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Sleep disturbances are common across all age groups, and they encompass a broad range of impairments of daytime functioning and comorbid various clinical conditions. However, current treatment methods for sleep disturbances have several limitations. As the ‘top-down’ pathway is known to play an important role in sleep-wake regulation, and as neuronal activity abnormalities have been reported as a potential pathological mechanism of sleep disturbances, the use of non-invasive brain stimulation—such as transcranial direct current stimulation (tDCS) in treating sleep disturbances—has emerged. In the present review, we first explain the mechanism of tDCS, and we also introduce recent studies that have applied tDCS to sleep disorders, along with other sleep-related tDCS studies. In conclusion, many studies have achieved improvements in sleep state, although some of these studies have reported inconsistent effects of tDCS according to the protocol and the conditions used. Further studies are needed to explore the optimal protocols to use when applying tDCS in each sleep disturbance and to enhance the evidence on the clinical efficacy of tDCS.

Objective: Internet addiction and smartphone addiction are emerging mental health concerns in adolescents. To determine the factors related to these conditions, we investigated their associations with sleep quality, chronotype, sleep patterns, daylight exposure, depression, and anxiety.Methods: Self-rated questionnaires, including the Internet Addiction Proneness Scale-Short Form, Smartphone Addiction Scale–short version, Insomnia Severity Index, Morningness–Eveningness Questionnaire, Epworth Sleepiness Scale, sleep-related questionnaires, and Hospital Anxiety and Depression Scale, were administered in a study population of 1,610 Korean high school students. Multiple linear regression analyses were performed to evaluate the associations with internet addiction and smartphone addiction.Results: Multiple linear regression showed significant associations of internet addiction with poor sleep quality (β=0.094, p

Seohyun Lee,1 Young-Ji Lee,2 SeYeon Kim,2 Wonil Choi,2 Yoojin Jeong,2 Nina Jinjoo Rhim,3 Ilwon Seo,4 Sun-Young Kim2,5 1Department of Global Public Administration, Yonsei University Mirae Campus, Wonju, Republic of Korea; 2Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea; 3Good Neighbors International, Seoul, Republic of Korea; 4Good Neighbors Ghana, Accra, Ghana; 5Institute of Health and Environment, Seoul National University, Seoul, Republic of KoreaCorrespondence: Sun-Young Kim, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea, Tel +82-2-880-2768, Fax +82-2-762-2888, Email sykim22@snu.ac.krPurpose: An electronic health record (EHR) system known as the e-Tracker was introduced in community health facilities in Ghana and numerous advantages were expected for clinical staff, as evidenced by previous literature. However, little is known about public health officials’ views, specifically in low-resource settings. This study aimed to investigate the perceptions of district health officers on data quality, use, and management following the adoption of tablet-based electronic health records in Ghana.Methods: A pre- and post-survey was conducted in two regions of Ghana that adopted the e-Tracker for the entire districts during the early stages of the national rollout. Sociodemographic information, internet connection environment, and perceptions on data quality, use, and management were measured. McNemar’s test and Wilcoxon Signed-Rank test were performed to identify changes in perceptions. Chi-square test and Mann–Whitney U-test were used to find any statistical differences in demographic characteristics between the two regions.Results: Twenty-four out of 25 districts in Volta and 24 out of 26 districts in Eastern regions participated in both pre- and post-surveys, with a total of 73 participants. In terms of efficiency in data management, the district health officers reported reduced time commitment in data validation and aggregation. Within less than a year, however, no statistically significant improvement was found in data quality and the use of electronic data for relevant tasks.Conclusion: A new EHR system in low-resource settings can rapidly improve efficiency in data management from the public health officials’ perspectives. Further impact evaluation is warranted to assess the long-term effect of the EHR system.Keywords: mHealth, maternal and child health, Sub-Saharan Africa, LMIC

