A control theoretic approach to evaluate and inform ecological momentary interventions
Open accessAbstract
Objectives. Ecological momentary interventions (EMI) are digital mobile health interventions administered in an individual's daily life to improve mental health by tailoring intervention components to person and context. Experience sampling via ecological momentary assessments (EMA) furthermore provides dynamic contextual information on an individual’s mental health state. We propose a personalized data-driven generic framework to select and evaluate EMI based on EMA. Methods. We analyze EMA/EMI time-series from 10 individuals, published in a previous study. The EMA consist of multivariate psychological Likert scales. The EMI are mental health trainings presented on a smartphone. We model EMA as linear dynamical systems (DS) and EMI as perturbations. Using concepts from network control theory, we propose and evaluate three personalized data-driven intervention delivery strategies. Moreover, we study putative change mechanisms in response to interventions.Results. We identify promising intervention delivery strategies that outperform empirical strategies in simulation. We pinpoint interventions with a high positive impact on the network, at low energetic costs. Although mechanisms differ between individuals - demanding personalized solutions - the proposed strategies are generic and applicable to various real-world settings.Conclusions. Combined with knowledge from mental health experts, DS and control algorithms may provide powerful data-driven and personalized intervention delivery and evaluation strategies.Keywords: ecological momentary assessment, ecological momentary intervention, mobile health, control theory, computational psychiatry
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Support for the applicability of process-based therapy has been reported from single-case studies.
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From Process-based therapy vs. routine-CBT for difficult-to-treat mood and anxiety disorders: study protocol for a randomized controlled trial (2024) · cites this paper as [58]
The Introduction describes a proof-of-concept application of network control theory to ecological momentary interventions, and the Conclusion discusses personalized intervention delivery. Neither passage reports single-case studies supporting the applicability of process-based therapy; searches for that term and for single-case applicability found no such passage in our copy. Page numbers were not provided. (LLM, conf=0.73)
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Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2649-2 · Cited by 23,826 · see citing works
Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N., et al. (2020) · RePEc: Research Papers in Economics · Cited by 17,844 · see citing works
Data Structures for Statistical Computing in Python
McKinney, W. (2010) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-92bf1922-00a · Cited by 11,511 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
Exploring Network Structure, Dynamics, and Function Using NetworkX
Hagberg, A. A., Schult, D. A., & Swart, P. J. (2008) · Proceedings of the Python in Science Conference · doi:10.25080/tcwv9851 · Cited by 7,477 · see citing works
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454 · see citing works
Network analysis: An integrative approach to the structure of psychopathology
Borsboom, D., & Cramer, A. O. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185608 · Cited by 4,359 · see citing works
A network theory of mental disorders
Borsboom, D. (2017) · World Psychiatry · doi:10.1002/wps.20375 · Cited by 3,482 · see citing works
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
Optimal control theory: An introduction
Levine, W. S. (1972) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1972.1100008 · Cited by 1,631 · see citing works
Heron, K. E., & Smyth, J. M. (2010) · British Journal of Health Psychology · doi:10.1348/135910709x466063 · Cited by 1,438 · see citing works
What do centrality measures measure in psychological networks?
Bringmann, L. F., Elmer, T., Epskamp, S., Krause, R. W., Schoch, D., Wichers, M., Wigman, J. T. W., & Snippe, E. (2019) · Journal of Abnormal Psychology · doi:10.1037/abn0000446 · Cited by 1,384 · see citing works
Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology
Fried, E. I., & Cramer, A. O. J. (2017) · Perspectives on Psychological Science · doi:10.1177/1745691617705892 · Cited by 957 · see citing works
Controllability of structural brain networks
Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., et al. (2015) · Nature Communications · doi:10.1038/ncomms9414 · Cited by 912 · see citing works
Experience sampling research in psychopathology: opening the black box of daily life
Myin-Germeys, I., Oorschot, M., Collip, D., Lataster, J., Delespaul, P., & van Os, J. (2009) · Psychological Medicine · doi:10.1017/s0033291708004947 · Cited by 817 · see citing works
Experience sampling methodology in mental health research: new insights and technical developments.
Myin‐Germeys, I., Kasanova, Z., Vaessen, T., Vachon, H., Kirtley, O., Viechtbauer, W., & Reininghaus, U. (2018) · World Psychiatry · doi:10.1002/wps.20513 · Cited by 709 · see citing works
Self-citationData-Driven Science and Engineering
Brunton, S. L., & Kutz, J. N. (2022) · Cambridge University Press eBooks · doi:10.1017/9781009089517 · Cited by 707 · see citing works
Controllability Metrics, Limitations and Algorithms for Complex Networks
Pasqualetti, F., Zampieri, S., & Bullo, F. (2014) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2014.2310254 · Cited by 624 · see citing works
Trull, T. J., & Ebner-Priemer, U. W. (2009) · Psychological Assessment · doi:10.1037/a0017653 · Cited by 550 · cited 1× · see citing works
“Mobile devices, such as smartphones and sensors, facilitate the rich and dynamic collection of information pertaining to an individual's mental state (Myin - Germeys et al., 2009; Schick et al., 2021; Shiffman et al., 2008; Trull & Ebner - Priemer, 2009).”
