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BACKGROUND: Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. OBJECTIVE: We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. METHODS: We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. RESULTS: Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. CONCLUSIONS: There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.
Assuntos
Algoritmos , Estilo de Vida , HumanosRESUMO
BACKGROUND: In families with children with cognitive impairments, both parents and children experience tension and have questions because of a lack of communication and adequate information. Therefore, there is a great need to develop tools that can help bridge the communication gap between patients and caregivers by stimulating conversations and providing psychoeducational tools. mHealth apps show great potential in this context. OBJECTIVE: The objective of this research is to discover the specific ways young people with cognitive impairments and their families interact with mHealth apps in the context of bridging the communication gap. This newly discovered information leads to potentially more impactful mHealth interventions in the future. Therefore, this paper documents the design and development of a mHealth app for a specific group of people with cognitive impairments-people with 22q11 deletion syndrome (22q11 DS)-and their caregivers, as well as key learnings from the evaluation of this app. METHODS: An iterative, user-centered design approach is used to design and develop the app. Design and evaluation happens in 2 phases. During the design phase, feedback is gathered from 2 medical experts and 3 human computer interaction (HCI) experts using a low-fidelity paper prototype. During the evaluation phase, feedback is gathered from 8 families with a child with 22q11 DS using a fully working proof of concept. This phase consists of a semistructured interview, a 2-4-week trial period, and a concluding semistructured interview. RESULTS: The evaluation results of the fully working proof of concept led to design recommendations related to four different topics: (1) overcoming usage barriers, (2) stimulating conversation through a mHealth app, (3) providing information, and (4) bringing continual added value. Results are presented according to six different categories obtained in a thematic analysis: (1) feedback about the app "as is," (2) difficulties, (3) comparison between physical and digital tool, (4) extensions, (5) intention, and (6) other. CONCLUSIONS: In this research, the need for apps that help bridge the communication gap between a person with cognitive impairment and their caregiver is confirmed. All participating families express their gratitude and mention the added value for other families. Therefore, it is highly encouraged for clinics and institutions to take action and develop an app to be used in practice. Furthermore, considerations when developing for people with 22q11 DS, or more broadly, people with cognitive impairments, are proposed. First, one should keep design principles in mind to overcome usage barriers. Next, recognition is a key concept when stimulating conversations through mobile apps. Third, information should be provided by a trusted source, and more than just clinical information can be considered valuable. Finally, having the possibility of using a digital tool that can be personalized brings continual added value.
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Due to their historical nature, humanistic data encompass multiple sources of uncertainty. While humanists are accustomed to handling such uncertainty with their established methods, they are cautious of visualizations that appear overly objective and fail to communicate this uncertainty. To design more trustworthy visualizations for humanistic research, therefore, a deeper understanding of its relation to uncertainty is needed. We systematically reviewed 126 publications from digital humanities literature that use visualization as part of their research process, and examined how uncertainty was handled and represented in their visualizations. Crossing these dimensions with the visualization type and use, we identified that uncertainty originated from multiple steps in the research process from the source artifacts to their datafication. We also noted how besides known uncertainty coping strategies, such as excluding data and evaluating its effects, humanists also embraced uncertainty as a separate dimension important to retain. By mapping how the visualizations encoded uncertainty, we identified four approaches that varied in terms of explicitness and customization. This work contributes with two empirical taxonomies of uncertainty and it's corresponding coping strategies, as well as with the foundation of a research agenda for uncertainty visualization in the digital humanities. Our findings further the synergy among humanists and visualization researchers, and ultimately contribute to the development of more trustworthy, uncertainty-aware visualizations.
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Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the humanities and suggest new directions for it. We first look into infovis from the humanistic perspective by exploring the humanistic literature around infovis. We validate and expand those findings though a co-design workshop with humanist and infovis experts. Then, we translate our findings into guidelines for designers and conduct a design critique exercise to explore their effect on the perception of humanist researchers. Based on these steps, we introduce Layers of Meaning, a framework to reduce the semantic distance between humanist researchers and visualizations of their research material, by grounding infovis tools in time and space, physicality, terminology, nuance, and provenance.
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BACKGROUND: Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this follow-up is conducted at separate moments in time with large intervals in between. Casual games, embedded into the daily life of older adults, may prove to be a less resource-intensive medium that yields continuous and rich data on a patient's cognition. OBJECTIVE: To explore whether digital biomarkers of cognitive performance, found in the casual card game Klondike Solitaire, can be used to train machine-learning models to discern games played by older adults living with MCI from their healthy counterparts. METHODS: Digital biomarkers of cognitive performance were captured from 23 healthy older adults and 23 older adults living with MCI, each playing 3 games of Solitaire with 3 different deck shuffles. These 3 deck shuffles were identical for each participant. Using a supervised stratified, 5-fold, cross-validated, machine-learning procedure, 19 different models were trained and optimized for F1 score. RESULTS: The 3 best performing models, an Extra Trees model, a Gradient Boosting model, and a Nu-Support Vector Model, had a cross-validated F1 training score on the validation set of ≥0.792. The F1 score and AUC of the test set were, respectively, >0.811 and >0.877 for each of these models. These results indicate psychometric properties comparative to common cognitive screening tests. CONCLUSION: The results suggest that commercial card games, not developed to address specific mental processes, may be used for measuring cognition. The digital biomarkers derived from Klondike Solitaire show promise and may prove useful to fill the current blind spot between consultations.
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BACKGROUND: Mild cognitive impairment (MCI), the intermediate cognitive status between normal cognitive decline and pathological decline, is an important clinical construct for signaling possible prodromes of dementia. However, this condition is underdiagnosed. To assist monitoring and screening, digital biomarkers derived from commercial off-the-shelf video games may be of interest. These games maintain player engagement over a longer period of time and support longitudinal measurements of cognitive performance. OBJECTIVE: This paper aims to explore how the player actions of Klondike Solitaire relate to cognitive functions and to what extent the digital biomarkers derived from these player actions are indicative of MCI. METHODS: First, 11 experts in the domain of cognitive impairments were asked to correlate 21 player actions to 11 cognitive functions. Expert agreement was verified through intraclass correlation, based on a 2-way, fully crossed design with type consistency. On the basis of these player actions, 23 potential digital biomarkers of performance for Klondike Solitaire were defined. Next, 23 healthy participants and 23 participants living with MCI were asked to play 3 rounds of Klondike Solitaire, which took 17 minutes on average to complete. A generalized linear mixed model analysis was conducted to explore the differences in digital biomarkers between the healthy participants and those living with MCI, while controlling for age, tablet experience, and Klondike Solitaire experience. RESULTS: All intraclass correlations for player actions and cognitive functions scored higher than 0.75, indicating good to excellent reliability. Furthermore, all player actions had, according to the experts, at least one cognitive function that was on average moderately to strongly correlated to a cognitive function. Of the 23 potential digital biomarkers, 12 (52%) were revealed by the generalized linear mixed model analysis to have sizeable effects and significance levels. The analysis indicates sensitivity of the derived digital biomarkers to MCI. CONCLUSIONS: Commercial off-the-shelf games such as digital card games show potential as a complementary tool for screening and monitoring cognition. TRIAL REGISTRATION: ClinicalTrials.gov NCT02971124; https://clinicaltrials.gov/ct2/show/NCT02971124.