BACKGROUND A timely decision in the initial stages for patients with an acute illness is important. However, only a few studies have determined the prognosis of patients based on insufficient laboratory data during the initial stages of treatment. OBJECTIVE This study aimed to develop and validate time adaptive prediction models to predict the severity of illness in the emergency department (ED) using highly sparse laboratory test data (test order status and test results) and a machine learning approach. METHODS This retrospective study used ED data from a tertiary academic hospital in Seoul, Korea. Two different models were developed based on laboratory test data order status only (OSO) and order status and results (OSR) models. A binary composite adverse outcome was used, including mortality or hospitalization in the intensive care unit. Both models were evaluated using various performance criteria, including the area under the receiver operating characteristic curve (AUC) and balanced accuracy (BA). Clinic, Kyunga Kim. Originally published in JMIR Medical Informatics (http//medinform.jmir.org), 26.03.2020.As interest in and use of telehealth during the COVID-19 global pandemic increase, the potential of digital health to increase access and quality of mental health is becoming clear. Although the world today must “flatten the curve” of spread of the virus, we argue that now is the time to “accelerate and bend the curve” on digital health. Increased investments in digital health today will yield unprecedented access to high-quality mental health care. Focusing on personal experiences and projects from our diverse authorship team, we share selected examples of digital health innovations while acknowledging that no single piece can discuss all the impressive global efforts past and present. Exploring the success of telehealth during the present crisis and how technologies like apps can soon play a larger role, we discuss the need for workforce training, high-quality evidence, and digital equity among other factors critical for bending the curve further. ©John Torous, Keris Jän Myrick, Natali Rauseo-Ricupero, Joseph Firth. Originally published in JMIR Mental Health (http//mental.jmir.org), 26.03.2020.BACKGROUND High-quality photos are critical for the remote diagnosis of dental trauma and thus are beneficial to the prognosis. The quality of the images obtained using a cell phone depends on the level of dental and photography knowledge of the person who is taking the photos. OBJECTIVE This study aimed to determine the efficacy of photography education in improving images used for the remote diagnosis of dental trauma. METHODS The subjects comprised 30 laypeople and 30 dentists who were randomly assigned to 15 subgroups with 2 subjects in each. Each subject was asked to take photos of their own anterior teeth and those of their partner on the assumption that an accident occurred using both an iPhone 4s and iPhone 6. Education about how to take an appropriate photo of the anterior teeth for teleconsultation purposes was then provided, after which photos were taken again. Photos were assessed by a dentist for their usefulness in diagnosis. RESULTS This study analyzed 965 photos 441 taken by laypeople and 524 taken by dentists. Photos taken after providing education had significantly higher scores for all assessment items than those taken before education (P less then .05). The scores were also significantly higher for photos taken using the rear camera than those taken using the front camera (P less then .02). The iPhone 6 did not have overwhelming advantages. The photos taken by dentists had significantly higher scores than those taken by laypeople for most of the evaluated items. CONCLUSIONS Both laypeople and dentists might find photography education useful for when they are taking photos to be used in teleconsultations. The type of cell phone does not significantly affect the usefulness of such photos. ©Jin-Sun Jeong, Nan-Sim Pang, Yiseul Choi, Kyeong-Mee Park, Taekbin Kim, Xin Xu, Wonse Park. Originally published in JMIR mHealth and uHealth (http//mhealth.jmir.org), 26.03.2020.BACKGROUND The gamification of digital health provisions for older adults (eg, for rehabilitation) is a growing trend; however, many older adults are not familiar with digital games. This lack of experience could cause stress and thus impede participants’ motivations to adopt these technologies. OBJECTIVE This crossover longitudinal multifactorial study aimed to examine the interactions between game difficulty, appraisal, cognitive ability, and physiological and cognitive responses that indicate game stress using the Affective Game Planning for Health Applications framework. METHODS A total of 18 volunteers (mean age 71 years, SD 4.5; 12 women) completed a three-session study to evaluate different genres of games in increasing order of difficulty (S1-BrainGame, S2-CarRace, and S3-Exergame). Each session included an identical sequence of activities (t1-Baseline, t2-Picture encode, t3-Play, t4-Stroop test, t5-Play, and t6-Picture recall), a repeated sampling of salivary cortisol, and time-tagged ambulatory datames for cognitive and physical stimulation by gamified interventions; however, the need for enjoyment of this type of challenging game must be addressed. ©Najmeh Khalili-Mahani, Atousa Assadi, Kate Li, Mahsa Mirgholami, Marie-Eve Rivard, Habib Benali, Kim Sawchuk, Bob De Schutter. Originally published in JMIR Mental Health (http//mental.jmir.org), 26.03.2020.BACKGROUND SMS text messaging is an inexpensive, private, and scalable technology-mediated assessment mode that can alleviate many barriers faced by the safety net population to receive depression screening. Some existing studies suggest that technology-mediated assessment encourages self-disclosure of sensitive health information such as depressive symptoms while other studies show the opposite effect. OBJECTIVE This study aimed to evaluate the validity of using SMS text messaging to screen depression and related conditions, including anxiety and functional disability, in a low-income, culturally diverse safety net primary care population. METHODS This study used a randomized design with 4 study groups that permuted the order of SMS text messaging and the gold standard interview (INTW) assessment. The participants for this study were recruited from the participants of the prior Diabetes-Depression Care-management Adoption Trial (DCAT). BDA-366 antagonist Depression was screened by using the 2-item and 8-item Patient Health Questionnaire (PHQ-2 and PHQ-8, respectively).