We describe the incidence and practice of prone positioning and determined the association of use of prone positioning with outcomes in invasively ventilated patients with acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) in a national, multicenter observational study, performed at 22 intensive care units in the Netherlands. Patients were categorized into 4 groups, based on indication for and actual use of prone positioning. The primary outcome was 28-day mortality. Secondary endpoints were 90-day mortality, and ICU and hospital length of stay. In 734 patients, prone positioning was indicated in 60%—the incidence of prone positioning was higher in patients with an indication than in patients without an indication for prone positioning (77 vs. 48%, p = 0.001). Patients were left in the prone position for median 15.0 (10.5–21.0) hours per full calendar day—the duration was longer in patients with an indication than in patients without an indication for prone positioning (16.0 (11.0–23.0) vs. 14.0 (10.0–19.0) hours, p < 0.001). Ventilator settings and ventilation parameters were not different between the four groups, except for FiO2 which was higher in patients having an indication for and actually receiving prone positioning. Our data showed no difference in mortality at day 28 between the 4 groups (HR no indication, no prone vs. no indication, prone vs. indication, no prone vs. indication, prone: 1.05 (0.76–1.45) vs. 0.88 (0.62–1.26) vs. 1.15 (0.80–1.54) vs. 0.96 (0.73–1.26) (p = 0.08)). Factors associated with the use of prone positioning were ARDS severity and FiO2. The findings of this study are that prone positioning is often used in COVID-19 patients, even in patients that have no indication for this intervention. Sessions of prone positioning lasted long. Use of prone positioning may affect outcomes.
Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.
Twitter timelines are increasingly populated with brand tweets that are linked to public events, a practice that is also known as real-time marketing (RTM). In two studies, we examine whether RTM is an effective strategy to boost sharing behavior, and if so, what event- and content-related characteristics are likely to contribute to its effectiveness. A content analysis of brand tweets from Nielsen’s top-100 advertisers (n=1500) shows that not all events are equally effective. RTM is only a more effective strategy (vs. no real-time marketing), when brand messages are linked with unpredictable events but not when brand messages are linked with predictable events. In a follow-up study, we examined what content characteristics improve the shareability of predictable RTM messages. A content analysis of RTM messages (n=143) from the Forbes top-100 brands showed that predictable events yield more retweets when the event is visually integrated in the brand tweet (vs. not visually integrated). The presence of event-driven hashtags did not lead to more retweets. Implications for theory and practice are discussed.
