Background: Emotional health plays a fundamental role in quality of life, particularly after the COVID-19 pandemic, which has increased stress and anxiety, especially among children and young people.
Objective: This study aimed to focus on the early identification of emotional processes that affect individuals' well-being and their effective management.
Methods: The open-source web app HealthTest was developed to help users understand and manage their emotions through tests focused on aspects such as stress, anxiety, and depression. The Open Source Scrum (OSCRUM) framework was used to optimize collaboration and effectively achieve objectives.
Results: HealthTest has established itself as a valuable tool for mental health professionals by gathering data from seventh-semester software engineering students and external users. It identifies trends in stress, anxiety, and depression through user self-assessments. In addition, it provides meditation and relaxation resources designed to support users in managing their emotional well-being.
Conclusions: This study promotes accessibility to self-care and health care tools. HealthTest reaffirms its commitment to benefiting both mental health professionals and patients, providing an effective avenue for controlling and improving emotional well-being.
Background: Socioeconomic and environmental factors influence youth mental well-being. Promoting mental well-being is essential to support youths' development toward adulthood with good mental health. Different Stockholm municipalities have adopted strategies to promote youth well-being. However, contextualizing and perceiving goals and mechanisms at the local municipal level is difficult. Thus, comparing or tracking their conception, purpose, and characteristics has been challenging.
Objective: We aimed to use data visualizations developed from a fusion of data sources to facilitate stakeholder conversations on promoting youth mental well-being within a municipality. We strive to demonstrate our methodology of using data visualizations as "boundary objects," which are cognitive artifacts that bridge knowledge from various domains to elicit understanding from specialized and siloed parts of a health delivery system.
Methods: Stakeholders from the municipalities of Lidingö and Nynäshamn participated in the study. A total of 15 workshops were conducted: 6 with only Lidingö participants, 6 with only Nynäshamn participants, and 3 with mixed participants. The sessions were conducted via Microsoft Teams or as physical sessions in Swedish and lasted between 60 and 90 minutes. Interactions were recorded with consent from participants. Recordings were transcribed using Amberscript software. We used matrix factorization with Kullback-Leibler divergence to extract 1000 features and created 10 topic clusters with 20 top words. We used the identified words and phrases to backtrack within the transcripts and to identify dialogues where they were used. We summarized participants' interactions across all the workshops to identify factors or strategies discussed for youth well-being.
Results: Participants noted that these sessions allowed them to contextualize their local observations from municipalities relative to the status of other municipalities in the national statistics. They indicated that they conceptualized well-being differently in their respective municipalities and between different professional backgrounds, and the sources of stress for youth differed. They noted the differences in the strategy and data collected for tracking youth well-being. Promotion of sports was a common strategy, while options for leisure activities differed between municipalities and professions.
Conclusions: Based on our observations and analysis of the transcripts from participatory workshops, we observed that the data-driven visualizations helped stakeholders from different departments of Lidingö and Nynäshamn municipalities to identify and bridge knowledge gaps caused by data silos. Participants noted proposals to modify future surveys and identified that this approach to visualizations would help them to share knowledge and maintain a long-term and sustainable
Background: In 2023, Cayuga County, a rural county in New York State, developed and published a publicly available, interactive overdose dashboard highlighting demographic, geographic, and time trends in suspected overdoses as well as substance use-related resources in the community. Despite the widespread use of data dashboards in the overdose crisis, there is little evidence to suggest that these dashboards can effectively disseminate data and enable public health data-driven decision-making, especially in a rural county. We conducted an evaluation of the Cayuga County Overdose Data Dashboard to fill this knowledge gap.
Objective: Our study aimed to evaluate the Cayuga County Overdose Data Dashboard's acceptability, use, and perceived effectiveness in disseminating overdose data and resources.
