Leveraging Contact Network Information in Clustered Randomized Studies of Contagion Processes.

In a randomized study, leveraging covariates related to the outcome (e.g. disease status) may produce less variable estimates of the effect of exposure.

Estimating longitudinal depressive symptoms from smartphone data in a transdiagnostic cohort.

Background: Passive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.

Methods: We enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors.

Interpretation Biases in Pain: Validation of Two New Stimulus Sets.

Adults with chronic pain interpret ambiguous information in a pain and illness related fashion. However, limitations have been highlighted with traditional experimental paradigms used to measure interpretation biases. Whilst ambiguous scenarios have been developed to measure interpretation biases in adolescents with pain, no scenario sets exist for use with adults.

Using behavioural insights to reduce sugar in primary school children's packed lunches in derby; A cluster randomised controlled trial.

Children's packed lunches contain more sugar than school-provided meals. Interventions to improve the provision of healthier packed lunches have modest effects on lunch contents. This cluster randomised controlled trial tested an intervention to encourage healthier provision of packed lunches by parents of primary school children in Derby.

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.

Background: Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.

Objective: To compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator.

Methods: Patients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio.

Design and results of a smartphone-based digital phenotyping study to quantify ALS progression.

Objective: The amyotrophic lateral sclerosis (ALS) trial outcome measures are clinic based. Active and passive smartphone data can provide important longitudinal information about ALS progression outside the clinic.

Methods: We used Beiwe, a research platform for smartphone-based digital phenotyping, to collect active (self-report ALSFRS-R surveys and speech recordings) and passive (phone sensors and logs) data from patients with ALS for approximately 24 weeks.

Exploring the relationship between gamma-band activity and maths anxiety.

Previous research has outlined high anxiety in connection with gamma modulation, identifying that gamma-band activity (40-100 Hz) correlates with processing of threat perception, attention and anxiety. Maths anxiety research has also noted the involvement of these aspects, yet this has not been investigated from a neurophysiological standpoint. Electroencephalography (EEG) was used to research gamma-band activity in relation to maths anxiety over two studies.

The HOPE Pilot Study: Harnessing Patient-Reported Outcomes and Biometric Data to Enhance Cancer Care.

Purpose: Integrating patient-reported outcomes (PROs) into clinical practice is an increasingly promising strategy for improving patients' symptoms, communication, and clinical outcomes. The objective of the current study was to assess the feasibility, acceptability, and perceived effectiveness of a mobile health intervention that was designed to collect PROs and activity data as a measure of health status.

Patients And Methods: This work was a pilot intervention with 10 patients with gynecologic cancers who received palliative chemotherapy.

Beyond smartphones and sensors: choosing appropriate statistical methods for the analysis of longitudinal data.

Objectives: As smartphones and sensors become more prominently used in mobile health, the methods used to analyze the resulting data must also be carefully considered. The advantages of smartphone-based studies, including large quantities of temporally dense longitudinally captured data, must be matched with the appropriate statistical methods in order draw valid conclusions. In this paper, we review and provide recommendations in 3 critical domains of analysis for these types of temporally dense longitudinal data and highlight how misleading results can arise from improper use of these methods.

Information-Based Medicine in Glioma Patients: A Clinical Perspective.

Glioma constitutes the most common type of primary brain tumor with a dismal survival, often measured in terms of months or years. The thin line between treatment effectiveness and patient harm underpins the importance of tailoring clinical management to the individual patient. Randomized trials have laid the foundation for many neuro-oncological guidelines.

Characterizing the clinical relevance of digital phenotyping data quality with applications to a cohort with schizophrenia.

Digital phenotyping, or the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices and smartphones, in particular, holds great potential for behavioral monitoring of patients. However, realizing the potential of digital phenotyping requires understanding of the smartphone as a scientific data collection tool. In this pilot study, we detail a procedure for estimating data quality for phone sensor samples and model the relationship between data quality and future symptom-related survey responses in a cohort with schizophrenia.

Relapse prediction in schizophrenia through digital phenotyping: a pilot study.

Among individuals diagnosed, hospitalized, and treated for schizophrenia, up to 40% of those discharged may relapse within 1 year even with appropriate treatment. Passively collected smartphone behavioral data present a scalable and at present underutilized opportunity to monitor patients in order to identify possible warning signs of relapse. Seventeen patients with schizophrenia in active treatment at a state mental health clinic in Boston used the Beiwe app on their personal smartphone for up to 3 months.

A comparison of passive and active estimates of sleep in a cohort with schizophrenia.

Unlabelled: Sleep abnormalities are considered an important feature of schizophrenia, yet convenient and reliable sleep monitoring remains a challenge. Smartphones offer a novel solution to capture both self-reported and objective measures of sleep in schizophrenia. In this three-month observational study, 17 subjects with a diagnosis of schizophrenia currently in treatment downloaded Beiwe, a platform for digital phenotyping, on their personal Apple or Android smartphones.

Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review.

