Cited by 43Mental Health Research TopicsDigital Mental Health InterventionsPsychotherapy Techniques and Applications

The use of digitally assessed stress levels to model change processes in CBT -A feasibility study on seven case examples

Open access
Hehlmann, Miriam I., Schwartz, Brian, Lutz, Teresa, Gómez Penedo, Juan Martín, Rubel, Julian A., Lutz, Wolfgang·2021·Frontiers in Psychiatry

Abstract

In psychotherapy research, the measurement of treatment processes and outcome are predominantly based on self-reports. However, given new technological developments, other potential sources can be considered to improve measurements. In a feasibility study, we examined whether Ecological Momentary Assessments (EMA) using digital phenotyping (stress level) can be a valuable tool to investigate change processes during cognitive behavioral therapy (CBT). Seven outpatients undergoing psychological treatment were assessed using EMA. Continuous stress levels (heart rate variability) were assessed via fitness trackers (Garmin) every 3 min over a 2-week time period (6,720 measurements per patient). Time-varying change point autoregressive (TVCP-AR) models were employed to detect both gradual and abrupt changes in stress levels. Results for seven case examples indicate differential patterns of change processes in stress. More precisely, inertia of stress level changed gradually over time in one of the participants, whereas the other participants showed both gradual and abrupt changes. This feasibility study demonstrates that intensive longitudinal assessments enriched by digitally assessed stress levels have the potential to investigate intra- and interindividual differences in treatment change processes and their relations to treatment outcome. Further, implementation issues and implications for future research and developments using digital phenotyping are discussed.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3389/fpsyt.2021.613085

Cites

17 of 17 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

  • More recent developments in data-informed psychotherapy deal with tools from e-mental health apps and intensive longitudinal assessments, which can also be used to create, improve and customize CF.

    “More recent developments in data-informed psychotherapy deal with tools from e-mental health apps and intensive longitudinal assessments, which can also be used to create, improve and customize CF (Hehlmann et al., 2021).”

    From Data-Informed Case Formulation with the Trier Treatment Navigator (2022) · cites this paper as Hehlmann et al., 2021

    The Introduction describes intensive repeated assessments using mobile devices, and the Limitations and Conclusion section says digitally assessed information might help therapists adapt treatment to individual patients. The paper does not say these tools can create, improve, or customize CF specifically; it discusses treatment monitoring and adaptation instead. No page numbers are available in our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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

