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Link to original content: https://pubmed.ncbi.nlm.nih.gov/28729233/
DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy - PubMed Skip to main page content
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. 2017 Jul 20;5(3):e20.
doi: 10.2196/medinform.6808.

DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy

Affiliations

DynAMo: A Modular Platform for Monitoring Process, Outcome, and Algorithm-Based Treatment Planning in Psychotherapy

Tim Kaiser et al. JMIR Med Inform. .

Abstract

Background: In recent years, the assessment of mental disorders has become more and more personalized. Modern advancements such as Internet-enabled mobile phones and increased computing capacity make it possible to tap sources of information that have long been unavailable to mental health practitioners.

Objective: Software packages that combine algorithm-based treatment planning, process monitoring, and outcome monitoring are scarce. The objective of this study was to assess whether the DynAMo Web application can fill this gap by providing a software solution that can be used by both researchers to conduct state-of-the-art psychotherapy process research and clinicians to plan treatments and monitor psychotherapeutic processes.

Methods: In this paper, we report on the current state of a Web application that can be used for assessing the temporal structure of mental disorders using information on their temporal and synchronous associations. A treatment planning algorithm automatically interprets the data and delivers priority scores of symptoms to practitioners. The application is also capable of monitoring psychotherapeutic processes during therapy and of monitoring treatment outcomes. This application was developed using the R programming language (R Core Team, Vienna) and the Shiny Web application framework (RStudio, Inc, Boston). It is made entirely from open-source software packages and thus is easily extensible.

Results: The capabilities of the proposed application are demonstrated. Case illustrations are provided to exemplify its usefulness in clinical practice.

Conclusions: With the broad availability of Internet-enabled mobile phones and similar devices, collecting data on psychopathology and psychotherapeutic processes has become easier than ever. The proposed application is a valuable tool for capturing, processing, and visualizing these data. The combination of dynamic assessment and process- and outcome monitoring has the potential to improve the efficacy and effectiveness of psychotherapy.

Keywords: algorithms; health information management; mental disorders; mental health; psychotherapeutic processes.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Data assessment interface on a mobile phone.
Figure 2
Figure 2
Vector autoregressive model, including auto- and cross-regressive parameters (one-headed arrows) and a synchronous association (double-headed arrows). Only statistically significant parameters are shown. Explained variance is indicated by R-squared values.
Figure 3
Figure 3
Time series plot showing answers to an intersession process item. A line smoothed by local regression scatterplot smoothing (LOESS) is added for easy interpretation of long-term change, including the 95% CI. The red line represents the measure of dynamic complexity. The x-axis represents the date of measurement (day and month).
Figure 4
Figure 4
Recurrence plot for the time series illustrated in Figure 2. Darker, red-colored areas of the plot indicate low Euclidean distance between the respective points of the time series whereas brighter, more yellow areas indicate greater Euclidean distance. Greater distance also implies that the time series currently describes a period that has not occurred before. Note that the periods of increased dynamic complexity in the time series depicted in Figure 2 are reflected in the recurrence plot. Both “time” axes refer to points of measurement in the time series.
Figure 5
Figure 5
Time series plot showing mean scores of the “Problem Solving” scale of the short intersession questionnaire. A line smoothed by local regression scatterplot smoothing (LOESS), including the 95% CI is added for easy interpretation of long-term change. The red line represents the measure of dynamic complexity that was rescaled from 0 (minimum complexity) to 100 (maximum complexity). Percentage values indicate weekly treatment outcome in percent symptom change.
Figure 6
Figure 6
Time series plot showing the (a) the “positive treatment-related emotion” and (b) the “negative treatment-related emotion” scores. The red box marks a significant drop in positive treatment-related emotions, as well as an increase in negative ones.

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