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Background: Wearable sensors (wearables) have been commonly integrated into a wide variety of commercial products and are increasingly being used to collect and process raw physiological parameters into salient digital health information. The data collected by wearables are currently being investigated across a broad set of clinical domains and patient populations. There is significant research occurring in the domain of algorithm development, with the aim of translating raw sensor data into fitness-or health-related outcomes of interest for users, patients, and health care providers. Objectives: The aim of this review is to highlight a selected group of fitness-and health-related indicators from wearables data and to describe several algorithmic approaches used to generate these higher order indicators. Methods: A systematic search of the Pubmed database was performed with the following search terms (number of records in parentheses): Fitbit algorithm (18), Apple Watch algorithm (3), Garmin algorithm (5), Microsoft Band algorithm (8), Samsung Gear algorithm (2), Xiaomi MiBand algorithm (1), Huawei Band (Watch) algorithm (2), photoplethysmography algorithm (465), accelerometry algorithm (966), ECG algorithm (8287), continuous glucose monitor algorithm (343). The search terms chosen for this review are focused on algorithms for wearable devices that dominated the commercial wearables market between 2014-2017 and that were highly represented in the biomedical literature. A second set of search terms included categories of algorithms for fitness-related and health-related indicators that are commonly used in wearable devices (e.g. accelerometry, PPG, ECG). These papers covered the following domain areas: fitness; exercise; movement; physical activity; step count; walking; running; swimming; energy expenditure; atrial fibrillation; arrhythmia; cardiovascular; autonomic nervous system; neuropathy; heart rate variability; fall detection; trauma; behavior change; diet; eating; stress detection; serum glucose monitoring; continuous glucose monitoring; diabetes mellitus type 1; diabetes mellitus type 2. All studies uncovered through this search on commercially available device algorithms and

Background:Collaborative care management (CCM) is an evidence-based model that contributes to better outcomes for depression treatment in the primary care setting. Tobacco use increases overall economic costs, morbidity, and mortality and has been shown to impact behavioral health outcomes. Our study aims to observe clinical outcomes for depression treatment for patients with comorbid tobacco use and depression within the CCM model.Methods:A retrospective chart review study of 2826 adult patients with depression enrolled in CCM was performed to determine the association between regular tobacco use and depression outcomes. Baseline intake data consisting of clinical and demographic variables along with 6-month follow-up of Patient Health Questionnaire-9 (PHQ-9) scores for smokers (n = 727, 25.7%) and nonsmokers (n = 2099, 74.3%) were obtained. Depression remission was defined as a PHQ-9 score <5 and persistent depressive symptoms (PDS) as a PHQ-9 score ≥10 at 6 months.Results:Using an intention-to-treat analysis, the multivariate modeling demonstrated that smokers, at 6 months, had an increased adjusted odds ratio (AOR) for PDS: 1.624 (95% CI: 1.353-1.949). Furthermore, smokers had a lower AOR of depression remission: 0.603 (95% CI: 0.492-0.739). Patient adherence to treatment was also lower in smokers with an AOR of 0.666 (95% CI: 0.553-0.802).Conclusions:Smokers enrolled in CCM were associated with reduced treatment adherence and worse outcomes for depression treatment at 6 months compared to nonsmokers, even when baseline clinical and demographic variables were controlled. Thus, new tailored practices may be warranted within the CCM model to treat comorbid depression and tobacco use disorders.

A prior project found that an intensive (12 weeks, thrice weekly sessions) in-person, supervised, exercise coaching intervention was effective for smoking cessation among depressed women smokers. However, the sample was 90% White and of high socioeconomic status, and the intensity of the intervention limits its reach. One approach to intervention scalability is to deliver the supervised exercise coaching using a robotic human exercise trainer. This is done in real time via an iPad tablet placed on a mobile robotic wheel base and controlled remotely by an iOS device or computer. As an initial step, this preliminary study surveyed potential receptivity to a robotic-assisted exercise coaching intervention among 100 adults recruited in two community settings, and explored the association of technology acceptance scores with smoking status and other demographics. Participants watched a brief demonstration of the robot-delivered exercise coaching and completed a 19-item survey assessing socio-demographics and technology receptivity measured by the 8-item Technology Acceptance Scale (TAS). Open-ended written feedback was obtained, and content analysis was used to derive themes from these data. Respondents were: 40% female, 56% unemployed, 41% racial minority, 38% current smoker, and 58% depression history. Mean total TAS score was 34.0 (SD = 5.5) of possible 40, indicating overall very good receptivity to the robotic-assisted exercise intervention concept. Racial minorities and unemployed participants reported greater technology acceptance than White (p = 0.015) and employed (p<0.001) respondents. No association was detected between the TAS score and smoking status, depression, gender or age groups. Qualitative feedback indicated the robot was perceived as a novel, motivating, way to increase intervention reach and accessibility, and the wave of the future. Robotic technology has potential applicability for exercise coaching in a broad range of populations, including depressed smokers. Our next step will be to conduct a pilot trial to assess acceptability and potential efficacy of the robotic-assisted exercise coaching intervention for smoking cessation.