Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)
Open accessAbstract
BACKGROUND: Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable technologies and depressive mood symptoms in affective disorders (unipolar disorder and bipolar disorder). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published. OBJECTIVE: The objectives of this systematic review were to 1) provide an overview of correlations between objective behavioral features and depressive mood symptoms reported in the literature, and 2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help in the identification on which objective features have shown most promising results across studies. METHODS: A systematic review of the scientific literature reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was conducted. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, Pubmed, DBLP computer science bibliography, HTA, DARE, Scopus and Science Direct were searched and supplemented by hand examination of reference lists. The search ended 04/27-2017 and was limited to studies published 2007-2017. RESULTS: A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features covering 17 various sensor data inputs. These features can be categorized into seven overall categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (e.g., the amount of home stay, sleep duration, vigorous activity), while others showed directionality discrepancies across the studies (e.g., amount of SMS sent, time you spend between locations, frequency of smartphone screen activity). CONCLUSIONS: Several studies showed consistent and statistically significant correlations between objective behavioral features collected by mobile and wearable technology and depressive mood symptoms. Hence, continuous and every-day monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure to estimate depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of 1) the collected objective features, 2) the mood assessment methodology, and 3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed making replication studies as well as meta-analyses possible.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Passive sensor data can be used to predict various symptoms of mental disorders or mental health problems.
“This type of research has established that passive sensor data can be used to predict various symptoms of mental disorders or mental health problems (23, 33–38), as well as their future development [e.g., (39)].”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [38]
The exploratory passive feature list was selected based on technical feasibility and associations with mood symptoms found in previous studies.
“This exploratory feature list was selected based on technical feasibility, as well as on associations with mood symptoms found in previous studies (38).”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [38]
Mobile phone technology provides an unobtrusive, continuous, and cost-efficient means to capture individuals' daily behaviors and statuses using embedded sensors such as accelerometers, GPS sensors, and Bluetooth sensors.
“Mobile phone technology provides an unobtrusive, continuous, and cost-efficient means to capture individuals’ daily behaviors and statuses using a number of embedded sensors, such as accelerometers, GPS sensors, and Bluetooth sensors [6].”
Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions.
“Inherent in these data sets are behavioral patterns: routines, rhythms, activities, and interactions that are useful in complementing traditional depression assessment methods, in studying the mental health of individuals, and in developing timely mental health interventions [14,22,25-28].”
The heterogeneity and inconsistency in correlation findings (linear relationships) in the field are common issues.
“The heterogeneity and inconsistency in correlation findings (ie, linear relationships) in the field are common issues [26].”
Rohani et al. meta-analytic review revealed that the association between time spent at home and depressive symptoms was the most consistently significant finding of any smartphone-derived feature.
“Rohani et al. (15) revealed that the association between time spent at home and depressive symptoms was the most consistently significant finding of any smartphone-derived feature in the analysis.”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823 · see citing works
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042 · see citing works
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910 · see citing works
Wang, G., Ma, R., Qiao, G., Wada, K., Aizawa, Y., & Satoh, T. (2015) · Integrative Medicine International · doi:10.1159/000368408 · Cited by 18,759 · see citing works
Utilization of the PICO framework to improve searching PubMed for clinical questions
Schardt, C., Adams, M. B., Owens, T., Keitz, S., & Fontelo, P. (2007) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-7-16 · Cited by 3,053 · see citing works
Straightforward Statistics for the Behavioral Sciences.
Wuensch, K. L., & Evans, J. D. (1996) · Journal of the American Statistical Association · doi:10.2307/2291607 · Cited by 2,417 · see citing works
Thomée, S., Härenstam, A., & Hagberg, M. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-66 · Cited by 1,269 · see citing works
Smartphones for smarter delivery of mental health programs: A systematic review
Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.-R., & Christensen, H. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2791 · Cited by 1,136 · see citing works
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116 · see citing works
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802 · see citing works
Saeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782 · see citing works
Harnessing context sensing to develop a mobile intervention for depression
Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1838 · Cited by 646 · cited 1× · see citing works
“However, studies have shown a statistically significant positive correlation between depressive symptoms and the feature screen active duration [78].”
