Personal Health Data Tracking by Blind and Low-Vision People: Survey Study.

BACKGROUND: Personal health technologies, including wearable tracking devices and mobile apps, have great potential to equip the general population with the ability to monitor and manage their health. However, being designed for sighted people, much of their functionality is largely inaccessible to the blind and low-vision (BLV) population, threatening the equitable access to personal health data (PHD) and health care services. OBJECTIVE: This study aims to understand why and how BLV people collect and use their PHD and the obstacles they face in doing so. Such knowledge can inform accessibility researchers and technology companies of the unique self-tracking needs and accessibility challenges that BLV people experience. METHODS: We conducted a web-based and phone survey with 156 BLV people. We reported on quantitative and qualitative findings regarding their PHD tracking practices, needs, accessibility barriers, and work-arounds. RESULTS: BLV respondents had strong desires and needs to track PHD, and many of them were already tracking their data despite many hurdles. Popular tracking items (ie, exercise, weight, sleep, and food) and the reasons for tracking were similar to those of sighted people. BLV people, however, face many accessibility challenges throughout all phases of self-tracking, from identifying tracking tools to reviewing data. The main barriers our respondents experienced included suboptimal tracking experiences and insufficient benefits against the extended burden for BLV people. CONCLUSIONS: We reported the findings that contribute to an in-depth understanding of BLV people's motivations for PHD tracking, tracking practices, challenges, and work-arounds. Our findings suggest that various accessibility challenges hinder BLV individuals from effectively gaining the benefits of self-tracking technologies. On the basis of the findings, we discussed design opportunities and research areas to focus on making PHD tracking technologies accessible for all, including BLV people.

Willingness to Share Wearable Device Data for Research Among Mechanical Turk Workers: Web-Based Survey Study.

BACKGROUND: Wearable devices that are used for observational research and clinical trials hold promise for collecting data from study participants in a convenient, scalable way that is more likely to reach a broad and diverse population than traditional research approaches. Amazon Mechanical Turk (MTurk) is a potential resource that researchers can use to recruit individuals into studies that use data from wearable devices. OBJECTIVE: This study aimed to explore the characteristics of wearable device users on MTurk that are associated with a willingness to share wearable device data for research. We also aimed to determine whether compensation was a factor that influenced the willingness to share such data. METHODS: This was a secondary analysis of a cross-sectional survey study of MTurk workers who use wearable devices for health monitoring. A 19-question web-based survey was administered from March 1 to April 5, 2018, to participants aged ≥18 years by using the MTurk platform. In order to identify characteristics that were associated with a willingness to share wearable device data, we performed logistic regression and decision tree analyses. RESULTS: A total of 935 MTurk workers who use wearable devices completed the survey. The majority of respondents indicated a willingness to share their wearable device data (615/935, 65.8%), and the majority of these respondents were willing to share their data if they received compensation (518/615, 84.2%). The findings from our logistic regression analyses indicated that Indian nationality (odds ratio [OR] 2.74, 95% CI 1.48-4.01, P=.007), higher annual income (OR 2.46, 95% CI 1.26-3.67, P=.02), over 6 months of using a wearable device (OR 1.75, 95% CI 1.21-2.29, P=.006), and the use of heartbeat and pulse tracking monitoring devices (OR 1.60, 95% CI 0.14-2.07, P=.01) are significant parameters that influence the willingness to share data. The only factor associated with a willingness to share data if compensation is provided was Indian nationality (OR 0.47, 95% CI 0.24-0.9, P=.02). The findings from our decision tree analyses indicated that the three leading parameters associated with a willingness to share data were the duration of wearable device use, nationality, and income. CONCLUSIONS: Most wearable device users indicated a willingness to share their data for research use (with or without compensation; 615/935, 65.8%). The probability of having a willingness to share these data was higher among individuals who had used a wearable for more than 6 months, were of Indian nationality, or were of American (United States of America) nationality and had an annual income of more than US $20,000. Individuals of Indian nationality who were willing to share their data expected compensation significantly less often than individuals of American nationality (P=.02).

Interrelationships Between Patients' Data Tracking Practices, Data Sharing Practices, and Health Literacy: Onsite Survey Study.

