Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data
Open accessAbstract
Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological momentary assessments (EMA) but also response times to EMA questions by a user. When observing this data, we see a huge variation between the patterns exhibited by different patients. Some are more stable while others vary a lot over time. This poses a challenging problem for the domain of artificial intelligence and makes on wondering whether it is possible to predict the future mental state of a patient using the data that is available. In the end, these predictions could potentially contribute to interventions that tailor the feedback to the user on a daily basis, for example by warning a user that a fall-back might be expected during the next days, or by applying a strategy to prevent the fall-back from occurring in the first place. In this work, we focus on short term mood prediction by considering the adherence and usage data as an additional predictor. We apply recurrent neural networks to handle the temporal aspects best and try to explore whether individual, group level, or one single predictive model provides the highest predictive performance (measured using the root mean squared error (RMSE)). We use data collected from patients from five countries who used the ICT4Depression/MoodBuster platform in the context of the EU E-COMPARED project. In total, we used the data from 143 patients (with between 9 and 425 days of EMA data) who were diagnosed with a major depressive disorder according to DSM-IV. Results show that we can make predictions of short term mood change quite accurate (ranging between 0.065 and 0.11). The past EMA mood ratings proved to be the most influential while adherence and usage data did not improve prediction accuracy. In general, group level predictions proved to be the most promising, however differences were not significant. Short term mood prediction remains a difficult task, but from this research we can conclude that sophisticated machine learning algorithms/setups can result in accurate performance. For future work, we want to use more data from the mobile phone to improve predictive performance of short term mood.
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There were a large variety of study types among the 17 studies, including pilot randomised controlled trials, observational studies, model evaluations, and a qualitative study.
“There were a large variety of study types among the 17 studies, including pilot randomised controlled trials (RCTs) [28–30], observational studies [31–35], model evaluations [36,37], and a qualitative study [38].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [36]
The most common use of artificial intelligence in the mental health apps was prediction (of mood, stress, and risk) (8/17 studies).
“The most common uses of artificial intelligence in the mental health apps were prediction (of mood, stress, and risk) (8/17 studies) [31–37,41]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [36]
6 of the 17 studies focused on depression.
“6 of the 17 studies focused on depression [29,31,36,40,42,44]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [36]
Continuous monitoring of patients' symptoms would allow clinicians to monitor treatment efficacy over time, predict short-term mood changes, detect symptom worsening early, and create continuous communication between clinicians and patients.
“the continuous monitoring of patients’ symptoms would allow clinicians to monitor the efficacy of a treatment over time [76], predict short-term mood changes [77], detect symptoms’ worsening in an early stage [78], and create continuous communication between clinicians and patients”
From Current state and future directions of technology-based ecological momentary assessment and intervention for major depressive disorder: a systematic review (2019) · cites this paper as [77]
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- Authors
- Mikus, A., Hoogendoorn, M., Rocha, A., Gama, J., Ruwaard, J., & Riper, H.
- Affiliations
- Vrije Universiteit Amsterdam (NL)
- INESC TEC (PT)
- Corresponding author
- Hoogendoorn, Mark
- Venue
- Internet Interventions
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 12
- Pages
- 105-110
- ISSN
- 2214-7829 (ISSN-L)
- PMC
- PMC6096213
- OpenAlex
- W2762937187
- Cited by
- 31
- References
- 16
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 6 · 2023: 2 · 2022: 2 · 2021: 5 · 2020: 8 · 2019: 4
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Internet Interventions (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (publishedVersion, other-oa)
- VU Research Portal (publishedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- FP7 (603098)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- mood prediction, major depressive disorder, ecological momentary assessment, medication adherence, recurrent neural networks, mobile mental health intervention
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