Flagged first, then by citations

Research works printed but not linked: 37

  1. Delgadillo, 2020, A development pathway towards precision mental health care, JAMA Psychiatry., 77, 889, 10.1001/jamapsychiatry.2020.1048 · DOI
  2. Cohen, 2018, Treatment selection in depression, Annu Rev Clin Psychol., 14, 209, 10.1146/annurev-clinpsy-050817-084746 · DOI · Paper page
  3. Delgadillo, 2020, Towards personalized allocation of patients to therapists, J Consult Clin Psychol, 88, 799, 10.1037/ccp0000507 · DOI
  4. Fisher, 2016, Enhancing the personalization of psychotherapy with dynamic assessment and modeling, Assessment., 23, 496, 10.1177/1073191116638735 · DOI
  5. Schwartz, 2020, Personalized treatment selection in routine care: integrating machine learning and statistical algorithms to recommend cognitive behavioral or psychodynamic therapy, Psychother Res, 31, 33, 10.1080/10503307.2020.1769219 · DOI
  6. Tschitsaz-Stucki, 2009, Identifikation und Aufklärung von Veränderungssprüngen im individuellen Psychotherapieverlauf, Zeitschrift Klin Psychol Psychother., 38, 13, 10.1026/1616-3443.38.1.13 · DOI
  7. Lutz, 2013, The ups and downs of psychotherapy: sudden gains and sudden losses identified with session reports, Psychother Res., 23, 14, 10.1080/10503307.2012.693837 · DOI
  8. Wucherpfennig, 2017, Sudden gains in routine care cognitive behavioral therapy for depression: a replication with extensions, Behav Res Ther., 89, 24, 10.1016/j.brat.2016.11.003 · DOI
  9. Lutz, 2020, Working towards the development and implementation of precision mental healthcare – an example, Adm Policy Ment Health., 47, 856, 10.1007/s10488-020-01053-y · DOI
  10. van de Leemput, 2014, Critical slowing down as early warning for the onset and termination of depression, Proc Natl Acad Sci USA., 111, 87, 10.1073/pnas.1312114110 · DOI
  11. Kuppens, 2010, Emotional inertia and psychological maladjustment, Psychol Sci., 21, 984, 10.1177/0956797610372634 · DOI
  12. Nelson, 2020, Everyday emotional dynamics in major depression, Emotion, 20, 179, 10.1037/emo0000541 · DOI
  13. Fisher, 2020, Identifying the presence and timing of discrete mood states prior to therapy, Behav Res Ther., 128, 103596, 10.1016/j.brat.2020.103596 · DOI
  14. Wright, 2019, Applied ambulatory assessment: integrating idiographic and nomothetic principles of measurement, Psychol Assess., 31, 1467, 10.1037/pas0000685 · DOI
  15. Pryss, 2019, Exploring the time trend of stress levels while using the crowdsensing mobile health platform, trackyourstress, and the influence of perceived stress reactivity: ecological momentary assessment pilot study, JMIR Mhealth Uhealth., 7, e13978, 10.2196/13978 · DOI
  16. Landmann, 2020, Changes in the daily life experience of patients with obsessive-compulsive disorder following mindfulness-based cognitive therapy: looking beyond symptom reduction using ecological momentary assessment, Psychiatry Res., 286, 112842, 10.1016/j.psychres.2020.112842 · DOI
  17. Husen, 2016, Daily affect dynamics predict early response in CBT: feasibility and predictive validity of EMA for outpatient psychotherapy, J Affect Disord., 206, 305, 10.1016/j.jad.2016.08.025 · DOI
  18. Lutz, 2018, Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders. A methodological proof-of-concept study, Sci Rep., 8, 7819, 10.1038/s41598-018-25953-0 · DOI
  19. Onnela, 2016, Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health, Neuropsychopharmacology., 41, 1691, 10.1038/npp.2016.7 · DOI · Paper page
  20. Wright, 2020, Personalized models of psychopathology, Ann Rev Clin Psychol., 16, 49, 10.1146/annurev-clinpsy-102419-125032 · DOI
  21. Asare, 2019, Towards early detection of depression through smartphone sensing, Assoc Comp Mach, 1158, 10.1145/3341162.3347075 · DOI · Paper page
  22. Blanck, 2019, Heart rate variability in individual psychotherapy: associations with alliance and outcome, J Nerv Ment Dis., 207, 451, 10.1097/NMD.0000000000000994 · DOI
  23. Jacobson, 2019, Using digital phenotyping to accurately detect depression severity, J Nerv Ment Dis., 207, 893, 10.1097/NMD.0000000000001042 · DOI · Paper page
  24. Albers, 2020, Inspecting gradual and abrupt changes in emotion dynamics with the time-varying change point autoregressive model, Eur J Psychol Assess., 36, 492, 10.1027/1015-5759/a000589 · DOI
  25. First, 2002, Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version
  26. Lutz, 2006, The development, validation, and reliability of short-forms of current instruments for the evaluation of therapeutic progress in psychotherapy and psychiatry, Diagnostica., 52, 11, 10.1026/0012-1924.52.1.11 · DOI
  27. Derogatis, 1992, SCL-90-R, Administration, Scoring and Procedures Manual-II for the R(evised) Version and Other Instruments of the Psychopathology Rating Scale Series
  28. Ellsworth, 2006, A comparison of the outcome questionnaire-45 and outcome questionnaire-30 in classification and prediction of treatment outcome, Clin Psychol Psychother., 13, 380, 10.1002/cpp.503 · DOI
  29. Collins, 2019, Version reporting and assessment approaches for new and updated activity and heart rate monitors, Sensors., 19, 1705, 10.3390/s19071705 · DOI
  30. Krone, 2017, A multivariate statistical model for emotion dynamics, Emotion., 18, 739, 10.1037/emo0000384 · DOI
  31. Koval, 2012, Changing emotion dynamics: individual differences in the effect of anticipatory social stress on emotional inertia, Emotion., 12, 256, 10.1037/a0024756 · DOI
  32. Nelson, 2017, Moving from static to dynamic models of the onset of mental disorder: a review, JAMA Psychiatry., 74, 528, 10.1001/jamapsychiatry.2017.0001 · DOI
  33. Bringmann, 2017, Changing dynamics: time-varying autoregressive models using generalized additive modeling, Psychol Methods., 22, 409, 10.1037/met0000085 · DOI
  34. Hamilton, 1989, a new approach to the economic analysis of nonstationary time series and the business cycle, Econometrica., 57, 357, 10.2307/1912559 · DOI
  35. Hamilton, 1994, Time Series Analysis, 10.1515/9780691218632 · DOI
  36. Wood, 2006, Generalized Additive Models: An Introduction With R, 10.1201/9781420010404 · DOI · Paper page
  37. Koval, 2012, Getting stuck in depression: the roles of rumination and emotional inertia, Cogn Emot., 26, 1412, 10.1080/02699931.2012.667392 · DOI

Cited by

Sorted by citations

Authors
Hehlmann, M. I., Schwartz, B., Lutz, T., Gómez Penedo, J. M., Rubel, J. A., & Lutz, W.
Affiliations
  • Universität Trier (DE)
  • Justus-Liebig-Universität Gießen (DE)
Corresponding author
Hehlmann, Miriam I.
Venue
Frontiers in Psychiatry
Published
2021-03-09
Volume
12
Pages
613085-613085
ISSN
1664-0640 (ISSN-L)
OpenAlex
W3134372219
Cited by
43
References
17
Citations by year
2026: 3 · 2025: 11 · 2024: 8 · 2023: 9 · 2022: 4 · 2021: 8
FWCI
5.32
Type
journal article
Language
en
Open access
Locations
  • Frontiers in Psychiatry (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • CONICET Digital (CONICET) (submittedVersion, cc-by)
  • Greater South Information System
Funders
Deutsche Forschungsgemeinschaft (LU 660-10/1, LU 660/8–1 and LU 660/10–1, 660/10-1, LU 660/8‐1)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
Keywords
cognitive behavioral therapy, digital phenotyping, ecological momentary assessment, heart rate variability, wearable activity trackers, stress levels, intensive longitudinal data, treatment outcomes, individual differences

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