BACKGROUND: Although the use of patient-generated data (PGD) in the optimization of patient care shows great promise, little is known about whether patients who track their PGD necessarily share the data with their clinicians. Meanwhile, health literacy-an important construct that captures an individual's ability to manage their health and to engage with their health care providers-has often been neglected in prior studies focused on PGD tracking and sharing. To leverage the full potential of PGD, it is necessary to bridge the gap between patients' data tracking and data sharing practices by first understanding the interrelationships between these practices and the factors contributing to these practices. OBJECTIVE: This study aims to systematically examine the interrelationships between PGD tracking practices, data sharing practices, and health literacy among individual patients. METHODS: We surveyed 109 patients at the time they met with a clinician at a university health center, unlike prior research that often examined patients' retrospective experience after some time had passed since their clinic visit. The survey consisted of 39 questions asking patients about their PGD tracking and sharing practices based on their current clinical encounter. The survey also contained questions related to the participants' health literacy. All the participants completed the survey on a tablet device. The onsite survey study enabled us to collect ecologically valid data based on patients' immediate experiences situated within their clinic visit. RESULTS: We found no evidence that tracking PGD was related to self-reports of having sufficient information to manage one's health; however, the number of data types participants tracked positively related to their self-assessed ability to actively engage with health care providers. Participants' data tracking practices and their health literacy did not relate to their data sharing practices; however, their ability to engage with health care providers positively related to their willingness to share their data with clinicians in the future. Participants reported several benefits of, and barriers to, sharing their PGD with clinicians. CONCLUSIONS: Although tracking PGD could help patients better engage with health care providers, it may not provide patients with sufficient information to manage their health. The gaps between tracking and sharing PGD with health care providers call for efforts to inform patients of how their data relate to their health and to facilitate efficient clinician-patient communication. To realize the full potential of PGD and to promote individuals' health literacy, empowering patients to effectively track and share their PGD is important-both technologies and health care providers can play important roles.

Databiting: Lightweight, Transient, and Insight Rich Exploration of Personal Data.

As mobile and wearable devices are becoming increasingly powerful, access to personal data is within reach anytime and anywhere. Currently, methods of data exploration while on-the-go and in-situ are, however, often limited to glanceable and micro visualizations, which provide narrow insight. In this article, we introduce the notion of databiting, the act of interacting with personal data to obtain richer insight through lightweight and transient exploration. We focus our discussion on conceptualizing databiting and arguing its potential values. We then discuss five research considerations that we deem important for enabling databiting: contextual factors, interaction modalities, the relationship between databiting and other forms of exploration, personalization, and evaluation challenges. We envision this line of work in databiting could enable people to easily gain meaningful personal insight from their data anytime and anywhere.

Envisioning the use of in-situ arm movement data in stroke rehabilitation: Stroke survivors' and occupational therapists' perspectives.

BACKGROUND: The key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice. OBJECTIVE: We aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders. METHODS: We conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach. RESULTS: Our study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice. CONCLUSIONS: These findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Understanding the Consequences of Moment-by-Moment Fluctuations in Mood and Social Experience for Paranoid Ideation in Psychotic Disorders.

Among individuals with psychotic disorders, paranoid ideation is common and associated with increased impairment, decreased quality of life, and a more pessimistic prognosis. Although accumulating research indicates negative affect is a key precipitant of paranoid ideation, the possible protective role of positive affect has not been examined. Further, despite the interpersonal nature of paranoid ideation, there are limited and inconsistent findings regarding how social context, perceptions, and motivation influence paranoid ideation in real-world contexts. In this pilot study, we used smartphone ecological momentary assessment to understand the relevance of hour-by-hour fluctuations in mood and social experience for paranoid ideation in adults with psychotic disorders. Multilevel modeling results indicated that greater negative affect is associated with higher concurrent levels of paranoid ideation and that it is marginally related to elevated levels of future paranoid ideation. In contrast, positive affect was unrelated to momentary experiences of paranoid ideation. More severe momentary paranoid ideation was also associated with an elevated desire to withdraw from social encounters, irrespective of when with familiar or unfamiliar others. These observations underscore the role of negative affect in promoting paranoid ideation and highlight the contribution of paranoid ideation to the motivation to socially withdraw in psychotic disorders.

The SleepFit Tablet Application for Home-Based Clinical Data Collection in Parkinson Disease: User-Centric Development and Usability Study.

