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Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones
Open accessAbstract
Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.
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“Multimodal and intensive sampling data streams (for example, experience sampling and passive measures gathered through smartphones and other electronic devices) used in some MBI studies133 and mental health research broadly134,135 might be particularly promising inputs for machine learning models, given that these models can often handle many correlated predictors136.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [136]
In the citing paper
“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 [39]
In the citing paper
“However, it has been recognised that the inherent clinical heterogeneity associated with mental disorders may necessitate a greater focus on inter-individual differences to ensure generalisability to unseen cases (39, 43, 44).”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [39]
In the citing paper
“only two studies have combined these data streams in models to monitor changing depressive symptoms (26, 27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
In the citing paper
“This has resulted in possible reliability problems, since these investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy (14–18, 23–27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
In the citing paper
“Those studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
In the citing paper
“Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) (27, 44) and HRV (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
In the citing paper
“Previous evaluations of models to estimate depression passively have primarily relied on examining correlations between estimated and observed symptoms (18, 26, 27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
In the citing paper
“was higher than the one of a previous model combining features from the fitbit and from smartphones (the best model yielded an r2 = 0.44 or r = 0.66) (27) and the one of a model aggregating mobile-based and physiological features (r = 0.58) (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
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BackgroundBrügger, V., Kowatsch, T., & Jovanova, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-08003-4 · Cited by 19
“…power calculation in machine learning or emerging person-specific approaches are often not considered 97 the sample size used in the prior analyses is comparable to prior studies developing similar predictive personalized, predictive models to allow for robust estimates (see example Refs 98,99 ).”
Langener, A. M., Bringmann, L. F., Kas, M. J., & Stulp, G. (2024) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-023-01328-0 · Cited by 12
“Both MAE and MAPE have been reported in previous papers that used passive smart-phone measures for prediction (e.g., Jacobson & Chung, 2020; Shah et al., 2021).”
BackgroundResultAcceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study
Wyant, K., Moshontz, H., Ward, S. B., Fronk, G. E., & Curtin, J. J. (2023) · JMIR mhealth and uhealth · doi:10.2196/41833 · Cited by 16
A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity
Zhuparris, A., Maleki, G., van Londen, L., Koopmans, I. W., Aalten, V., Yocarini, I. E., Exadaktylos, V., van Hemert, A. M., Cohen, A. F., Gal, P., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-46075-2 · Cited by 15
Schmitter‐Edgecombe, M., Luna, C. M., Dai, S., & Cook, D. J. (2024) · The Clinical Neuropsychologist · doi:10.1080/13854046.2024.2330143 · Cited by 12
“…that the intensive longitudinal data collected from passive sensor sources can be used to predict mental health problems (Choi et al., 2022; Jacobson & Chung, 2020), recognize daily activities (e.g. Paraschiakos et al., 2020; Plotz & Guan, 2018), and detect intervention impact (Hehlmann et…”
"What Does Your Robot Do?" A Tabletop Role-Playing Game to Support Robot Design
Collins, S., & Šabanović, S. (2021) · IEEE RO-MAN · doi:10.1109/ro-man50785.2021.9515554 · Cited by 12
Kadirvelu, B., Bel, T. B., Wu, X., Burmester, V., Ananth, S., Branco, B. C. C. C., Serrano, B. G., Gledhill, J. A., Simplicio, M. C. D., Nicholls, D., et al. (2023) · JMIR Formative Research · doi:10.2196/44877 · Cited by 12
Webb, C. A., Ren, B., Rahimi-Eichi, H., Gillis, B. W., Chung, Y., & Baker, J. T. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03394-4 · Cited by 15
“Second, prior studies have typically collected passive sensing data over relatively short timeframes ranging from a few days to a few weeks [14, 20 – 29] (For an exception, see [30]).”
