Sürdürülebilir Kalkınma Amaçları
Yazarlar
Yayınlar
- MakaleAçık Erişim2026The Effect of a Psychiatric Nurse–Led AI-Based Online Group Guided Self-Help Program on Eating Behaviours and Body Image among Overweight and Obese Women Nurses: A Randomized Controlled Trial
Tuncer, Gülsüm Zekiye; Duman, Zekiye Çetinkaya; Tuncer, Metin
Aim: This study aimed to evaluate the effectiveness of a psychiatric nurse–led, AI-supported online group guided self-help programme on eating behaviours, body image, and body mass index (BMI) among overweight and obese women nurses. Method: A randomised controlled experimental design with pre-test, post-test, and three-month follow-up assessments was employed. Forty-four overweight and obese women nurses were randomly assigned to intervention (n = 22) and control (n = 22) groups. The intervention consisted of an 8-week online group guided self-help programme supported by a large language model (LLM), under the leadership of a psychiatric nurse. Participants did not interact directly with the LLM; all AI-generated feedback was clinically reviewed and delivered by the researcher. Results: The intervention group demonstrated significant reductions in emotional eating and external eating, along with significant improvements in body image scores and adaptive coping strategies. A significant decrease in BMI was also observed from baseline to three-month follow-up. Between-group comparisons at post-test and follow-up indicated statistically significant differences favouring the intervention for eating behaviours and selected body image–related outcomes, whereas between-group differences in BMI were not statistically significant. Conclusion: A multi-component online group guided self-help intervention incorporating structured AI-assisted feedback under psychiatric nurse leadership may represent a promising approach to improving eating behaviours, body image, and weight-related outcomes among overweight and obese women nurses.
- MakaleAçık Erişim2021Modal control of cantilever beam using a gyrostabilizer
Çuvalci, Olkan; Ünker, Faruk; Baturalp, Turgut Batuhan; Gülbulak, Utku; Ertas, Atila
In this paper, an experimental model of a horizontal cantilever beam with a rotating/oscillating attached to the shaker for harmonic excitation at the one end and a gyrostabilizer at the other end is built to verify the equations of the Lagrangian model. The primary focus of the study was to investigate the parameters of excitation amplitude, natural frequency, rotating mass (disk mass), and disk speed of gyro that would minimize the amplitude of the beam to identify these effects. Numerical and experimental results indicate that the angular momentum of the gyrostabilizer is the most effective parameter in the reduction of beam displacement.
- MakaleAçık Erişim2020Machine learning and classical forecasting methods based decision support systems for covid-19
Ünlü, Ramazan; Namli, Ersin
From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory (LSTM) forecasting models are applied to the novel COVID-19 dataset. According to the results, the Prophet model gives the lowest Root Mean Squared Error (RMSE) score compared to the other three models. Besides, according to this model, a projection for the future COVID-19 predictions of Turkey has been drawn and aimed to shape the current measures against the coronavirus.
- DergiInternational Journal of Mental Health Promotion
- DergiComputers, Materials and Continua
- DergiSound and Vibration