BACKGROUND Intimate partner violence (IPV) is an underreported public health crisis primarily affecting women associated with severe health conditions and a high rate of homicide. Due to the COVID-19 pandemic, more women with IPV experiences visited online health communities (OHCs) to seek help because of anonymity. However, little is known regarding whether their help requests were answered and whether information provided was delivered in an appropriate manner. To understand the help-seeking information sought and given in OHCs, extracting postings and linguistic features could be helpful to develop automated models to improve future help-seeking experiences. OBJECTIVE The objective of this study is to examine the types and patterns (i.e., communication styles) of the advice offered by OHCs members and whether the information received from women matched their expressed needs in their initial postings. METHODS We examined data from Reddit using data from subreddit community r/domestic violence posts from November 14, 2020, through November 14, 2021, during COVID-19. We included posts from women 18 years or older who self-identified/described experiencing IPV and requested advice or help in this subreddit community. Posts from non-abused women, women under 18, non-English posts, good news announcements, gratitude posts without any advice-seeking, and posts related to advertisements were excluded. We developed a codebook and annotated the postings in an iterative fashion. Original posts were also quantified using the Linguistic Inquiry Word Count (LIWC) to categorize linguistic and postings features. Postings were then classified into two categories (i.e., matched needs and unmatched needs) according to the types of help sought and received in OHCs to capture the help-seeking result. Non-parametric statistical analysis (i.e., t-test or Mann-Whitney U) were used to compare the linguistic/postings features between matched and unmatched needs. RESULTS A total of (N=250) postings were included, 80% (n=200) of posting response comments matched with the type of help requested in initial postings with legal advice and IPV knowledge achieving the highest matching rate. 17 linguistic or postings features were found to be significantly different between the two groups (i.e., matched help and unmatched help). Negative title sentiment and linguistic features in postings containing health and wellness wordings were associated with unmatched needs postings, while the other 14 features were associated with postings with matched needs. CONCLUSIONS OHCs can extract the linguistic and posting features to understand the help-seeking result among women with IPV experiences. Features identified in this corpus reflected the differences found between the two groups. This is the first study that leveraged LIWC to shed light on generating predictive features from unstructured text in OHCs, which could guide future algorithms development to detect help-seeking results within OHCs effectively. CLINICALTRIAL N/A

BACKGROUND Family caregivers of individuals with gynecologic cancer experience high levels of distress. Web based caregiver support interventions have demonstrated efficacy in improving caregiver outcomes. However, the lack of portability could be a limitation. mHealth apps could fill this gap and facilitate communication between patient-caregiver dyads. OBJECTIVE To obtain information on desired usage and features to be used to design an mHealth self-management support app targeting dyads. METHODS We conducted Zoom focus groups with women who had been treated for gynecologic cancers (ovarian, fallopian, primary peritoneal, uterine, endometrial, cervical, vulvar) and their self-identified closest support person. A semi-structured interview guide informed by the Consolidated Framework for Implementation Research (CFIR) was used to elicit information on intervention characteristics, outer setting, inner setting, characteristics of individuals, and processes relevant to app design and implementation. After transcription, rapid qualitative analysis using a thematic matrix was used to identify common themes across groups. RESULTS The final sample included 41 individuals with gynecologic cancer and 22 support persons/caregivers (total n=63). Patients were aged between 32-84 years old, and most (94%) were white and married. For caregivers, 15 individuals identified as male and 7 as female, with ages ranging between 19 and 81 years old. Almost 60% of CGs were spouses. Seven themes emerged related to our three topic areas: 1) Gaps in Information & Support: i) Finding relevant information is time-consuming and emotionally draining; ii) Caregivers and patients lack confidence in determining when, how, and from whom to obtain assistance with new or concerning problems; 2) Desired features of mhealth app: Caregivers and patients desire iii) centralized, trustworthy information; iv) timely recommendations tailored to specific personal and cancer related needs; v) opportunities to interact with clinical and peer experts through the app; 3) Interest and preferences for app usage: Caregivers and patients were interested in the app, but expressed needs to vi) have private space in the app for each member of the dyad; and vii) have control over sharing of information with other family members. CONCLUSIONS Designing a single mHealth app to be used by both members of the cancer dyad presents unique challenges for intervention designers and app developers. Results of the present study highlight the need for flexibility in app functionality and sharing information between members of the dyad. CLINICALTRIAL Not applicable.

Summary of 44 retained introns

Differential expression of 44 significant retained introns between races and four subtypes

Background Online health communities (OHCs) can be a source for clinicians to learn the needs of cancer patients and caregivers. Ovarian cancer (OvCa) patients and caregivers deal with a wide range of unmet needs, many of which are expressed in OHCs. An automated need classification model could help clinicians more easily understand and prioritize information available in the OHCs. Objective The aim of this study was to use initial OHC postings to develop an automated model for the classification of OvCa patient and caregiver needs. Methods We collected data from the OvCa OHC and analyzed the initial postings of patients and caregivers (n = 853). Two annotators coded each posting with 12 types of needs. Then, we applied the machine learning approach with bag-of-words features to build a model to classify needs. F1 score, an indicator of model accuracy, was used to evaluate the model. Results The most reported needs were information, social, psychological/emotional, and physical. Thirty-nine percent of postings described information and social needs in the same posting. Our model reported a high level of accuracy for classifying those top needs. Psychological terms were important for classifying psychological/emotional and social needs. Medical terms were important for physical and information needs. Conclusions We demonstrate the potential of using OHCs to supplement traditional needs assessment. Further research would incorporate additional information (eg, trajectory, stage) for more sophisticated models. Implications for practice This study shows the potential of automated classification to leverage OHCs for needs assessment. Our approach can be applied to different types of cancer and enhanced by using domain-specific information.

