Abstract
The prevalence of mental health problems is rising in the college-going population. To predict the mental health of students using smartphone usage and sensor data is an intriguing research problem. In this study, we aim to engineer feature variables related to daily-living behavior using smartphone usage and sensor data. Further, to develop models using these feature variables to predict if anybody is having a mental health issue or not. Independent-samples t-test has been used to compare the variation in means between the healthy group and group with mental illness. Correlation analysis is used to see the strength of the relationship between the independent and dependent variables. The classification model has been developed to predict mental health, (baseline: n = 45). The difference in means of various feature variables among the two groups is statistically significant (p ≤ 0.05). Many variables are strongly correlated with various mental health predictors. The area under curve of the prediction model for predicting stress is 82.6% and that for the depression is 74%. Our results are quite encouraging and point towards the novel application of smartphone-based data sensing in tracking or predicting mental health issues. The study has some implications for practice such as developing a smartphone-based automated system for predicting mental health that could be a useful tool for professionals in predicting mental health, especially in academic institutions.
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Abdullah S, Matthews M, Murnane EL, Gay G, Choudhury T (2014) Towards circadian computing, in: proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing—UbiComp ’14 Adjunct. ACM Press, New York. pp. 673–684. Doi:10.1145/2632048.2632100
Aharony N, Pan W, Ip C, Khayal I, Pentland A (2011) Social fMRI: investigating and shaping social mechanisms in the real world. Pervasive Mob Comput 7:643–659. https://doi.org/10.1016/j.pmcj.2011.09.004
Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185. https://doi.org/10.1080/00031305.1992.10475879
American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders. American Psychiatric Publishing, Arlington
Aminikhanghahi S, Cook DJ (2017) Using change point detection to automate daily activity segmentation, in: 2017 IEEE international conference on pervasive computing and communications workshops (PerCom Workshops). IEEE, pp. 262–267. Doi: 10.1109/PERCOMW.2017.7917569
Anchala R, Kannuri NK, Pant H, Khan H, Franco OH, Di Angelantonio E, Prabhakaran D (2014) Hypertension in India: a systematic review and meta-analysis of prevalence, awareness, and control of hypertension. J Hypertens 32:1170–1177. https://doi.org/10.1097/HJH.0000000000000146
Ani C, Bazargan M, Hindman D, Bell D, Farooq MA, Akhanjee L, Yemofio F, Baker R, Rodriguez M (2008) Depression symptomatology and diagnosis: discordance between patients and physicians in primary care settings. BMC Fam Pract 9:1. https://doi.org/10.1186/1471-2296-9-1
Arlot S, Celisse A (2010) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79. https://doi.org/10.1214/09-SS054
Beierle F, Tran VT, Allemand M, Neff P, Schlee W, Probst T, Zimmermann J, Pryss R (2020) What data are smartphone users willing to share with researchers?: designing and evaluating a privacy model for mobile data collection apps. J Ambient Intell Hum Comput 11:2277–2289. https://doi.org/10.1007/s12652-019-01355-6
Bermingham ML, Pong-Wong R, Spiliopoulou A, Hayward C, Rudan I, Campbell H, Wright AF, Wilson JF, Agakov F, Navarro P, Haley CS (2015) Application of high-dimensional feature selection: evaluation for genomic prediction in man. Sci Rep 5:10312. https://doi.org/10.1038/srep10312
Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Burba F, Ferraty F, Vieu P (2009) k -Nearest Neighbour method in functional nonparametric regression. J Nonparametr Stat 21:453–469. https://doi.org/10.1080/10485250802668909
Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ (1989) The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Res 28:193–213. https://doi.org/10.1016/0165-1781(89)90047-4
Cao H, Lin M (2017) Mining smartphone data for app usage prediction and recommendations: a survey. Pervasive Mob Comput 37:1–22. https://doi.org/10.1016/j.pmcj.2017.01.007
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357. https://doi.org/10.1613/JAIR.953
Chen S-B, Zhang Y-M, Ding CHQ, Zhang J, Luo B (2019) Extended adaptive Lasso for multi-class and multi-label feature selection. Knowl Based Syst 173:28–36. https://doi.org/10.1016/J.KNOSYS.2019.02.021
Cohen S, Kamarck T, Mermelstein R (1983) A global measure of perceived stress. J Health Soc Behav 24:385–396
Cox DR (1958) The regression analysis of binary sequences. Stat Soc Ser B J R. https://doi.org/10.2307/2983890
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines : and other kernel-based learning methods. Cambridge University Press, Cambridge
Das B, Krishnan NC, Cook DJ (2015) RACOG and wRACOG: two probabilistic oversampling techniques. IEEE Trans Knowl Data Eng 27:222–234. https://doi.org/10.1109/TKDE.2014.2324567
de Arriba-Pérez F, Caeiro-Rodríguez M, Santos-Gago JM (2018) How do you sleep? Using off the shelf wrist wearables to estimate sleep quality, sleepiness level, chronotype and sleep regularity indicators. J Ambient Intell Humaniz Comput 9:897–917. https://doi.org/10.1007/s12652-017-0477-5
Diener E, Wirtz D, Tov W, Kim-Prieto C, Choi D, Oishi S, Biswas-Diener R (2010) New well-being measures: short scales to assess flourishing and positive and negative feelings. Soc Indic Res 97:143–156. https://doi.org/10.1007/s11205-009-9493-y
Dimitrov DV (2016) Medical internet of things and big data in healthcare. Healthc Inform Res 22:156–163. https://doi.org/10.4258/hir.2016.22.3.156
Dinga R, Marquand AF, Veltman DJ, Beekman ATF, Schoevers RA, van Hemert AM, Penninx BWJH, Schmaal L (2018) Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Transl Psychiatry 8:241. https://doi.org/10.1038/s41398-018-0289-1
Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform 35:352–359. https://doi.org/10.1016/S1532-0464(03)00034-0
Ebert DD, Buntrock C, Mortier P, Auerbach R, Weisel KK, Kessler RC, Cuijpers P, Green JG, Kiekens G, Nock MK, Demyttenaere K, Bruffaerts R (2019) Prediction of major depressive disorder onset in college students. Depress Anxiety 36:294–304. https://doi.org/10.1002/da.22867
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise, in: KDD’96 proceedings of the second international conference on knowledge discovery and data mining. pp. 226--231
Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119–139. https://doi.org/10.1006/JCSS.1997.1504
Freund Y, Schapire RE (1999) A short introduction to boosting. J Jpn Soc Artif Intell 14:771–780
Granitto PM, Furlanello C, Biasioli F, Gasperi F (2006) Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemom Intell Lab Syst 83:83–90. https://doi.org/10.1016/J.CHEMOLAB.2006.01.007
Greenberg PE, Fournier A-A, Sisitsky T, Pike CT, Kessler RC (2015) The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry 76:155–162. https://doi.org/10.4088/JCP.14m09298
Grunerbl A, Muaremi A, Osmani V, Bahle G, Ohler S, Troster G, Mayora O, Haring C, Lukowicz P (2015) Smartphone-based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform 19:140–148. https://doi.org/10.1109/JBHI.2014.2343154
Guyon I, Andre E (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36. https://doi.org/10.1148/radiology.143.1.7063747
Harari GM, Lane ND, Wang R, Crosier BS, Campbell AT, Gosling SD (2016) Using smartphones to collect behavioral data in psychological science. Perspect Psychol Sci 11:838–854. https://doi.org/10.1177/1745691616650285
