Abstract
Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user’s experience in real time. In this paper, we describe the design and development of Psychlog, a mobile phone platform designed to collect users’ psychological, physiological, and activity information for mental health research. The tool allows administering self-report questionnaires at specific times or randomly within a day. The system also permits to collect heart rate and activity information from a wireless electrocardiogram equipped with a three-axial accelerometer. By combining self-reports with heart rate and activity data, the application makes it possible to investigate the relationship between psychological, physiological, and behavioral variables, as well as to monitor their fluctuations over time. The software runs on Windows mobile operative system and is available as open source (http://sourceforge.net/projects/psychlog/).
Similar content being viewed by others
References
Larson RW, Csikszentmihalyi M (1983) The experience sampling method. In: Reis H (ed) New directions for naturalistic methods in the behavioral sciences. Jossey-Bass, San Francisco, pp 41–56
Stone AA, Shiffman S (1994) Ecological momentary assessment (EMA) in behavioral medicine. Annal Behav Med 16:199–202
Barrett LF, Barrett DJ (2001) An introduction to computerized experience sampling in psychology. Soc Sci Comput Rev 19(2):175–185
Conner TS, Tennen H, Fleeson W, Feldman BL (2009) Experience sampling methods: a modern idiographic approach to personality research. Social Person Psychol Compass 13(3):292–313
Fischer JE (2009) Experience sampling tools: a critical review. In: Proceedings of MobileHCI’09, 15–18 Sept 2009, ACM, Bonn, Germany
Intille SS, Rondoni J, Kukla C, Ancona I, Bao L (2003) A context-aware experience sampling tool. In: Proceedings of human factors in computing systems (CHI 03), ACM, New York, USA, pp 972–973
Yin J, Yang Q, Pan JJ (2008) Sensor-based abnormal human-activity detection. IEEE Trans knowl Data Eng 20(8):1082–1090
Raento M, Oulasvirta A, Eagle N (2009) Smartphones: an emerging tool for social scientists. Sociol Method Res 37:426–454
Pantelopoulos A, Bourbakis NG (2011) A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man and Cyber 41(1):1–12
Asada HH, Shaltis P, Reisner A, Rhee S, Hutchinson RC (2003) Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Eng Med Biol Mag 22(3):28–40
Budinger TF (2003) Biomonitoring with wireless communications. Annu Rev Biomed Eng 5:383–412
Sun L, Zhang D, Li B, Guo B, Li S (2010) Activity recognition on an accelerometer embedded mobile phone with varying positions and orientations. Lect Notes Comput Sci 6406:548–562
Aminian K, Robert P, Buchser E, Rutschmann B, Hayoz D, Depairon M (1999) Physical activity monitoring based on accelerometry: validation and comparison with video observation. Med Biol Eng Comput 37(1):304–308
Hicks J, Ramanathan N, Kim D, Monibi M, Selsky J, Hansen M, Estrin D (2010) AndWellness: an open mobile system for activity and experience sampling. In Proceedings of wireless health 2010 academic and research conference, La Jolla, CA, 5–7 Oct 2010 (WH ‘10). ACM, New York, USA
Hektner JM, Schmidt JA, Csikszentmihalyi M (2007) Experience sampling method: measuring the quality of everyday life. Sage, Thousand Oaks
Reis HT, Gable SL (2000) Event-sampling and other methods for studying everyday experience. In: Reis HT, Judd CM (eds) Handbook of research methods in social and personality psychology. Cambridge University Press, New York, pp 190–222
Wheeler L, Reis HT (1991) Self-recording of everyday life events: origins, types, and uses. J Pers 59:339–354
Shiffman S, Stone AA, Hufford MR (2008) Ecological momentary assessment. Annu Rev Clin Psychol 4:1–32
Moskowitz DS, Young SN (2006) Ecological momentary assessment: what it is and why it is a method of the future in clinical psychopharmacology. Journal Psychiatry Neurosci 31(1):13–20
Barge-Schaapveld DQ, Nicolson NA (2002) Effects of antidepressant treatment on the quality of daily life: an experience sampling study. J Clin Psychiatry 63(6):477–485
Barrett LF, Barrett DJ (2001) An Introduction to computerized experience sampling in psychology. Soc Sci Comput Rev Summer 19(2):175–185
Froehlich J, Chen MY, Consolvo S, Harrison B, Landay JA (2007) MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. MobiSys ‘07. ACM, New York, USA, pp 57–70
Khan VJ, Markopoulos P, Eggen B (2009) Features for the future Experience Sampling Tool. In: Proceedings of MobileHCI ‘09, 15–18 Sept 2009, ACM, Bonn, Germany
Berntson GG, Cacioppo JT (2004) Heart rate variability: stress and psychiatric conditions. In: Malik M, Camm J (eds) Dynamic electrocardiography. Wiley-Blackwell, Hoboken, pp 57–64
Kimhy D, Delespaul P, Ahn H, Cai S, Shikhman M, Lieberman JA, Malaspina D, Sloan RP (2010) Concurrent measurement of “real-world” stress and arousal in individuals with psychosis: assessing the feasibility and validity of a novel methodology. Schizophr Bull 36(6):1131–1139
Pan J, Tompkins WJ (1985) A real-time QRS detection algorithm. IEEE Trans Biomed Eng 32:230–236
Jönsson P (2007) Respiratory sinus arrhythmia as a function of state anxiety in healthy individuals. Int J Psychophysiol 63(1):48–55
Jönsson P, Hansson-Sandsten M (2008) Respiratory sinus arrhythmia in response to fear-relevant and fear-irrelevant stimuli. Scand J Psychol 49(2):123–131
Sloan RP, Shapiro PA, Bagiella E, Boni SM, Paik M, Bigger JT Jr, Steinman RC, Gorman JM (1994) Effect of mental stress throughout the day on cardiac autonomic control. Biol Psychol 37:89–99
Jacobs N, Myin-Germeys I, Derom C, Delespaul P, van Os J, Nicolson NA (2007) A momentary assessment study of the relationship between affective and adrenocortical stress responses in daily life. Biol Psychol 74:60–66
Larson RW, Delespaul P (1992) Analyzing Experience Sampling data: a guide book for the perplexed. In: de Vries MW (ed) The experience of psychopathology. Cambridge University Press, New York, pp 58–78
Mateo J, Laguna P (2003) Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE TransBiomed Eng 50(3):334–343
Malik M, Bigger JT, Camm AJ, Kleiger R, Malliani A, Moss A et al (1996) Task force of the European society of cardiology and the North American society of pacing and electrophysiology, heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation 93(5):1043–1065
Acknowledgments
This work was supported by the European funded project “Interstress-Interreality in the management and treatment of stress-related disorders”, FP7-247685.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gaggioli, A., Pioggia, G., Tartarisco, G. et al. A mobile data collection platform for mental health research. Pers Ubiquit Comput 17, 241–251 (2013). https://doi.org/10.1007/s00779-011-0465-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-011-0465-2