Parents' perspectives on a smartwatch intervention for children with ADHD: Rapid deployment and feasibility evaluation of a pilot intervention to support distance learning during COVID-19.

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children's learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google calendars were synchronized across devices to guide children through daily schedules. After the sixth week, we conducted parent interviews to understand the use of smartwatches and the impact on children's functioning, and we collected physiological data directly from the smartwatch. Our results demonstrated that children successfully adopted the use of the smartwatch, and parents believed the intervention was helpful, especially in supporting the development of organizational skills in their children. Overall, we illustrate how even simple DHIs, such as using smartwatches to promote daily organization and task completion, have the potential to support children and families, particularly during periods of distance learning. We include practical suggestions to help professionals teach children with ADHD to use smartwatches to improve organization and task completion, especially as it applies to supporting remote instruction.

Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign.

BACKGROUND: Patient-generated health data (PGHD) are health-related data created or recorded by patients to inform their self-care and understanding about their own health. PGHD is different from other patient-reported outcome data because the collection of data is patient-driven, not practice- or research-driven. Technical applications for assisting patients to collect PGHD supports self-management activities such as healthy eating and exercise and can be important for preventing and managing disease. Technological innovations (eg, activity trackers) are making it more common for people to collect PGHD, but little is known about how PGHD might be used in outpatient clinics. OBJECTIVE: The objective of our study was to examine the experiences of health care professionals who use PGHD in outpatient clinics. METHODS: We conducted an evaluation of Project HealthDesign Round 2 to synthesize findings from 5 studies funded to test tools designed to help patients collect PGHD and share these data with members of their health care team. We conducted semistructured interviews with 13 Project HealthDesign study team members and 12 health care professionals that participated in these studies. We used an immersion-crystallization approach to analyze data. Our findings provide important information related to health care professionals' attitudes toward and experiences with using PGHD in a clinical setting. RESULTS: Health care professionals identified 3 main benefits of PGHD accessibility in clinical settings: (1) deeper insight into a patient's condition; (2) more accurate patient information, particularly when of clinical relevance; and (3) insight into a patient's health between clinic visits, enabling revision of care plans for improved health goal achievement, while avoiding unnecessary clinic visits. Study participants also identified 3 areas of consideration when implementing collection and use of PGHD data in clinics: (1) developing practice workflows and protocols related to PGHD collection and use; (2) data storage, accessibility at the point of care, and privacy concerns; and (3) ease of using PGHD data. CONCLUSIONS: PGHD provides value to both patients and health care professionals. However, more research is needed to understand the benefit of using PGHD in clinical care and to identify the strategies and clinic workflow needs for optimizing these tools.

Supplementing Public Health Inspection via Social Media.

Foodborne illness is prevented by inspection and surveillance conducted by health departments across America. Appropriate restaurant behavior is enforced and monitored via public health inspections. However, surveillance coverage provided by state and local health departments is insufficient in preventing the rising number of foodborne illness outbreaks. To address this need for improved surveillance coverage we conducted a supplementary form of public health surveillance using social media data: Yelp.com restaurant reviews in the city of San Francisco. Yelp is a social media site where users post reviews and rate restaurants they have personally visited. Presence of keywords related to health code regulations and foodborne illness symptoms, number of restaurant reviews, number of Yelp stars, and restaurant price range were included in a model predicting a restaurant's likelihood of health code violation measured by the assigned San Francisco public health code rating. For a list of major health code violations see (S1 Table). We built the predictive model using 71,360 Yelp reviews of restaurants in the San Francisco Bay Area. The predictive model was able to predict health code violations in 78% of the restaurants receiving serious citations in our pilot study of 440 restaurants. Training and validation data sets each pulled data from 220 restaurants in San Francisco. Keyword analysis of free text within Yelp not only improved detection of high-risk restaurants, but it also served to identify specific risk factors related to health code violation. To further validate our model we applied the model generated in our pilot study to Yelp data from 1,542 restaurants in San Francisco. The model achieved 91% sensitivity 74% specificity, area under the receiver operator curve of 98%, and positive predictive value of 29% (given a substandard health code rating prevalence of 10%). When our model was applied to restaurant reviews in New York City we achieved 74% sensitivity, 54% specificity, area under the receiver operator curve of 77%, and positive predictive value of 25% (given a prevalence of 12%). Model accuracy improved when reviews ranked highest by Yelp were utilized. Our results indicate that public health surveillance can be improved by using social media data to identify restaurants at high risk for health code violation. Additionally, using highly ranked Yelp reviews improves predictive power and limits the number of reviews needed to generate prediction. Use of this approach as an adjunct to current risk ranking of restaurants prior to inspection may enhance detection of those restaurants participating in high risk practices that may have gone previously undetected. This model represents a step forward in the integration of social media into meaningful public health interventions.

