How Does Fitbit Measure Brainwaves

Abstract

Consumer sleep-tracking devices provide an unobtrusive and affordable way to learn about personal sleep habits. Recent research focused primarily on the information provided by such devices, i.e., whether the information is accurate and meaningful to people. However, little is known about how people judge the credibility of such information, and how the functionality and the design may influence such judgements. Hence, the aim of this research was to examine how consumers assess the credibility of sleep-tracking devices. We conducted a qualitative study with 22 participants who tracked their sleep for 3 nights with three different devices: Fitbit Charge 2, Neuroon EEG, and SleepScope, a medical sleep monitor. Based on semi-structured interviews, we found that people assess the credibility of sleep-tracking devices based not only on the credibility of sleep data per se, but also on device functionality, interface design and physical appearance. People found it difficult to judge credibility, because of the complexities of sleep stages and micro-arousals (sleep fallacy) and the black boxed nature of devices (black box fallacy), and also because of the misalignment between objective sleep measures and subjective sleep quality. We discuss the significance of design and functionality on the credibility of personal health technologies and highlight design challenges and opportunities to enhance their credibility. CCS Concepts: • Human-centered computing → Empirical studies in HCI.

Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review

“…Perceived credibility also affects the adoption and retention of health technology [123]. Credibility has been previously studied in general RS [124] and quantifiedself sleep-tracking technologies [12] and was further reaffirmed as a crucial property for CASHRS [42].…”

“…Empathy building starts with goal setting by defining target sleep measures. Prior studies found that subjective sleep quality was the most targeted and improved dimension of sleep by mHealth sleep apps [44], while wearable device users target a wider spectrum of measures, ranging from SOL and TST to the ratio of deep sleep and REM sleep [12,21]. Existing CASHRS support users to set goals of sleep hours and sleep schedule (i.e., bedtime and wake up time) but seldom target other sleep metrics such as sleep efficiency and sleep stages.…”

“…Popular consumer sleep-tracking wearables (e.g., Fitbit, Apple Watch, Oura Ring) and mobile apps (e.g., SleepAsAndroid, SleepCycle) have attracted increasing interest in the research community as well as among end users. A large body of research surrounding these technologies has intensively studied their accuracy and validity [7][8][9][10], as well as userperceived properties including usability and credibility [11][12][13]. Some studies have also attempted to develop novel sleep staging algorithms that work with the processed data available from consumer sleep trackers [14][15][16], as well as devising new analytic methods for personalized sleep data analysis and knowledge discovery [17][18][19].…”

A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers

“…The electrodes of the Sleep Scope were placed in the middle of the forehead and behind one ear. Details of the two devices are available in Liang and Ploderer (12). Demographic information, such as age, sex, and subjective sleep quality measured by PSQI questionnaire (32) was also collected.…”

“…Activity trackers are among the most popular consumer sleep tracking technologies. The opportunities and challenges pertaining the personal use of these devices have been well-studied (8)(9)(10)(11)(12). In the meantime, recent advances in wearable technology has seen a rise in employing consumer activity trackers, such as Fitbit in research studies for measuring sleep outcomes (13)(14)(15)(16).…”