Undiagnosed and untreated depressive disorders have become a serious public health issue and it is prevalent among people of all ages, gender and race. Social media sites, such as Twitter, have become a major venue for people to express/disclose their thoughts and feelings. The tweets from these micro-blogging sites could be used to screen for and potentially detect depression. To date, studies in this area have focused on developing and validating the terms and vocabulary used by users with depression, or evaluating tweets related to depression by using terms that are synonymous with depression. This approach has not produced reliable findings. In this study, we depart from this approach and instead, base our analysis on research on depressive disorders, which indicates the critical significance of repetitive thoughts and ruminating behavior of people with depression. The current study and findings hold important implications for research on depression, social media, and public health informatics.

The use of mobile technology and mobile apps has become pervasive in our daily lives for completing a variety of daily tasks. Mobile health (mHealth) apps can provide an accessible platform for self-management among breast cancer (BC) survivors, as they recover from not just the intensive cancer treatments, but also their associated side-effects. They also offer a means to learn about survivorship topics and connect with peer survivors online, irrespective of their geographical location. This study is an attempt to assess the availability and characterize the self-management features of free mobile apps for breast cancer survivors on the Google Play (Android) and Apple App Store (iOS). Out of 249 such apps for the Android, only eight satisfied initial criteria, while only one of 174 iOS apps that met inclusion criteria was included for further analysis. A content analysis of the nine apps that met inclusion criteria was conducted to assess the inclusion of the following mHealth self-management features derived from the Chronic Care Model: symptom tracking; survivorship education; information-sharing with family and/or caregivers; scheduling follow-up visits; personal alerts and reminders; and social networking. Survivorship education was found to be the most common self-management feature among the apps reviewed, followed by social networking. The results of this study highlight the dearth of available mHealth resources for BC survivors. Future efforts in app development should involve survivors and healthcare providers to ensure comprehensive resources that address their unmet needs are made more accessible.

Using a personal decision support-based tool can serve as a training tool and resource, providing these patients with pertinent information about the various aspects of their long-term health, while educating them about any related side effects and symptoms. It is hoped that making such tools more accessible could help in engaging survivors to play an active role in managing their health and encourage shared decision-making with their providers.

Background Breast cancer is the most common form of cancer among American women, accounting for 23% of all cancer survivors nationally. Yet, the availability of adequate resources and tools for supporting breast cancer survivors has not kept up with the rapid advancement in treatment options, resulting in unmet supportive care needs, particularly among low-income and minority populations. This study explores an alternative means of delivering breast cancer survivorship care plans (SCPs), with the aim of improving survivor morbidity, patient knowledge, and self-management of treatment-related symptoms, as well as addressing inconsistencies in follow-up care visits. Objective The overall goal of this study is to improve the uptake of SCP recommendations via an educational intervention for breast cancer survivors, to improve treatment-related morbidity, patient knowledge, self-management, and adherence to follow-up visits. The specific aims of the study are to (1) evaluate the feasibility of the online SCP, and (2) assess the impact of the online SCP on survivorship outcomes. Methods We will enroll 50 breast cancer survivors who have completed initial breast cancer treatment into a 2-armed, randomized, waitlist-controlled pilot trial, and collect data at baseline and 6 months. For the first aim, we will use mixed methods, including surveys and personal interviews among the intervention group, to determine the feasibility of providing an online, interactive SCP (called ACESO) based on the survivors’ online user experience and their short-term adoption. For the secondary aim, we will compare the 2 groups to assess the primary outcomes of survivor knowledge, self-efficacy for self-management, perceived peer support, and adherence to SCP-recommended posttreatment follow-up visits to oncology and primary care; and the secondary outcomes of treatment-related morbidity (body weight, fatigue, depression, anxiety, sexual function, distress, and sleep quality). We assess these outcomes by using measurements from validated instruments with robust psychometric properties. Results We have developed and refined the online breast cancer survivorship plan, ACESO, with consultation from breast cancer oncologists, nurses, and survivors. Approval for the study protocol has been obtained from the Institutional Review Board. An advisory board has also been established to provide oversight and recommendations on the conduct of the study. The study will be completed over a period of 2 years. Conclusions The results of this pilot study will inform the feasibility and design of a larger-scale pragmatic trial to evaluate the impact of an online breast cancer SCP on treatment-related morbidity and self-efficacy for self-management. International Registered Report Identifier (IRRID) PRR1-10.2196/23414

Background: Obesity is a continuing national epidemic, and the condition can have a physical, psychological, as well as social impact on one's well-being. Consequently, it is critical to diagnose and document obesity accurately in the patient's electronic medical record (EMR), so that the information can be used and shared to improve clinical decision making and health communication and, in turn, the patient's prognosis. It is therefore worthwhile identifying the various factors that play a role in documenting obesity diagnosis and the methods to improve current documentation practices. Method: We used a retrospective cross-sectional design to analyze outpatient EMRs of patients at an academic outpatient clinic. Obese patients were identified using the measured body mass index (BMI; 30 kg/m 2) entry in the EMR, recorded at each visit, and checked for documentation of obesity in the EMR problem list. Patients were categorized into two groups (diagnosed or undiagnosed) based on a documented diagnosis (or omission) of obesity in the EMR problem list and compared. Results: A total of 10,208 unique patient records of obese patients were included for analysis, of which 4119 (40%) did not have any documentation of obesity in their problem list. Chi-square analysis between the diagnosed and undiagnosed groups revealed significant associations between documentation of obesity in the EMR and patient characteristics. Conclusion: EMR designers and developers must consider employing automated decision support techniques to populate and update problem lists based on the existing recorded BMI in the EMR in order to prevent omissions occurring from manual entry.