Developing a model for understanding patient collection of observations of daily living: A qualitative meta-synthesis of the Project HealthDesign Program.

We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health.

Organizational routines, innovation, and flexibility: the application of narrative networks to dynamic workflow.

OBJECTIVE: The purpose of this paper is to demonstrate how current visual representations of organizational and technological processes do not fully account for the variability present in everyday practices. We further demonstrate how narrative networks can augment these representations to indicate potential areas for successful or problematic adoption of new technologies and potential needs for additional training. METHODS: We conducted a qualitative study of the processes and routines at a major academic medical center slated to be supported by the development and installation of a new comprehensive HIT system. We used qualitative data collection techniques including observations of the activities to be supported by the new system and interviews with department heads, researchers, and both clinical and non-clinical staff. We conducted a narrative network analysis of these data by choosing exemplar processes to be modeled, selecting and analyzing narrative fragments, and developing visual representations of the interconnection of these narratives. RESULTS: Narrative networks enable us to view the variety of ways work has been and can be performed in practice, influencing our ability to design for innovation in use. DISCUSSION: Narrative networks are a means for analyzing and visualizing organizational routines in concert with more traditional requirements engineering, workflow modeling, and quality improvement outcome measurement. This type of analysis can support a deeper and more nuanced understanding of how and why certain routines continue to exist, change, or stop entirely. At the same time, it can illuminate areas in which adoption may be slow, more training or communication may be needed, and routines preferred by the leadership are subverted by routines preferred by the staff.

Improving communication between patients and providers using health information technology and other quality improvement strategies: focus on Asian Americans.

Disparities in provider-patient communication have been shown to exist among Asian Americans, especially those who are low-income and have limited English proficiency. These disparities have resulted in unmet health care needs and poor quality care. To identify strategies for improving provider-patient communication in this population, we conducted a systematic review of the literature and in-depth interviews with key informants. Little published literature on interventions focused on Asian Americans was found. Most interventions were conducted among White populations and occurred in the waiting room before patients' visits with their providers. Interventions ranged from a leaflet encouraging patients to ask more questions, to more intensive face-to-face coaching before office visits. Health information technology (health IT) has not been widely used to improve communication, especially among patients with limited English proficiency. More research is needed using new health IT strategies to improve care for Asian Americans and other vulnerable populations.

Improving communication between patients and providers using health information technology and other quality improvement strategies: focus on low-income children.

Effective communication between providers and patients has been linked to improved outcomes. Previous reviews of quality improvement strategies, including health information technology (health IT), have not focused on the needs of low-income children. The authors conducted a systematic review of the literature on studies of communication surrounding the care of low-income children, with an emphasis on interventions and health IT.The search yielded six studies that focused on low-income children; three of the studies used health IT. Key informant interviews provided insight to the current use of health IT for provider-patient communication in geographically diverse, underresourced settings.The authors identify gaps between existing literature and clinical practice. Future research should focus on the specific impact of health IT in pediatric medicine, particularly in underresourced and safety net settings. These efforts should focus on the use of technological innovations to improve care for low-income children and their families.