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General methodological considerations for the assessment of nutritional influences on human cognitive functions
The premise that cognitive functioning can be influenced through dietary means has gained widespread interest. The assessment of cognitive functioning is a key method to scientifically substantiate such nutritional effects on cognition. The current paper provides a basic overview of the main concepts, issues and pitfalls of human cognitive research. General methods of cognitive assessment, selection of appropriate tests, factors that may mediate task performance and issues pertaining to the interpretation of the results are discussed.
Journal Article
Cancer Care for the Whole Patient
Cancer care today often provides state-of-the-science biomedical treatment, but fails to address the psychological and social (psychosocial) problems associated with the illness. This failure can compromise the effectiveness of health care and thereby adversely affect the health of cancer patients. Psychological and social problems created or exacerbated by cancer-including depression and other emotional problems; lack of information or skills needed to manage the illness; lack of transportation or other resources; and disruptions in work, school, and family life-cause additional suffering, weaken adherence to prescribed treatments, and threaten patients' return to health.
Today, it is not possible to deliver high-quality cancer care without using existing approaches, tools, and resources to address patients' psychosocial health needs. All patients with cancer and their families should expect and receive cancer care that ensures the provision of appropriate psychosocial health services.
Cancer Care for the Whole Patient recommends actions that oncology providers, health policy makers, educators, health insurers, health planners, researchers and research sponsors, and consumer advocates should undertake to ensure that this standard is met.
eBook
Mental Health Chatbot for Young Adults With Depressive Symptoms During the COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial
Depression has a high prevalence among young adults, especially during the COVID-19 pandemic. However, mental health services remain scarce and underutilized worldwide. Mental health chatbots are a novel digital technology to provide fully automated interventions for depressive symptoms. The purpose of this study was to test the clinical effectiveness and nonclinical performance of a cognitive behavioral therapy (CBT)–based mental health chatbot (XiaoE) for young adults with depressive symptoms during the COVID-19 pandemic. In a single-blind, 3-arm randomized controlled trial, participants manifesting depressive symptoms recruited from a Chinese university were randomly assigned to a mental health chatbot (XiaoE; n=49), an e-book (n=49), or a general chatbot (Xiaoai; n=50) group in a ratio of 1:1:1. Participants received a 1-week intervention. The primary outcome was the reduction of depressive symptoms according to the 9-item Patient Health Questionnaire (PHQ-9) at 1 week later (T1) and 1 month later (T2). Both intention-to-treat and per-protocol analyses were conducted under analysis of covariance models adjusting for baseline data. Controlled multiple imputation and δ-based sensitivity analysis were performed for missing data. The secondary outcomes were the level of working alliance measured using the Working Alliance Questionnaire (WAQ), usability measured using the Usability Metric for User Experience-LITE (UMUX-LITE), and acceptability measured using the Acceptability Scale (AS). A CBT-based chatbot is a feasible and engaging digital therapeutic approach that allows easy accessibility and self-guided mental health assistance for young adults with depressive symptoms. A systematic evaluation of nonclinical metrics for a mental health chatbot has been established in this study. In the future, focus on both clinical outcomes and nonclinical metrics is necessary to explore the mechanism by which mental health chatbots work on patients. Further evidence is required to confirm the long-term effectiveness of the mental health chatbot via trails replicated with a longer dose, as well as exploration of its stronger efficacy in comparison with other active controls.
Journal Article
Applying the Rasch Model
Recognised as the most influential publication in the field, ARM facilitates deep understanding of the Rasch model and its practical applications. The authors review the crucial properties of the model and demonstrate its use with examples across the human sciences. Readers will be able to understand and critically evaluate Rasch measurement research, perform their own Rasch analyses and interpret their results. The glossary and illustrations support that understanding, and the accessible approach means that it is ideal for readers without a mathematical background.
Intended as a text for graduate courses in measurement, item response theory, (advanced) research methods or quantitative analysis taught in psychology, education, human development, business and other social and health sciences. Professionals in these areas will also appreciate the book’s accessible introduction.
Highlights of the new edition include:
More learning tools to strengthen readers’ understanding including chapter introductions, boldfaced key terms, chapter summaries, activities and suggested readings.
Greater emphasis on the use of R packages; readers can download the R code from the Routledge website.
Explores the distinction between numerical values, quantity and units, to understand the measurement and the role of the Rasch logit scale (Chapter 4).
A new four-option data set from the IASQ (Instrumental Attitude toward Self-assessment Questionnaire) for the Rating Scale Model (RSM) analysis exemplar (Chapter 6).
