In total 104 (45.2% female) white-collar workers participated in the study. To provide anonymity we asked the age of participants in form of categories: < 25 years (0%), 25–29 years (7.7%), 30–34 years (5.8%), 35–39 years (13.5%), 40–44 years (5.8%), 45–49 years (10.6%), 50–54 years (18.3%), 55–59 years (21.1%), 60–64 years (10.6%) and no statement (6.7%). Participants worked at a certain department of a public institution in Austria. The majority of them worked fulltime: ≤ 25 h per week (10.6%), 26–45 h per week (8.7%) and ≥ 36 h per week (68.3%), no statement (12.5%). Work activity of participants included both in-house activities and on-the-road activities, visiting customers. Regardless of activity, employees could easily take the fitness tracker (wrist band) with them all day.
Participation was fully voluntary. As a benefit for taking part, each participant received individual feedback on their health parameters and some suggestions on how to improve their health at the end of the study. Participants needed to register for participation in advance in order to take part at the opening event of the study where they were informed about content and purpose of the study and gave consent. The field study was approved in terms of ethical compliance by the committee of the public institution’s workers' council. At the opening event participants also received a fitness tracker, which they chose randomly. Overall, they could wear the fitness tracker for fourteen consecutive days, but because of work-related appointments some participants received their device one day later and/or returned it one day earlier. Therefore, in terms of data continuity, we excluded the first and the last two days of data collected and included ten days for further analyses, because this was the official time of data collection.
We used the XIAOMI “Mi Band 2” fitness tracker for several reasons. First, it had enough internal storage to save all data during the duration of data collection. We only synchronized the data when the participants returned their trackers. Second, the trackers had a long-lasting battery which kept the effort for participants to a minimum. They did not have to charge the device every other day, which was important for both participants’ commitment and continuous collection of data. Finally, third, the trackers had an application programming interface that we used to pair the devices exclusively with our own application based on the open source app Gadgetbridge to ensure data security. When participants returned their fitness tracker, they received the link to an online questionnaire that included standard sociodemographic data (age, gender, educational level, marital status, working hours) as well as measures about sleep quality, sleep duration, burnout, and wellbeing. It also included other health parameters (e.g., physical activity) that are not part of the current research model. We then used the serial number of the fitness tracker as the identification code for each participant to match the objective sleep data with the data of the online survey.
Self-reported Sleep Quality
To assess self-reported sleep quality (SSQ), we used the German version of the Jenkins Sleep Quality Index . The index consists of four items that ask about the most common parameters for sleep quality, for example, trouble falling asleep or not feeling refreshed after a sufficient time of sleep. Items were originally rated on a 6-point scale, expressing how often the condition appeared in the last month. We changed the instructions from “in the last month” to “in the last 14 days.” To fit the design of the current study, the answering scale was adapted from: “not at all”, “1 to 3 days”, “4 to 7 days”, “8 to 14 days”, “15 to 21 days” and “22 to 31 days” to: 1 “to a very large extent”, 2 “to a large extent”, 3 “somewhat”, 4 “to a small extent” and 5 “to a very small extent.” Originally, high sleep quality was demonstrated with a low score, but for statistical analyses, items were revised so that a high sleep quality was demonstrated with a high score. Observed reliability within this sample was Cronbach’s α (alpha) = 0.81.
Self-reported Sleep Duration (SSD)
To assess SSD, we used the single item for sleep duration from the Pittsburgh Sleep Quality Index: “How many hours of actual sleep do you get at night? (This may be different than the number of hours you spend in bed)” . To fit the study design, we adapted the instructions slightly from “… do you get at night” to “… did you on average get at night in the last 14 days.” Participants responded with the average hours of sleep, for example, 6.5 h or 7 h.
Objective Sleep Duration (OSD)
To record OSD, we used the Xiaomi “Mi Band 2” tracker and extracted the data with our own application based on the open source app, Gadgetbridge. With this software we were able to extract the raw data to a database and divided it into the columns: id, mac-address, timestamp in 60 s steps, raw intensity, heart rate, and raw kind. The first two columns (id and mac-address) were necessary for unique identification of data set with person; raw intensity and raw kind were indicators for specific activities like sitting, running, or sleeping. After some investigation at the official github wiki and the relevant reddit forum, we identified the raw kind codes for sleep as 105, 106, 112, 121, 122, 123, 124 for the latest version of Gadgetbridge, numbered 0.24.2. We verified the codes using a small test study with 8 participants, who recorded their sleeping time every night by hand as well. We identified the relevant raw data (according to the raw kind codes) and calculated sum scores of sleeping hours per night and person. We used the sum scores to build an individual mean of hours of sleep for each person. We examined the accuracy of the Mi Band 2 in measuring sleep duration in a diary-study with 45 participants, ages ranging from 18 to 59 years (manuscript in preparation). Participants wore the fitness tracker on 10 consecutive days and filled out a daily sleep protocol. The protocol involved questions on the times of going to bed and waking up, sleep quality, and concrete hours of sleep. The results of multilevel analyses show a significant relation between objective and self-reported hours of sleep. Thus, the Mi Band 2 is, at least to some extent, suitable for measuring objective sleep duration (manuscript in preparation).
Objective Sleep Quality (OSQ)
The regularity of the sleep schedule is an indicator for better sleep . Therefore, we used the day-to-day variability of sleep duration as indicator for objective sleep quality. We calculated the day-to-day variability based on the objective hours of sleep from the Mi Band 2. We divided the individual variance of sleep duration with the individual mean of sleep duration and multiplied it with 100 as suggested by Lemola et al. . This procedure results in the coefficient of variance. A higher coefficient indicates a higher variability of sleep duration from day-to-day whereas a smaller coefficient indicates a lower variability .
To measure personal burnout, we used the German version of the Copenhagen Burnout Inventory (CBI) . The authors refer to personal burnout in the following way “Personal burnout is the degree of physical and psychological fatigue and exhaustion experienced by the person”, p. 197 . The CBI consists of 6 items that ask about physical and psychological fatigue and exhaustion, for example, “How often are you emotionally exhausted” or “How often are you physically exhausted.” Participants can choose 100% “always”, 75% “often”, 50% “sometimes”, 25% “seldom” and 0% “never/almost never” to answer the items. In order to make it easier to interpret the burnout score, we transformed answer options to a 5-point scale for statistical analysis (100% “always” = 5, 75% “often” = 4, 50% “sometimes” = 3, 25% “seldom” = 2 and 0% “never/almost never” = 1). Cronbach’s α (alpha) was 0.89, indicating adequate internal consistency.
We assessed well-being with the WHO-5 Well-Being Index . The five items can be answered on a 6-point scale ranging from 0 “at no time” to 5 “at all times.” The questionnaire contains items about general well-being, for example, “I have felt calm and relaxed” or “My daily life has been filled with things that interest me.” To fit the design of the current study, we adapted the instruction slightly from “Please respond to each item by marking one box per row regarding how you felt in the last two weeks” to “Please respond to each item by marking one box per row regarding how you felt in the last ten days.” Internal consistency using Cronbach’s α (alpha) was 0.85.