Personalized Stress Management: Enabling Stress Monitoring with LifelogExplorer

In its 2009 report, The European Agency for Safety and Health at Work (OHSA) named stress to be the second most frequent work-related health problem, affecting 22 % of workers from the EU27 [4]. Today’s society imposes multitude of different responsibilities and demands constant multitasking, which leads to unprecedented surges in stress levels [7]. In order to cope, many people resort to popular momentary relaxation and reflection techniques [14]. Still, overwhelmed and unable to see dangerous trends in their stress levels, people often start reflecting on their stress patterns and possible stress coping strategies too late, when one of the stress-related diseases affects them. Especially stress responses experienced over longer periods of time, that build up, can lead to health related problems [6].

There exist individual patterns in person’s behavior that lead to increases in the level of stress or to better relaxation [11, 13]. A behavioral pattern can manifest itself in different contexts and the intensity of the stressful response can depend on the context, which makes pattern identification difficult. On the other hand, thanks to the digitalization of the world and recent advances in sensor technologies it becomes possible to facilitate the discovery of behavioral patterns [3].

In this paper, we look at peoples’ behavior in relation to their physiological responses and visualize the obtained information using a tool called LifelogExplorer. We combine arousal information, tightly linked to stress, with contextual information in form of artifacts produced by users’ activity in the digital world such as calendar activities, e-mails, (logs of) phone calls. The tool allows users to explore correlations between their activities and their emotional responses and reflect on their behavior. By realizing such relations they may identify possible problems, attempt a change in their behavior, and observe its effects. The first version of LifelogExplorer was presented in [8] and we further improved it based on user feedback from multiple studies.

In our previous case studies [8, 9] we evaluated our approach with LifelogExplorer and the arousal data obtained with the DTI-2 sensor wristband [19]. The focus of those studies was on the evaluation of the quality of measurements and the understandability of the visualizations generated by LifelogExplorer. Since we aimed at an objective evaluation and thus avoided interfering with the users’ perception of the experienced stress during the studies, the users were only given a possibility to use LifelogExplorer and explore their data at the end of the studies. The obtained results showed that long-term measurements of arousal allows to reveal people information about their behavioral patterns perceived as meaningful and useful.

The question we address in this paper is whether the users are actually interested in seeing their behavioral patterns on a regular basis, able to learn from them and, if needed, to act upon this knowledge to undertake a behavior change intervention. We carried out a case study with teachers of vocational school, organized with the help of Human Capital Care (HCC)—an occupational health organization. We show the analysis users’ activities on exploring their data with LifelogExplorer during the four weeks of the study.

The rest of the paper is organised as follows: In Sect. 2, we describe our information sources, their interpretation for generating the LifelogExplorer views and the general functionality of the tool. In Sect. 3, we present the case study and its results. Finally, in Sect. 5, we draw conclusions from our work.

The area of behavior change in context of wellbeing management focuses on changing user’s unhealthy behavior into a more healthy one. Most of the strategies employed are derived from stage-based model [15], setting theory [10], and cognitive dissonance theory [5]. Although focused on various aspects, a common requirement for all of them is the need of first making the user aware of the need for behavior changes. In the context of stress management, it means that the user needs to become aware of the main sources of stress, related behavioral patterns and be able to monitor the results of interventions aimed at behavior changes.

Monitoring stress and arousal became an active research field due to recent advances in wearable sensor technologies. Unobtrusive and continuous measurements of skin conductance and/or heart rate variability supplemented with information about physical activities, can be used to estimate the stress level [18].

Several approaches for the continuous monitoring of stress and arousal were proposed in the last years. In the Feel project, the authors created a system for automated annotation and monitoring of phone calls and stress responses of the users  [1] using a mobile phone and a wearable biosensor. The presented visualization of the data is restricted to calendar views: day, week, month and a list view for events, limiting the ability of the user to spot non-time-related trends and reflect on them. AffectAura [12] is another system aimed at enabling self-reflection on emotional states over an extended period of time. The authors employ multiple sensors in order to capture the precise context of emotional responses, which limits the applicability of the approach to office-workers. Also here, the visualization of data is driven by the time dimension only. The AffectiveHealth project focuses on live presentation of the physiological signals with the main focus on providing real-time, short-term feedback to the user; the measurements are visualized in the form of a dynamic spiral to encourage personal reflection on body reactions throughout the day [16]. The recall and the pattern discovery are out of the scope in that approach. An extensive survey of recent work done in the context of sensor-based personal wellness management systems is presented in [20].

