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Bipolar Disorder

Bipolar Disorder

Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.

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Predicting Affective Episodes in Bipolar Disorder Using Statistical Process Control Analysis of GPS-Based Mobility

Marvin Guth¹, Carl Bittendorf², Clemens Krug³

¹Department of eHealth and Sports Analytics, Faculty of Sport Science, Ruhr University Bochum, Bochum, Germany.

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|June 22, 2026

Summary

This summary is machine-generated.

Spatial behavior patterns from GPS data show potential for predicting bipolar disorder episodes, but current methods require further refinement for robust clinical use.

Keywords:

bipolar disorder digital phenotyping mobile sensing spatial analysis spatial data statistical process control unique places

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Area of Science:

Digital health
Psychiatry
Data science

Background:

Bipolar disorders (BD) cause severe affective episodes impacting quality of life.
Early prediction of BD episodes is challenging.
Mobile sensing and digital phenotypes, like geolocation, offer new detection possibilities.

Purpose of the Study:

To assess if spatial exploratory behavior, via GPS, predicts depressive and manic episodes in BD.
To evaluate unique places visited and mobility metrics for predicting prodromal states and ongoing episodes.
To apply statistical process control (SPC) for deviation detection.

Main Methods:

Utilized the BipoSense dataset with high-resolution GPS data.
Applied Density-Based Spatial Clustering to extract mobility indicators (unique places, location changes, time per location).
Implemented exponentially weighted moving average (EWMA)-based SPC to detect deviations from individual baselines.

Main Results:

Median time spent at locations best indicated depressive and (hypo)manic episodes.
Number of unique clusters visited did not significantly correlate with phase transitions.
EWMA-SPC detected behavioral deviations, but no single indicator consistently met high sensitivity and specificity thresholds for prediction.
Optimized SPC improved performance, yet individual indicators lacked robust predictive accuracy for prodromal or acute episodes.

Conclusions:

While unique place data alone is insufficient, EWMA-SPC applied to GPS data shows promise for digital phenotypes.
Current predictive accuracy for upcoming episodes is not robust, but the framework is promising for individualized monitoring.
Further research is essential to refine digital biomarkers and validate their clinical utility in managing BD phases.