Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review

Abstract

BackgroundSeveral 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.ObjectiveThe 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.MethodsWe 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.ResultsA 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).ConclusionsSeveral 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.

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder

“…For example, MONARCA, PRIORI, and Bi‐Affect use patterns of speech and behavior from recorded calls, keystrokes, number of phone calls, and duration of phone calls to predict mood. Wearable devices, such as activity trackers, also offer direct and indirect measurements of a wide range of clinically important variables of BP such as mood, physical activity, heart rate variability, sleep patterns, and circadian rhythms . Yet, it remains unknown whether these passive approaches improve engagement in monitoring compared to active monitoring through smartphone apps.…”

“…Wearable devices, such as activity trackers, also offer direct and indirect measurements of a wide range of clinically important variables of BP such as mood, physical activity, heart rate variability, sleep patterns, and circadian rhythms. 6,22,23 Yet, it remains unknown whether these passive approaches improve engagement in monitoring compared to active monitoring through smartphone apps.…”

Creating a Digital Health Smartphone App and Digital Phenotyping Platform for Mental Health and Diverse Healthcare Needs: an Interdisciplinary and Collaborative Approach

“…Trust is critical for any new data collection tool in healthcare, especially new digital data streams. Given that many mobile app studies correlating sensors to metrics like mood have failed to replicate (Asselbergs et al 2016;Rohani et al 2018), we are cautious to make any claims about this data. Instead, our team is committed to building trust through conducting rigorous, fully reproducible research on all LAMP data streams including surveys, sensors, and cognitive tests.…”

Towards early detection of depression through smartphone sensing

“…The ability to detect the early onset of MDD in individuals will have significant impact on addressing MDD. The current advancement in smartphone sensing, Experience Sampling Method (ESM) and human behaviour modelling using affective computing, multi-modal data fusion and machine learning algorithms [5,18,19] has enabled the instrumentation smartphones to unobtrusively detect MDD in individuals beyond laboratory confinements.…”