60.0% ranging from 3.5% to 100%. The means of positive and negative affect were 18.34 of 28 and 19.09 of 63. The mean stress was 17.92 of 40. Participants responded that the mobile application correctly measured their affect (4.34 ± 0.83) and stress (4.48 ± 0.62) of 5-point Likert scale.
CONCLUSIONS
Our study investigated the process of utilizing a mobile application to assess momentary affect and stress at repeated times. We found challenges regarding adherence to the research protocol, such as completion and delay of answering after alarm notification. Despite this inherent issue of adherence to the research protocol, the EMA still has advantages of reducing recall bias and assessing the actual moment of interest at multiple time points that improves ecological validity.

Keyword

Ecological Momentary Assessment; Mobile Applications; Smartphone; Affect; Psychological Stress

MeSH Terms

Bias (Epidemiology)
Gyeonggi-do
Humans
Korea
Methyltestosterone
Mobile Applications*
Police
Smartphone
Stress, Psychological
Methyltestosterone

Figure

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  Figure 1 Structure of mobile ecological momentary assessment (EMA) service. FCM: Firebase Cloud Messaging, APNs: Access Point Names, API: application programming interface.

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  Figure 2 Flow of mobile ecological momentary assessment (EMA) service.

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  Figure 3 Screen examples of mobile EMA application.

Reference

1. Stone AA, Shiffman S. Ecological momentary assessment (EMA) in behavioral medicine. Annu Behav Med. 1994; 16(3):199–202.

2. Shiffman S, Stone AA, Hufford MR. Ecological momentary assessment. Annu Rev Clin Psychol. 2008; 4:1–32.

3. Bolger N, Laurenceau JP. Intensive longitudinal methods: an introduction to diary and experience sampling research. New York (NY): Guilford Press;2013.

4. Doherty ST, Lemieux CJ, Canally C. Tracking human activity and well-being in natural environments using wearable sensors and experience sampling. Soc Sci Med. 2014; 106:83–92.

5. Shiffman S. Dynamic influences on smoking relapse process. J Pers. 2005; 73(6):1715–1748.

6. Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H. Smartphones for smarter delivery of mental health programs: a systematic review. J Med Internet Res. 2013; 15(11):e247.

7. Myin-Germeys I, Oorschot M, Collip D, Lataster J, Delespaul P, van Os J. Experience sampling research in psychopathology: opening the black box of daily life. Psychol Med. 2009; 39(9):1533–1547.

8. Schwartz JE, Stone AA. Strategies for analyzing ecological momentary assessment data. Health Psychol. 1998; 17(1):6–16.

9. Ebner-Priemer UW, Trull TJ. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol Assess. 2009; 21(4):463–475.

10. Band R, Barrowclough C, Emsley R, Machin M, Wearden AJ. Significant other behavioural responses and patient chronic fatigue syndrome symptom fluctuations in the context of daily life: An experience sampling study. Br J Health Psychol. 2016; 21(3):499–514.

11. Clasen PC, Fisher AJ, Beevers CG. Mood-reactive self-esteem and depression vulnerability: person-specific symptom dynamics via smart phone assessment. PLoS One. 2015; 10(7):e0129774.

12. Cook PF, Schmiege SJ, Bradley-Springer L, Starr W, Carrington JM. Motivation as a mechanism for daily experiences' effects on HIV medication adherence. J Assoc Nurses AIDS Care. 2018; 29(3):383–393.

13. Faherty LJ, Hantsoo L, Appleby D, Sammel MD, Bennett IM, Wiebe DJ. Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy. J Am Med Inform Assoc. 2017; 24(4):746–753.

14. Fuller-Tyszkiewicz M, Karvounis T, Pemberton R, Hartley-Clark L, Richardson B. Determinants of depressive mood states in everyday life: an experience sampling study. Motiv Emot. 2017; 41(4):510–521.

15. Verhagen SJ, Berben JA, Leue C, Marsman A, Delespaul PA, van Os J, et al. Demonstrating the reliability of transdiagnostic mHealth Routine Outcome Monitoring in mental health services using experience sampling technology. PLoS One. 2017; 12(10):e0186294.

16. Powell DJH, Liossi C, Schlotz W, Moss-Morris R. Tracking daily fatigue fluctuations in multiple sclerosis: ecological momentary assessment provides unique insights. J Behav Med. 2017; 40(5):772–783.

17. Jones KK, Zenk SN, McDonald A, Corte C. Experiences of African-American women with smartphone-based ecological momentary assessment. Public Health Nurs. 2016; 33(4):371–380.