Harari GM, Gosling SD, Wang R, Chen F, Chen Z, Campbell AT (2017) Patterns of behavior change in students over an academic term: a preliminary study of activity and sociability behaviors using smartphone sensing methods. Comput Human Behav 67:129–138. https://doi.org/10.1016/j.chb.2016.10.027
Harris TL, Molock SD (2000) Cultural orientation, family cohesion, and family support in suicide ideation and depression among African American college students. Suicide Life Threat Behav 30:341–353
India: Health of the Nation’s States (2017) New Delhi, India
Jaisoorya TS, Rani A, Menon PG, Jeevan CR, Revamma M, Jose V, Radhakrishnan KS, Kishore A, Thennarasu K (2017) Psychological distress among college students in Kerala, India—prevalence and correlates. Asian J Psychiatr 28(28):31. https://doi.org/10.1016/J.AJP.2017.03.026
Khalil A, Abdallah S (2013) Harnessing social dynamics through persuasive technology to promote healthier lifestyle. Comput Human Behav 29:2674–2681. https://doi.org/10.1016/j.chb.2013.07.008
Koyanagi A, DeVylder JE, Stubbs B, Carvalho AF, Veronese N, Haro JM, Santini ZI (2018) Depression, sleep problems, and perceived stress among informal caregivers in 58 low-, middle-, and high-income countries: a cross-sectional analysis of community-based surveys. J Psychiatr Res 96:115–123. https://doi.org/10.1016/J.JPSYCHIRES.2017.10.001
Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 16:606–613. https://doi.org/10.1046/j.1525-1497.2001.016009606.x
Kroenke K, Spitzer RL, Williams JBW, Löwe B (2009a) An ultra-brief screening scale for anxiety and depression: The PHQ-4. Psychosomatics 50:613–621. https://doi.org/10.1176/appi.psy.50.6.613
Kroenke K, Strine TW, Spitzer RL, Williams JBW, Berry JT, Mokdad AH (2009b) The PHQ-8 as a measure of current depression in the general population. J Affect Disord 114:163–173. https://doi.org/10.1016/j.jad.2008.06.026
Kusaslan Avci D (2018) Evaluation of the relationship between loneliness and medication adherence in patients with diabetes mellitus: a cross-sectional study. J Int Med Res 46:3149–3161. https://doi.org/10.1177/0300060518773223
Le H-N, Boyd RC (2006) Prevention of major depression: early detection and early intervention in the general population. Clin Neuropsychiatry. J Treat Eval 3(1):6–22
Liouane Z, Lemlouma T, Roose P, Weis F, Messaoud H (2020) An intelligent knowledge system for designing, modeling, and recognizing the behavior of elderly people in smart space. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01876-5
Liu CH, Stevens C, Wong SHM, Yasui M, Chen JA (2019) The prevalence and predictors of mental health diagnoses and suicide among US college students: Implications for addressing disparities in service use. Depress Anxiety 36:8–17. https://doi.org/10.1002/da.22830
Mohr DC, Ho J, Duffecy J, Baron KG, Lehman KA, Jin L, Reifler D (2010) Perceived barriers to psychological treatments and their relationship to depression. J Clin Psychol. https://doi.org/10.1002/jclp.20659
Moreira MWL, Rodrigues JJPC, Korotaev V, Al-Muhtadi J, Kumar N (2019a) A comprehensive review on smart decision support systems for health care. IEEE Syst J. https://doi.org/10.1109/JSYST.2018.2890121
Moreira MWL, Rodrigues JJPC, Kumar N, Saleem K, Illin IV (2019b) Postpartum depression prediction through pregnancy data analysis for emotion-aware smart systems. Inf Fusion 47:23–31. https://doi.org/10.1016/j.inffus.2018.07.001
