Related Experiment Video
Updated: Feb 6, 2026

Author Spotlight: Unveiling the Connection Between Sleep Disorders and Cognitive Symptoms in Depression
Published on: April 26, 2024
Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood
Darius A Rohani1,2, Maria Faurholt-Jepsen3, Lars Vedel Kessing3,4
1Embedded Systems Engineering, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.
Background:
Several studies have recently reported on the correlation between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms in patients with affective disorders (unipolar and bipolar disorders). However, individual studies have reported on different and sometimes contradicting results, and no quantitative systematic review of the correlation between objective behavioral features and depressive mood symptoms has been published.
Objective:
The objectives of this systematic review were to (1) provide an overview of the correlations between objective behavioral features and depressive mood symptoms reported in the literature and (2) investigate the strength and statistical significance of these correlations across studies. The answers to these questions could potentially help identify which objective features have shown most promising results across studies.
Methods:
We conducted a systematic review of the scientific literature, reported according to the preferred reporting items for systematic reviews and meta-analyses guidelines. IEEE Xplore, ACM Digital Library, Web of Sciences, PsychINFO, PubMed, DBLP computer science bibliography, HTA, DARE, Scopus, and Science Direct were searched and supplemented by hand examination of reference lists. The search ended on April 27, 2017, and was limited to studies published between 2007 and 2017.
Results:
A total of 46 studies were eligible for the review. These studies identified and investigated 85 unique objective behavioral features, covering 17 various sensor data inputs. These features were divided into 7 categories. Several features were found to have statistically significant and consistent correlation directionality with mood assessment (eg, the amount of home stay, sleep duration, and vigorous activity), while others showed directionality discrepancies across the studies (eg, amount of text messages [short message service] sent, time spent between locations, and frequency of mobile phone screen activity).
Conclusions:
Several studies showed consistent and statistically significant correlations between objective behavioral features collected via mobile and wearable devices and depressive mood symptoms. Hence, continuous and everyday monitoring of behavioral aspects in affective disorders could be a promising supplementary objective measure for estimating depressive mood symptoms. However, the evidence is limited by methodological issues in individual studies and by a lack of standardization of (1) the collected objective features, (2) the mood assessment methodology, and (3) the statistical methods applied. Therefore, consistency in data collection and analysis in future studies is needed, making replication studies as well as meta-analyses possible.
More Related Videos
07:18A Behavioral Test Battery for the Repeated Assessment of Motor Skills, Mood, and Cognition in Mice
Published on: March 2, 2019
06:55An Unpredictable Chronic Mild Stress Protocol for Instigating Depressive Symptoms, Behavioral Changes and Negative Health Outcomes in Rodents
Published on: December 2, 2015
Related Concept Videos
Depressive Disorders: Etiology
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
Depressive Disorders: MDD and Dysthymia
Correlations
REM Sleep Behavior Disorder
RBD is significantly associated with...
Long-term Depression
Review and Preview
Percentiles are a type of fractile that partition data into...