Posted on by bardram

Visiting the HCI Group at the University of Konstanz

Recently I had the pleasure to visit old friends at the Human-Computer Interaction Lab at the University of Konstanz. This was a really nice and relaxed visit, while also super busy. The first day I was the external thesis opponent on the PhD Thesis of Jens Müller. He did a really nice presentation of his work on co-located and distributed augmented reality, as presented in his CHI 2017 paper. The next day, we had a chance to go over the SmartAct Project and dig into some very technical details on how to collect sensor data from the Movisens sensor via BTLE. It was nice to study code rather seeing polished presentations.

Is there a correlation between e.g. location and depression?

Talking about presentations, I also did a (polished ;-)) presentation of my work. The slides are attached here for download. The talk was entitled “Is there a correlation between e.g. location and depression?” and the main focus was a presentation of our recent systematic review on “Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders” published in the Journal of Medical Internet Research (JMIR) [1] . A summary of this paper is also available at the CACHET homepage.

The main message in the paper is that there is a disturbing lack of standardisation in the work on “digital phenotyping in mental health”. Despite all the visions and promises for the use of smartphone technology in mental health, the (systematic) evidence is still very limited and fragmented. In the paper we discuss that standardization is needed in three main areas:

  •  Standardise data collection and features extraction methods – the way that physical activity, social activity, and mobility features based on accelerometer and GPS data are extracted should be standardized across studies.
  • Standardise mood assessment tools – a wide range of clinical (n=11) and nonclinical (n=9) mood rating scales were used and it is hard to compare correlations across studies when such different scales are used.
  • Standardise statistical correlation methodology – studies applied more than 11 different methods for correlation values, with different time windows.

One of the ways forward in terms of standardisation is the Open mHealth initiative, which is now part of the IEEE P1752 standardization group. I’m active in this standardization group and has recently released an open source implementation of the Open mHealth schemas for Flutter developers.

References

[1] [pdf] [doi] D. A. Rohani, M. Faurholt-Jepsen, V. L. Kessing, and J. E. Bardram, “Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review,” JMIR Mhealth Uhealth, vol. 6, iss. 8, p. e165, 2018.
[Bibtex]
@article{jmir-rohani2018b,
abstract = {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.},
author = {Rohani, A Darius and Faurholt-Jepsen, Maria and Kessing, Vedel Lars and Bardram, E Jakob},
doi = {10.2196/mhealth.9691},
file = {:Users/bardram/Documents/Mendeley Desktop/fd000374d85f1e70f1c78103e4d84e5a.pdf:pdf},
issn = {2291-5222},
journal = {JMIR Mhealth Uhealth},
keywords = {affective disorder,behavior,bipolar disorder,correlation,depression,depressive mood symptoms,mobile phone,mood disorder,objective features,sensor data,systematic review,wearable devices},
month = {aug},
number = {8},
pages = {e165},
title = {{Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review}},
url = {http://mhealth.jmir.org/2018/8/e165/},
volume = {6},
year = {2018}
}