BackgroundCan Mobile Phone Apps Influence People’s Health Behavior Change? An Evidence Review
Zhao, J., Freeman, B., & Li, M. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5692 · Cited by 618 · see citing works
Mental Health Mobile Apps for Preadolescents and Adolescents: A Systematic Review.
Grist, R., Porter, J., & Stallard, P. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7332 · Cited by 518 · see citing works
Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477 · see citing works
Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T. (2015) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000130 · Cited by 429 · see citing works
Personality, self-esteem, and demographic predictions of happiness and depression
Cheng, H., & Furnham, A. (2003) · Personality and Individual Differences · doi:10.1016/s0191-8869(02)00078-8 · Cited by 413 · see citing works
Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2343154 · Cited by 384 · see citing works
Greenhalgh, J., & Meadows, K. (1999) · Journal of Evaluation in Clinical Practice · doi:10.1046/j.1365-2753.1999.00209.x · Cited by 364 · see citing works
A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression
Huguet, A., Rao, S., McGrath, P. J., Wozney, L., Wheaton, M., Conrod, J., & Rozario, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154248 · Cited by 328 · see citing works
The relationship between mobile phone location sensor data and depressive symptom severity
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313 · see citing works
Baker, D. A., & Algorta, G. P. (2016) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2016.0206 · Cited by 286 · cited 2× · see citing works
“We did not find any research article that explains how social patterns change with depression, but the review article by Baker et al [27] on online social networks suggests a complex relation involving factors that mediate or moderate the correlation and increase the variability in the findings.”
BackgroundActivity monitoring in patients with depression: A systematic review
Burton, C., McKinstry, B., Szentagotai Tătar, A., Serrano-Blanco, A., Pagliari, C., & Wolters, M. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.07.001 · Cited by 249 · see citing works
Voice analysis as an objective state marker in bipolar disorder
Faurholt‐Jepsen, M., Busk, J., Frost, M. M., Vinberg, M., Christensen, E. M., Winther, O., Bardram, J. E., & Kessing, L. V. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.123 · Cited by 236 · see citing works
Self-citationAutomatic detection of social rhythms in bipolar disorder
Abdullah, S., Matthews, M. L., Frank, E., Doherty, G. J., Gay, G. K., & Choudhury, T. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv200 · Cited by 225 · see citing works
Asselbergs, J., Ruwaard, J., Ejdys, M., Schrader, N., Sijbrandij, M., & Riper, H. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5505 · Cited by 192 · see citing works
Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study
Beiwinkel, T., Kindermann, S., Maier, A., Kerl, C., Moock, J., Barbian, G., & Rössler, W. (2016) · JMIR Mental Health · doi:10.2196/mental.4560 · Cited by 191 · see citing works
Activation in Bipolar Disorders
Scott, J. L., Murray, G., Henry, C., Morken, G., Scott, E. M., Angst, J., Merikangas, K. R., & Hickie, I. B. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.3459 · Cited by 187 · see citing works
Smartphone data as objective measures of bipolar disorder symptoms
Faurholt-Jepsen, M., Frost, M., Vinberg, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2014) · Psychiatry Research · doi:10.1016/j.psychres.2014.03.009 · Cited by 185 · see citing works
Self-citationDetecting Bipolar Depression From Geographic Location Data