Objective: Accurate measurement of surgical outcomes is highly desirable to optimize surgical decision-making. An important element of surgical decision making is identification of the patient cohort that will benefit from surgery before the intervention. Machine learning (ML) enables computers to learn from previous data to make accurate predictions on new data.

Characterizing Smartphone Engagement for Schizophrenia: Results of a Naturalist Mobile Health Study.

Introduction: Despite growing interest in smartphone apps for schizophrenia, little is known about how these apps are utilized in the real world. Understanding how app users are engaging with these tools outside of the confines of traditional clinical studies offers an important information on who is most likely to use apps and what type of data they are willing to share.

Methods: The Schizophrenia and Related Disorders Alliance of America, in partnership with Self Care Catalyst, has created a smartphone app for schizophrenia that is free and publically available on both Apple iTunes and Google Android Play stores.

Using a network-based approach and targeted maximum likelihood estimation to evaluate the effect of adding pre-exposure prophylaxis to an ongoing test-and-treat trial.

Background: Several cluster-randomized trials are underway to investigate the implementation and effectiveness of a universal test-and-treat strategy on the HIV epidemic in sub-Saharan Africa. We consider nesting studies of pre-exposure prophylaxis within these trials. Pre-exposure prophylaxis is a general strategy where high-risk HIV- persons take antiretrovirals daily to reduce their risk of infection from exposure to HIV.

Dietary nitrate supplementation and exercise tolerance in patients with heart failure with reduced ejection fraction.

Endothelial dysfunction and reduced nitric oxide (NO) signaling are key abnormalities leading to skeletal muscle oxygen delivery-utilization mismatch and poor physical capacity in patients with heart failure with reduced ejection fraction (HFrEF). Oral inorganic nitrate supplementation provides an exogenous source of NO that may enhance locomotor muscle function and oxygenation with consequent improvement in exercise tolerance in HFrEF. Thirteen patients (left ventricular ejection fraction ≤40%) were enrolled in a double-blind, randomized crossover study to receive concentrated nitrate-rich (nitrate) or nitrate-depleted (placebo) beetroot juice for 9 days.

Corrigendum: Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers.

[This corrects the article on p. 180 in vol. 10, PMID: 27148026.

Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps: An Internet Survey of Younger US Consumers.

Background: While clinical evidence for the efficacy of brain training remains in question, numerous smartphone applications (apps) already offer brain training directly to consumers. Little is known about why consumers choose to download these apps, how they use them, and what benefits they perceive. Given the high rates of smartphone ownership in those with internet access and the younger demographics, we chose to approach this question first with a general population survey that would capture primarily this demographic.

Incorporating Contact Network Structure in Cluster Randomized Trials.

Whenever possible, the efficacy of a new treatment is investigated by randomly assigning some individuals to a treatment and others to control, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, clusters of individuals are assigned to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.

Utilizing a Personal Smartphone Custom App to Assess the Patient Health Questionnaire-9 (PHQ-9) Depressive Symptoms in Patients With Major Depressive Disorder.

Background: Accurate reporting of patient symptoms is critical for diagnosis and therapeutic monitoring in psychiatry. Smartphones offer an accessible, low-cost means to collect patient symptoms in real time and aid in care.

Objective: To investigate adherence among psychiatric outpatients diagnosed with major depressive disorder in utilizing their personal smartphones to run a custom app to monitor Patient Health Questionnaire-9 (PHQ-9) depression symptoms, as well as to examine the correlation of these scores to traditionally administered (paper-and-pencil) PHQ-9 scores.

Realizing the potential of mobile mental health: new methods for new data in psychiatry.

Smartphones are now ubiquitous and can be harnessed to offer psychiatry a wealth of real-time data regarding patient behavior, self-reported symptoms, and even physiology. The data collected from smartphones meet the three criteria of big data: velocity, volume, and variety. Although these data have tremendous potential, transforming them into clinically valid and useful information requires using new tools and methods as a part of assessment in psychiatry.

Attentional biases towards familiar and unfamiliar foods in children. The role of food neophobia.

Familiarity of food stimuli is one factor that has been proposed to explain food preferences and food neophobia in children, with some research suggesting that food neophobia (and familiarity) is at first a predominant of the visual domain. Considering visual attentional biases are a key factor implicated in a majority of fear-related phobias/anxieties, the purpose of this research was to investigate attentional biases to familiar and unfamiliar fruit and vegetables in 8 to 11 year old children with differing levels of food neophobia. To this end, 70 primary aged children completed a visual-probe task measuring attentional biases towards familiar and unfamiliar fruit/vegetables, as well as the food neophobia, general neophobia and willingness to try self-report measures.

A cultural consultation service in East London: experiences and outcomes from implementation of an innovative service.

This paper reports on a feasibility study and evaluation of a new type of cultural consultation service (CCS). This multi-component and systemic complex intervention was offered over 18 months to specialist mental health providers in one of the poorest regions of the UK. The service received 900 clinically related contacts and 99 in-depth consultations.