Abstract
Due to their widespread adoption, frequent use, and diverse sensor capabilities, smartphones have become a powerful tool for academic studies focused on sampling human behaviour. While packing many technological advances, the need for researchers to develop their own software packages in order to run smartphone-based studies has resulted in a clear barrier to entry for researchers without the financial means, time, or technical knowledge required to overcome this technical barrier. We present AWARE-Light, a new smartphone application for data collection from study participants, which is accompanied by a website that provides any researcher the possibility to easily configure their own study. To highlight the possibilities of our tool, we present a research scenario on digital phenotyping for mental health. Furthermore, we describe the methodological configuration possibilities offered by our tool, and complement the technical configuration possibilities with recommendations from the existing literature.
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References
Raento M, Oulasvirta A, Eagle N (2009) Smartphones: an emerging tool for social scientists. Sociol Methods Res 37(3):426–454. https://doi.org/10.1177/0049124108330005
van Berkel N, Goncalves J, Wac K, Hosio S, Cox AL (2020) Human accuracy in mobile data collection. Int J Hum Comput Stud 137. https://doi.org/10.1016/j.ijhcs.2020.102396
Larson R, Csikszentmihalyi M (2014) The Experience Sampling Method. Springer, Dordrecht, pp 21–34. https://doi.org/10.1007/978-94-017-9088-8_2
van Berkel N, Ferreira D, Kostakos V (2017) The experience sampling method on mobile devices. ACM Comput Surv 50(6):93–19340. https://doi.org/10.1145/3123988
Miller G (2012) The smartphone psychology manifesto. Perspect Psychol Sci 7(3):221–237. https://doi.org/10.1177/1745691612441215
Rough D, Quigley A (2015) Jeeves - a visual programming environment for mobile experience sampling. In: 2015 IEEE symposium on visual languages and human-centric computing (VL/HCC), IEEE, pp 121–129
Xiong H, Huang Y, Barnes LE, Gerber MS (2016) Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies. In: Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing. UbiComp ’16, Association for Computing Machinery, New York, pp 415–426. https://doi.org/10.1145/2971648.2971711
Shevchenko Y, Kuhlmann T, Reips U-D (2021) Samply: a user-friendly smartphone app and web-based means of scheduling and sending mobile notifications for experience-sampling research. Behav Res Methods 53(4):1710–1730. https://doi.org/10.3758/s13428-020-01527-9
Ferreira D, Kostakos V, Dey AK (2015) AWARE: mobile context instrumentation framework. Frontiers in ICT 2:6
Langer SL, Romano JM, Todd M, Strauman TJ, Keefe FJ, Syrjala KL, Bricker JB, Ghosh N, Burns JW, Bolger N, Puleo BK, Gralow JR, Shankaran V, Westbrook K, Zafar SY, Porter LS (2018) Links between communication and relationship satisfaction among patients with cancer and their spouses: results of a fourteen-day smartphone-based ecological momentary assessment study. Front Psychol 9:1843. https://doi.org/10.3389/fpsyg.2018.01843
van Berkel N, Goncalves J, Lovén L, Ferreira D, Hosio S, Kostakos V (2019) Effect of experience sampling schedules on response rate and recall accuracy of objective self-reports. Int J Hum Comput 125:118–128. https://doi.org/10.1016/j.ijhcs.2018.12.002
Soong A, Chen JC, Borzekowski DL (2015) Using ecological momentary assessment to study tobacco behavior in urban India: there’s an app for that. JMIR Res Protoc 4(2):76
Howison J, Herbsleb JD (2011) Scientific software production: incentives and collaboration. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work. CSCW ’11, Association for Computing Machinery, New York, pp 513–522. https://doi.org/10.1145/1958824.1958904
Insel TR (2017) Digital phenotyping: technology for a new science of behavior. JAMA 318(13):1215–1216. https://doi.org/10.1001/jama.2017.11295
Camacho TC, Roberts RE, Lazarus NB, Kaplan GA, Cohen RD (1991) Physical activity and depression: evidence from the Alameda County Study. Am J Epidemiol 134(2):220–231. https://doi.org/10.1093/oxfordjournals.aje.a116074
Nezlek JB, Hampton CP, Shean GD (2000) Clinical depression and day-to-day social interaction in a community sample. J Abnorm Psychol 109(1):11–19. https://doi.org/10.1037/0021-843X.109.1.11
Rush AJ, Trivedi MH, Ibrahim HM, Carmody TJ, Arnow B, Klein DN, Markowitz JC, Ninan PT, Kornstein S, Manber R, Thase ME, Kocsis JH, Keller MB (2003) The 16-item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression. Biol Psychiatry 54(5):573–583. https://doi.org/10.1016/S0006-3223(02)01866-8
D’Alfonso S, Carpenter N, Alvarez-Jimenez M (2018) Making the MOST out of smartphone opportunities for mental health. In: Proceedings of the 30th Australian Conference on Computer-Human Interaction. OzCHI ’18, Association for Computing Machinery, New York, pp 577–581. https://doi.org/10.1145/3292147.3292230