Self-citationCollins, A. C., Price, G. D., Woodworth, R. J., & Jacobson, N. C. (2023) · The Journal of Positive Psychology · doi:10.1080/17439760.2023.2254743 · Cited by 11
“…Boosted Tree (xgboost) (Ramraj et al., 2016) regressor, a well-documented machine learning algorithm for detecting mental health constructs (Jacobson & Chung, 2020; Jacobson et al., 2019a, 2019b; Sharma & Verbeke, 2020), to detect 1) change in total AHI and 2) change in total CES-D from…”
MethodSelf-citationPlaitano, E. G., McNeish, D., Bartels, S. M., Bell, K. R., Dallery, J., Grabinski, M. J., Kiernan, M., Lavoie, H. A., Lemley, S. M., Lowe, M. R., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1467772 · Cited by 10
Leimhofer, J., Petrovic, M., Dominik, A., Heider, D., & Hegerl, U. (2025) · Interactive Journal of Medical Research · doi:10.2196/69686 · Cited by 7
“For example, a study by Jacobson and Chung [40] illustrates how GPS location data can be enriched with weather conditions by retrieving temperature, humidity, and precipitation data from the National Weather Service API at a specific time and location.”
Receptivity to mobile health interventions
Keller, R., von Wangenheim, F., Mair, J. L., & Kowatsch, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00006-x · Cited by 7
Blending digital therapeutics within the healthcare system
Keller, O. C., Budney, A. J., Struble, C. A., & Teepe, G. W. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00016-2 · Cited by 9
HADD: High-Accuracy Detection of Depressed Mood
Liu, Y., Kang, K., & Doe, M. J. (2022) · Technologies · doi:10.3390/technologies10060123 · Cited by 8
Adolescents’ daily social media use and mood during the COVID-19 lockdown period
Dreier, M. J., Boyd, S. I., Jorgensen, S. L., Merai, R., Fedor, J., Durica, K. C., Low, C. A., & Hamilton, J. L. (2024) · Current Research in Ecological and Social Psychology · doi:10.1016/j.cresp.2024.100196 · Cited by 8
Garzón‐Partida, A. P., Padilla-Gómez, C. B., Martínez-Fernández, D. E., Garcı́a‐Estrada, J., Luquı́n, S., & Fernández-Quezada, D. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1595243 · Cited by 10
Webb, C. A., & Fisher, H. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.19054 · Cited by 7
Vincent, P. M. D. R., Mahendran, N., Nebhen, J., Deepa, N., Srinivasan, K., & Hu, Y. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/9950332 · Cited by 7
“Major depressive disorder (MDD) is the most common mental disorder in the present day as all individuals’ lives, irrespective of being employed or unemployed, is going through the depression phase at least once in their lifetime.”
MethodInfluentialLee, J., Solomonov, N., Banerjee, S., Alexopoulos, G. S., & Sirey, J. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.732773 · Cited by 9
“Types of machine learning and artificial intelligence algorithms for passive sensing data range from feature extraction and selection (33), gradient boosting (6), to artificial neural networks (34).”
BackgroundMethodLee, T., Lee, H.-J., Lee, J.-B., & Kim, J.-D. (2023) · Italian National Conference on Sensors · doi:10.3390/s23208544 · Cited by 8
“A previous study [1] predicted changes in depressive moods among 31 college students and used passive sensor data collected from smartphones to predict depressive moods over time.”
Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review
Dumas, A., Hokayem, J., Goodman, G. R., Venkatasubramanian, K., & Chai, P. R. (2026) · Journal of Medical Internet Research · doi:10.2196/84146 · Cited by 8
“Over the years, studies have shown that passive sensor data collected from smartphones can be associated with depression [38-40].”
Vega, J., Bell, B. T., Taylor, C. S., Xie, J., Ng, H., Honary, M., & McNaney, R. (2022) · JMIR Mental Health · doi:10.2196/32146 · Cited by 7
“For example, a scenario where most of these indicators are related to affective states or mental health disorders such as depression would support the idea of leveraging previous works on general or personalized mobile monitoring of these constructs [81].”