BACKGROUND: Parkinson disease (PD) is a common, multifaceted neurodegenerative disorder profoundly impacting patients' autonomy and quality of life. Assessment in real-life conditions of subjective symptoms and objective metrics of mobility and nonmotor symptoms such as sleep disturbance is strongly advocated. This information would critically guide the adaptation of antiparkinsonian medications and nonpharmacological interventions. Moreover, since the spread of the COVID-19 pandemic, health care practices are being reshaped toward a more home-based care. New technologies could play a pivotal role in this new approach to clinical care. Nevertheless, devices and information technology tools might be unhandy for PD patients, thus dramatically limiting their widespread employment. OBJECTIVE: The goals of the research were development and usability evaluation of an application, SleepFit, for ecological momentary assessment of objective and subjective clinical metrics at PD patients' homes, and as a remote tool for researchers to monitor patients and integrate and manage data. METHODS: An iterative and user-centric strategy was employed for the development of SleepFit. The core structure of SleepFit consists of (1) an electronic finger-tapping test; (2) motor, sleepiness, and emotional subjective scales; and (3) a sleep diary. Applicable design, ergonomic, and navigation principles have been applied while tailoring the application to the specific patient population. Three progressively enhanced versions of the application (alpha, v1.0, v2.0) were tested by a total of 56 patients with PD who were asked to perform multiple home assessments 4 times per day for 2 weeks. Patient compliance was calculated as the proportion of completed tasks out of the total number of expected tasks. Satisfaction on the latest version (v2.0) was evaluated as potential willingness to use SleepFit again after the end of the study. RESULTS: From alpha to v1.0, SleepFit was improved in graphics, ergonomics, and navigation, with automated flows guiding the patients in performing tasks throughout the 24 hours, and real-time data collection and consultation were made possible thanks to a remote web portal. In v2.0, the kiosk-mode feature restricts the use of the tablet to the SleepFit application only, thus preventing users from accidentally exiting the application. A total of 52 (4 dropouts) patients were included in the analyses. Overall compliance (all versions) was 88.89% (5707/6420). SleepFit was progressively enhanced and compliance increased from 87.86% (2070/2356) to 89.92% (2899/3224; P=.04). Among the patients who used v2.0, 96% (25/26) declared they would use SleepFit again. CONCLUSIONS: SleepFit can be considered a state-of-the-art home-based system that increases compliance in PD patients, ensures high-quality data collection, and works as a handy tool for remote monitoring and data management in clinical research. Thanks to its user-friendliness and modular structure, it could be employed in other clinical studies with minimum adaptation efforts. TRIAL REGISTRATION: ClinicalTrials.gov NCT02723396; https://clinicaltrials.gov/ct2/show/NCT02723396.

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones.

We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology.

Reaching Broader Audiences With Data Visualization.

The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this article, we discuss four research topics-personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization-that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible.

Investigating data accessibility of personal health apps.

OBJECTIVE: Despite the potential values self-tracking data could offer, we have little understanding of how much access people have to "their" data. Our goal of this article is to unveil the current state of the data accessibility-the degree to which people can access their data-of personal health apps in the market. MATERIALS AND METHODS: We reviewed 240 personal health apps from the App Store and selected 45 apps that support semi-automated tracking. We characterized the data accessibility of these apps using two dimensions-data access methods and data types. RESULTS: More than 90% of our sample apps (n = 41) provide some types of data access support, which include synchronizing data with a health platform (ie, Apple Health), file download, and application program interfaces. However, the two approachable data access methods for laypeople-health platform and file download-typically put a significant limit on data format, granularity, and amount, which constrains people from easily repurposing the data. DISCUSSION: Personal data should be accessible to the people who collect them, but existing methods lack sufficient support for people in accessing the fine-grained data. Lack of standards in personal health data schema as well as frequent changes in market conditions are additional hurdles to data accessibility. CONCLUSIONS: Many stakeholders including patients, healthcare providers, researchers, third-party developers, and the general public rely on data accessibility to utilize personal data for various goals. As such, improving data accessibility should be considered as an important factor in designing personal health apps and health platforms.

Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation.

In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.

A novel upper-limb function measure derived from finger-worn sensor data collected in a free-living setting.

The use of wrist-worn accelerometers has recently gained tremendous interest among researchers and clinicians as an objective tool to quantify real-world use of the upper limbs during the performance of activities of daily living (ADLs). However, wrist-worn accelerometers have shown a number of limitations that hinder their adoption in the clinic. Among others, the inability of wrist-worn accelerometers to capture hand and finger movements is particularly relevant to monitoring the performance of ADLs. This study investigates the use of finger-worn accelerometers to capture both gross arm and fine hand movements for the assessment of real-world upper-limb use. A system of finger-worn accelerometers was utilized to monitor eighteen neurologically intact young adults while performing nine motor tasks in a laboratory setting. The system was also used to monitor eighteen subjects during the day time of a day in a free-living setting. A novel measure of real-world upper-limb function-comparing the duration of activities of the two limbs-was derived to identify which upper limb subjects predominantly used to perform ADLs. Two validated handedness self-reports, namely the Waterloo Handedness Questionnaire and the Fazio Laterality Inventory, were collected to assess convergent validity. The analysis of the data recorded in the laboratory showed that the proposed measure of upper-limb function is suitable to accurately detect unilateral vs. bilateral use of the upper limbs, including both gross arm movements and fine hand movements. When applied to recordings collected in a free-living setting, the proposed measure showed high correlation with self-reported handedness indices (i.e., ρ = 0.78 with the Waterloo Handedness Questionnaire scores and ρ = 0.77 with the Fazio Laterality Inventory scores). The results herein presented establish face and convergent validity of the proposed measure of real-world upper-limb function derived using data collected by means of finger-worn accelerometers.