In metastatic breast cancer (MBC), positive estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status allow for more long-term, sequential treatment options compared to ER-negative and HER2-negative diseases. It is unclear if end-of-life care (timely integration of palliative care, discontinuation of chemotherapy, and enrollment into hospice) in MBC is now tailored to the ER and HER2 status.This article explores the association between ER and HER2 status and the quality of end-of-life care received among patients with MBC.A 20-year MBC clinical database captured demographics, tumor characteristics, and treatment histories of deceased patients with MBC (N = 1,258) at a tertiary hospital located in Pittsburgh, Pennsylvania. Descriptive and inferential statistics were used.Patients with ER-positive MBC had greater odds of receiving quality end-of-life care than those with ER-negative MBC. HER2 status was not associated with differences in the quality of end-of-life care.

Background: Without specific attention to health equity considerations in design, implementation, and evaluation, the rapid expansion of digital health approaches threatens to exacerbate rather than ameliorate existing health disparities. Methods: We explored known factors that increase digital health inequity to contextualize the need for equity-centered informatics. This work used a narrative review method to summarize issues about inequities in digital health and to discuss future directions for researchers and clinicians. We searched literature using a combination of relevant keywords (e.g., “digital health”, “health equity”, etc.) using PubMed and Google Scholar. Results: We have highlighted strategies for addressing medical marginalization in informatics according to vectors of power such as race and ethnicity, gender identity and modality, sexuality, disability, housing status, citizenship status, and criminalization status. Conclusions: We have emphasized collaboration with user and patient groups to define priorities, ensure accessibility and localization, and consider risks in development and utilization of digital health tools. Additionally, we encourage consideration of potential pitfalls in adopting these diversity, equity, and inclusion (DEI)-related strategies.

Objective: Resilience-promoting program which covers strategies to improve sleep health through modulation of sleep-related habits has been lacking. The authors aimed to develop a resilience-promoting program that incorporates the encouragement of healthy sleep habits. Methods: Fifty-eight adolescents (27 female, 46.6%; aged 13.78±0.82 years) were recruited to test the effectiveness of the program. Dur- ing the first and final sessions, several self-reported questionnaires, including the Connor-Davidson Resilience Scale (CD-RISC), the Smart- phone Addiction Scale–short version (SAS-SV), Hospital Anxiety and Depression Scale (HADS), Composite Scale of Morningness (CSM), In- somnia Severity Index (ISI), and a sleep habit-related questionnaires were administered to the adolescents. After the first session, adolescents were provided with an Actiwatch and were asked to wear the watch to the last session date. Multiple linear regression analyses were used to identify predictors of resilience and sleep quality.Results: The participants’ resilience, morningness, sleep quality, wake after sleep onset (WASO), and wakening frequency during the night changed significantly. Resilience at the last session was related to sleep quality (β=-0.394, p=0.005) at the last session when controlling for age, sex, and morningness. Sleep quality at the last session was related to the mean sun- light exposure time (β=-0.363, p=0.037) at the last session when controlling for age, sex, and WASO during the third week.Conclusion: This study found that four weekly sessions of resilience-promoting program improved resilience, sleep quality including objective actigraphic measures, and morningness. Improved resilience was related to better sleep quality.

Domestic violence (DV) is a public health crisis that threatens both the mental and physical health of people. With the unprecedented surge in data available on the internet and electronic health record systems, leveraging machine learning (ML) to detect obscure changes and predict the likelihood of DV from digital text data is a promising area health science research. However, there is a paucity of research discussing and reviewing ML applications in DV research. Methods: We extracted 3588 articles from four databases. Twenty-two articles met the inclusion criteria. Results: Twelve articles used the supervised ML method, seven articles used the unsupervised ML method, and three articles applied both. Most studies were published in Australia (n = 6) and the United States (n = 4). Data sources included social media, professional notes, national databases, surveys, and newspapers. Random forest (n = 9), support vector machine (n = 8), and naïve Bayes (n = 7) were the top three algorithms, while the most used automatic algorithm for unsupervised ML in DV research was latent Dirichlet allocation (LDA) for topic modeling (n = 2). Eight types of outcomes were identified, while three purposes of ML and challenges were delineated and are discussed. Conclusions: Leveraging the ML method to tackle DV holds unprecedented potential, especially in classification, prediction, and exploration tasks, and particularly when using social media data. However, adoption challenges, data source issues, and lengthy data preparation times are the main bottlenecks in this context. To overcome those challenges, early ML algorithms have been developed and evaluated on DV clinical data.