Palmius, N., Tsanas, A., Saunders, K. E. A., Bilderbeck, A. C., Geddes, J. R., Goodwin, G. M., & De Vos, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2611862 · Cited by 180 · see citing works
Dogan, E., Sander, C., Wagner, X., Hegerl, U., & Kohls, E. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7006 · Cited by 177 · see citing works
Physical Activity and Depression in Young Adults
McKercher, C., Schmidt, M. D., Sanderson, K. A., Patton, G. C., Dwyer, T., & Venn, A. (2008) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2008.09.036 · Cited by 174 · see citing works
Chow, P. I., Fua, K., Huang, Y., Bonelli, W., Xiong, H., Barnes, L. E., & Teachman, B. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6820 · Cited by 172 · see citing works
Smartphone data as an electronic biomarker of illness activity in bipolar disorder
Faurholt‐Jepsen, M., Vinberg, M., Frost, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2015) · Bipolar Disorders · doi:10.1111/bdi.12332 · Cited by 169 · see citing works
Self-citationStrategies for mHealth Research: Lessons from 3 Mobile Intervention Studies
Ben‐Zeev, D., Schueller, S. M., Begale, M. J., Duffecy, J. L., Kane, J. M., & Mohr, D. C. (2014) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-014-0556-2 · Cited by 167 · see citing works
Disabato, D. J., Kashdan, T. B., Short, J. L., & Jarden, A. (2016) · Cognitive Therapy and Research · doi:10.1007/s10608-016-9785-x · Cited by 151 · see citing works
Pachucki, M. C., Ozer, E. J., Barrat, A., & Cattuto, C. (2014) · Social Science & Medicine · doi:10.1016/j.socscimed.2014.04.015 · Cited by 150 · see citing works
Faurholt-Jepsen, M., Vinberg, M., Frost, M., Debel, S., Margrethe Christensen, E., Bardram, J. E., & Kessing, L. V. (2016) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1502 · Cited by 146 · see citing works
Self-citationBerle, J. Ø., Hauge, E. R., Oedegaard, K. J., Holsten, F., & Fasmer, O. B. (2010) · BMC Research Notes · doi:10.1186/1756-0500-3-149 · Cited by 140 · see citing works
Predicting students' happiness from physiology, phone, mobility, and behavioral data
Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344575 · Cited by 140 · see citing works
Mobile Sensing at the Service of Mental Well-being: a Large-scale Longitudinal Study
Servia-Rodríguez, S., Rachuri, K. K., Mascolo, C., Rentfrow, P. J., Lathia, N., & Sandstrom, G. M. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052618 · Cited by 134 · see citing works
Pillai, V., Steenburg, L. A., Ciesla, J. A., Roth, T., & Drake, C. L. (2014) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2014.05.004 · Cited by 133 · cited 1× · see citing works
“2) 29 10 Actigraph Pillai et al, 2014 [50]”
Activities on Facebook Reveal the Depressive State of Users
Park, S., Lee, S. W., Kwak, J.-A., Cha, M., & Jeong, B. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2718 · Cited by 128 · see citing works
Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data
Farhan, A. A., Yue, C., Morillo, R., Ware, S., Lu, J., Bi, J., Kamath, J., Russell, A., Bamis, A., & Wang, B. (2016) · 2016 IEEE Wireless Health (WH) · doi:10.1109/wh.2016.7764553 · Cited by 115 · see citing works
Actigraphic Assessment of Motor Activity in Acutely Admitted Inpatients with Bipolar Disorder
Krane‐Gartiser, K., Henriksen, T. E. G., Morken, G., Vaaler, A., & Fasmer, O. B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089574 · Cited by 112 · see citing works
Glenn, T., & Monteith, S. (2014) · Current Psychiatry Reports · doi:10.1007/s11920-014-0523-3 · Cited by 104 · see citing works
Dickerson, R. F., Gorlin, E. I., & Stankovic, J. A. (2011) · doi:10.1145/2077546.2077552 · Cited by 103 · see citing works
Supporting disease insight through data analysis
