Un modelo de teoría de juegos

First of all, we asked for reasons to not use the CommonSense Tracker app. Only one user mentioned privacy (uncertainty about ownership of the data). Users were more concerned about battery use, or having no interest in the collected data.

In general, there is a large difference between users, on which information they would want to collect in a user model and in what amount of detail (exact development over the day, coarse development over the day, or only summary

per day), see Table 7.3. We asked users to explain their choices. We found that the type of information that users want to collect for own insight depends on their personal interests and goals (e.g. regarding behavioral change). The amount of detail depends on the benefit they see.

Type of information exact

mental workload 4 (c,f,h,i) 4 (b,d,e,g) 0 1 (a)

stress 5 (c,d,f,h,i) 2 (b,g) 1 (e) 1 (a)

Table 7.3: Which information do users want to collect for own insight and in what detail. (Amount of users that chose the option denoted, together with user ids.)

In general, there is a difference between users, in which information they consider privacy sensitive (see Table 7.4). As slight trend we see that mental workload and stress is often perceived as privacy sensitive. On the other side, steps/time active is often perceived as less privacy sensitive. Users are mainly concerned of misuse of the data by colleagues, the boss or externals (e.g. insur-ance companies).

Type of information very sensitive a little sensi-tive

Table 7.4: How privacy sensitive the users consider each type of information.

(Note: due to a technical issue responses for participants ‘a’ and ‘g’ on ‘a little sensitive’ and ‘not very sensitive’ were not properly recorded.)

In the current set-up the collected user model data is only available for own insight in the CommonSense tracker app. We asked the participants whether they would want to share the collected information for use in the NiceWork e-coach app. We explained per type of information how this could improve the content and timing of provided tips. All participants, except participant

‘a’ (who had not used the tracking app) wanted to connect the CommonSense Tracker app to the NiceWork app. In general, there is again much difference between users, which information they would want to share with the NiceWork e-coach app. Again, we asked users to explain their choices. We identified sev-eral different strategies. Some people stated that they want to share everything they collect with the e-coach. Their rationale behind this is that privacy should be warranted and then all data would be safe. Other people stated that they would only want to share data which is relevant to their personal goal, or that is necessary for the e-coach to work well.

In general, there is much difference between users, which sensor data they would want to use, see Table7.5. Again, we asked users to explain their choices.

Some people based their decision to collect sensor data on the sensitivity of the particular sensor. Some people based their decisions on a trade-off between usefulness of the data for their personal goal, and how sensitive they find the data. Other users stated that the most important factor for their decision is whether privacy is handled adequately.

motion sensors 8 (b,c,d,e,f,g,h,i) 0 1 (a) 0

location detection 3 (d,e,i) 3 (c,g,h) 2 (a,f) 1 (b)

light sensor 3 (e,f,h) 3 (c,d,g) 2 (b,i) 1 (a)

sound sensor 2 (e,f) 2 (c,h) 3 (b,d,i) 2 (a,g)

digital communication 2 (e,f) 1 (h) 3 (b,c,d) 3 (a,g,i)

computer interactions 3 (e,f,h) 1 (d) 3 (b,c,i) 2 (a,g)

computer content 1 (e) 1 (h) 5 (b,c,d,f,i) 2 (a,g)

webcam 2 (e,f) 2 (d,h) 2 (b,c) 3 (a,g,i)

Kinect 3D camera 3 (d,f,g) 2 (e,h) 3 (b,c,i) 1 (a)

hart rate sensor 5 (b,d,e,f,g) 2 (c,h) 1 (i) 1 (a)

skin conductance sensor 4 (d,e,f,g) 3 (b,c,h) 1 (i) 1 (a)

Table 7.5: Which sensors may be used to collect data and in what detail.

(Amount of users that chose the option denoted, together with user IDs.)

In general, we find that many sensors are perceived as very privacy sensitive (see Table 7.6). Only the motion sensor is experienced as less privacy sensitive, which is in line with the fact that participants judge information on steps/ time active as not very sensitive. The body sensors for collecting heart rate and skin conductance are generally perceived as neutral or only little concerning. In general, many participants express a strong concern for the webcam, sound sensor, computer content and digital communication. Still there is variation between users regarding the exact privacy sensitivity of each sensor. We asked users again to explain what they are afraid of. Users are mainly concerned of misuse of the data, privacy and being controlled and judged.

Sensor no

location detection 0 1 (i) 0 3 (c,d,h) 4 (a,b,e,f)

light sensor 0 5 (c,d,e,f,h) 1 (i) 1 (b) 1 (a)

sound sensor 0 2 (c,f) 0 2 (b,d) 5 (a,e,g,h,i)

digital communication 0 1 (f) 0 2 (c,d) 6 (a,b,e,g,h,i)

computer interactions 0 2 (f,d) 0 2 (c,h) 5 (a,b,e,g,i)

computer content 0 0 1 (d) 2 (c,i) 6 (a,b,e,f,g,h)

webcam 0 0 1 (f) 2 (c,d) 6 (a,b,e,g,h,i)

Kinect 3D camera 0 2 (c,f) 2 (d,i) 1 (h) 3 (a,b,e)

hart rate sensor 0 4 (b,c,d,f) 2 (e,h) 1 (i) 1 (a)

skin conductance sensor 0 6 (b,c,d,e,f,h) 1 (i) 0 1 (a)

Table 7.6: How privacy sensitive the users consider each sensor. (Note: due to a technical issue responses for participants ‘a’ and ‘g’ on ‘a little sensitive’ and

‘not very sensitive’ were not properly recorded.)

We also asked participants to explicitly choose their preferences for a list of conflicting requirements (identified in Section7.4). In summary, we can say that different users make different choices in the trade-offs presented to them. For desired functionality the users are willing to hand in a bit privacy or do extra effort. There are no options that all users prefer in general, but we see some trends (see Table7.7). Most users prefer full control over the system over a sys-tem that is as simple as possible. Most users like the idea of combining different types of data for more insight. Most users prefer to store data on a server (such that it is accessible from different locations). For the other settings, choices are more mixed. We also asked the users to explain their choices, i.e. which choices were easy to make, which difficult and why. What makes decisions difficult is the fact that time and knowledge are necessary to take good decisions. A partic-ipant suggested to take over settings from ‘power users’ who have delved into various trade-offs. Moreover, some users noted that the system may also change over time, e.g. from a learning period in the beginning to more automation, or from detailed support to providing overviews. We can conclude that not only the setting should be flexible enough to account for different user’s preferences, but the system also should account for changes in preferences over time.