BackgroundOn Submodularity and Controllability in Complex Dynamical Networks
Summers, T. H., Cortesi, F. L., & Lygeros, J. (2015) · IEEE Transactions on Control of Network Systems · doi:10.1109/tcns.2015.2453711 · Cited by 500 · see citing works
The physics of brain network structure, function and control
Lynn, C. W., & Bassett, D. S. (2019) · Nature Reviews Physics · doi:10.1038/s42254-019-0040-8 · Cited by 438 · see citing works
Revealing the dynamic network structure of the Beck Depression Inventory-II
Bringmann, L. F., Lemmens, L. H. J. M., Huibers, M. J. H., Borsboom, D., & Tuerlinckx, F. (2015) · Psychological Medicine · doi:10.1017/s0033291714001809 · Cited by 371 · see citing works
A complex network perspective on clinical science
Hofmann, S. G., Curtiss, J., & McNally, R. J. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616639283 · Cited by 366 · see citing works
Psychopathological networks: Theory, methods and practice
Bringmann, L. F., Albers, C. J., Bockting, C., Borsboom, D., Ceulemans, E., Cramer, A. O. J., Epskamp, S., Eronen, M. I., Hamaker, E. L., Kuppens, P., et al. (2021) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104011 · Cited by 297 · see citing works
Ecological momentary interventions in psychiatry
Myin‐Germeys, I., Klippel, A., Steinhart, H., & Reininghaus, U. (2016) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000255 · Cited by 223 · see citing works
Self-citationModeling Affect Dynamics: State of the Art and Future Challenges
Hamaker, E. L., Ceulemans, E., Grasman, R. P. P. P., & Tuerlinckx, F. (2015) · Emotion Review · doi:10.1177/1754073915590619 · Cited by 207 · see citing works
Bell, I. H., Lim, M. H., Rossell, S. L., & Thomas, N. (2017) · Psychiatric Services · doi:10.1176/appi.ps.201600523 · Cited by 189 · see citing works
Deep learning for small and big data in psychiatry
Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189 · see citing works
Self-citationEcological momentary interventions for depression and anxiety
Schueller, S. M., Aguilera, A., & Mohr, D. C. (2017) · Depression and Anxiety · doi:10.1002/da.22649 · Cited by 168 · see citing works
Assessing time-varying causal effect moderation in mobile health
Boruvka, A., Almirall, D., Witkiewitz, K., & Murphy, S. A. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1305274 · Cited by 152 · see citing works
Seppälä, 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
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model
Bringmann, L. F., Ferrer, E. E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018) · Multivariate Behavioral Research · doi:10.1080/00273171.2018.1439722 · Cited by 144 · see citing works
Reconstructing computational system dynamics from neural data with recurrent neural networks
Durstewitz, D., Koppe, G., & Thurm, M. I. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00740-7 · Cited by 133 · see citing works
Self-citationKarrer, T. M., Kim, J. Z., Stiso, J., Kahn, A. E., Pasqualetti, F., Habel, U., & Bassett, D. S. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab6e8b · Cited by 121 · see citing works
Wigman, J. T. W., van Os, J., Thiery, E. W., Derom, C. A., Collip, D., Jacobs, N., & Wichers, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059559 · Cited by 98 · see citing works
On the Control of Psychological Networks
Henry, T. R., Robinaugh, D. J., & Fried, E. I. (2021) · Psychometrika · doi:10.1007/s11336-021-09796-9 · Cited by 96 · see citing works
Designing m-Health interventions for precision mental health support
Bidargaddi, N., Schrader, G., Klasnja, P., Licinio, J., & Murphy, S. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00895-2 · Cited by 92 · see citing works
Use of Ecological Momentary Assessment and Intervention in Treatment With Adults
McDevitt‐Murphy, M. E., Luciano, M. T., & Zakarian, R. J. (2018) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/appi.focus.20180017 · Cited by 81 · see citing works
Controllability metrics, limitations and algorithms for complex networks
Pasqualetti, F., Zampieri, S., & Bullo, F. (2014) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.1109/acc.2014.6858621 · Cited by 79 · see citing works
Rauschenberg, C., Boecking, B., Paetzold, I., Schruers, K., Schick, A., van Amelsvoort, T., & Reininghaus, U. (2021) · JMIR Mental Health · doi:10.2196/25650 · Cited by 63 · see citing works
Self-citationAmbulatory assessment for precision psychiatry: Foundations, current developments and future avenues
