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Cities worldwide are attempting to be claimed as smart, but truly classifying as such remains a great challenge. This paper aims to use artificial intelligence AI to classify the smart city's performance as well as the factors linked to it. This is based on the perceptions of residents on issues related to structures and technology applications available in their cities. To achieve this goal, the study included 200 cities worldwide. For 147 cities we captured the perceptions of 120 residents in each city, by answering a survey of 39 questions evolving around two main Pillars: ‘Structures’ that refers to the existing infrastructure of the city and the ‘Technology’ pillar that describes the technological provisions and services available to the inhabitants. And each one is evaluated under five key areas: health and safety, mobility, activities, opportunities, and governance. The final score of the other 53 cities, was measured by using the data openly available on the internet. And this by means of different algorithms of machine learning such as Random Forest RF, Artificial Neural Network ANN, Support Vector Machine (SVM), and Gradient Boost (XGB). These algorithms have been compared and evaluated in order to select the best one. The tests showed that Random Forest RF alongside with Artificial Neural Network ANN, with the highest level of accuracy, are the best trained model. This study will enable other researches to use machine learning in the identification process of smart cities.

Background: Internet-based interventions offer a way to meet the high demand for psychological support. However, this setting also has disadvantages, such as the lack of personal contact and the limited ability to respond to crises. Blended care combines Internet-based interventions with face-to-face psychotherapy and merges the benefits of both settings. To ensure the uptake of blended care in routine care, Internet-based interventions need to be suitable for different therapeutic approaches and mental disorders. Objective: This paper describes the participatory development process of the Internet-based intervention “TONI” using a common therapeutic language and content on various transdiagnostic topics to be integrated into routine outpatient psychotherapy. Methods: To develop this intervention in a participatory manner, we followed the Integrate, Design, Assess, and Share (IDEAS) framework. In a multilevel development process, we used a combination of interviews, focus groups, and proofreading to optimally tailor online modules to routine outpatient psychotherapy. Building on well-established cognitive-behavioral online content, we included expert interviews with psychodynamic (n = 20) and systemic psychotherapists (n = 9) as well as focus groups with psychotherapists of different approaches (n = 10) and persons with lived experience of mental illness (PWLE; n = 10). Results: We describe the development process of TONI step-by-step, outlining the specific requirements that therapists from different therapeutic approaches as well as PWLE have and how we implemented them in our intervention. This includes the content and specific exercises in the online modules, aspects of data protection, language, design, and usability. Conclusion: Internet-based interventions that use a common therapeutic language and address therapeutic principles across different approaches have the potential to advance digitalization in psychotherapy. Involving psychotherapists and PWLE in intervention development may positively impact acceptance and usage in practice. This study shows how participatory intervention development involving both psychotherapists and PWLE can be carried out.