Communicating Bad News: Insights for the Design of Consumer Health Technologies.

BACKGROUND: As people increasingly receive personal health information through technology, there is increased importance for this information to be communicated with empathy and consideration for the patient's experience of consuming it. Although technology enables people to have more frequent and faster access to their health information, it could also cause unnecessary anxiety, distress, or confusion because of the sensitive and complex nature of the information and its potential to provide information that could be considered bad news. OBJECTIVE: The aim of this study was to uncover insights for the design of health information technologies that potentially communicate bad news about health such as the result of a diagnosis, increased risk for a chronic or terminal disease, or overall declining health. METHODS: On the basis of a review of established guidelines for clinicians on communicating bad news, we developed an interview guide and conducted interviews with patients, patients' family members, and clinicians on their experience of delivering and receiving the diagnosis of a serious disease. We then analyzed the data using a thematic analysis to identify overall themes from a perspective of identifying ways to translate these strategies to technology design. RESULTS: We describe qualitative results combining an analysis of the clinical guidelines for sharing bad health news with patients and interviews on clinicians' specific strategies to communicate bad news and the emotional and informational support that patients and their family members seek. Specific strategies clinicians use included preparing for the patients' visit, anticipating patients' feelings, building a partnership of trust with patients, acknowledging patients' physical and emotional discomfort, setting up a scene where patients can process the information, helping patients build resilience and giving hope, matching the level of information to the patients' level of understanding, communicating face-to-face, if possible, and using nonverbal means. Patient and family member experiences included internal turmoil and emotional distress when receiving bad news and emotional and informational support that patients and family members seek. CONCLUSIONS: The results from this study identify specific strategies for health information technologies to better promote empathic communication when they communicate concerning health news. We distill the findings from our study into design hypotheses for ways technologies may be able to help people better cope with the possibility of receiving bad health news, including tailoring the delivery of information to the patients' individual preferences, supporting interfaces for sharing patients' context, mitigating emotional stress from self-monitoring data, and identifying clear, actionable steps patients can take next.

Persuasive Data Videos: Investigating Persuasive Self-Tracking Feedback with Augmented Data Videos.

Self-tracking feedback with engaging and persuasive visualizations not only helps convey data but can also affect people's attitudes and behaviors. We investigate persuasive self-tracking feedback by augmenting data videos (DVs)-novel, engaging storytelling media. We introduce a new class of DVs, called Persuasive Data Videos (PDVs), by incorporating four persuasive elements-primary task, dialogue, system credibility, and social supports-drawn from the Persuasive System Design Model. We describe the iterative design of PDVs and a within-subjects preliminary validation to check their persuasive potential. We then assess PDVs' feasibility using the Persuasive Potential Questionnaire in a between-subjects study comparing a PDV against a conventional DV on Amazon Mechanical Turk (N = 252). Our results indicate the feasibility of using PDVs in providing individuals' self-tracking feedback to convey persuasive health messages, based on which we discuss opportunities for designing persuasive behavioral feedback in an engaging way.

A New Prospective, Home-Based Monitoring of Motor Symptoms in Parkinson's Disease.

BACKGROUND: Subjective symptoms, which are retrospectively assessed during clinical interviews in the office, may be influenced by patient recall in Parkinson's disease (PD). Prospective collection of subjective data might be an effective tool to overcome this bias. OBJECTIVE: We investigated the correspondence between prospectively and retrospectively assessed motor symptoms in PD. METHODS: Forty-two consecutive patients (9 females, 67±9.8 years old) with mild to moderate PD reported their symptoms four times a day for two weeks, using the "SleepFit" application (app) for tablets. This app incorporates a new Visual Analogue Scale assessing global mobility (m-VAS), and the Scales for Outcome in Parkinson Assessment Diary Card (SCOPA-DC). At day 14, the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and IV questionnaires were completed at the hospital. Agreement (root mean square difference) and the tendency to under- or overestimate their symptoms by patients (relative difference after normalization) were calculated to compare prospectively vs. retrospectively collected information. RESULTS: Although agreement was good for overall scores (m-VAS: 10.0%; SCOPA-DC: 18.3%), and for single motor symptoms (involuntary movements, hand dexterity, walking, changing position; each <20%), some individuals with more advanced disease, higher fatigue or worse sleep quality showed poor symptom recall in retrospect. Moreover, a subgroup of patients (16.7%) either over- or underestimated symptom severity. CONCLUSIONS: Regular, prospective monitoring of motor symptoms is suitable in PD patients. SleepFit might be a useful tool in routine practice to identify patients tending to under- or overestimate their symptoms, and for their follow-up.

