Rationale and Objectives: To retrospectively analyze the chest imaging findings in patients with coronavirus disease 2019 (COVID-19) on thin-section CT.Materials and Methods: Fifty-three patients with confirmed COVID-19 infection underwent thin-section CT examination. Two chest radiologists independently evaluated the imaging in terms of distribution, ground-glass opacity (GGO), consolidation, air bronchogram, stripe, enlarged mediastinal lymph node, and pleural effusion.Results: Fourty-seven cases (88.7%) had findings of COVID-19 infection, and the other six (11.3%) were normal. Among the 47 cases, 78.7% involved both lungs, and 93.6% had peripheral infiltrates distributed along the subpleural area. All cases showed GGO, 59.6% of which were round and 40.4% patchy. Other imaging features included "crazy-paving pattern" (89.4%), consolidation (63.8%), and air bronchogram (76.6%). Air bronchograms were observed within GGO (61.7%) and consolidation (70.3%). Neither enlarged mediastinal lymph nodes nor pleural effusion were present. Thirty-three patients (62.3%) were followed an average interval of 6.2 § 2.9 days. The lesions increased in 75.8% and resorbed in 24.2% of patients.Conclusion: COVID-19 showed the pulmonary lesions in patients infected with COVID-19 were predominantly distributed peripherally in the subpleural area.

Background Individuals with mild cognitive impairment and dementia have impaired physical and cognitive functions, leading to a reduced quality of life compared with those without such impairment. Exergaming, which is defined as a combination of exercise and gaming, is an innovative, fun, and relatively safe way to exercise in a virtual reality or gaming environment. Therefore, exergaming may help people living with mild cognitive impairment or dementia to overcome obstacles that they may experience regarding regular exercise and activities. Objective The aim of this systematic review was to review studies on exergaming interventions administered to elderly individuals with mild cognitive impairment and dementia, and to summarize the results related to physical and cognitive functions such as balance, gait, executive function, and episodic memory. Methods We searched Cochrane Central Register of Controlled Trials (CENTRAL), Medline, Embase, PsycINFO, Amed, and Nursing Database for articles published from the inception of the respective databases to January 2019. We included all clinical trials of exergaming interventions in individuals with mild cognitive impairment and dementia for review. The risk of bias was independently evaluated by two reviewers using the Cochrane Collaboration and Risk of Bias in Non-randomized Studies of Interventions tools. Results Ten studies involving 702 participants were included for review. There was consistent evidence from 7 studies with a low risk of bias showing statistically significant effects of exergaming on cognitive functioning in people with mild cognitive impairment and dementia. With respect to physical function, 3 of 5 full-scale studies found positive results, and the intensity of most games was classified as moderate. Conclusions Overall, exergaming is an innovative tool for improving physical and cognitive function in people with mild cognitive impairment or dementia, although there is high heterogeneity among studies in terms of the duration, frequency, and gaming platform used. The quality of the included articles was moderate to high. More high-quality studies with more accurate outcome indicators are needed for further exploration and validation of the benefits of exergaming for this population.

BackgroundType 2 diabetes mellitus (T2DM) is a major public health burden. Self-management of diabetes including maintaining a healthy lifestyle is essential for glycemic control and to prevent diabetes complications. Mobile-based health data can play an important role in the forecasting of blood glucose levels for lifestyle management and control of T2DM.ObjectiveThe objective of this work was to dynamically forecast daily glucose levels in patients with T2DM based on their daily mobile health lifestyle data including diet, physical activity, weight, and glucose level from the day before.MethodsWe used data from 10 T2DM patients who were overweight or obese in a behavioral lifestyle intervention using mobile tools for daily monitoring of diet, physical activity, weight, and blood glucose over 6 months. We developed a deep learning model based on long short-term memory–based recurrent neural networks to forecast the next-day glucose levels in individual patients. The neural network used several layers of computational nodes to model how mobile health data (food intake including consumed calories, fat, and carbohydrates; exercise; and weight) were progressing from one day to another from noisy data.ResultsThe model was validated based on a data set of 10 patients who had been monitored daily for over 6 months. The proposed deep learning model demonstrated considerable accuracy in predicting the next day glucose level based on Clark Error Grid and ±10% range of the actual values.ConclusionsUsing machine learning methodologies may leverage mobile health lifestyle data to develop effective individualized prediction plans for T2DM management. However, predicting future glucose levels is challenging as glucose level is determined by multiple factors. Future study with more rigorous study design is warranted to better predict future glucose levels for T2DM management.

Establishing a conclusive positive or negative association between BMD and SES proved to be difficult. However, individuals who are at an extreme SES disadvantage are the most vulnerable to have relatively low BMD in the study population. Efforts to promote bone health may benefit from focusing on men with low education levels and women with low individual income.

