Moment-to-Moment Continuous Attention Fluctuation Monitoring through Consumer-Grade EEG Device.

While numerous studies have explored using various sensing techniques to measure attention states, moment-to-moment attention fluctuation measurement is unavailable. To bridge this gap, we applied a novel paradigm in psychology, the gradual-onset continuous performance task (gradCPT), to collect the ground truth of attention states. GradCPT allows for the precise labeling of attention fluctuation on an 800 ms time scale. We then developed a new technique for measuring continuous attention fluctuation, based on a machine learning approach that uses the spectral properties of EEG signals as the main features. We demonstrated that, even using a consumer grade EEG device, the detection accuracy of moment-to-moment attention fluctuations was 73.49%. Next, we empirically validated our technique in a video learning scenario and found that our technique match with the classification obtained through thought probes, with an average F1 score of 0.77. Our results suggest the effectiveness of using gradCPT as a ground truth labeling method and the feasibility of using consumer-grade EEG devices for continuous attention fluctuation detection.

Perceptions and Acceptability of Digital Interventions Among Tuberculosis Patients in Cambodia: Qualitative Study of Video-Based Directly Observed Therapy.

BACKGROUND: Despite the development of effective drugs for treatment, tuberculosis remains one of the leading causes of death from an infectious disease worldwide. One of the greatest challenges to tuberculosis control is patient adherence to treatment. Recent research has shown that video-based directly observed therapy is a feasible and effective approach to supporting treatment adherence in high-income settings. However, few studies have explored the potential for such a solution in a low- or middle-income country setting. Globally, these countries' rapidly rising rate of mobile penetration suggests that the potential for translation of these results may be high. OBJECTIVE: We sought to examine patient perceptions related to the use of mobile health, and specifically video-based directly observed therapy, in a previously unstudied patient demographic: patients with tuberculosis in a low-income country setting (Cambodia). METHODS: We conducted a cross-sectional qualitative study in urban and periurban areas in Cambodia, consisting of 6 focus groups with tuberculosis patients who were receiving treatment (standard directly observed therapy) through a nongovernmental organization. RESULTS: Familiarity with mobile technology and apps was widespread in this population, and overall willingness to consider a mobile app for video-based directly observed therapy was high. However, we identified potential challenges. First, patients very much valued their frequent in-person interactions with their health care provider, which may be reduced with the video-based directly observed therapy intervention. Second, there may be technical issues to address, including how to make the app suitable for illiterate participants. CONCLUSIONS: While video-based directly observed therapy is a promising technology, even in country settings where mobile penetration is reportedly almost universal, it should be introduced with caution. However, the results were generally promising and yielded important insights that not only will be translated into the further adaptation of key features of video-based directly observed therapy for tuberculosis patients in Cambodia, but also can inform the future design and successful implementation of video-based directly observed therapy interventions in low- and middle-income settings more generally.

Preliminary Effects of a Mobile Interactive Supervised Therapy Intervention on People Living With HIV: Pilot Randomized Controlled Trial.

BACKGROUND: As people living with HIV infection require lifelong treatment, nonadherence to medication will reduce their chance of maintaining viral suppression and increase the risk of developing drug resistance and HIV transmission. OBJECTIVE: This study aimed to evaluate the efficacy of a mobile app, Mobile Interactive Supervised Therapy (MIST), for improving adherence to oral HIV medications among HIV-infected adults in Singapore. METHODS: We conducted a two-group pilot randomized controlled trial (RCT) with a process evaluation, in which 40 HIV-infected participants with once-daily medication regimes were recruited from a public tertiary hospital in Singapore and randomly assigned equally to either the intervention (receiving MIST and routine care) or control (receiving routine care only) groups. The intervention lasted for 2 months. The outcome of antiretroviral therapy (ART) adherence was measured by a 7-day recall self-report (SR), pill count (PC), an electronic medical device-Medication Event Monitoring System (MEMS)-and a mobile app-MIST (for the intervention group only). In total, 20 participants from the intervention group were interviewed at the end of the intervention to assess the acceptability of MIST. Data were collected at baseline and at 1-month and 2-month postintervention. RESULTS: All participants had excellent medication adherence at baseline (median 100, IQR 100-100). The use of MIST did not result in a significant improvement in ART adherence when measured by the SR, PC, and MEMS, as compared with the control group at 1-month (P values >.99, .86, and .74, respectively) and 2-month (P values=.80, .84, and .82, respectively) postintervention. ART adherence also did not improve in each group over the same period. MIST was perceived to be a beneficial tool based on the process evaluation results. CONCLUSIONS: Although MIST did not enhance medication adherence to HIV treatments, mainly owing to the ceiling effect, it was perceived to be beneficial among the participants of this study. Our process evaluation provided useful data to further develop MIST for bigger and long-term mobile phone app-assisted intervention RCTs in the future. TRIAL REGISTRATION: ClinicalTrials.gov NCT03794648; https://clinicaltrials.gov/ct2/show/NCT03794648.

