Daily Motivational Text Messages to Promote Physical Activity in University Students: Results From a Microrandomized Trial.

BACKGROUND: Low physical activity is an important risk factor for common physical and mental disorders. Physical activity interventions delivered via smartphones can help users maintain and increase physical activity, but outcomes have been mixed. PURPOSE: Here we assessed the effects of sending daily motivational and feedback text messages in a microrandomized clinical trial on changes in physical activity from one day to the next in a student population. METHODS: We included 93 participants who used a physical activity app, "DIAMANTE" for a period of 6 weeks. Every day, their phone pedometer passively tracked participants' steps. They were microrandomized to receive different types of motivational messages, based on a cognitive-behavioral framework, and feedback on their steps. We used generalized estimation equation models to test the effectiveness of feedback and motivational messages on changes in steps from one day to the next. RESULTS: Sending any versus no text message initially resulted in an increase in daily steps (729 steps, p = .012), but this effect decreased over time. A multivariate analysis evaluating each text message category separately showed that the initial positive effect was driven by the motivational messages though the effect was small and trend-wise significant (717 steps; p = .083), but not the feedback messages (-276 steps, p = .4). CONCLUSION: Sending motivational physical activity text messages based on a cognitive-behavioral framework may have a positive effect on increasing steps, but this decreases with time. Further work is needed to examine using personalization and contextualization to improve the efficacy of text-messaging interventions on physical activity outcomes. CLINICALTRIALS.GOV IDENTIFIER: NCT04440553.

Understanding the Use of the Term "Weaponized Autism" in An Alt-Right Social Media Platform.

BACKGROUND: The term "weaponized autism" is frequently used on extremist platforms. To better understand this, we conducted a discourse analysis of posts on Gab, an alt-right social media platform. METHODS: We analyzed 711 posts spanning 2018-2019 and filtered for variations on the term "weaponized autism". RESULTS: This term is used mainly by non-autistic Gab users. It refers to exploitation of perceived talents and vulnerabilities of "Weaponized autists", described as all-powerful masters-of-technology who are devoid of social skills. CONCLUSIONS: The term "weaponized autism" is simultaneously glorified and derogatory. For some autistic people, the partial acceptance offered within this community may be preferable to lack of acceptance offered in society, which speaks to improving societal acceptance as a prevention effort.

Adaptive learning algorithms to optimize mobile applications for behavioral health: guidelines for design decisions.

OBJECTIVE: Providing behavioral health interventions via smartphones allows these interventions to be adapted to the changing behavior, preferences, and needs of individuals. This can be achieved through reinforcement learning (RL), a sub-area of machine learning. However, many challenges could affect the effectiveness of these algorithms in the real world. We provide guidelines for decision-making. MATERIALS AND METHODS: Using thematic analysis, we describe challenges, considerations, and solutions for algorithm design decisions in a collaboration between health services researchers, clinicians, and data scientists. We use the design process of an RL algorithm for a mobile health study "DIAMANTE" for increasing physical activity in underserved patients with diabetes and depression. Over the 1.5-year project, we kept track of the research process using collaborative cloud Google Documents, Whatsapp messenger, and video teleconferencing. We discussed, categorized, and coded critical challenges. We grouped challenges to create thematic topic process domains. RESULTS: Nine challenges emerged, which we divided into 3 major themes: 1. Choosing the model for decision-making, including appropriate contextual and reward variables; 2. Data handling/collection, such as how to deal with missing or incorrect data in real-time; 3. Weighing the algorithm performance vs effectiveness/implementation in real-world settings. CONCLUSION: The creation of effective behavioral health interventions does not depend only on final algorithm performance. Many decisions in the real world are necessary to formulate the design of problem parameters to which an algorithm is applied. Researchers must document and evaulate these considerations and decisions before and during the intervention period, to increase transparency, accountability, and reproducibility. TRIAL REGISTRATION: clinicaltrials.gov, NCT03490253.

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study.

INTRODUCTION: Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention. METHODS AND ANALYSIS: In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up. ETHICS AND DISSEMINATION: The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings. TRIAL REGISTRATION NUMBER: NCT03490253; pre-results.

Cell-free Epstein-Barr virus-DNA in patients with nasopharyngeal carcinoma: Plasma versus urine.

BACKGROUND: The purpose of this study was to explore urinary Epstein-Barr virus (EBV)-DNA as a potential biomarker in patients with nasopharyngeal carcinoma (NPC). METHODS: EBV-DNA copies were estimated in plasma/urine of patients with NPC (n = 76) by real-time polymerase chain reaction (PCR) at baseline, during therapy, and at follow-up. Their correlation with EBV-RNA expression in tissues (n = 53) was used to assess sensitivity and specificity of plasma/urine EBV-DNA. Correlation of urine and plasma EBV-DNA with each other and with radiological response was evaluated. RESULTS: This study demonstrated that urine EBV-DNA has high sensitivity (96%) at diagnosis and it correlates well with plasma EBV-DNA at baseline and after neoadjuvant chemotherapy. The EBV-DNA copies reduced significantly with therapy (plasma: p < .001; urine: p = .011). Patients with low EBV-DNA copies demonstrated improved survival (plasma: p = .023; urine: p = .083). CONCLUSION: Plasma EBV-DNA is a good prognostic marker, whereas further study on a larger cohort may help in developing urine EBV-DNA as a surrogate prognostic marker for patients with NPC. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1666-E1673, 2016.

Cardioprotective radiotherapy: the circadian way.

Radiotherapy (RT) has been established to improve both local control as well as overall survival rates in breast cancer. However, RT especially in left-sided breast cancer also irradiates a portion of the heart. Radiation associated toxicity to the heart assumes significance because of improval in survival of breast cancer patients. A circadian pattern has been reported in the myocardial oxygen demand and myocardial ischaemia with the cardiac tissue being more susceptible to injury between 6 am and noon. Radiation damages blood vessels of all sizes causing an increase in capillary wall permeability and dilatation of vessels leading to the characteristic radiation erythema followed by an inflammatory cell infiltrate. Coronary artery spasm may be the reason behind some cases of sudden death occurring in patients after radiation therapy. Endothelial behaviour also has a circadian variation and vasodilation is significantly attenuated in the morning. Critical coronary artery disease occurs 10-15 years after radiotherapy. Radiation in the morning hours may be one of the associated risk factor. The application of chrono-therapeutics with radiation therapy in carcinoma breast and in other chest wall irradiation, could possibly decrease the radiation associated cardiac toxicity.