Smart Phone Sensing to Examine Effects of Social Interactions and Non-sedentary Work Time on Mood Changes

Abstract

Abstract. Study of mood and in turn mood changes is an important index of general wellbeing state and can also be an indicator of various mood disorders including clinical depression and bipolar disorder. While there have been clinical studies of mood, less emphasis has been placed on the factors that affect the mood, especially in workplace. The typical approach taken by these studies is to use clinical questionnaires in order to record the activities that impact the mood. However, recording activities that may have an influence on the mood in this manner suffers from a number of issues including floor effect and difficulty in recalling past activities. Our work instead has focused on using unobtrusive monitoring technology to study mood changes during office hours and a set of associated factors that influence these changes. We have primarily studied two factors and their impact on the mood changes, namely social activity and nonsedentary work style. The pilot study ran over the course of 7 days of measurements with the participation of 9 knowledge workers. The results have shown that mood changes are highly correlated with both social interactions and non-sedentary work style. This study is the first to investigate the correlation between mood changes and non-sedentary behavior patterns, opening up a research avenue to explore psychological effects of increasing prevalence of sedentary behavior.

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

“…Some of these features are quite creative and worth mentioning. The most promising results for the nonclinical samples include the time spent in break rooms (ρ=−0.21, nonsignificant) [16], and less SD of stillness amount, which can be interpreted as a more uniform activity pattern (beta=−3.3, P <.001) [46]. For the clinical samples, it includes the increased amount of time with no sound detection ( speech pauses ; beta=0.34, P =.004) [55], increased number of calls missed (beta=0.05, P =.006) [6], and fewer incidences of quick or sudden movements ( jerk ; t =4.06, P <.001).…”

“…The lack of detailed reporting on analysis methods was clearly demonstrated in a study by Beiwinkel et al [22], where a between-subject (cross-sectional analysis) relationship yielded a statistically nonsignificant ( P =.82) regression coefficient of −0.04, while a within-subject (longitudinal analysis) relationship yielded a statistically significant ( P =.03) regression coefficient of −0.11, on the feature of cell tower ID. Data aggregation length was also a concern because the duration of studies included in this review spans from 7 days [16,47] to 12 months [22]. Canzian & Musolesi [9] presented results on the correlation between PHQ-8 and different mobility features for 1 to 14 days of aggregation.…”

Workplace Indicators of Mood

“…One of the few studies to examine mood in the workplace using unobtrusive measures is a pilot study of nine information workers by Matic et al [31]. This study used smartphones to sense activity and correlated that with experience sampling of mood and active badges to detect social interactions.…”

Correlations between objective behavioral features collected from mobile and wearable devices, and depressive mood symptoms in affective disorders: A systematic review (Preprint)

“…The lack of detailed reporting on analysis methods was clearly demonstrated in Beiwinkel et al [21] where a between-subject (crosssectional analysis) relationship yielded a statistically non-significant (p = .82) regression coefficient of -.04, while a within-subject (longitudinal analysis) relationship yielded a statistically significant (p < .03) regression coefficient of -.11, on the feature of cell tower ID. Data aggregation lengths was also a concern since duration of studies included in this review spans from 7 days [16,48] to 12 months [21]. Canzian & Musolesi [9] presented results on the correlation between PHQ-8 and different mobility features for 1 to 14 days of aggregation.…”

“…A digital marker has been defined as a consumer generated physiological and behavioral measure collected from digital tool that can be used to explain, influence and/or predict health related outcomes [13]. Many studies have found statistically significant correlations between objective behavioral features collected from mobile and wearable technology and mood symptoms in non-clinical samples of participants without psychiatric illness (e.g., [14][15][16][17]) as well as in clinical samples of patients diagnosed with psychiatric disorders (e.g., [11,[18][19][20]), and this has raised great enthusiasm in terms of using mobile and wearable technology in the treatment and monitoring of depression and other affective disorders. It has been argued that such an approach may provide an easy and objective way to monitor illness activity and could serve as a digital marker of mood symptoms in affective disorders [18].…”