Frost, M. M., Doryab, A., Faurholt‐Jepsen, M., Kessing, L. V., & Bardram, J. E. (2013) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2493432.2493507 · Cited by 100 · see citing works
Self-citationHauge, E. R., Berle, J. Ø., Oedegaard, K. J., Holsten, F., & Fasmer, O. B. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016291 · Cited by 98 · see citing works
The quantitative assessment of motor activity in mania and schizophrenia
Minassian, A., Henry, B. L., Geyer, M. A., Paulus, M. P., Young, J. W., & Perry, W. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2009.04.018 · Cited by 93 · see citing works
Monitoring activity of patients with bipolar disorder using smart phones
Osmani, V., Maxhuni, A., Grunerbl, A., Lukowicz, P., Haring, C., & Mayora, O. (2013) · Advances in Mobile Multimedia · doi:10.1145/2536853.2536882 · Cited by 89 · see citing works
Edwards, M. K., & Loprinzi, P. D. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.03.021 · Cited by 88 · see citing works
Mehrotra, A., Hendley, R., & Musolesi, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968299 · Cited by 77 · see citing works
O'Brien, J. T., Gallagher, P., Stow, D., Hammerla, N., Ploetz, T., Firbank, M., Ladha, C., Ladha, K., Jackson, D., McNaney, R., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291716002166 · Cited by 76 · see citing works
Faurholt‐Jepsen, M., Brage, S., Vinberg, M., Christensen, E. M., Knorr, U. B., Jensen, H. M., & Kessing, L. V. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.02.020 · Cited by 73 · see citing works
Self-citationTell me your apps and I will tell you your mood
Alvarez-Lozano, J., Osmani, V., Mayora, O., Frost, M. M., Bardram, J. E., Faurholt‐Jepsen, M., & Kessing, L. V. (2014) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2674396.2674408 · Cited by 71 · see citing works
Self-citationLoprinzi, P. D., & Mahoney, S. E. (2014) · Journal of Affective Disorders · doi:10.1016/j.jad.2014.04.073 · Cited by 69 · see citing works
Predicting Mood Changes in Bipolar Disorder Through Heartbeat Nonlinear Dynamics
Valenza, G., Nardelli, M., Lanatà, A., Gentili, C., Bertschy, G., Kosel, M., & Scilingo, E. P. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2554546 · Cited by 69 · see citing works
Bohnert, K. M., & Breslau, N. (2011) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.331 · Cited by 67 · see citing works
Objective Measurement of Sociability and Activity: Mobile Sensing in the Community
Berke, E. M., Choudhury, T., Ali, S., & Rabbi, M. (2011) · The Annals of Family Medicine · doi:10.1370/afm.1266 · Cited by 66 · see citing works
Daily Actigraphy Profiles Distinguish Depressive and Interepisode States in Bipolar Disorder
Gershon, A., Ram, N., Johnson, S. L., Harvey, A. G., & Zeitzer, J. M. (2016) · Clinical Psychological Science · doi:10.1177/2167702615604613 · Cited by 66 · see citing works
Todder, D., Caliskan, S., & Baune, B. T. (2009) · The World Journal of Biological Psychiatry · doi:10.3109/15622970701403081 · Cited by 63 · see citing works
Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls
Muaremi, A., Gravenhorst, F., Grünerbl, A., Bert Arnrich, A., & Tröster, G. (2014) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-11564-1_11 · Cited by 62 · see citing works
Sleep disturbances in bipolar disorder during remission
St-Amand, J., Provencher, M. D., Bélanger, L., & Morin, C. M. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2012.05.057 · Cited by 59 · see citing works
Roll-over Detection and Sleep Quality Measurement using a Wearable Sensor
Miwa, H., Sasahara, S., & Matsui, T. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4352587 · Cited by 59 · see citing works
Smartphone-based point-of-care testing of salivary α-amylase for personal psychological measurement.