Prospective single-arm interventional pilot study to assess a smartphone-based system for measuring and supporting adherence to medication.

OBJECTIVES: Suboptimal medication adherence for infectious diseases such as tuberculosis (TB) results in poor clinical outcomes and ongoing infectivity. Directly observed therapy (DOT) is now standard of care for TB treatment monitoring but has a number of limitations. We aimed to develop and evaluate a smartphone-based system to facilitate remotely observed therapy via transmission of videos rather than in-person observation. DESIGN: We developed an integrated smartphone and web-based system (Mobile Interactive Supervised Therapy, MIST) to provide regular medication reminders and facilitate video recording of pill ingestion at predetermined timings each day, for upload and later review by a healthcare worker. We evaluated the system in a single arm, prospective study of adherence to a dietary supplement. Healthy volunteers were recruited through an online portal. Entry criteria included age ≥21 and owning an iOS or Android-based device. Participants took a dietary supplement pill once, twice or three-times a day for 2 months. We instructed them to video each pill taking episode using the system. OUTCOME: Adherence as measured by the smartphone system and by pill count. RESULTS: 42 eligible participants were recruited (median age 24; 86% students). Videos were classified as received-confirmed pill intake (3475, 82.7% of the 4200 videos expected), received-uncertain pill intake (16, <1%), received-fake pill intake (31, <1%), not received-technical issues (223, 5.3%) or not received-assumed non-adherence (455, 10.8%). Overall median estimated participant adherence by MIST was 90.0%, similar to that obtained by pill count (93.8%). There was a good relationship between participant adherence as measured by MIST and by pill count (Spearmans rs 0.66, p<0.001). CONCLUSIONS: We have demonstrated the feasibility, acceptability and accuracy of a smartphone-based adherence support and monitoring system. The system has the potential to supplement and support the provision of DOT for TB and also to improve adherence in other conditions such as HIV and hepatitis C.

Neurite Tracing With Object Process.

In this paper we present a pipeline for automatic analysis of neuronal morphology: from detection, modeling to digital reconstruction. First, we present an automatic, unsupervised object detection framework using stochastic marked point process. It extracts connected neuronal networks by fitting special configuration of marked objects to the centreline of the neurite branches in the image volume giving us position, local width and orientation information. Semantic modeling of neuronal morphology in terms of critical nodes like bifurcations and terminals, generates various geometric and morphology descriptors such as branching index, branching angles, total neurite length, internodal lengths for statistical inference on characteristic neuronal features. From the detected branches we reconstruct neuronal tree morphology using robust and efficient numerical fast marching methods. We capture a mathematical model abstracting out the relevant position, shape and connectivity information about neuronal branches from the microscopy data into connected minimum spanning trees. Such digital reconstruction is represented in standard SWC format, prevalent for archiving, sharing, and further analysis in the neuroimaging community. Our proposed pipeline outperforms state of the art methods in tracing accuracy and minimizes the subjective variability in reconstruction, inherent to semi-automatic methods.

A stochastic model for automatic extraction of 3D neuronal morphology.

Tubular structures are frequently encountered in bio-medical images. The center-lines of these tubules provide an accurate representation of the topology of the structures. We introduce a stochastic Marked Point Process framework for fully automatic extraction of tubular structures requiring no user interaction or seed points for initialization. Our Marked Point Process model enables unsupervised network extraction by fitting a configuration of objects with globally optimal associated energy to the centreline of the arbors. For this purpose we propose special configurations of marked objects and an energy function well adapted for detection of 3D tubular branches. The optimization of the energy function is achieved by a stochastic, discrete-time multiple birth and death dynamics. Our method finds the centreline, local width and orientation of neuronal arbors and identifies critical nodes like bifurcations and terminals. The proposed model is tested on 3D light microscopy images from the DIADEM data set with promising results.