An examination of the quality of late-life depression websites on the Internet (Preprint)

BACKGROUND The Internet can increase the accessibility of mental health information and improve the mental health literacy of older adults. The quality of mental health information on the Internet can be inaccurate or biased, leading to misinformation OBJECTIVE This study’s objectives were to evaluate the quality, usability, and readability of websites providing information concerning depression in later life. METHODS Websites were identified through a Google search, and evaluated by assessing quality (DISCERN), usability (Patient Education Materials Assessment Tool; PEMAT) and readability (Simple Measure of Gobbledygook; SMOG). RESULTS The overall quality of late-life depression websites (N = 19) was moderate, usability was low, and readability was poor. No significant relationship was found between quality and readability of websites. CONCLUSIONS Websites can be improved by enhancing information quality, usability, and readability related to late-life depression. The use of high-quality websites may improve mental health literacy and shared treatment decision-making for older adults.

eMental Health Literacy and Psychological Distress as Predictors of Barriers to Mental Health Services

Since the COVID-19 pandemic, psychologists have begun to rely heavily on technology to provide mental health information and services (APA, 2020). As the older adult population increases, the number of older adults in need of mental health services also increases; however, little is known about the way older adults might utilize technology to inform mental health-related decisions. This study expands on the construct of eHealth Literacy by examining eMental Health Literacy, which is defined as the degree to which individuals seek, find, understand, and appraise basic mental health information and services online that are needed to inform mental health-related decisions. A sample of 244 older adults (M=68.34, range=65-82 years) were recruited online through Amazon Mechanical Turk. A structural equation model was estimated specifying eMental Health Literacy and psychological distress as predictors of extrinsic and intrinsic barriers to mental health services. After adding three correlated errors, the model achieved good fit (χ2(110)=329.20, p<.001, SRMR=.08, CFI=.93, TLI=.91, GFI=.86, RMSEA=.09). All indicators were significantly related to their latent construct (p<.001). The results indicated that, controlling for psychological distress, higher eMental health literacy was significantly related to fewer reported intrinsic (b=-.386, p<.001) and extrinsic barriers (b=-.315, p<.001) to mental health services. Higher distress was also significantly related to more intrinsic (b=.537, p<.001) and extrinsic barriers (b=.645, p<.001) to mental health services. These findings suggest that, as we move towards a more digital world, eMental health literacy could play a significant role in the way older adults navigate through the mental healthcare system.

Acute-Stage Mental Health Symptoms by Natural Disaster Type: Consultations of Disaster Psychiatric Assistance Teams (DPATs) in Japan

This study analyzed the support activities that the Disaster Psychiatric Assistance Team (DPAT) in Japan provided following four previous disasters (a volcanic eruption, a mudslide, a flood, and an earthquake) to identify links between the disaster type and the characteristics of acute stage mental disorders observed. Using Disaster Mental Health Information Support System database records of consultations with patients supported by the DPAT during the survey period from 2013 (when DPAT was launched) to 2016, we performed cross-tabulations and investigated significant differences using chi-squared tests. For expected values less than 5, Fisher’s exact test was performed. Frequently occurring acute-stage symptoms after a disaster include anxiety, sleep problems, mood and affect, and physical symptoms. The affected population characteristics, victim attributes, severity of damage sustained, and evacuation status were the chief factors that influenced acute-stage mental health symptoms. The psychiatric symptoms detected in our study together with the results of diagnoses are important for determining the types of early interventions needed during the acute stage of a disaster. By sharing baseline mental health information, together with disaster-related characteristics highlighted in this study, mental health providers are better able to predict future possible mental disorders and symptoms.

The impact of mental health information overload on community education programs to enhance mental health-care seeking

Purpose Education programs seek to increase the public’s mental health literacy so they are better able to, among other things, help others engage in care when in need. This task may be diminished when such programs overwhelm participants with too much information. In addition, participants might arrive to the program with information overload related to the covered health topic. Information overload about health topics has been shown to influence attitudes and behavioral intentions. The overall purpose of the current study was to examine the relationship between mental health information overload, topic interest, and care seeking recommendations. Design/methodology/approach The current study tested a path suggesting high mental health information overload diminishes interest in learning about mental health, which in turn reduces recommendations to others to seek appropriate help when in need. Participants completed online measures of mental health information overload, topic interest and recommendations for care seeking. The data set analyzed represents valid responses from 221 participants. Structural equation modeling was completed to confirm the path model hypothesized for this study. Findings Structural equation modeling showed satisfactory fit and significant betas for the hypothesized path. Originality/value This study adds to the emerging literature on the impact of health information overload and is the first to the best of the authors’ knowledge to measure mental health information overload. Program developers should consider information overload in the ongoing development of public mental health education programs.

