Real-world clinical outcomes and treatment patterns in patients with MDD treated with vortioxetine: a retrospective study.

BACKGROUND: This study included evaluation of the effectiveness of vortioxetine, a treatment for adults with major depressive disorder (MDD), using patient-reported outcome measures (PROMs) in a real-world setting. METHODS: This retrospective chart review analyzed the care experiences of adult patients with a diagnosis of MDD from Parkview Physicians Group - Mind-Body Medicine, Midwestern United States. Patients with a prescription for vortioxetine, an initial baseline visit, and ≥ 2 follow-up visits within 16 weeks from September 2014 to December 2018 were included. The primary outcome measure was effectiveness of vortioxetine on depression severity as assessed by change in Patient Health Questionnaire-9 (PHQ-9) scores ~ 12 weeks after initiation of vortioxetine. Secondary outcomes included changes in depression-related symptoms (i.e., sexual dysfunction, sleep disturbance, cognitive function, work/social function), clinical characteristics, response, remission, and medication persistence. Clinical narrative notes were also analyzed to examine sleep disturbance, sexual dysfunction, appetite, absenteeism, and presenteeism. All outcomes were examined at index (start of vortioxetine) and at ~ 12 weeks, and mean differences were analyzed using pairwise t tests. RESULTS: A total of 1242 patients with MDD met inclusion criteria, and 63.9% of these patients had ≥ 3 psychiatric diagnoses and 65.9% were taking ≥ 3 medications. PHQ-9 mean scores decreased significantly from baseline to week 12 (14.15 ± 5.8 to 9.62 ± 6.03, respectively; p < 0.001). At week 12, the response and remission rates in all patients were 31.0% and 23.1%, respectively, and 67% continued vortioxetine treatment. Overall, results also showed significant improvements by week 12 in anxiety (p < 0.001), sexual dysfunction (p < 0.01), sleep disturbance (p < 0.01), cognitive function (p < 0.001), work/social functioning (p = 0.021), and appetite (p < 0.001). A significant decrease in presenteeism was observed at week 12 (p < 0.001); however, no significant change was observed in absenteeism (p = 0.466). CONCLUSIONS: Using PROMs, our study results suggest that adults with MDD prescribed vortioxetine showed improvement in depressive symptoms in the context of a real-world clinical practice setting. These patients had multiple comorbid psychiatric and physical diagnoses and multiple previous antidepressant treatments had failed.

Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations.

BACKGROUND: There is worldwide demand for an affordable hemoglobin measurement solution, which is a particularly urgent need in developing countries. The smartphone, which is the most penetrated device in both rich and resource-constrained areas, would be a suitable choice to build this solution. Consideration of a smartphone-based hemoglobin measurement tool is compelling because of the possibilities for an affordable, portable, and reliable point-of-care tool by leveraging the camera capacity, computing power, and lighting sources of the smartphone. However, several smartphone-based hemoglobin measurement techniques have encountered significant challenges with respect to data collection methods, sensor selection, signal analysis processes, and machine-learning algorithms. Therefore, a comprehensive analysis of invasive, minimally invasive, and noninvasive methods is required to recommend a hemoglobin measurement process using a smartphone device. OBJECTIVE: In this study, we analyzed existing invasive, minimally invasive, and noninvasive approaches for blood hemoglobin level measurement with the goal of recommending data collection techniques, signal extraction processes, feature calculation strategies, theoretical foundation, and machine-learning algorithms for developing a noninvasive hemoglobin level estimation point-of-care tool using a smartphone. METHODS: We explored research papers related to invasive, minimally invasive, and noninvasive hemoglobin level measurement processes. We investigated the challenges and opportunities of each technique. We compared the variation in data collection sites, biosignal processing techniques, theoretical foundations, photoplethysmogram (PPG) signal and features extraction process, machine-learning algorithms, and prediction models to calculate hemoglobin levels. This analysis was then used to recommend realistic approaches to build a smartphone-based point-of-care tool for hemoglobin measurement in a noninvasive manner. RESULTS: The fingertip area is one of the best data collection sites from the body, followed by the lower eye conjunctival area. Near-infrared (NIR) light-emitting diode (LED) light with wavelengths of 850 nm, 940 nm, and 1070 nm were identified as potential light sources to receive a hemoglobin response from living tissue. PPG signals from fingertip videos, captured under various light sources, can provide critical physiological clues. The features of PPG signals captured under 1070 nm and 850 nm NIR LED are considered to be the best signal combinations following a dual-wavelength theoretical foundation. For error metrics presentation, we recommend the mean absolute percentage error, mean squared error, correlation coefficient, and Bland-Altman plot. CONCLUSIONS: We addressed the challenges of developing an affordable, portable, and reliable point-of-care tool for hemoglobin measurement using a smartphone. Leveraging the smartphone's camera capacity, computing power, and lighting sources, we define specific recommendations for practical point-of-care solution development. We further provide recommendations to resolve several long-standing research questions, including how to capture a signal using a smartphone camera, select the best body site for signal collection, and overcome noise issues in the smartphone-captured signal. We also describe the process of extracting a signal's features after capturing the signal based on fundamental theory. The list of machine-learning algorithms provided will be useful for processing PPG features. These recommendations should be valuable for future investigators seeking to build a reliable and affordable hemoglobin prediction model using a smartphone.

Supporting Accurate Interpretation of Self-Administered Medical Test Results for Mobile Health: Assessment of Design, Demographics, and Health Condition.

BACKGROUND: Technological advances in personal informatics allow people to track their own health in a variety of ways, representing a dramatic change in individuals' control of their own wellness. However, research regarding patient interpretation of traditional medical tests highlights the risks in making complex medical data available to a general audience. OBJECTIVE: This study aimed to explore how people interpret medical test results, examined in the context of a mobile blood testing system developed to enable self-care and health management. METHODS: In a preliminary investigation and main study, we presented 27 and 303 adults, respectively, with hypothetical results from several blood tests via one of the several mobile interface designs: a number representing the raw measurement of the tested biomarker, natural language text indicating whether the biomarker's level was low or high, or a one-dimensional chart illustrating this level along a low-healthy axis. We measured respondents' correctness in evaluating these results and their confidence in their interpretations. Participants also told us about any follow-up actions they would take based on the result and how they envisioned, generally, using our proposed personal health system. RESULTS: We find that a majority of participants (242/328, 73.8%) were accurate in their interpretations of their diagnostic results. However, 135 of 328 participants (41.1%) expressed uncertainty and confusion about their ability to correctly interpret these results. We also find that demographics and interface design can impact interpretation accuracy, including false confidence, which we define as a respondent having above average confidence despite interpreting a result inaccurately. Specifically, participants who saw a natural language design were the least likely (421.47 times, P=.02) to exhibit false confidence, and women who saw a graph design were less likely (8.67 times, P=.04) to have false confidence. On the other hand, false confidence was more likely among participants who self-identified as Asian (25.30 times, P=.02), white (13.99 times, P=.01), and Hispanic (6.19 times, P=.04). Finally, with the natural language design, participants who were more educated were, for each one-unit increase in education level, more likely (3.06 times, P=.02) to have false confidence. CONCLUSIONS: Our findings illustrate both promises and challenges of interpreting medical data outside of a clinical setting and suggest instances where personal informatics may be inappropriate. In surfacing these tensions, we outline concrete interface design strategies that are more sensitive to users' capabilities and conditions.

Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health.

OBJECTIVE: To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. MATERIALS AND METHODS: Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. RESULTS: Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. DISCUSSION: Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. CONCLUSION: This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management.