Zhang, L., Yang, W.-T., Yang, Y.-K., Liu, H., & Gu, Z.-Z. (2015) · In Analysis · doi:10.1039/c5an01664a · Cited by 57 · see citing works
The Relationship Between Affective State and the Rhythmicity of Activity in Bipolar Disorder
Gonzalez, R., Tamminga, C. A., Tohen, M. F., & Suppes, T. M. (2014) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.13m08506 · Cited by 50 · see citing works
PRISM: A DATA-DRIVEN PLATFORM FOR MONITORING MENTAL HEALTH
Kamdar, M. R., & Wu, M. J. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0031 · Cited by 47 · see citing works
Automatic analysis of speech F0 contour for the characterization of mood changes in bipolar patients
Guidi, A., Vanello, N., Bertschy, G., Gentili, C., Landini, L., & Scilingo, E. P. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2014.10.011 · Cited by 43 · see citing works
Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks
Li, X., Hu, B., Shen, J., Xu, T., & Retcliffe, M. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0345-9 · Cited by 41 · see citing works
Use of Computer and Mobile Technologies in the Treatment of Depression
Callan, J. A., Wright, J. H., Siegle, G. J., Howland, R. H., & Kepler, B. B. (2016) · Archives of Psychiatric Nursing · doi:10.1016/j.apnu.2016.10.002 · Cited by 39 · see citing works
Relationship between social interaction and mental health
Ono, E., Nozawa, T., Ogata, T., Motohashi, M., Higo, N., Kobayashi, T., Ishikawa, K., Ara, K., Yano, K., & Miyake, Y. (2011) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii.2011.6147454 · Cited by 38 · see citing works
Doryab, A., Frost, M. M., Faurholt‐Jepsen, M., Kessing, L. V., & Bardram, J. E. (2014) · Personal and Ubiquitous Computing · doi:10.1007/s00779-014-0826-8 · Cited by 38 · see citing works
Self-citationSmartphone Data as an Electronic Biomarker of Illness Activity in Bipolar Disorder
Faurholt‐Jepsen, M., Vinberg, M., Frost, M. M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2015) · European Psychiatry · doi:10.1016/s0924-9338(15)30186-3 · Cited by 37 · see citing works
Self-citationUser-centered Depression Prevention: An EEG approach to pervasive healthcare
Peng, H., Hu, B., Liu, Q., Dong, Q., Zhao, Q., & Moore, P. T. (2011) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2011.246025 · Cited by 34 · see citing works
Gentili, C., Valenza, G., Nardelli, M., Lanatà, A., Bertschy, G., Weiner, L., Mauri, M., Scilingo, E. P., & Pietrini, P. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.11.008 · Cited by 32 · see citing works
New algorithm for the depression diagnosis using HRV: A neuro-fuzzy approach
Zhang, Z., Tian, X.-W., & Lim, J. S. (2011) · doi:10.1109/isbb.2011.6107702 · Cited by 29 · see citing works
Workplace Indicators of Mood: Behavioral and Cognitive Correlates of Mood Among Information Workers
Mark, G., Czerwinski, M., Iqbal, S., & Johns, P. (2016) · Proceedings of the 6th International Conference on Digital Health Conference · doi:10.1145/2896338.2896360 · Cited by 24 · see citing works
Using opportunistic face logging from smartphone to infer mental health
Wang, R., Campbell, A. T., & Zhou, X. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2800835.2804391 · Cited by 22 · see citing works
Faurholt‐Jepsen, M., Brage, S., Vinberg, M., Jensen, H. M., Christensen, E. M., Knorr, U. B., & Kessing, L. V. (2014) · Nordic Journal of Psychiatry · doi:10.3109/08039488.2014.936501 · Cited by 21 · see citing works
Self-citationDetecting change in depressive symptoms from daily wellbeing questions, personality, and activity
Demasi, Aguilera, A., & Recht (2016) · Wireless Health · doi:10.1109/wh.2016.7764552 · Cited by 20 · see citing works
A vocal modulation model with application to predicting depression severity
Horwitz-Martin, R. L., Quatieri, T. F., Godoy, E., et al. (2016) · IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN) · doi:10.1109/bsn.2016.7516268 · Cited by 19 · see citing works
A cross-sectional study of the association between mobile phone use and symptoms of ill health
Cho, Y. M., Lim, H., Jang, H. A., Kim, K.-H., Choi, J. W., Shin, C., Lee, S. K., Kwon, J. H., & Kim, N. (2016) · Environmental Health and Toxicology · doi:10.5620/eht.e2016022 · Cited by 19 · see citing works