Clarifies the relationship between Rasch measurement, path analysis and SEM, with a host of new examples of Rasch measurement applied across health sciences, education and psychology (Chapter 10).
eBook
A comprehensive AI policy education framework for university teaching and learning
This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies. Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities, using both quantitative and qualitative research methods. Based on the findings, the study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning. This framework is organized into three dimensions: Pedagogical, Governance, and Operational. The Pedagogical dimension concentrates on using AI to improve teaching and learning outcomes, while the Governance dimension tackles issues related to privacy, security, and accountability. The Operational dimension addresses matters concerning infrastructure and training. The framework fosters a nuanced understanding of the implications of AI integration in academic settings, ensuring that stakeholders are aware of their responsibilities and can take appropriate actions accordingly.HighlightsProposed AI Ecological Education Policy Framework for university teaching and learning.Three dimensions: Pedagogical, Governance, and Operational AI Policy Framework.Qualitative and quantitative data collected from students, teachers, and staff.Ten key areas identified for planning an AI policy in universities.Students should play an active role in drafting and implementing the policy.
Journal Article
Scale development: ten main limitations and recommendations to improve future research practices
The scale development process is critical to building knowledge in human and social sciences. The present paper aimed (a) to provide a systematic review of the published literature regarding current practices of the scale development process, (b) to assess the main limitations reported by the authors in these processes, and (c) to provide a set of recommendations for best practices in future scale development research. Papers were selected in September 2015, with the search terms “scale development” and “limitations” from three databases: Scopus, PsycINFO, and Web of Science, with no time restriction. We evaluated 105 studies published between 1976 and 2015. The analysis considered the three basic steps in scale development: item generation, theoretical analysis, and psychometric analysis. The study identified ten main types of limitation in these practices reported in the literature: sample characteristic limitations, methodological limitations, psychometric limitations, qualitative research limitations, missing data, social desirability bias, item limitations, brevity of the scale, difficulty controlling all variables, and lack of manual instructions. Considering these results, various studies analyzed in this review clearly identified methodological weaknesses in the scale development process (e.g., smaller sample sizes in psychometric analysis), but only a few researchers recognized and recorded these limitations. We hope that a systematic knowledge of the difficulties usually reported in scale development will help future researchers to recognize their own limitations and especially to make the most appropriate choices among different conceptions and methodological strategies.
Journal Article
Evaluating the effectiveness of behavior change techniques in health-related behavior: a scoping review of methods used
Behavior change interventions typically contain multiple potentially active components: behavior change techniques (BCTs). Identifying which specific BCTs or BCT combinations have the potential to be effective for a given behavior in a given context presents a major challenge. The aim of this study was to review the methods that have been used to identify effective BCTs for given behaviors in given contexts and evaluate their strengths and limitations. A scoping review was conducted of studies that had sought to identify effective BCTs. Articles referring to “behavio(u)r change technique(s)” in the abstract/text were located, and ones that involved identification of effective BCTs were selected. The methods reported were coded. The methods were analyzed in general terms using “PASS” criteria: Practicability (facility to apply the method appropriately), Applicability (facility to generalize from findings to contexts and populations of interest), Sensitivity (facility to identify effective BCTs), and Specificity (facility to rule out ineffective BCTs). A sample of 10% of the studies reviewed was then evaluated using these criteria to assess how far the strengths and limitations identified in principle were borne out in practice. One hundred and thirty-five studies were identified. The methods used in those studies were experimental manipulation of BCTs, observational studies comparing outcomes in the presence or absence of BCTs, meta-analyses of BCT comparisons, meta-regressions evaluating effect sizes with and without specific BCTs, reviews of BCTs found in effective interventions, and meta-classification and regression trees. The limitations of each method meant that only weak conclusions could be drawn regarding the effectiveness of specific BCTs or BCT combinations. Methods for identifying effective BCTs linked to target behavior and context all have important inherent limitations. A strategy needs to be developed that can systematically combine the strengths of the different methods and that can link these constructs in an ontology of behavior change interventions.
Journal Article
Dietary assessment methods in epidemiological research: current state of the art and future prospects version 1; peer review: 3 approved
Self-reported dietary intake is assessed by methods of real-time recording (food diaries and the duplicate portion method) and methods of recall (dietary histories, food frequency questionnaires, and 24-hour dietary recalls). Being less labor intensive, recall methods are more frequently employed in nutritional epidemiological investigations. However, sources of error, which include the participants' inability to fully and accurately recall their intakes as well as limitations inherent in the food composition databases applied to convert the reported food consumption to energy and nutrient intakes, may limit the validity of the generated information. The use of dietary biomarkers is often recommended to overcome such errors and better capture intra-individual variability in intake; nevertheless, it has its own challenges. To address measurement error associated with dietary questionnaires, large epidemiological investigations often integrate sub-studies for the validation and calibration of the questionnaires and/or administer a combination of different assessment methods (e.g. administration of different questionnaires and assessment of biomarker levels). Recent advances in the omics field could enrich the list of reliable nutrition biomarkers, whereas new approaches employing web-based and smart phone applications could reduce respondent burden and, possibly, reporting bias. Novel technologies are increasingly integrated with traditional methods, but some sources of error still remain. In the analyses, food and nutrient intakes always need to be adjusted for total daily energy intake to account for errors related to reporting.
Journal Article