Notes

Vast deviation associated with winter-induced continual winter power dissipation inside conifers: a common-garden examine.
was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation.
This study supports utilizing HRV alongside an accelerometer as a means for distinguishing sleep from wake and for identifying sleep stages. The Firstbeat method was able to detect light sleep and slow wave sleep with no statistically significant difference to PSG. Firstbeat underestimated REM sleep and overestimated wake time. This study suggests that Firstbeat is a feasible method with sufficient validity to measure nocturnal sleep stage variation.
Evaluation of patients with serious mental illness (SMI) relies largely on patient or caregiver self-reported symptoms. New digital technologies are being developed to better quantify the longitudinal symptomology of patients with SMI and facilitate disease management. However, as these new technologies become more widely available, psychiatrists may be uncertain about how to integrate them into daily practice. To better understand how digital tools might be integrated into the treatment of patients with SMI, this study examines a case study of a successful technology adoption by physicians endocrinologists' adoption of digital glucometers.

This study aims to understand the key facilitators of and barriers to clinician and patient adoption of digital glucose monitoring technologies to identify lessons that may be applicable across other chronic diseases, including SMIs.

We conducted focus groups with practicing endocrinologists from 2 large metropolitan areas using a semistructured discussion guide desi management, streamlined software, and standardized metrics.
Specifying the determinants of using health apps has been an important research topic for health scholars as health apps have proliferated during the past decade. Socioeconomic status (SES) has been revealed as a significant determinant of using health apps, but the cognitive mechanisms underlying the relationship between SES and health app use are unknown.

This study aims to examine the cognitive mechanisms underlying the relationships between SES and use of health apps, applying the integrative model of behavioral prediction (IM). The model hypothesizes the indirect influences of SES on intentions to use health apps, which in turn predict actual use of health apps. The relationships between SES and intentions to use health apps were assumed to be mediated by proximal variables (attitudes, perceived behavioral control [PBC], injunctive norms, and descriptive norms).

We conducted path analyses using data from a two-wave opt-in panel survey of Korean adults who knew about health apps. The number of respoe.

We found that PBC over using health apps may be the most important factor in predicting health app use. This suggests the necessity of designing and promoting health apps in a user-friendly way. Our findings also imply that socioeconomic inequalities in using health apps may be reduced by increasing positive attitudes toward, and boosting PBC over, health app use among individuals with low income.
We found that PBC over using health apps may be the most important factor in predicting health app use. This suggests the necessity of designing and promoting health apps in a user-friendly way. Our findings also imply that socioeconomic inequalities in using health apps may be reduced by increasing positive attitudes toward, and boosting PBC over, health app use among individuals with low income.
Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients.

This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients.

PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram-CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices.

PPGs from 100 patients were used to develop the spectrogram-CNN index. Cobimetinib When there was pain, the mean (95% CI) spectrogram-CNN index value increased significantly-baseline 28.5 (24.2-30.7) versus recovery area 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram-CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%.

Although there were limitations to the study design, we confirmed that the spectrogram-CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram-CNN index's feasibility and prevent overfitting to various populations, including patients under general anesthesia.

Clinical Research Information Service KCT0002080; https//cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638.
Clinical Research Information Service KCT0002080; https//cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638.
In recent years, digital tools have become a viable means for patients to address their health and information needs. Governments and health care organizations are offering digital tools such as self-assessment tools, symptom tracking tools, or chatbots. Other sources of digital tools, such as those offered through patient platforms, are available on the internet free of charge. We define patient platforms as health-specific websites that offer tools to anyone with internet access to engage them in their health care process with peer networks to support their learning. Although numerous social media platforms engage users without up-front charges, patient platforms are specific to health. As little is known about their business model, there is a need to understand what else these platforms are trying to achieve beyond supporting patients so that patients can make informed decisions about the benefits and risks of using the digital tools they offer.

The aim of this study is to explore what patient platforms are trying to achieve beyond supporting patients and how their digital tools can be used to generate income.
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