Predicting ADHD Symptoms Using Smartphone Sensing Data
Ware, S., Knouse, L. E., Draz, I., & Enikeeva, A. (2022) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3544793.3563430 · Cited by 7
“Recent studies have used smartphone data for mental health prediction–for example, depression [5, 8, 9, 11, 16, 18–20].”
Neural Mixed Effects for Nonlinear Personalized Predictions
Wörtwein, T., Allen, N. B., Sheeber, L., Auerbach, R. P., Cohn, J. F., & Morency, L. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3614115 · Cited by 6
“Inspired by previous phone-based mood prediction work [2, 17, 27, 44], we extracted the following features: LIWC dimensions [40] and sentiment from Vader [16] of the typed text, the number of words, total time typing, the mean and variance of the typing speed, the weekday, the number of visited places based on GPS dat…”
MethodBrown, R. D., Bondy, E., Prim, J., Dichter, G. S., & Schiller, C. E. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1448914 · Cited by 7
Weingarden, H. M., Meng, X., Armey, M. F., Onnela, J.-P. “., Jaroszewski, A. C., Armstrong, C. H., & Wilhelm, S. (2025) · Internet Interventions · doi:10.1016/j.invent.2025.100833 · Cited by 5
“…and call duration (Cao et al., 2020) (potential proxies for social withdrawal) correlate with measures of mood and anxiety in adolescent, community, undergraduate, and depressed samples (Stamatis et al., 2024; Chow et al., 2017; Cao et al., 2020; Jacobson et al., 2020; Jacobson and Chung, 2020).”
Fisher, H., Jaffe, N. M., Rahimi-Eichi, H., Forbes, E. E., Pizzagalli, D. A., Baker, J. T., & Webb, C. A. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-025-00045-w · Cited by 5
“Growing evidence suggests smartphone sensors can be used to predict daily and even hourly fl uctuations in depressed mood [12], including in youth [13 – 15].”
Müller-Bardorff, M., Schulz, A., Paersch, C., Recher, D., Schlup, B., Seifritz, E., Kolassa, I., Kowatsch, T., Fisher, A. J., Galatzer‐Levy, I. R., et al. (2022) · JMIR Research Protocols · doi:10.2196/42547 · Cited by 11
“…assesses the key psychological and neurobiological variables previously associated with treatment response [12,13] and enriched by smartphone-based and passive sensing predictors (ie, features indexed in the daily lives of patients using digital diaries) as well as passive mobile sensing [14,15].”
BackgroundUsing Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement
Sahoo, S., Shende, C., Hossain, M. Z., Patel, P. A., Wang, X., Mahmud, M. I., Bi, J., Kamath, J., Russell, A. C., Song, D., et al. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00012 · Cited by 5
“For example, the study in [30] predicts moment-to-moment depressed mood among undergraduate students with clinical levels of depression using data collected passively from smartphones and wearable sensors.”
BackgroundTechnological Advances in Clinical Assessment
Heinz, M. V., Thomas, N., Nguyen, N. D., Griffin, T. Z., & Jacobson, N. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00171-0 · Cited by 6
Self-citationEdler, J.-S., Terhorst, Y., Pryss, R. C., Baumeister, H., & Cohrdes, C. (2024) · Journal of Medical Internet Research · doi:10.2196/45530 · Cited by 4
“In contrast to previous findings [21,22], other smartphone-based social interaction parameters (weekly averaged use duration of phone calls and social media, as well as weekly averaged total smartphone use duration) did not contribute to the prediction of depressive or anxiety symptoms in the…”
Garzón‐Partida, A. P., Magaña-Plascencia, K., Martínez-Fernández, D. E., Garcı́a‐Estrada, J., Luquı́n, S., & Fernández-Quezada, D. (2025) · JMIR Research Protocols · doi:10.2196/71374 · Cited by 5
“Similarly, high levels of acceptance have been observed among patients with BD, schizophrenia, anxiety, and major neurocognitive disorders, highlighting the feasibility of using such technologies in mental health research and clinical practice [34,37,56,60,93,97-102].”