In our approach we aim at facilitating self-reflection and identification of stress-related patterns by visualizing links between stress-related data and different aspects of person’s activities, to raise awareness of the contexts leading to stress unbalance.

We conducted a non-experimental study at a vocational school. We were mainly interested in evaluating user engagement in viewing their own data, their ability to learn from it and to apply self-coaching. To evaluate these aspects LifelogExplorer logged participant’s use of the system, hence avoiding affecting the users’ natural behavior and preserving ecological validity. We also discuss here what functionalities of the tool were mostly used, and what kinds of visualizations users prefer. Since observation of behavior using logs does not give insight in the reasons of users’ choices and preferences, an on-line questionnaire and semi-structured interviews have been employed at the end of the study. The interviews were conducted by a coaching expert from HCC and focused on discovering if the users were able to make sense of the visualized information, learn from it and make self-triggered interventions to alter their behavior.

Participants The study involved 21 participants (15 males, 6 females). Their age ranged from 23 to 56 \((\mu =41.0, \sigma =11.59)\). They were not using drugs, antidepressants and were not suffering from a disease resulting in a fever or excessive sweating. There was no preselection related to stress or fitness. 2 of the 21 participants joined the study a week later (and finishing it a week later), after learning about it from their colleagues at the vocational school and expressing their interest to participate. The participants did not get any financial compensation; we were interested to see whether they are intrinsically interested in seeing information extracted from their data.

During the study, the participants downloaded the measurements from the DTI-2 wristband by connecting it to a PC with the LifelogExplorer tool running at the moments of their choice, with a request to do it at least once: at the end of the study. LifelogExplorer automatically detects device connection, downloads the data and updates all visualization elements. To provide context to the measurements, the participant’s work schedules were automatically uploaded to LifelogExplorer. The participants were free to decide when and which views on their data they want to explore, if any. The participants were not supervised directly; interventions were made only at participant’s requests.

We have collected 20 days of measurements per participant on average \((\sigma = 5.92)\) along with an average of 90 calendar entries per participant \((\sigma = 31.30)\). 7 of the participants chose to wear the device also during some of the weekend days and 5 of the participants expressed their interest in using the system for an additional week, which they were allowed to do. Out of the full group, two persons did not collect any data—one due to getting ill at the beginning of the study and another because of consistently forgetting to wear the device. Two more participants are excluded from further analysis due to the low quality of collected physiological measurements (with more than 50 % of the data filtered out as too noisy).

Analysis of tool use The average number of sessions with the LifelogExplorer amounted to 18.2 \((\sigma =14.1)\) per user (compared to 20 days with measurements per user, on average). In principle, the users could have just used the tool for downloading the data from the device without exploring it any further. To differentiate between “data upload only” sessions and sessions in which the user actually viewed his data and avoid the bias the arousal visualisation set as default view, we configured the tool so that the initial view showed the calendar without arousal information. To see a view with some arousal information, the user had to click a button choosing the visualization type. We call a session active session if the user viewed his/her arousal information. 86.7 % of user sessions were active sessions. Further we give statistics for active sessions only.

The number of active sessions of two users were outliers, being very high: 41 and 47, respectively. After removing these two outliers, the data for the number of active sessions per user fits the normal distribution with the mean value 11.6 and standard deviation 6.0, according to Shapiro–Wilk Normality Test [17], with \(W= 0.89\) for \(p=0.05\).

During the study, the users spent a total of 95 min using the tool on average per user. The mean duration of each session was 6.5 min and the average number of actions per session amounted to 25. In terms of visualization style preference, the participants looked at stripe glyph views in 91 % of the active sessions, while they looked at line glyph views in 46 % of the sessions. Only in 6.6 % of the active sessions the users did not use any of the calendar views on the arousal (but aggregated views and histogram views).