Murray CJL, Atkinson C, Bhalla K, Birbeck G, Burstein R, Chou D, Dellavalle R, Danaei G, Ezzati M, Fahimi A, Flaxman D, Foreman GS, Gakidou E, Kassebaum N, Khatibzadeh S, Lim S, Lipshultz SE, London S, Lopez M, MacIntyre F, Mokdad AH, Moran A, Moran AE, Mozaffarian D, Murphy T, Naghavi M, Pope C, Roberts T, Salomon J, Schwebel DC, Shahraz S, Sleet DA, Murray AJ, Ali Mohammed K, Atkinson C, Bartels DH, Bhalla K, Birbeck G, Burstein R, Chen H, Criqui MH, Dahodwala J, Ding EL, Dorsey ER, Ebel BE, Ezzati M, Fahami FS, Flaxman AD, Gonzalez-Medina D, Grant B, Hagan H, Hoffman H, Kassebaum N, Khatibzadeh S, Leasher JL, Lin J, Lipshultz SE, Lozano R, Lu Y, Mallinger L, McDermott MM, Micha R, Miller TR, Mokdad AA, Mokdad AH, Mozaffarian D, Naghavi M, Narayan KMV, Omer SB, Pelizzari PM, Phillips D, Ranganathan D, Rivara FP, Roberts T, Sampson U, Sanman E, Sapkota A, Schwebel DC, Sharaz S, Shivakoti R, Singh GM, Singh D, Tavakkoli M, Towbin JA, Wilkinson JD, Zabetian A, Murray AJ, Ali Mohammad K, Alvardo M, Atkinson C, Baddour LM, Benjamin EJ, Bhalla K, Birbeck G, Bolliger I, Burstein R, Carnahan E, Chou D, Chugh SS, Cohen A, Colson KE, Cooper LT, Couser W, Criqui MH, Dabhadkar KC, Dellavalle RP, Jarlais DD, Dorsey ER, Duber H, Ebel BE, Engell RE, Ezzati M, Felson DT, Finucane MM, Flaxman S, Flaxman AD, Fleming T, Foreman FMH, Freedman G, Freeman MK, Gakidou E, Gillum RF, Gonzalez-Medina D, Gosselin R, Gutierrez HR, Hagan H, Havmoeller R, Hoffman H, Jacobsen KH, James SL, Jasrasaria R, Jayarman S, Johns N, Kassebaum N, Khatibzadeh S, Lan Q, Leasher JL, Lim S, Lipshultz SE, London S, Lopez Lozano R, Lu Y, Mallinger L, Meltzer M, Mensah GA, Michaud C, Miller TR, Mock C, Moffitt TE, Mokdad AA, Mokdad AH, Moran A, Naghavi M, Narayan KMV, Nelson RG, Olives C, Omer SB, Ortblad K, Ostro B, Pelizzari PM, Phillips D, Raju M, Razavi H, Ritz B, Roberts T, Sacco RL, Salomon J, Sampson U, Schwebel DC, Shahraz S, Shibuya K, Silberberg D, Singh JA, Steenland K, Taylor JA, Thurston GD, Vavilala MS, Vos T, Wagner GR, Weinstock MA, Weisskopf MG, Wulf S, Murray S, U.S. Burden of Disease Collaborators (2013) The State of US Health, 1990–2010. JAMA 310:591. https://doi.org/10.1001/jama.2013.13805
Naslund JA, Aschbrenner KA, Bartels SJ (2016) Wearable devices and smartphones for activity tracking among people with serious mental illness. Ment Health Phys Act 10:10–17. https://doi.org/10.1016/j.mhpa.2016.02.001
O’Donoghue G, Cunningham C, Murphy F, Woods C, Aagaard-Hansen J (2014) Assessment and management of risk factors for the prevention of lifestyle-related disease: a cross-sectional survey of current activities, barriers and perceived training needs of primary care physiotherapists in the Republic of Ireland. Physiotherapy 100:116–122. https://doi.org/10.1016/j.physio.2013.10.004
Patel V, Ramasundarahettige C, Vijayakumar L, Thakur J, Gajalakshmi V, Gururaj G, Suraweera W, Jha P, Million Death Study Collaborators (2012) Suicide mortality in India: a nationally representative survey. Lancet 379:2343–2351. https://doi.org/10.1016/S0140-6736(12)60606-0
Pavel M, Jimison HB, Korhonen I, Gordon CM, Saranummi N (2016) Behavioral informatics and computational modeling in support of proactive health management and care. IEEE Trans Biomed Eng 116:1477–1490. https://doi.org/10.1161/CIRCRESAHA.116.303790.The
Pedregosa F, Michel V, Grisel Oliviergrisel O, Blondel M, Prettenhofer P, Weiss R, Vanderplas J, Cournapeau D, Pedregosa F, Varoquaux G, Gramfort A, Thirion B, Grisel O, Dubourg V, Passos A, Brucher M, Perrot andÉdouardand, Duchesnay A, Duchesnay Edouardduchesnay, Fré (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Perry GR (1990) Loneliness and coping among tertiary-level adult cancer patients in the home. Cancer Nurs 13:293–302
Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards 69:749–779. https://doi.org/10.1007/s11069-013-0728-5
Powers DMW (2011) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. Int J Mach Learn Technol 2:37–63
Radhakrishnan R, Andrade C (2012) Suicide: an Indian perspective. Indian J Psychiatry 54:304–319. https://doi.org/10.4103/0019-5545.104793
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318–362
Russell D, Peplau LA, Ferguson ML (1978) Developing a measure of loneliness. J Pers Assess 42:290–294. https://doi.org/10.1207/s15327752jpa4203_11
