## 1 Introduction  
Many people have experienced forced awakening using mobile alarm applications. [PERSON] et al. [1] found that the alarm clock is one of the most crucial features of smartphones, and the most popular sleep-related apps were alarm apps [2]. Additionally, [PERSON] et al. [3] conducted surveys of mobile phone usage pattern, and the results showed that many participants used alarm applications very frequently. The latest mobile alarm apps offer wake-up task alarms that do not allow the user to dismiss an alarm until they complete a specific task that we call a wake-up task in this study. According to relevant studies [4, 5] and the 14 most downloaded alarm apps in Google Play (using the search keyword \"alarm\"), including our app, Alarmy (deployed in 2013), most alarm apps provide alarms with wake-up tasks. The types of wake-up tasks are diverse; they include inputting text (e.g., Earlybird Alarm Clock), taking a picture (e.g., Alarmy and Mimicker Alarm), reading a given sentence aloud (e.g., EarlyBird Alarm Clock), solving a math problem (e.g., Alarmy), walking some steps [4], playing a game (e.g., Alarmmon), and haptic features such as following a particular pattern (e.g., FreeAlarm Clock). These tasks may aggravate users by enforcing higher cognitive and physical efforts relative to the general touch-to-dismiss type of alarm. Task-based alarm users need to go through these to achieve their initial interaction goal (i.e., dismissing an alarm), and this would increase cognitive load and frustration [6]. Despite such inconvenience, the large number of downloads and amount of usage shows that many users are willing to conduct the task to get up at the right time. The principle behind task-based alarm apps is linked to the relationship between neural activation and wakefulness [7; 8; 9]. From the perspective of the systemic-structural activity theory (SSAT) [10], prior studies revealed that task complexity could be related to the activation level of the brain [8; 9]. In particular, [PERSON] [7] pointed out that the higher the level of activation is, the higher the level of wakefulness. Based on these studies, performing wake-up tasks in alarm apps can be seen as activating neurons that help users to wake. While SSAT is helpful in understanding the general working mechanism of wake-up tasks in mobile alarm apps, it rarely considers users' task selection and specific usage contexts. In most task-based alarm apps, users can select one of the wake-up tasks according to their preferences and needs. Even though the particular tasks can increase wakefulness, users may rarely choose them due to the ultra high level of the task difficulty. Furthermore, wake-up alarms have been relatively under-studied in human-computer interaction research (HCI). Many HCI researchers have mainly focused on using mobile sensing technologies to support healthy sleep behaviors such as monitoring sleep states [11] and measuring sleep quality [12; 13]. Particularly, mobile alarm usage behavior goes through a self-regulative process: setting up the alarm to plan one's upcoming wake-up time and selecting a task to make oneself adhere to the mobile alarm's \"instruction\" to complete a task. This study was inspired by [PERSON]'s behavior model [14], which explains how one's motivation, ability, and a prompt to a task initiate an action. When a person is motivated to wake up on time, their ability may not be sufficient at the time of waking up. Therefore, a certain prompt must exist that could actually lead to one's action of waking up. Therefore, we aimed to analyze the different usage patterns and insights of the particular group of people whom we call hard-taskers, who actively use what could be inconvenient and frustrating task-based alarms. Understanding the in situ moments of awakening with task-based alarms would be of great value in the future design of mobile alarms that could be appropriate for users with different preferences and wake-up needs.