Methods: Following the launch of the dashboard, an online Qualtrics survey collected feedback from individuals older than 18 years of age living or working in Cayuga County, asking respondents to reflect upon their experience using the dashboard. The 10-minute survey assessed usage patterns and motivations to access the dashboard as well as the dashboard's ease of use, most valued design features, and overall perceived effectiveness in communicating information on overdoses and local resources. Data were analyzed using descriptive statistics.
Results: From May to December 2023, a total of 61 individuals from Cayuga County completed the survey, including those with lived substance use experience (n=8, 13%) as well as their close contacts (n=28, 46%), health care providers (n=12, 20%), law enforcement (n=11, 18%), and local public health and mental health care professionals (n=27, 44%). The user-friendly design and frequent updates facilitate engagement, as 54% (n=33) of respondents reported accessing the dashboard at least monthly and 75% (n=46) using it to inform decision-making. Most thought that the dashboard was easy to use (n=59, 97%) and very effective in disseminating information (n=46, 76%). From the 8 different types of overdose-related information portrayed on the dashboard, the most valued were the locations of treatment and recovery services, scoring an average of 4.75 (SD 0.65) on a 5-point scale (1="Not important" to 5="Most important"), followed by the locations of free, publicly accessible Naloxone (mean 4.58, SD 0.89) and trends in fatal and nonfatal overdoses (mean 4.48, SD 0.81).
Conclusions: Overall, this study suggests that the Cayuga County Overdose Data Dashboard effectively disseminates information and enables data-driven decision-making in the region. When developing a community-level dashboard, our findings underscore the necessity of a user-friendly design, frequent data updates, and inclusion of key information and visuals on local overdose trends and resources.
Background: One Health is a collaborative approach that can be used to evaluate and enhance the fields of human, animal, and environmental health and to emphasize their sectoral interconnectedness. Empirical evaluation of the One Health performance of a country in the form of an index, provides direction for actionable interventions such as targeted funding, prioritized resource allocation, rigorous data management, and evidence-based policy decisions. These efforts, along with public engagement and awareness on disease management; environmental degradation, and preparedness toward disease outbreaks, contribute to strengthening global health security. Thus, developing a One Health Index (OHI) calculator for India is a significant step toward evidence-based One Health governance in the context of low-and middle-income countries.
Objective: This study aimed to (1) develop a OHI Calculator for India using efficient and user-friendly weighting methods and demonstrate the calculation of the OHI; (2) develop India-specific datasets through secondary data collection from reliable data sources; and (3) determine data gaps for policy stewardship.
Methods: We proposed a OHI calculator to measure the OHI from an Indian context by adopting the Global One Health Index framework that comprises 3 categories: 13 key indicators, 57 indicators, and 216 subindicators. Secondary data collection was conducted to create a dataset for specific to India from reliable sources. For measuring OHI, we demonstrated two mathematical weighting methods: an efficient expert-based rating using fuzzy extent analysis and a modified entropy-based weightage method.
Results: We demonstrate the step-by-step OHI calculation by determining indicator scores using both fuzzy extent analysis and modified entropy-based weightage method. Through secondary data collection an India-specific dataset was created using reliable sources. For the datasets from India, data for 156/216 subindicators were available, while that for the remaining 60 indicators were unavailable. Further, a pilot correlation analysis was performed between 20 indicator scores and relevant budget allocations for the years 2022-2023, 2023-2024, and 2024-2025. It was found that increases in the budget allocation across consecutive years improved indicator scores or better performance and vice versa.
Conclusions: The demonstrated OHI calculator has the potential to serve as a governance tool while promoting data transparency and ethical data management. There is a need for a collaborative data federation approach to resolve data gaps, including incomplete, missing, or unavailable data. Further, the correlation analysis between budgetary allocation and performance of indicators provides empirical evidence for policymakers to improve intersectoral communication, multistakeholder engagement, concerted interventions, and
Background: The outbreak of COVID-19 in 2019 led governments worldwide to introduce various public health measures, which included restrictions on travel and public gatherings, effectively reducing the spread of the virus and associated mortality rates. In Japan, nonlegally binding restrictions on outings effectively curbed infections, as in other countries. However, the restrictions impacted lifestyles, including reduced physical activity, increased frailty, and overeating issues, beyond the effect of preventing the spread of infection. Various factors such as personality, age, and cultural norms influenced outing behavior during the pandemic, which varied by activity type.