All users accessed the aggregated views at some point, with the average number of 8 aggregated views per user during the whole study period; 89 % of the users went there further than looking at the default aggregated view selection (“Days of the Week”). The aggregated views were used in 37 % of the sessions and the custom days views in 26 % of sessions. 37 % of the custom days views were called from an aggregated view, to understand on which days certain things from the aggregated view happened, in the other 63 % of the custom views, the user defined the days he wants to see and compare. 14 % of the users also made use of the trend view functionality with tracking trends by weeks being the most popular option (which is logical, taking into account the duration of the study and weekly schedules of the users). The histogram view was looked at in 23 % of the sessions and in 53 % of these sessions the users customized the categorization of arousal.

Interestingly, 59 of 300 active sessions were started within 1 h after a previous active session of this user (the mean time till the follower session was 11.54 min, \(\sigma = 11.98\,\)min). 16 of 19 users had such “follower” sessions. We considered three clusters of sessions: follower sessions (following another session within 1 h), sessions being followed, and stand-alone sessions and stated that the three clusters have very similar characteristics both from the perspective of the session duration and the number of user activities in the sessions. The only difference was related to uploading the sensor data: it took place in 67 % of sessions being followed, only 15 % of the follower sessions and in 67 % of the stand alone sessions, indicating that the follower sessions were primarily used for taking another look at the data. Because of the anonymity, we could not contact the users having such follower sessions, and we can only make a conjecture that the follower sessions are caused by self-reflection taking place after the first session and the need for more information due to it.

61 % of the sessions took place in the first two weeks of the study, which means that the users explored their data in the second half of the study less frequently but did not lose interest in it. Moreover, 58 % of the users spent more time per session, on average, in the last 2 weeks of the study than in the first 2 weeks.

The big variation in the number of sessions per user suggests big differences between users’ interest in exploring their data. The relatively high number of activities and time spent on average per session suggests users interest in the information they get. Statistics for the use of aggregated views suggests that the participants were interested in exploring their long-term behavior patterns, which is especially encouraging taking into account the study duration. The use of histogram views and custom days views suggest that some users are interested in having control over the way the information is shown to them. Finally, clear usage preference towards the color based visualization in the calendar indicates that most of the users prefer interpreted visualizations of their arousal.

12 out of 15 participants who provided feedback indicated that they have learned something new about themselves based on the presented information, providing comments like “Recognizable, but also surprising to see that certain activities, such as meetings, are experienced as stressful.”, “You learn and see what particular activities have impact on your energy and state of arousal” Most negative comments focused on the belief of the users that they already knew about their stress reactions. "I knew that classes require varying energy.” “I knew that repetitive activities can cost equal energy.”

Although 12 out of 15 participants indicated they learnt about themselves, only 5 (33 %) indicated that they are able to change something in their lives based on what they have learned. Some of their comments are “Yes, my stress level was going down during the breaks. Probably, it might help to take breaks between classes and meetings more often”; “Yes, dropping activities that did not require direct action helped me to lower my stress level”; “Yes, I better schedule my time”. Participants who felt they were not able to act upon the presented information indicated that stress they experience is part of their job and they do not think it can be reduced. Some mentioned that the results can only be obtained if weekly meetings with a discussion of stress-related issues would take place and the changes are tried out. Some of their comments are: “Some results are just part of the job.”; “I still need to give lessons to these groups.”; “No, I do not think I can do anything to reduce my stress, especially on busier days.” These results suggest that many people, even being able to identify behavioural patterns linked to stress, are not able to translate this knowledge into self-advices and need an additional assistance of a coach or peers.

In this paper we presented the results of an unsupervised study on longitude monitoring of arousal in the context of daily events of the user and evaluated how the participants used LifelogExplorer—a tool visualizing relations between arousal and different aspects of daily events. The results of the study show that most participants increased their self-awareness of arousal-related patterns and some of them were able to generate practically relevant interventions to improve their stress balance based on what they learnt. This is especially encouraging since the study was unsupervised and it was conducted in a real-life setting.

To further support users in identifying meaningful stress patterns and to reduce incompleteness of the lifelog we plan to include additional information sources, like telephone and internet activities logs. We would also like to further explore possibilities of engaging users in effective and practically useful behavior change interventions, allowing options for automated monitoring of the intervention effects and generating recommendations guiding the user to the objectives (s)he chooses.