Background: Although geriatric depression is prevalent, diagnosis using self-reporting instruments has limitations when measuring the depressed mood of older adults in a community setting. Ecological momentary assessment (EMA) by using wearable devices could be used to collect data to classify older adults into depression groups. Objective: The objective of this study was to develop a machine learning algorithm to predict the classification of depression groups among older adults living alone. We focused on utilizing diverse data collected through a survey, an Actiwatch, and an EMA report related to depression. Methods: The prediction model using machine learning was developed in 4 steps: (1) data collection, (2) data processing and representation, (3) data modeling (feature engineering and selection), and (4) training and validation to test the prediction model. Older adults (N=47), living alone in community settings, completed an EMA to report depressed moods 4 times a day for 2 weeks between May 2017 and January 2018. Participants wore an Actiwatch that measured their activity and ambient light exposure every 30 seconds for 2 weeks. At baseline and the end of the 2-week observation, depressive symptoms were assessed using the Korean versions of the Short Geriatric Depression Scale (SGDS-K) and the Hamilton Depression Rating Scale (K-HDRS). Conventional classification based on binary logistic regression was built and compared with 4 machine learning models (the logit, decision tree, boosted trees, and random forest models). Results: On the basis of the SGDS-K and K-HDRS, 38% (18/47) of the participants were classified into the probable depression group. They reported significantly lower scores of normal mood and physical activity and higher levels of white and red, green, and blue (RGB) light exposures at different degrees of various 4-hour time frames (all P<.05). Sleep efficiency was chosen for modeling through feature selection. Comparing diverse combinations of the selected variables, daily mean EMA score, daily mean activity level, white and RGB light at 4:00 pm to 8:00 pm exposure, and daily sleep efficiency were selected for modeling. Conventional classification based on binary logistic regression had a good model fit (accuracy: 0.705; precision: 0.770; specificity: 0.859; and area under receiver operating characteristic curve or AUC: 0.754). Among the 4 machine learning models, the logit model had the best fit compared with the others (accuracy: 0.910; precision: 0.929; specificity: 0.940; and AUC: 0.960). Conclusions: This study provides preliminary evidence for developing a machine learning program to predict the classification of depression groups in older adults living alone. Clinicians should consider using this method to identify underdiagnosed subgroups and monitor daily progression regarding treatment or therapeutic intervention in the community setting. Furthermore, more efforts are needed for researchers and clinicians to diversify data collection methods by using a survey, EMA, and a sensor.

The coronavirus pandemic highlights the urgent need for increased support related to mental health concerns. This study aimed to synthesize the findings of empirical studies reporting the post‐traumatic stress symptoms in the general population during the coronavirus pandemic. Whittemore and Knafl’s (Journal of Advanced Nursing, 52, 546, 2005) integrative review methodology was used to analyse and synthesize the peer‐reviewed studies. Five electronic databases, PubMed, CINAHL, PsychINFO, Cochrane and Google Scholar were searched using terms related to the coronavirus pandemic and post‐traumatic stress symptoms. The quality of the studies was screened and evaluated using the Mixed Methods Appraisal Tool. The sample size of the 16 studies included in this review ranged from 41 to 3480 participants, with a total of 18 039 participants. The majority of the participants’ ages ranged from 30 to 39 years, and 57% of the participants were female. The following factors related to post‐traumatic stress symptoms during the coronavirus pandemic were identified as follows: (i) risk factors included social discrimination, fear of uncontrolled contagion and financial burden or economic instability; and, (ii) protective factors included social support and timely government action. A traumatic experience itself can trigger the onset of post‐traumatic stress disorder; however, depending on the risk and protection factors, each individual can experience different post‐traumatic stress symptoms. Thus, mental health nurses should comprehensively understand how to reduce the influence of risk factors and enhance protective factors when dealing with the pandemic and related trauma. This study’s findings are beneficial for identifying, preventing and managing post‐traumatic stress symptoms associated with the coronavirus and future pandemics.