Factors affecting perception of Indian adolescent students toward interactive online mental health information during COVID-19

Purpose This study aims to identify the critical factors affecting the perception of adolescent students toward interactive online mental health information available on health-related websites. Design/methodology/approach The primary data was collected with the help of an online self–structured questionnaire. The questionnaire includes the identified variables extracted from previous literature related to the mental health information websites using the Likert scale. The respondents include the adolescent school students belonging to the northern region of India: semi-urban/rural locations of Uttar Pradesh (Agra and Mathura) and urban cities (Faridabad, Gaziabad, Delhi and NCR). The criteria for selecting respondents were that students must have visited any online health information-related websites at least once. Exploratory factor analysis was used to explore the factors with the help of SPSS.20. Findings The identified factors that include information delivery medium/mode, websites’ navigation structure, customized information or content, ability to form a virtual relationship and supplementary features of the websites may benefit the health communication system of any country and the health-care industry. Research limitations/implications There are some limitations such as a limited number of respondents and even on that sample was taken for teenagers; thereby creating fewer generalizations related to the present context. Further, only exploratory factor analysis is applied in the study to identify the factors but future researchers may proceed to develop the conceptual model of perception toward online information with the help of confirmatory factor analysis and structural equation modeling techniques. Practical implications The results of this study are useful for government officials especially those related to the ministry of health care and public health organizations of various countries, who usually invest in co-designing authentic, reliable and high interactive online information-sharing websites. Social implications The results of this study are helpful for government officials, especially those related to the ministry of health care and public health organizations of various countries, who usually invest in co-designing authentic, reliable and high interactive online information-sharing websites. Originality/value The study is unique as it provides insight into the opinion of the adolescent students, primarily upon encountering the online mental health information concerning the Indian perspective. Future researchers, health-care policymakers and health-care professionals may use the study to capture a complete picture of a relevant phenomenon in their work.

Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers

We aimed to develop a quantitative instrument to assist with the automatic evaluation of the actionability of mental healthcare information. We collected and classified two large sets of mental health information from certified mental health websites: generic and patient-specific mental healthcare information. We compared the performance of the optimised classifier with popular readability tools and non-optimised classifiers in predicting mental health information of high actionability for people with mental disorders. sensitivity of the classifier using both semantic and structural features as variables achieved statistically higher than that of the binary classifier using either semantic (p < 0.001) or structural features (p = 0.0010). The specificity of the optimized classifier was statistically higher than that of the classifier using structural variables (p = 0.002) and the classifier using semantic variables (p = 0.001). Differences in specificity between the full-variable classifier and the optimised classifier were statistically insignificant (p = 0.687). These findings suggest the optimised classifier using as few as 19 semantic-structural variables was the best-performing classifier. By combining insights of linguistics and statistical analyses, we effectively increased the interpretability and the diagnostic utility of the binary classifiers to guide the development, evaluation of the actionability and usability of mental healthcare information.

Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features

Background: Online mental health information represents important resources for people living with mental health issues. Suitability of mental health information for effective self-care remains understudied, despite the increasing needs for more actionable mental health resources, especially among young people. Objective: We aimed to develop Bayesian machine learning classifiers as data-based decision aids for the assessment of the actionability of credible mental health information for people with mental health issues and diseases. Methods: We collected and classified creditable online health information on mental health issues into generic mental health (GEN) information and patient-specific (PAS) mental health information. GEN and PAS were both patient-oriented health resources developed by health authorities of mental health and public health promotion. GENs were non-classified online health information without indication of targeted readerships; PASs were developed purposefully for specific populations (young, elderly people, pregnant women, and men) as indicated by their website labels. To ensure the generalisability of our model, we chose to develop a sparse Bayesian machine learning classifier using Relevance Vector Machine (RVM). Results: Using optimisation and normalisation techniques, we developed a best-performing classifier through joint optimisation of natural language features and min-max normalisation of feature frequencies. The AUC (0.957), sensitivity (0.900), and specificity (0.953) of the best model were statistically higher (p < 0.05) than other models using parallel optimisation of structural and semantic features with or without feature normalisation. We subsequently evaluated the diagnostic utility of our model in the clinic by comparing its positive (LR+) and negative likelihood ratios (LR−) and 95% confidence intervals (95% C.I.) as we adjusted the probability thresholds with the range of 0.1 and 0.9. We found that the best pair of LR+ (18.031, 95% C.I.: 10.992, 29.577) and LR− (0.100, 95% C.I.: 0.068, 0.148) was found when the probability threshold was set to 0.45 associated with a sensitivity of 0.905 (95%: 0.867, 0.942) and specificity of 0.950 (95% C.I.: 0.925, 0.975). These statistical properties of our model suggested its applicability in the clinic. Conclusion: Our study found that PAS had significant advantage over GEN mental health information regarding information actionability, engagement, and suitability for specific populations with distinct mental health issues. GEN is more suitable for general mental health information acquisition, whereas PAS can effectively engage patients and provide more effective and needed self-care support. The Bayesian machine learning classifier developed provided automatic tools to support decision making in the clinic to identify more actionable resources, effective to support self-care among different populations.