Bewernick, B. H., Urbach, A. S., Bröder, A., Kayser, S., & Schlaepfer, T. E. (2016) · Psychiatry Research · doi:10.1016/j.psychres.2016.09.009 · Cited by 18 · see citing works
Predicting Subjective Well-Being by Smartphone Usage Behaviors
Gao, Y., Li, H., & Zhu, T. (2014) · International Conference on Health Informatics · doi:10.5220/0004800203170322 · Cited by 16 · see citing works
Jeon, S., Han, C.-S., Ko, Y.-H., Yoon, S., Pae, C., Choi, J., Park, Y., Kim, J.-W., Yoon, H.-K., Ko, S.-D., et al. (2017) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.2017.15.1.28 · Cited by 16 · see citing works
‘Smartphine': Supporting students' well-being according to their calendar and mood
Baras, K., Soares, L., Paulo, N. P., & Barros, R. (2016) · International Conference on Smart and Sustainable Technologies (SpliTech) · doi:10.1109/splitech.2016.7555919 · Cited by 15 · see citing works
Matic, A., Osmani, V., Popleteev, A., & Mayora, O. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24279-3_21 · Cited by 14 · see citing works
Proposal of a depression detector
Mok, W. T., Sing, R., Jiang, X., & See, S. L. (2014) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2014.7041742 · Cited by 9 · see citing works
Quantifying the mental state on the basis of physical and social activities
Ishimaru, S., & Kise, K. (2015) · doi:10.1145/2800835.2807934 · Cited by 8 · see citing works
Facial electromyography for characterization of emotions using LabVIEW
Thulkar, D., Bhaskarwar, T., & Hamde, S. T. (2015) · doi:10.1109/iic.2015.7150829 · Cited by 7 · see citing works
Garcia-Chamizo J.M., F. G., Fortino, G., García‐Chamizo, J. M., Fortino, G., & Ochoa, S. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26401-1 · Cited by 7 · see citing works
Mestry, M., Mehta, J., Mishra, A., & Gawande, K. (2015) · International Conference on Communication, Information & Computing Technology · doi:10.1109/iccict.2015.7045656 · Cited by 6 · see citing works
Speech analysis and depression
Shannon, T. T. E., Jingwen, A. D., & Lan, S. S. (2016) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipa.2016.7820752 · Cited by 5 · see citing works
Data Quality Issues in Environmental Sensing with Smartphones
de Araújo, T., Silva, L. T., & Moreira, A. J. C. (2017) · International Conference on Sensor Networks · doi:10.5220/0006201600590068 · Cited by 5 · see citing works
Smartphone-based Device for Checking Mental Status in Real Time
Oyama-Higa, M., Wang, W., Kaizu, S., et al. (2016) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0005655401370142 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [18]Doryab A Min J Wiese J Zimmerman J Hong J Detection of behavior change in people with depression 2014 AAAI July 27-31, 2014 Quebec, Canada 12
- [91]Zhang X Hu B Zhou L Moore P Chen J An EEG based pervasive depression detection for females Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) Internet Springer, Berlin, Heidelberg 2013 ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World November 28-30, 2012 Istanbul, Turkey 848 861
Cited by
Sorted by citations
Digital interventions for the treatment of depression: a meta-analytic review
Moshe, I., Terhorst, Y., Philippi, P., Domhardt, M., Cuijpers, P., Cristea, I., Pulkki-Råback, L., Baumeister, H., & Sander, L. B. (2021) · Psychological Bulletin · doi:10.1037/bul0000334 · Cited by 390 · see citing works
Thieme, A., Belgrave, D., & Doherty, G. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3398069 · Cited by 406 · see citing works
Stein, D. J., Shoptaw, S. J., Vigo, D. V., Lund, C., Cuijpers, P., Bantjes, J., Sartorius, N., & Maj, M. (2022) · World Psychiatry · doi:10.1002/wps.20998 · Cited by 331 · see citing works
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.625247 · Cited by 261 · see citing works
Digital Health Tools for the Passive Monitoring of Depression: A Systematic Review of Methods
de Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., Lavelle, G., Matcham, F., Pace, A., Mohr, D. C., et al. (2021) · doi:10.1101/2021.07.19.21260786 · Cited by 227 · see citing works
Torous, J., Wisniewski, H., Bird, B., Carpenter, E., David, G., Elejalde, E., Fulford, D., Guimond, S., Hays, R., Henson, P., et al. (2019) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-019-00095-w · Cited by 202 · see citing works