Objective: To elucidate the association between personality traits and changes in outing behaviors during the COVID-19 pandemic, as well as to clarify age-specific differences in outing behaviors, focusing on different types of outings.
Methods: A cross-sectional survey was conducted using a web-based questionnaire in January 2021, when Japan announced its second emergency declaration during the pandemic. Overall, 1236 participants were recruited, with an equal number of participants for each gender and 10-year age group. The survey included questions regarding changes in the frequency of three types of outings-medical institution visits, eating out, and traveling-in addition to participants' personality traits, such as sociability and morality. Multinomial logistic regression analysis was performed to analyze the association between personality traits and changes in different outing behaviors. Stratified analysis by age group was also performed.
Results: The findings revealed that 790 participants reported no change in medical institution visits, although the frequency of eating out and traveling decreased during the pandemic. Regarding an age-wise comparison, a higher percentage of older people reported no change in medical institution visits but reported a decrease in eating out and traveling than younger people. Multinomial logistic regression analysis stratified by age showed that sociable people were more likely to report a decrease in the frequency of medical institution visits and an increase in the frequency of eating out (odds ratio [OR] 1.92, 95% CI 1.36-2.71, P<.001; OR 2.57, 95% CI 1.19-5.54, P=.016, respectively), and participants with a strong sense of responsibility were more likely to report a decrease in the frequency of traveling (OR 1.76, 95% CI 1.14-2.72, P=.011) among younger adults. Among older adults, strongly responsible individuals were less likely to eating out frequently (OR 2.56, 95% CI 1.12-5.82, P=.026).
Conclusions: We examined various behavioral changes observed during the pandemic for different types of outings and their associations with personality traits, as well as differences between age groups. The findings could help promote
Background: In response to the increasing incidence and prevalence of hypertension, Ethiopia has been piloting hypertension control at the primary health care level in selected sentinel sites. However, no evaluation has been conducted and its success and failures have not been ascertained.
Objective: This study aimed to evaluate on whether sentinel hypertension surveillance system in Mojo City were operating efficiently and effectively.
Methods: A concurrently embedded mixed design (quantitative or qualitative) study was conducted in 2 sentinel health centers in Mojo city, Oromia region of Ethiopia. The usefulness and 9 system attributes were assessed via key informant interviews, observations, and record reviews. The qualitative data were analyzed manually via thematic analysis, whereas quantitative data were analyzed via SPSS Software version 25.0 (IBM Corp).
Results: The study invited 14 key informants, and all were willing to participate in the interview. The completeness and timeliness of reports were 98% and 100%, respectively. The sensitivity, positive predictive value, and representativeness were 45.3%, 92.6%, and 22%, respectively. Nearly three-fourths (10/14, 71%) of key informants perceived the system as flexible, while half thought it as unstable due to factors such as inadequate training and lack of supportive supervision and feedback system. Health facilities did not conduct routine data analysis and interpretation, nor did they use for action.
Conclusions: The surveillance system in Mojo city was simple, flexible, acceptable, and predictive but less sensitive, unrepresentative, and unstable. There is a need for implementing routine data analysis and use for action, adequate training, and feedback system for optimizing the system's performance and to ensure its sustainability.
Background: During the COVID-19 pandemic in 2020, hospitals encountered numerous challenges that compounded their difficulties. Some of these challenges directly impacted patient care, such as the need to expand capacities, adjust services, and use new knowledge to save lives in an ever-evolving situation. In addition, hospitals faced regulatory challenges.
Objective: This paper presents the findings of a qualitative study that aimed to compare the effects of reporting requirements on a small independent hospital and a large network hospital during the COVID-19 pandemic.
