Designing and Developing an eHealth Program for Patients With Persistent Physical Symptoms: Usability Study.

BACKGROUND: Patients with persistent physical symptoms presenting in primary care are often affected by multiple symptoms and reduced functioning. The medical and societal costs of these patients are high, and there is a need for new interventions tailored to both the patients and health care system. OBJECTIVE: This study aimed to examine the usability of an unguided, self-help treatment program, "My Symptoms," developed to assist patients and general practitioners in symptom management. METHODS: In all, 11 users (4 patients with persistent physical symptoms and 7 laypeople) participated in web-based thinking-aloud interviews involving the performance of predefined tasks in the program. Thematic analysis was used to categorize the severity of usability issues. General usability heuristics were cross-referenced with the usability issues. RESULTS: The analysis identified important usability issues related to functionality, navigation, and content. The study shows how therapeutic knowledge in some cases was lost in the translation of face-to-face therapy to a digital format. The user testing helped uncover how the functionality of the digital elements and general navigation of the program played a huge part in locating and accessing the needed treatment. Examples of redesign to mediate the therapeutic value in the digital format involving health care professionals, web developers, and users are provided. The study also highlights the differences of involving patients and laypeople in the interviews. CONCLUSIONS: Taking the experience of common symptoms as a point of departure, patients and laypeople contributed to finding usability issues on program functionality, navigation, and content to improve the program and make the treatment more accessible to users.

Steering carbon dioxide reduction toward C-C coupling using copper electrodes modified with porous molecular films.

Copper offers unique capability as catalyst for multicarbon compounds production in the electrochemical carbon dioxide reduction reaction. In lieu of conventional catalysis alloying with other elements, copper can be modified with organic molecules to regulate product distribution. Here, we systematically study to which extent the carbon dioxide reduction is affected by film thickness and porosity. On a polycrystalline copper electrode, immobilization of porous bipyridine-based films of varying thicknesses is shown to result in almost an order of magnitude enhancement of the intrinsic current density pertaining to ethylene formation while multicarbon products selectivity increases from 9.7 to 61.9%. In contrast, the total current density remains mostly unaffected by the modification once it is normalized with respect to the electrochemical active surface area. Supported by a microkinetic model, we propose that porous and thick films increase both local carbon monoxide partial pressure and the carbon monoxide surface coverage by retaining in situ generated carbon monoxide. This reroutes the reaction pathway toward multicarbon products by enhancing carbon-carbon coupling. Our study highlights the significance of customizing the molecular film structure to improve the selectivity of copper catalysts for carbon dioxide reduction reaction.

A Neural Network Approach for Property Determination of Molecular Solar Cell Candidates.

The dihydroazulene/vinylheptafulvene (DHA/VHF) photocouple is a promising candidate for molecular solar heat batteries, storing and releasing energy in a closed cycle. Much work has been done on improving the energy storage capacity and the half-life of the high-energy isomer via substituent functionalization, but similarly important is keeping these improved properties in common polar solvents, along with being soluble in these, which is tied to the dipole properties. However, the number of possible derivatives makes an overview of this combinatorial space impossible both for experimental work and traditional computational chemistry. Due to the time-consuming nature of running many thousands of computations, we look to machine learning, which bears the advantage that once a model has been trained, it can be used to rapidly estimate approximate values for the given system. Applying a convolutional neural network, we show that it is possible to reach good agreement with traditional computations on a scale that allows us to rapidly screen tens of thousands of the DHA/VHF photocouple, eliminating bad candidates and allowing computational resources to be directed toward meaningful compounds.