Bauer, M., Glenn, T., Geddes, J., Gitlin, M., Grof, P., Kessing, L. V., Monteith, S., Faurholt-Jepsen, M., Severus, E., & Whybrow, P. C. (2020) · International Journal of Bipolar Disorders · doi:10.1186/s40345-019-0164-x · Cited by 163 · see citing works
Self-citationSeppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., Feldman, Y., Grasa, E., Corripio, I., Berdun, J., et al. (2019) · JMIR Mental Health · doi:10.2196/mental.9819 · Cited by 148 · see citing works
Opoku Asare, K., Terhorst, Y., Vega, J., Peltonen, E., Lagerspetz, E., & Ferreira, D. (2021) · JMIR mHealth and uHealth · doi:10.2196/26540 · Cited by 145 · see citing works
Tazawa, Y., Liang, K.-C., Yoshimura, M., Kitazawa, M., Kaise, Y., Takamiya, A., Kishi, A., Horigome, T., Mitsukura, Y., Mimura, M., et al. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e03274 · Cited by 101 · see citing works
Using digital phenotyping to accurately detect depression severity
Jacobson, N. C., Weingarden, H., & Wilhelm, S. (2019) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0000000000001042 · Cited by 94 · see citing works
Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Matcham, F., et al. (2021) · JMIR mHealth and uHealth · doi:10.2196/29840 · Cited by 68 · see citing works
Busk, J., Faurholt-Jepsen, M., Frost, M., Bardram, J. E., Vedel Kessing, L., & Winther, O. (2020) · JMIR mhealth and uhealth · doi:10.2196/15028 · Cited by 58 · see citing works
Personalised depression forecasting using mobile sensor data and ecological momentary assessment
Kathan, A., Harrer, M., Küster, L., Triantafyllopoulos, A., He, X., Milling, M., Gerczuk, M., Yan, T., Rajamani, S. T., Heber, E., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.964582 · Cited by 47 · see citing works
Lorenz, N., Spada, J., Sander, C., Riedel-Heller, S. G., & Hegerl, U. (2019) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2019.06.022 · Cited by 38 · see citing works
Kim, J. S., Wang, B., Kim, M., Lee, J., Kim, H., Roh, D., Lee, K. H., Hong, S.-B., Lim, J. S., Kim, J.-W., et al. (2023) · JMIR Formative Research · doi:10.2196/45991 · Cited by 25 · see citing works
Towards early detection of depression through smartphone sensing
Opoku Asare, K., Visuri, A., & Ferreira, D. S. T. (2019) · Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers · doi:10.1145/3341162.3347075 · Cited by 24 · see citing works
Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder
Tønning, M. L., Faurholt-Jepsen, M., Frost, M., Bardram, J. E., & Kessing, L. V. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.701360 · Cited by 17 · see citing works
Self-citationRottstädt, F., Becker, E., Wilz, G., Croy, I., Baumeister, H., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1335776 · Cited by 11 · see citing works
Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial
Terhorst, Y., Weilbacher, N., Suda, C., Simon, L., Messner, E.-M., Sander, L. B., & Baumeister, H. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1075266 · Cited by 10 · see citing works
Knauer, J., Baumeister, H., Schmitt, A., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1352762 · Cited by 2 · see citing works
“In order to translate encouraging fi ndings from smart sensing studies (37, 38, 83) into tangible healthcare solutions, it is essential to address underlying processes governing both initial and long-term use.”
Background
- Authors
- Rohani, D. A., Faurholt-Jepsen, M., Kessing, L. V., & Bardram, J. E.
- Affiliations
- Copenhagen Center for Health Technology (DK)
- University of Copenhagen (DK)
- Corresponding author
- Rohani, Darius Adam
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2017-12-19
- Volume
- 6
- Issue
- 8
- Pages
- e165-e165
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6111148
- OpenAlex
- W2809351553
- Cited by
- 195
- References
- 96
- Citations by year
- 2026: 21 · 2025: 21 · 2024: 21 · 2023: 22 · 2022: 33 · 2021: 26 · 2020: 32 · 2019: 16 · 2018: 3
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, other-oa)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- mood disorders, major depressive disorder, bipolar disorder, mobile devices, wearable sensors, sleep duration, physical activity, text messaging, location tracking, mood tracking, digital phenotyping
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
