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Background Adequate sleep is crucial for child health and development; however, sleep problems are common, affecting 25% of children. Psychotherapeutic stories are an effective tool in child therapy, with key elements including relatable characters, clear messages, emotional engagement, and interactivity. Such stories can help children regulate emotions, develop problem-solving skills, and model positive behaviors. Research on therapeutic stories for sleep issues is limited. Objective The aim of this study is to investigate whether trained reviewers can identify artificial intelligence (AI)-constructed psychotherapeutic sleep stories for children. Materials and methods Four reviewers trained in developing therapeutic stories evaluated 20 bedtime stories (10 psychotherapist-constructed and 10 AI-constructed) assigned to them at random. The AI-constructed stories were created using ChatGPT. Participants completed a questionnaire after reading each story, focusing on various aspects of the content. In a second step, the reviewers participated in a 30-minute interview for qualitative feedback. Results Analysis of 80 questionnaires from four reviewers revealed that 85% of all stories were correctly categorized, with 95% of AI stories identified as such. Expert stories were categorized correctly in 75% of the cases. However, results also show that AI-constructed stories lacked in sleep-related cognitions, emotions, and daytime consequences, but included sleep-related problems and recommendations. Reviewers noted similarities in story structure and repetition in AI-generated stories. Conclusion The need for prevention tools for sleep disturbances is urgent, with early interventions for different age groups essential. This pilot study showed that AI- and expert-constructed psychotherapeutic stories share similarities in addressing sleep-related problems; however, the latter seem to be superior regarding key elements of such stories. Future research should involve a wider range of professionals and parents. Ultimately, tailored stories may play a key role in early interventions for children’s sleep health.
Background Adequate sleep is crucial for child health and development; however, sleep problems are common, affecting 25% of children. Psychotherapeutic stories are an effective tool in child therapy, with key elements including relatable characters, clear messages, emotional engagement, and interactivity. Such stories can help children regulate emotions, develop problem-solving skills, and model positive behaviors. Research on therapeutic stories for sleep issues is limited. Objective The aim of this study is to investigate whether trained reviewers can identify artificial intelligence (AI)-constructed psychotherapeutic sleep stories for children. Materials and methods Four reviewers trained in developing therapeutic stories evaluated 20 bedtime stories (10 psychotherapist-constructed and 10 AI-constructed) assigned to them at random. The AI-constructed stories were created using ChatGPT. Participants completed a questionnaire after reading each story, focusing on various aspects of the content. In a second step, the reviewers participated in a 30-minute interview for qualitative feedback. Results Analysis of 80 questionnaires from four reviewers revealed that 85% of all stories were correctly categorized, with 95% of AI stories identified as such. Expert stories were categorized correctly in 75% of the cases. However, results also show that AI-constructed stories lacked in sleep-related cognitions, emotions, and daytime consequences, but included sleep-related problems and recommendations. Reviewers noted similarities in story structure and repetition in AI-generated stories. Conclusion The need for prevention tools for sleep disturbances is urgent, with early interventions for different age groups essential. This pilot study showed that AI- and expert-constructed psychotherapeutic stories share similarities in addressing sleep-related problems; however, the latter seem to be superior regarding key elements of such stories. Future research should involve a wider range of professionals and parents. Ultimately, tailored stories may play a key role in early interventions for children’s sleep health.
Malnutrition is a growing public health problem leading to increased morbidity and mortality worldwide. Up to 50% of elderly patients are hospitalized due to this condition. In this review, we focused on analyzing the current diagnostic criteria for malnutrition among the elderly population and proposing promising solutions. Currently used diagnostic methods such as BMI or serum albumin levels are not sufficient to indicate malnutrition, which is affected by many factors, including the number of chronic diseases, multiple medications taken, or physical condition. Moreover, current recommendations are inadequate because they fail to account for various factors such as chronic illnesses, multiple medications, and bodily changes that are crucial in diagnostic evaluations. There is a noticeable gap between these recommendations and actual clinical practice. Nevertheless, developing more precise, non-invasive biomarkers and personalized nutrition strategies has to be explored. One of these strategies we discuss in our review is multidisciplinary approaches that combine nutrition, physical activity, and psychosocial support. Addressing malnutrition among the elderly should rely on standardized protocols and personalized interventions to enhance their nutritional health and overall well-being.
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