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Background: Reddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research. Objective: This review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention. Methods: A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions. Results: The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources. Conclusions: This systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research. [ABSTRACT FROM AUTHOR]

Background: Persons diagnosed with serious chronic illnesses and their caretakers experience multiple types of financial costs that strain their income and generate financial distress. Many turn to medical crowdfunding (MCF) to mitigate the harms of these costs on their health and quality of life. Objective: This scoping review aims to summarize the research on MCF for persons diagnosed with serious chronic illness regarding study designs and methods; the responsible conduct of research practices; and study foci as they relate to stress, stress appraisals, and the coping processes. Methods: This review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eligible studies were conducted in countries designated as high income by the World Bank and focused on beneficiaries diagnosed with serious chronic illness. The findings of the included studies were summarized as they related to the key concepts in a conceptual framework derived from an established stress, appraisal, and coping framework and a conceptual model of financial toxicity in pediatric oncology. Results: Overall, 26 studies were eligible for inclusion in the review. The main findings included a lack of integration of qualitative and quantitative approaches and the inconsistent reporting of the responsible conduct of research practices. The included studies focused on financial stressors that contributed to financial burden, such as out-of-pocket payments of medical bills, basic living expenses, medical travel expenses, and lost income owing to illness-related work disruptions. Few studies addressed stress appraisals as threatening or the adequacy of available financial resources. When mentioned, appraisals related to the global financial struggle during the COVID-19 pandemic or the capacity of social network members to donate funds. The consequences of MCF included the receipt of 3 forms of social support (tangible, informational, and emotional), privacy loss, embarrassment, and the propagation of scientifically unsupported information. Studies found that friends and family tended to manage MCF campaigns. Although most of the studies (21/26, 81%) focused on monetary outcomes, a few (5/26, 19%) concentrated on peoples' experiences with MCF. Conclusions: The identified methodological gaps highlight the need for more robust and reproducible approaches to using the copious data available on public MCF platforms. The integration of quantitative and qualitative methods will allow for nuanced explorations of the MCF experience. A more consistent elaboration of strategies to promote the responsible conduct of research is warranted to minimize risk to populations that are vulnerable and express concerns regarding the loss of privacy. Finally, an examination of the unanticipated consequences of MCF is critical for the development of future interventions to optimize existing supports while providing needed supports, financial and nonfinancial, that are lacking. [ABSTRACT FROM AUTHOR]

Background: Increasing adoption of sensor-based digital health technologies (sDHTs) in recent years has cast light on the many challenges in implementing these tools into clinical trials and patient care at scale across diverse patient populations; however, the methodological approaches taken toward sDHT usability evaluation have varied markedly., Objective: This review aims to explore the current landscape of studies reporting data related to sDHT human factors, human-centered design, and usability, to inform our concurrent work on developing an evaluation framework for sDHT usability., Methods: We conducted a scoping review of studies published between 2013 and 2023 and indexed in PubMed, in which data related to sDHT human factors, human-centered design, and usability were reported. Following a systematic screening process, we extracted the study design, participant sample, the sDHT or sDHTs used, the methods of data capture, and the types of usability-related data captured., Results: Our literature search returned 442 papers, of which 85 papers were found to be eligible and 83 papers were available for data extraction and not under embargo. In total, 164 sDHTs were evaluated; 141 (86%) sDHTs were wearable tools while the remaining 23 (14%) sDHTs were ambient tools. The majority of studies (55/83, 66%) reported summative evaluations of final-design sDHTs. Almost all studies (82/83, 99%) captured data from targeted end users, but only 18 (22%) out of 83 studies captured data from additional users such as care partners or clinicians. User satisfaction and ease of use were evaluated for 83% (136/164) and 91% (150/164) of sDHTs, respectively; however, learnability, efficiency, and memorability were reported for only 11 (7%), 4 (2%), and 2 (1%) out of 164 sDHTs, respectively. A total of 14 (9%) out of 164 sDHTs were evaluated according to the extent to which users were able to understand the clinical data or other information presented to them (understandability) or the actions or tasks they should complete in response (actionability). Notable gaps in reporting included the absence of a sample size rationale (reported for 21/83, 25% of all studies and 17/55, 31% of summative studies) and incomplete sociodemographic descriptive data (complete age, sex/gender, and race/ethnicity reported for 14/83, 17% of studies)., Conclusions: Based on our findings, we suggest four actionable recommendations for future studies that will help to advance the implementation of sDHTs: (1) consider an in-depth assessment of technology usability beyond user satisfaction and ease of use, (2) expand recruitment to include important user groups such as clinicians and care partners, (3) report the rationale for key study design considerations including the sample size, and (4) provide rich descriptive statistics regarding the study sample to allow a complete understanding of generalizability to other patient populations and contexts of use., (©Animesh Tandon, Bryan Cobb, Jacob Centra, Elena Izmailova, Nikolay V Manyakov, Samantha McClenahan, Smit Patel, Emre Sezgin, Srinivasan Vairavan, Bernard Vrijens, Jessie P Bakker, Digital Health Measurement Collaborative Community (DATAcc) hosted by DiMe. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.11.2024.)