Background Self-monitoring is key to successful behavior change in diabetes and obesity, and the use of traditional paper-based methods of self-monitoring may be time-consuming and burdensome. Objective This study aimed to explore participant experiences while using technology-assisted self-monitoring of lifestyle behaviors and health indicators among overweight or obese adults with type 2 diabetes. Methods Qualitative data collected from the intervention group of a 6-month, three-arm (control, paper diary, and technology-assisted self-monitoring groups) randomized clinical trial were analyzed. Study participants in the intervention group monitored their diet, exercise, and weight using the LoseIt! app, and their blood glucose levels using a glucometer and the Diabetes Connect app. Semistructured group discussions were conducted at 6 weeks (n=10) from the initiation of the behavioral lifestyle intervention and again at 6 months (n=9). All group interviews were audiotaped and transcribed verbatim. Using a combination of thematic and comparative analysis approaches, two trained professionals coded the transcriptions independently and then discussed and concluded common themes for the 6-week and 6-month discussions separately. Results The sample (n=10), which primarily involved African American participants (n=7) and female participants (n=8), had a mean age of 59.4 years. The following eight themes emerged: (1) perceived benefits of technology-assisted self-monitoring; (2) perceived ease of use (eg, barriers: technical difficulties and lack of self-discipline; facilitators: help from family, friends, and the program); (3) use of technology-assisted self-monitoring; (4) facilitators of engaging in healthy lifestyle behaviors (eg, visualization and awareness of calorie input/expenditure); (5) positive lifestyle change; (6) barriers of engaging in healthy lifestyle behaviors (eg, event influence); (7) learning curve; and (8) monitored data sharing. The first six of these themes were shared between the 6-week and 6-month timepoints, but the codes within these themes were not all the same and differed slightly between the two timepoints. These differences provide insights into the evolution of participant thoughts and perceptions on using technology for self-monitoring and subsequent behavioral lifestyle changes while participating in lifestyle interventions. The findings from the 6-week and 6-month data helped to paint a picture of participant comfort and the integration of technology and knowledge overtime, and clarified participant attitudes, difficulties, behavioral processes, and modifications, as well as health indicators that were experienced throughout the study. Conclusions Although there were some barriers, participants were able to identify various individual and external facilitators to adjust to and engage in technology-assisted self-monitoring, and it was concluded that the technology-assisted self-monitoring approach was beneficial, safe, and feasible to use for positive lifestyle change. These patient perspectives need to be considered in future research studies when investigating the effectiveness of using technology-assisted self-monitoring, as well as in clinical practice when recommending technology-assisted self-monitoring of lifestyle behaviors and health indicators to improve health outcomes.

Background An increasing number of mobile and wearable devices are available in the market. However, the extent to which these devices can be used to assist older adults to age in place remains unclear. Objective This study aimed to assess older adults’ perceptions of using mobile and connected health technologies. Methods Using a cross-sectional design, a total of 51 participants were recruited from a senior community center. Demographics and usage of mobile or wearable devices and online health communities were collected using a survey questionnaire. Descriptive statistics assessed usage of devices and online health communities. The Fisher exact test was used to examine the relationship between technology usage and having access to a smartphone. Results The sample was primarily comprised non-Hispanic white (35/51, 69%), educated (39/51, 76% any college), and female (36/51, 71%) participants, with an average age of 70 (SD 8) years. All participants were insured and nearly all lived at home (49/51, 94%). A total of 86% (44/51) of the participants had heard of wearable health devices, but only 18 out of 51 (35%) had ever used them. Over 80% (42/51) expressed interest in using such devices and were interested in tracking exercise and physical activity (46/51, 90%), sleep (38/51, 75%), blood pressure (34/51, 67%), diet (31/51, 61%), blood sugar (28/51, 55%), weight (26/51, 51%), and fall risk (23/51, 45%). The greatest concerns about using wearable devices were cost (31/51, 61%), safety (14/51, 28%), and privacy (13/51, 26%); one-fourth (12/51) reported having no concerns. They were mostly interested in sharing data from mobile and connected devices with their health care providers followed by family, online communities, friends, and no one. About 41% (21/51) of the older adults surveyed reported having ever heard of an online health community, and roughly 40% (20/51) of the participants reported being interested in joining such a community. Most participants reported having access to a smartphone (38/51, 74%), and those with such access were significantly more likely to show interest in using a wearable health device ( P <.001) and joining an online health community ( P =.05). Conclusions Our findings suggest that, although few older adults are currently using mobile and wearable devices and connected health technologies for managing health, they are open to this idea and are mostly interested in sharing such data with their health care providers. Further studies are warranted to explore strategies to balance the data sharing preference of older adults and how to best integrate mobile and wearable device data with clinical workflow for health care providers to promote healthy aging in place.

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Health disparities exist among older adult populations; the combined effects of minority and immigrant status can be approximated from the results in this study. Health care accessibility and the quality of care should be promoted in minority/immigrant populations. Public health nurses have a strong potential to aide in reducing health disparities among an aging American population that continues to exhibit increasing racial/ethnic diversity.