Care for the Mind amid Chronic Diseases: An Interpretable AI Approach Using IoT
Xie, J., Zhao, X., Liu, X., & Fang, X. (2025) · Management Science · doi:10.1287/mnsc.2023.04183 · Cited by 6
Thapa, S., Tay, L., & Hou, D. (2021) · Research in occupational stress and well being · doi:10.1108/s1479-355520210000019007 · Cited by 5
Social aspects of the COVID-19 pandemic in the education system
Mukhametzyanov, I. S. (2021) · SHS Web of Conferences · doi:10.1051/shsconf/202110103006 · Cited by 4
Ghorbankhani, M., & Safara, M. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103320 · Cited by 5
Orgaz, G. B., Menéndez, H., Quintana-Quiroga, F., & Díaz-Álvarez, A. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109683 · Cited by 3
Gyorda, J. A., Lekkas, D., & Jacobson, N. C. (2024) · Digital Biomarkers · doi:10.1159/000540546 · Cited by 5
“Passive sensing technologies, such as wearable devices (i.e., smartwatches) [21, 22] and smartphones [23], collect a wide array of information from individuals, including physiological (e.g., heart rate, movement, sleep) and behavioral (e.g., phone usage, location) data.”
BackgroundSelf-citationKilshaw, R. E., Boggins, A., Everett, O., Butner, E., Leifker, F. R., & Baucom, B. (2024) · JMIR Research Protocols · doi:10.2196/53857 · Cited by 3
“Finally, a growing body of research demonstrates that passive sensor data from PDDs can also be used to identify common risk factors for mental health conditions (eg, stress, depressed mood, and anxiety) in clinical [34] and student samples [35].”
Jacob, J., & Kannan, K. S. (2024) · Informatics and Automation · doi:10.15622/ia.23.6.7 · Cited by 3
Walsh, A. E. L., Naughton, G., Sharpe, T., Zajkowska, Z., Malys, M. K., van Heerden, A. C., & Mondelli, V. (2022) · medRxiv · doi:10.1101/2022.06.16.22276510 · Cited by 3
Kadirvelu, B., Bel, T. B., Freccero, A., Simplicio, M. C. D., Nicholls, D., & Faisal, A. A. (2025) · doi:10.2196/preprints.72501 · Cited by 3
Comparing training window selection methods for prediction in non‐stationary time series
Petersen, F., Haslbeck, J. M. B., Tendeiro, J. N., Langener, A. M., Kas, M. J., Rizopoulos, D., & Bringmann, L. F. (2026) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.70018 · Cited by 2
“ESM involves participants repeatedly completing questionnaires to capture dynamic behaviours, emotions and contexts (Shiffman et al., 2008), while passive sensing collects smartphone sensor data, such as location data via GPS (Jacobson & Chung, 2020; Torous et al., 2016).”
Jacobucci, R., Shao, W., Kobrinsky, V., & Ammerman, B. A. (2026) · JMIR Mental Health · doi:10.2196/90581 · Cited by 4
“Personalized models calibrated to an individual’s baseline were markedly more accurate than models applied to new individuals, aligning with the nonergodic character of many psychological processes [27,65] and consistent empirical findings in digital phenotyping [28,66].”
Metadata
- Authors
- Jacobson, N. C., & Chung, Y. J.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Jacobson, Nicholas C.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-06-24
- Volume
- 20
- Issue
- 12
- Pages
- 3572
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7349045
- OpenAlex
- W3037651932
- Cited by
- 139
- References
- 69
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 23 · 2023: 18 · 2022: 23 · 2021: 21 · 2020: 3
- FWCI
- 15.72
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Drug Abuse (P30DA029926)
- 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 › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- passive sensing, major depressive disorder, smartphone sensor data, ecological momentary assessment, just-in-time adaptive interventions
- MeSH terms
- Smartphone, Affect, Depressive Disorder, Major, Major Depressive Disorder, Humans, Students
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