Methods: We used both quantitative and qualitative analyses and conducted 51 interviews, which were thematically analyzed. We quantified the changes in regulatory reporting requirements during the first 14 months of the pandemic.
Results: Reporting requirements placed a substantial time burden on key clinical personnel at the small independent hospital, consequently reducing the time available for patient care. Conversely, the large network hospital had dedicated nonclinical staff responsible for reporting duties, and their robust health information system facilitated this work.
Conclusions: The discrepancy in health IT capabilities suggests that there may be significant institutional inequities affecting smaller hospitals' ability to respond to a pandemic and adequately support public health efforts. Electronic certification guidelines are essential to addressing the substantial equity issues. We discuss in detail the health care policy implications of these findings.
Background: Although many studies have used smartphone apps to examine alcohol consumption, none have clearly delineated long-term (>1 year) consumption among the general population.
Objective: The objective of our study is to elucidate in detail the alcohol consumption behavior of alcohol drinkers in Japan using individual real-world data. During the state of emergency associated with the COVID-19 outbreak, the government requested that people restrict social gatherings and stay at home, so we hypothesize that alcohol consumption among Japanese working people decreased during this period due to the decrease in occasions for alcohol consumption. This analysis was only possible with individual real-world data. We also aimed to clarify the effects of digital interventions based on notifications about daily alcohol consumption.
Methods: We conducted a retrospective study targeting 5-year log data from January 1, 2018, to December 31, 2022, obtained from a commercial smartphone health care app (CALO mama Plus). First, to investigate the possible size of the real-world data, we investigated the rate of active users of this commercial smartphone app. Second, to validate the individual real-world data recorded in the app, we compared individual real-world data from 9991 randomly selected users with government-provided open data on the number of daily confirmed COVID-19 cases in Japan and with nationwide alcohol consumption data. To clarify the effects of digital interventions, we investigated the relationship between 2 types of notification records (ie, "good" and "bad") and a 3-day daily alcohol consumption log following the notification. The protocol of this retrospective study was approved by the Ethics Committee of the Kyoto University Graduate School and Faculty of Medicine (R4699).
Background: Clinical risk prediction models integrated into digitized health care informatics systems hold promise for personalized primary prevention and care, a core goal of precision health. Fairness metrics are important tools for evaluating potential disparities across sensitive features, such as sex and race or ethnicity, in the field of prediction modeling. However, fairness metric usage in clinical risk prediction models remains infrequent, sporadic, and rarely empirically evaluated.
Objective: We seek to assess the uptake of fairness metrics in clinical risk prediction modeling through an empirical evaluation of popular prediction models for 2 diseases, 1 chronic and 1 infectious disease.
Methods: We conducted a scoping literature review in November 2023 of recent high-impact publications on clinical risk prediction models for cardiovascular disease (CVD) and COVID-19 using Google Scholar.
Results: Our review resulted in a shortlist of 23 CVD-focused articles and 22 COVID-19 pandemic-focused articles. No articles evaluated fairness metrics. Of the CVD-focused articles, 26% used a sex-stratified model, and of those with race or ethnicity data, 92% had study populations that were more than 50% from 1 race or ethnicity. Of the COVID-19 models, 9% used a sex-stratified model, and of those that included race or ethnicity data, 50% had study populations that were more than 50% from 1 race or ethnicity. No articles for either disease stratified their models by race or ethnicity.
Conclusions: Our review shows that the use of fairness metrics for evaluating differences across sensitive features is rare, despite their ability to identify inequality and flag potential gaps in prevention and care. We also find that training data remain largely racially and ethnically homogeneous, demonstrating an urgent need for diversifying study cohorts and data collection. We propose an implementation framework to initiate change, calling for better connections between theory and practice when it comes to the adoption of fairness metrics for clinical risk prediction. We hypothesize that this integration will lead to a more equitable prediction world.
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