Eisele G, Vachon H, Lafit G, Kuppens P, Houben M, Myin-Germeys I, Viechtbauer W (2020) The effects of sampling frequency and questionnaire length on perceived burden, compliance, and careless responding in experience sampling data in a student population. Assessment. https://doi.org/10.1177/1073191120957102
Consolvo S, Walker M (2003) Using the experience sampling method to evaluate ubicomp applications. IEEE Pervasive Comput 2(2):24–31. https://doi.org/10.1109/MPRV.2003.1203750
Hektner JM, Schmidt JA, Csikszentmihalyi M (2007) Experience Sampling Method: measuring the quality of everyday life. Sage
Palmier-Claus JE, Myin-Germeys I, Barkus E, Bentley L, Udachina A, Delespaul PAEG, Lewis SW, Dunn G (2011) Experience sampling research in individuals with mental illness: reflections and guidance. Acta Psychiatr Scand 123(1):12–20. https://doi.org/10.1111/j.1600-0447.2010.01596.x
Buck B, Scherer E, Brian R, Wang R, Wang W, Campbell A, Choudhury T, Hauser M, Kane JM, Ben-Zeev D (2019) Relationships between smartphone social behavior and relapse in schizophrenia: a preliminary report. Schizophr Res 208:167–172. https://doi.org/10.1016/j.schres.2019.03.014
Tausczik YR, Pennebaker JW (2010) The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol 29(1):24–54. https://doi.org/10.1177/0261927X09351676
Bedi G, Carrillo F, Cecchi GA, Slezak DF, Sigman M, Mota NB, Ribeiro S, Javitt DC, Copelli M, Corcoran CM (2015) Automated analysis of free speech predicts psychosis onset in high-risk youths. npj Schizophr 1(1):15030. https://doi.org/10.1038/npjschz.2015.30
Eichstaedt JC, Smith RJ, Merchant RM, Ungar LH, Crutchley P, Preoţiuc-Pietro D, Asch DA, Schwartz HA (2018) Facebook language predicts depression in medical records. Proc Natl Acad Sci 115(44):11203–11208. https://doi.org/10.1073/pnas.1802331115
Mastoras R-E, Iakovakis D, Hadjidimitriou S, Charisis V, Kassie S, Alsaadi T, Khandoker A, Hadjileontiadis LJ (2019) Touchscreen typing pattern analysis for remote detection of the depressive tendency. Sci Rep 9(1):13414. https://doi.org/10.1038/s41598-019-50002-9
Zulueta J, Piscitello A, Rasic M, Easter R, Babu P, Langenecker SA, McInnis M, Ajilore O, Nelson PC, Ryan K, Leow A (2018) Predicting mood disturbance severity with mobile phone keystroke metadata: a biaffect digital phenotyping study. J Med Internet Res 20(7):241. https://doi.org/10.2196/jmir.9775
Anagnostopoulos T, Garcia J, Goncalves J, Ferreira D, Hosio S, Kostakos V (2017) Environmental exposure assessment using indoor/outdoor detection on smartphones. Pers Ubiquitous Comput 21(4):761–773. https://doi.org/10.1007/s00779-017-1028-y
Choudhury T, Borriello G, Consolvo S, Haehnel D, Harrison B, Hemingway B, Hightower J, Klasnja PP, Koscher K, LaMarca A, Landay JA, LeGrand L, Lester J, Rahimi A, Rea A, Wyatt D (2008) The mobile sensing platform: an embedded activity recognition system. IEEE Pervasive Comput 7(2):32–41. https://doi.org/10.1109/MPRV.2008.39
Lathia N, Rachuri KK, Mascolo C, Rentfrow PJ (2013) Contextual dissonance: design bias in sensor-based experience sampling methods. In: Proceedings of the 2013 ACM international joint conference on pervasive and ubiquitous computing. UbiComp ’13, Association for Computing Machinery, New York, pp 183–192. https://doi.org/10.1145/2493432.2493452
van Berkel N (2019) Data quality and quantity in mobile experience sampling. PhD thesis, University of Melbourne
van Berkel N, Kostakos V (2021) In: Karapanos, E., Gerken, J., Kjeldskov, J., Skov, M.B. (eds.) Recommendations for conducting longitudinal experience sampling studies, Springer, Cham, pp 59–78. https://doi.org/10.1007/978-3-030-67322-2_4
Melcher J, Hays R, Torous J (2020) Digital phenotyping for mental health of college students: a clinical review. Evid Based Ment Health 23(4):161–166. https://doi.org/10.1136/ebmental-2020-300180
Boonstra TW, Larsen ME, Townsend S, Christensen H (2017) Validation of a smartphone app to map social networks of proximity. PLoS ONE 12(12):1–13. https://doi.org/10.1371/journal.pone.0189877
Saeb S, Lattie EG, Schueller SM, Kording KP, Mohr DC (2016) The relationship between mobile phone location sensor data and depressive symptom severity. PeerJ 4:2537
Saeb S, Lattie EG, Kording KP, Mohr DC (2017) Mobile phone detection of semantic location and its relationship to depression and anxiety. JMIR Mhealth Uhealth 5(8):112
Taylor K, Silver L (2009) Smartphone ownership is growing rapidly around the world, but not always equally. Pew Research Center 5
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van Berkel, N., D’Alfonso, S., Kurnia Susanto, R. et al. AWARE-Light: a smartphone tool for experience sampling and digital phenotyping. Pers Ubiquit Comput 27, 435–445 (2023). https://doi.org/10.1007/s00779-022-01697-7
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DOI: https://doi.org/10.1007/s00779-022-01697-7