Saeb S, Zhang M, Karr CJ, Schueller SM, Corden ME, Kording KP, Mohr DC (2015) Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J Med Internet Res 17:e175. https://doi.org/10.2196/jmir.4273
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:e2537. https://doi.org/10.7717/peerj.2537
Sanchez W, Martinez A, Hernandez Y, Estrada H, Gonzalez-Mendoza M (2018) A predictive model for stress recognition in desk jobs. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-1149-9
Schaefer JD, Caspi A, Belsky DW, Harrington H, Houts R, Horwood LJ, Hussong A, Ramrakha S, Poulton R, Moffitt TE (2017) Enduring mental health: prevalence and prediction. J Abnorm Psychol 126:212–224. https://doi.org/10.1037/abn0000232
Schumacher M, Roßner R, Vach W (1996) Neural networks and logistic regression: part I. Comput Stat Data Anal 21:661–682. https://doi.org/10.1016/0167-9473(95)00032-1
Silva BMC, Rodrigues JJPC, de la Torre Diez I, Lopez-Coronado M, Saleem K (2015) Mobile-health: a review of current state in 2015. J Biomed Inform 56:265–272. https://doi.org/10.1016/j.jbi.2015.06.003
Smyth MSNC (2012) The pittsburgh sleep quality index (PSQI), The Hartford Institute for Geriatric Nursing. New York University, College of Nursing, New york
Sneha S (2009) Enabling ubiquitous patient monitoring: model, decision protocols, opportunities and challenges. Decis Support Syst 46:606–619. https://doi.org/10.1016/j.dss.2008.11.014
Soto CJ (2019) How replicable are links between personality traits and consequential life outcomes? The life outcomes of personality replication project. Psychol Sci. https://doi.org/10.1177/0956797619831612
Sprint G, Cook DJ, Schmitter-Edgecombe M (2016) Unsupervised detection and analysis of changes in everyday physical activity data. J Biomed Inform 63:54–65. https://doi.org/10.1016/J.JBI.2016.07.020
Thakur SS, Roy RB (2018) Smartphone-based ubiquitous data sensing and analysis for personalized preventive care: a conceptual framework. In: Nishchal K, Verma A, Ghosh K (eds) Advances in intelligent systems and computing, vol 798. Springer, Singapore, pp 119–132. https://doi.org/10.1007/978-981-13-1132-1_10
Tibshirani R (1996) Regression shrinkage and selection via the lasso. Stat Soc Ser B J R. https://doi.org/10.2307/2346178
Tuv E, Borisov A, Runger G, Torkkola K, Guyon I, Saffari AR (2009) Feature Selection with ensembles, artificial variables, and redundancy elimination. J Mach Learn Res. 10:1341–1366
Walker SH, Duncan DB (1967) Estimation of the probability of an event as a function of several independent variables. Biometrika 54:167. https://doi.org/10.2307/2333860
Wang R, Chen F, Chen Z, Li T, Harari G, Tignnor S, Zhou X, Ben-Zeev D, Campbell Andrew T (2014) StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones. UbiComp. ACM 3–14
Wang R, Dasilva A, Huckins JF, Kelley WM, Heatherton TF, Campbell AT, Wang W, Todd Heatherton F (2018) Tracking depression dynamics in college students using mobile phone and wearable sensing. Proc ACM Interact Mob Wearable Ubiquitous Technol 2(1):43. https://doi.org/10.1145/3191775
WHO | Depression Statistics [WWW Document], 2017. . WHO. URL https://www.who.int/mediacentre/factsheets/fs369/en/. Accessed 6 Feb 17
Wiese J, Min J-K, Hong JI, Zimmerman J (2015) You never call, you never write: call and sms logs do not always indicate tie strength, in: Proceedings of the 18th ACM conference on computer supported cooperative work and social computing. pp. 765–774
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Thakur, S.S., Roy, R.B. Predicting mental health using smart-phone usage and sensor data. J Ambient Intell Human Comput 12, 9145–9161 (2021). https://doi.org/10.1007/s12652-020-02616-5
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DOI: https://doi.org/10.1007/s12652-020-02616-5