Background: With the world's population aging, more health-conscious older adults are seeking health information to make better-informed health decisions. The rapid growth of the internet has empowered older adults to access web-based health information sources. However, research explicitly exploring older adults' online health information seeking (OHIS) behavior is still underway.Objective: This systematic scoping review aims to understand older adults' OHIS and answer four research questions: (1) What types of health information do older adults seek and where do they seek health information on the internet? (2) What are the factors that influence older adults' OHIS? (3) What are the barriers to older adults' OHIS? (4) How can we intervene and support older adults' OHIS?Methods: A comprehensive literature search was performed in November 2020, involving the following academic databases: Web of Science; Cochrane Library database; PubMed; MEDLINE; CINAHL Plus; APA PsycINFO; Library and Information Science Source; Library, Information Science and Technology Abstracts; Psychology and Behavioral Sciences Collection; Communication & Mass Media Complete; ABI/INFORM; and ACM Digital Library. The initial search identified 8047 publications through database search strategies. After the removal of duplicates, a data set consisting of 5949 publications was obtained for screening. Among these, 75 articles met the inclusion criteria. Qualitative content analysis was performed to identify themes related to the research questions.Results: The results suggest that older adults seek 10 types of health information from 6 types of internet-based information sources and that 2 main categories of influencing factors, individual-related and source-related, impact older adults' OHIS. Moreover, the results reveal that in their OHIS, older adults confront 3 types of barriers, namely individual, social, and those related to information and communication technologies. Some intervention programs based on educational training workshops have been created to intervene and support older adults' OHIS.Conclusions: Although OHIS has become increasingly common among older adults, the review reveals that older adults' OHIS behavior is not adequately investigated. The findings suggest that more studies are needed to understand older adults' OHIS behaviors and better support their medical and health decisions in OHIS. Based on the results, the review proposes multiple objectives for future studies, including (1) more investigations on the OHIS behavior of older adults above 85 years; (2) conducting more longitudinal, action research, and mixed methods studies; (3) elaboration of the mobile context and cross-platform scenario of older adults' OHIS; (4) facilitating older adults' OHIS by explicating technology affordance; and (5) promoting and measuring the performance of OHIS interventions for older adults. [ABSTRACT FROM AUTHOR]

Background: Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC.Objective: We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings.Methods: We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data.Results: We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%).Conclusions: We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings. [ABSTRACT FROM AUTHOR]

Background: Delirium prevention is crucial, especially in critically ill patients. Nonpharmacological multicomponent interventions for preventing delirium are increasingly recommended and technology-based interventions have been developed to support them. Despite the increasing number and diversity in technology-based interventions, there has been no systematic effort to create an overview of these interventions for in-hospital delirium prevention and reduction.Objective: This systematic scoping review was carried out to answer the following questions: (1) what are the technologies currently used in nonpharmacological technology-based interventions for preventing and reducing delirium? and (2) what are the strategies underlying these currently used technologies?Methods: A systematic search was conducted in Scopus and Embase between 2015 and 2020. A selection was made in line with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Studies were eligible if they contained any type of technology-based interventions and assessed delirium-/risk factor-related outcome measures in a hospital setting. Data extraction and quality assessment were performed using a predesigned data form.Results: A total of 31 studies were included and analyzed focusing on the types of technology and the strategies used in the interventions. Our review revealed 8 different technology types and 14 strategies that were categorized into the following 7 pathways: (1) restore circadian rhythm, (2) activate the body, (3) activate the mind, (4) induce relaxation, (5) provide a sense of security, (6) provide a sense of control, and (7) provide a sense of being connected. For all technology types, significant positive effects were found on either or both direct and indirect delirium outcomes. Several similarities were found across effective interventions: using a multicomponent approach or including components comforting the psychological needs of patients (eg, familiarity, distraction, soothing elements).Conclusions: Technology-based interventions have a high potential when multidimensional needs of patients (eg, physical, cognitive, emotional) are incorporated. The 7 pathways pinpoint starting points for building more effective technology-based interventions. Opportunities were discussed for transforming the intensive care unit into a healing environment as a powerful tool to prevent delirium.Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020175874; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175874. [ABSTRACT FROM AUTHOR]

Background: Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC., Objective: We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings., Methods: We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data., Results: We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%)., Conclusions: We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings., (©Samira Abbasgholizadeh Rahimi, France Légaré, Gauri Sharma, Patrick Archambault, Herve Tchala Vignon Zomahoun, Sam Chandavong, Nathalie Rheault, Sabrina T Wong, Lyse Langlois, Yves Couturier, Jose L Salmeron, Marie-Pierre Gagnon, Jean Légaré. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 03.09.2021.)