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The Geriatrics 5Ms: Medications, Mind, Mobility, what Matters most and Multicomplexity is a framework to address the complex needs of older adults. Intelligent Voice Assistants (IVAs) are increasingly popular and have potential to support health-related needs of older adults. We utilized previously collected qualitative data on older adults’ views of how an IVA may address their health-related needs and ascertained their fit into the Geriatrics 5Ms framework. The codes describing health challenges and potential IVA solutions fit the framework: (1) Medications: difficulty remembering medications. Solution: reminders. (2) Mind: isolation, anxiety, memory loss. Solution: companionship, memory aids. (3) Mobility: barriers to exercise. Solution: incentives, exercise ideas. (4) Matters most: eating healthy foods. Solution: suggest and order nutritious foods, (5) Multicomplexity; managing multimorbidity. Solution: symptom tracking and communicating with health care professionals. Incorporating the 5Ms framework into IVA design can aid in addressing health care priorities of older adults.

Abstract Background Excess weight gain in young adulthood is associated with future weight gain and increased risk of chronic disease. Although multimodal, technology-based weight-loss interventions have the potential to promote weight loss among young adults, many interventions have limited personalization, and few have been deployed and evaluated for longer than a year. We aim to assess the effects of a highly personalized, 2-year intervention that uses popular mobile and social technologies to promote weight loss among young adults. Methods The Social Mobile Approaches to Reducing Weight (SMART) 2.0 Study is a 24-month parallel-group randomized controlled trial that will include 642 overweight or obese participants, aged 18–35 years, from universities and community colleges in San Diego, CA. All participants receive a wearable activity tracker, connected scale, and corresponding app. Participants randomized to one intervention group receive evidence-based information about weight loss and behavior change techniques via personalized daily text messaging (i.e., SMS/MMS), posts on social media platforms, and online groups. Participants in a second intervention group receive the aforementioned elements in addition to brief, technology-mediated health coaching. Participants in the control group receive a wearable activity tracker, connected scale, and corresponding app alone. The primary outcome is objectively measured weight in kilograms over 24 months. Secondary outcomes include anthropometric measurements; physiological measures; physical activity, diet, sleep, and psychosocial measures; and engagement with intervention modalities. Outcomes are assessed at baseline and 6, 12, 18, and 24 months. Differences between the randomized groups will be analyzed using a mixed model of repeated measures and will be based on the intent-to-treat principle. Discussion We hypothesize that both SMART 2.0 intervention groups will significantly improve weight loss compared to the control group, and the group receiving health coaching will experience the greatest improvement. We further hypothesize that differences in secondary outcomes will favor the intervention groups. There is a critical need to advance understanding of the effectiveness of multimodal, technology-based weight-loss interventions that have the potential for long-term effects and widespread dissemination among young adults. Our findings should inform the implementation of low-cost and scalable interventions for weight loss and risk-reducing health behaviors. Trial registration ClinicalTrials.gov NCT03907462 . Registered on April 9, 2019

Automated patient monitoring systems suer from several design problems. Among them, alarm fatigue is one of the most critical issues, as evidenced by the Sentinel Event Alert that The Joint Commission – the U.S. hospital-accrediting body – recently issued. In this study, we explore fast-and-frugal heuristics that may be used to prioritize patient alarms, while continuing to monitor patient physiological state. By using a combination of human factors methodologies and the theory of Distributed Cognition (DCog), we studied alarm fatigue and its relationship to the underlying hospital systems. We identified three specific factors that we envision to be helpful for clinical personnel: ventilator presence, number of intravenous drips, and number of medications. We discuss their application in daily hospital operation. We also address cost-benefit considerations and possible monitor designs.