OneNote Meal: A Photo-Based Diary Study for Reflective Meal Tracking.

When a self-monitoring tool is used to enhance behavior awareness, the tool should afford reflection by design. This work examines the "valence of meal" (i.e., healthy versus unhealthy meal) as a means to support reflection on a person's diet in photo-based meal tracking. To study the effect of imposing valence on meal tracking, we designed two conditions-one focusing on capturing healthy meals, the other capturing unhealthy meals-and conducted a between-subjects diary study with 22 college students over four weeks. According to their group assignment, participants tracked only healthy or unhealthy meals by taking photos and rationalizing in texts why their meals were particularly healthy or unhealthy. We found that participants in both groups became more aware of their diet, but the valence of meal influenced them differently regarding their meal assessment, self-reflection, and food choice intention. We discuss ways to leverage valence in designing reflective meal tracking systems.

Expanding Research Methods for a Realistic Understanding of Personal Visualization.

Personal visualizations have the great potential to provide the benefits of visualizations to everyone in their everyday lives. Their diverse goals combined with the personal data they contain and the contexts in which they are being used, however, make their evaluation particularly challenging and call for a wider perspective on empirical approaches. We need to devise new methods and adapt existing methods from other fields to account for the specific goals and challenges in this emerging research area. An open-minded approach to empirical methods may help us gain a more realistic understanding of personal visualizations.

Sharing Patient-Generated Data in Clinical Practices: An Interview Study.

Patients are tracking and generating an increasingly large volume of personal health data outside the clinic due to an explosion of wearable sensing and mobile health (mHealth) apps. The potential usefulness of these data is enormous as they can provide good measures of everyday behavior and lifestyle. However, how we can fully leverage patient-generated data (PGD) and integrate them in clinical practice is less clear. In this interview study, we aim to understand how patients and clinicians currently share patient-generated data in clinical care practice. From the study, we identified technical, social, and organizational challenges in sharing and fully leveraging patient-generated data in clinical practices. Our findings can provide researchers potential avenues for enablers and barriers in sharing patient-generated data in clinical settings.

Revealing Visualization Insights from Quantified-Selfers' Personal Data Presentations.

Data visualization and analytics research has great potential to empower people to improve their lives by leveraging their own personal data. However, most Quantified-Selfers are neither visualization experts nor data scientists. Consequently, their visualizations of their data are often not ideal for conveying their insights. Aiming to design a visualization system to help non-experts explore and present their personal data, we conducted a pre-design empirical study. Through the lens of Quantified-Selfers, we examined what insights people gain specifically from their personal data and how they use visualizations to communicate their insights. Based on our analysis of 30 Quantified Self presentations, we characterized eight insight types (detail, self-reflection, trend, comparison, correlation, data summary, distribution, outlier) and mapped the visual annotations used to communicate them. We further discussed four areas for the design of personal visualization systems, including support for encouraging self-reflection, gaining valid insight, communicating insight, and using visual annotations.

Consumer Sleep Technologies: A Review of the Landscape.

OBJECTIVE: To review sleep related consumer technologies, including mobile electronic device "apps," wearable devices, and other technologies. Validation and methodological transparency, the effect on clinical sleep medicine, and various social, legal, and ethical issues are discussed. METHODS: We reviewed publications from the digital libraries of the Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and PubMed; publications from consumer technology websites; and mobile device app marketplaces. Search terms included "sleep technology," "sleep app," and "sleep monitoring." RESULTS: Consumer sleep technologies are categorized by delivery platform including mobile device apps (integrated with a mobile operating system and utilizing mobile device functions such as the camera or microphone), wearable devices (on the body or attached to clothing), embedded devices (integrated into furniture or other fixtures in the native sleep environment), accessory appliances, and conventional desktop/website resources. Their primary goals include facilitation of sleep induction or wakening, self-guided sleep assessment, entertainment, social connection, information sharing, and sleep education. CONCLUSIONS: Consumer sleep technologies are changing the landscape of sleep health and clinical sleep medicine. These technologies have the potential to both improve and impair collective and individual sleep health depending on method of implementation.