Reichert, M., Gan, G., Renz, M. P., Braun, U., Brüßler, S., Timm, I., Ma, R., Berhe, O., Benedyk, A., Moldavski, A., et al. (2021) · Experimental Neurology · doi:10.1016/j.expneurol.2021.113807 · Cited by 55 · see citing works
Recurrent Neural Networks in Mobile Sampling and Intervention
Koppe, G., Gülöksüz, S., Reininghaus, U., & Durstewitz, D. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby171 · Cited by 50 · see citing works
Self-citationReininghaus, U., Paetzold, I., Rauschenberg, C., Hirjak, D., Banaschewski, T., Meyer-Lindenberg, A., Boehnke, J. R., Boecking, B., & Schick, A. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbac212 · Cited by 42 · see citing works
Self-citationSchick, A., Paetzold, I., Rauschenberg, C., Hirjak, D., Banaschewski, T., Meyer-Lindenberg, A., Boehnke, J. R., Boecking, B., & Reininghaus, U. (2021) · JMIR Research Protocols · doi:10.2196/27462 · Cited by 30 · see citing works
Self-citationEcological Momentary Interventions in Public Mental Health Provision
Schulte‐Strathaus, J. C. C., Rauschenberg, C., Baumeister, H., & Reininghaus, U. (2022) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-98546-2_25 · Cited by 29 · see citing works
Self-citationThe Symptom and Domain Structure of Psychotic Disorders: A Network Analysis Approach
Peralta, V., Gil‐Berrozpe, G. J., Librero, J., Sánchez-Torres, A. M., & Cuesta, M. J. (2020) · Schizophrenia Bulletin Open · doi:10.1093/schizbullopen/sgaa008 · Cited by 25 · see citing works
Paetzold, I., Schick, A., Rauschenberg, C., Hirjak, D., Banaschewski, T., Meyer‐Lindenberg, A., Butz, S., Floesser, C., Schültke, L., Boehnke, J. R., et al. (2022) · JMIR Formative Research · doi:10.2196/39511 · Cited by 18 · see citing works
Self-citationOptimizing mHealth Interventions with a Bandit
Rabbi, M., Klasnja, P., Choudhury, T., Tewari, A., & Murphy, S. A. (2019) · Studies in neuroscience, psychology and behavioral economics · doi:10.1007/978-3-030-31620-4_18 · Cited by 17 · see citing works
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time SeriesPreprint
Kramer, D., Bommer, P. L., Tombolini, C., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02922 · Cited by 8 · see citing works
Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural NetworksPreprint
Durstewitz, D., Koppe, G., & Thurm, M. I. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.31.514408 · Cited by 6 · see citing works
Self-citationThome, J., Pinger, M., Durstewitz, D., Sommer, W. H., Kirsch, P., & Koppe, G. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1077735 · Cited by 5 · see citing works
Self-citationIntegrating Multimodal Data for Joint Generative Modeling of Complex DynamicsPreprint
Brenner, M., Hess, F., Koppe, G., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.07892 · Cited by 4 · see citing works
Self-citationThome, J., Pinger, M., Durstewitz, D., et al. (2022) · doi:10.31234/osf.io/s7wda · Cited by 1 · see citing works
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Stangier, U., Kohl, V., Görg, N., Sendig, L., Hufschmidt, B., Bonarius, D., Nemani, A., Ebert, M., & Hofmann, S. G. (2024) · Trials · doi:10.1186/s13063-024-08689-3 · Cited by 2 · see citing works
- Authors
- Fechtelpeter, J., Rauschenberg, C., Jamalabadi, H., Boecking, B., van Amelsvoort, T., Reininghaus, U., Durstewitz, D., & Koppe, G.
- Affiliations
- Heidelberg University (DE)
- Charité – Universitätsmedizin Berlin (DE)
- King's College London (GB)
- Corresponding author
- Fechtelpeter, Janik
- ORCID
- Venue
- International Journal of Methods in Psychiatric Research
- Publisher
- Center for Open Science
- Published
- 2023-05-17
- Volume
- 33
- Issue
- 4
- Pages
- e70001-e70001
- ISSN
- 1049-8931 (ISSN-L)
- PMC
- PMC11495417
- OpenAlex
- W4403635208
- Cited by
- 16
- References
- 55
- Citations by year
- 2026: 7 · 2025: 5 · 2024: 3 · 2023: 1
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Methods in Psychiatric Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- European Commission (945263, HORIZON2020)
- Deutsche Forschungsgemeinschaft (TRR 265, TRR‐265 (project A06), 389624707)
- Ministerium für Wissenschaft, Forschung und Kunst Baden-Württemberg (31-7547.223-7/3/2)
- Horizon 2020 Framework Programme (945263)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- ecological momentary intervention, ecological momentary assessment, mobile health, mental health, network control theory, linear dynamical systems, intervention evaluation, smartphone-based training, mechanisms of change
- MeSH terms
- Smartphone, Ecological Momentary Assessment, Adult, Female, Humans, Male, Telemedicine
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