Geriatric depression is a societal problem, specifically in those living alone in Korea. This study aims are to investigate (1) how sociodemographic factors, health status, and health behaviors are differently associated with depressive symptoms in older Koreans living alone compared to those living with others and (2) how living arrangements attenuated or strengthened the associations between four types of health behaviors and depressive symptoms. This secondary data analysis was conducted using data from the 2014 Korean Longitudinal Study of Aging. A structured survey assessing sociodemographic factors, health status, and health behaviors was conducted with people aged 65 or older who lived alone (n = 1359) and living with others (n = 2864). A multiple linear regression with interaction terms was conducted between mean-centered health behaviors and the status of living alone. All statistical analyses were performed using SPSS Statistics 23.0, and the two-tailed level of significance was set at 0.05. Those living alone reported higher levels of depressive symptoms than those living with others (Mdiff = 2.129, SE = 0.005, p < 0.001). The variance of depressive symptoms explained by 13 variables was 18.1% for those living alone compared to 23.7% for those living with others. Compared to health behaviors, sociodemographic factors and health status more explained depressive symptoms, specifically with psychiatric disorders, pain, and impaired functionality as risk factors. Smoking, alcohol abstinence, physical inactivity, and social inactivity were associated with more depressive symptoms. Living arrangements moderated the association between depressive symptoms and each health behavior, except for physical inactivity (all p values < 0.001). Older Koreans living alone were exposed to different risk factors for depressive symptoms compared to those living with others. Non-modifiable sociodemographic and health status factors were highly associated with depressive symptoms relative to health behaviors; thus, it is important to conduct early assessment and classification of vulnerable subgroups regarding geriatric depression. Specific assessment instruments should be prepared in practice according to living arrangements among older Koreans. Targeted interventions are essential to addressing living arrangements and modifying health behaviors to reduce smoking, alcohol consumption, and social inactivity, specifically in those living alone.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: In Korea, most self-management of chronic diseases are trained through face-to-face education primarily at healthcare setting. Therefore, older Koreans living in the community continue to seek other training opportunities to learn chronic disease management using new training methods, such as mobile health (mHealth), which are valuable in community settings. Objective: This feasibility study (1) developed an mHealth training protocol to empower older adults to manage their chronic diseases at home, (2) examined the feasibility of the mHealth training protocol using mobile tablets and applications (apps), and (3) discussed contextual and methodological challenges when applying an mHealth training protocol for older Koreans with chronic diseases. Methods: The mHealth training protocol was developed based on the eHealth Enhanced Chronic Care Model and consisted of four phases. Phase 1 included mobile tablet training and standardized technology training with guidebooks, demonstrations, and guided practice. Phase 2 included standardized information for disease management retrieved from both governmental and professional healthcare organizations. Phase 3 included training to use the pre-selected high-quality mHealth apps to consider types of individuals’ chronic disease. Phase 4 included encouragement to practice self-selected mHealth apps based on their individual needs. Quantitatively descriptive and qualitative content analyses of user evaluations were completed to assess feasibility and user acceptance. Results: Among the 27 older adults, 25 completed mHealth training for 4 weeks. The attrition rate was 7.4% (2/27), due to time conflicts, emotional distress, and/or family discouragement. The men required little or no training for Phase 1, while the women seemed to depend more on the mHealth trainers for Phase 3 than men. At least 4 weeks of weekly meetings were required to complete the training with mHealth training protocol. Gender, education level, and previous experience using smartphones were associated with learning speed, confidence, and competence. Conclusions: A tailored and personalized approach is required to develop mHealth training protocols for older adults. Self-management of chronic diseases via mHealth training requires careful consideration regarding the complex nature of human behavior, emotional response, and family influence; therefore, integrating a theoretical-clinical-technical approach is necessary for the development and implementation of mHealth training targeting older adults with chronic diseases in a community setting.

The Swedish Occupational Fatigue Inventory (SOFI) has been tested in different languages and populations; thus, there is a need for a culturally adapted Korean version. We evaluated the psychometric properties of a Korean version of the SOFI among construction workers. The SOFI was translated into Korean and reviewed through a back-translation process involving standardized scaling procedures. Its reliability and validity were evaluated with a sample of 193 construction workers using internal consistency, item–subscale correlations, test–retest reliability, and content, construct, and concurrent validity. The Cronbach’s alpha coefficients of the total scale and each subscale were satisfactory. Item–subscale correlations and test–retest reliability were both at acceptable levels. Confirmatory factor analyses revealed that the five-factor model had acceptable model fits corresponding to the structure of the original instrument. However, some modifications were made to improve in the new context from model fit (such as χ²(95) = 113.905 (p = 0.091), CFI = 0.994, and RMSEA = 0.033, as well as the lowest AIC = 383.905). Correlation analysis showed a significant relationship of SOFI with other fatigue measures in terms of total and subscale scores. Occupational fatigue is one of the important risk factors associated with workers’ health and safety at work. The new translated instrument is a reliable and valid tool for assessing fatigue among Korean construction workers. However, this instrument should be tested extensively in other working populations to devise specific interventions concerning fatigue reduction.