Lullaby

Kay, Matthew; Choe, Eun Kyoung; Shepherd, Jesse; Greenstein, Benjamin; Watson, Nathaniel F.; Consolvo, Sunny; Kientz, Julie A.

doi:10.1145/2370216.2370253

Cited by 165 publications

(26 citation statements)

References 18 publications

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“…Human-computer interaction researchers have intensively studied how users interact with sleep-tracking technologies. Previous studies have qualitatively examined how users make sense of sleep data from single source [47,60], the impact of consumer sleep-tracking devices on sleep hygiene [77], and the opportunities for sleep-tracking tools to enhance sleep hygiene [14]. There is strong evidence that sleep-tracking increases people's awareness of sleep health and support healthy sleep behaviors [61].…”

Section: Sleep Tracking and Monitoring Technologiesmentioning

confidence: 99%

“…Some of the main obstacles include technology transparency, not tracking triggers, not giving actionable knowledge and not providing personalized recommendations [61]. Consequently, new sleep-tracking and analysis tools were developed to lower tracking effort [15], to explore the associations between sleep and external factors [47,60], to support behavior changes for better sleep [5] and to support self-experimentation [17,84].…”

Section: Sleep Tracking and Monitoring Technologiesmentioning

confidence: 99%

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How Does Fitbit Measure Brainwaves

Liang

Ploderer

2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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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.

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Section: Sleep Tracking and Monitoring Technologiesmentioning

confidence: 99%

Section: Sleep Tracking and Monitoring Technologiesmentioning

confidence: 99%

How Does Fitbit Measure Brainwaves

Liang

Ploderer

2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Mobile apps that track physical activity and increase user awareness of activity levels can increase motivation for behavior change [15,16]. Other systems have been designed to help end users identify factors that influence their wellness behaviors and outcomes, for example, as discussed in Bentley et al [17] and Kay et al [18]. These life-logging and tracking tools have the potential to make continuous, objective, and precise data available for clinical care.…”

Section: Introductionmentioning

confidence: 99%

More Than Telemonitoring: Health Provider Use and Nonuse of Life-Log Data in Irritable Bowel Syndrome and Weight Management

Chung¹,

Cook²,

Bales³

et al. 2015

J Med Internet Res

116

158

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BackgroundThe quantified self, self-monitoring or life-logging movement is a trend to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (eg food consumed), states (eg mood), and performance (mental and physical). Consumer self-monitoring mobile phone apps have been widely studied and used to promote healthy behavior changes. Data collected through life-logging apps also have the potential to support clinical care.ObjectiveWe sought to develop an in-depth understanding of providers’ facilitators and barriers to successfully integrating life-log data into their practices and creating better experiences. We specifically investigated three research questions: How do providers currently use patient-collected life-log data in clinical practice? What are provider concerns and needs with respect to this data? What are the constraints for providers to integrate this type of data into their workflows?MethodsWe interviewed 21 health care providers—physicians, dietitians, a nurse practitioner, and a behavioral psychologist—who work with obese and irritable bowel syndrome patients. We transcribed and analyzed interviews according to thematic analysis and an affinity diagramming process.ResultsProviders reported using self-monitoring data to enhance provider-patient communication, develop personalized treatment plans, and to motivate and educate patients, in addition to using them as diagnostic and adherence tools. However, limitations associated with current systems and workflows create barriers to regular and effective review of this data. These barriers include a lack of time to review detailed records, questions about providers' expertise to review it, and skepticism about additional benefits offered by reviewing data. Current self-monitoring tools also often lack flexibility, standardized formats, and mechanisms to share data with providers.ConclusionsVariations in provider needs affect tracking and reviewing needs. Systems to support diagnosis might require better reliability and resolution, while systems to support interaction should support collaborative reflection and communication. Automatic synthesis of data logs could help providers focus on educational goals while communication of contextual information might help providers better understand patient values. We also discuss how current mobile apps and provider systems do, and do not, support these goals, and future design opportunities to realize the potential benefits of using life-logging tools in clinical care.

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“…Our goal was to elicit conversations about current tracking practices as a family and to identify opportunities. Our range of tools included pen and paper checklists, diet journals [12], self-report-based sleep journaling [5], and sensor-based sleep systems such as Fitbit 1 , Jawbone 2 , and Lullaby [34]. Table 1: Overview of study participants.…”

Section: Interview Protocolmentioning

confidence: 99%

From Personal Informatics to Family Informatics

Pina

Sien

Ward

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

Self Cite

140

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In families composed of parents and children, the health of parents and children is often interrelated: the health of children can have an impact on the health of parents, and vice versa. However, the design of health tracking technologies typically focuses on individual self-tracking and self-management, not yet addressing family health in a unified way. To examine opportunities for family-centered health informatics, we interviewed 14 typically healthy families, interviewed 10 families with a child with a chronic condition, and conducted three participatory design sessions with children aged 7 to 11. Although we identified similarities between family-centered tracking and personal self-tracking, we also found families want to: (1) identify ripple effects between family members;(2) consider both caregivers and children as trackers to support distributing the burdens of tracking across family members; and (3) identify and pursue health guidelines that consider the state of their family (e.g., specific health guidelines for families that include a child with a chronic condition). We contribute to expanding the design lens from self-tracking to family-centered health tracking.

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Lullaby

Cited by 165 publications

References 18 publications

How Does Fitbit Measure Brainwaves

How Does Fitbit Measure Brainwaves

More Than Telemonitoring: Health Provider Use and Nonuse of Life-Log Data in Irritable Bowel Syndrome and Weight Management

From Personal Informatics to Family Informatics

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