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European thought is often said to be a gift to the rest of the world, but what if there is no gift as such? What if there is only an economy where every giving is also a taking, and every taking is also a giving? This book extends the question of economies by making a case for an \"economy of thought\" and a \"political economy.\" It argues that all thinking and doing presupposes taking, and therefore giving, as the price to pay for taking; or that there exists a \"cost of living,\" which renders the idea of free thinking and living untenable. The argument is developed against the Enlightenment directive to think for oneself as the means of becoming autonomous and shows that this \"light,\" given to the rest of the world as a gift, turns out to be nothing.

The “one-off” approach of systematic reviews is no longer sustainable; we need to move toward producing “living” evidence syntheses (i.e., comprehensive, based on rigorous methods, and up-to-date). This implies rethinking the evidence synthesis ecosystem, its infrastructure, and management. The three distinct production systems—primary research, evidence synthesis, and guideline development—should work together to allow for continuous refreshing of synthesized evidence and guidelines. A new evidence ecosystem, not just focusing on synthesis, should allow for bridging the gaps between evidence synthesis communities, primary researchers, guideline developers, health technology assessment agencies, and health policy authorities. This network of evidence synthesis stakeholders should select relevant clinical questions considered a priority topic. For each question, a multidisciplinary community including researchers, health professionals, guideline developers, policymakers, patients, and methodologists needs to be established and commit to performing the initial evidence synthesis and keeping it up-to-date. Encouraging communities to work together continuously with bidirectional interactions requires greater incentives, rewards, and the involvement of health care policy authorities to optimize resources. A better evidence ecosystem with collaborations and interactions between each partner of the network of evidence synthesis stakeholders should permit living evidence syntheses to justify their status in evidence-informed decision-making.

Background The gait speed and handgrip strength represented the core determinants of physical frailty and sarcopenia, which were reported to be associated with cognitive impairment and decline. Different physical measures might differentially affect cognitive changes, such as higher-level cognitive change and global cognitive decline. This study examined the differential associations of gait speed and handgrip strength with 10-year cognitive changes among community-dwelling older people. Methods Participants aged 60 years and over living in the community were invited for study. Gait speed and handgrip strength were classified into 5 groups based on quintiles at baseline. Cognitive functions were assessed using the Mini-Mental State Examination (MMSE) and Digit Symbol Substitution Test (DSST) every 2 years from baseline for a period of 10 years. Linear mixed effects models were used to determine the role of gait speed and handgrip strength in the prediction of 10-year cognitive changes by adjusting covariates, including age, gender, education, depressive symptoms, marital status, smoking status, instrumental activities of daily life (IADL), Charlson Comorbidity Index (CCI), and body mass index (BMI) at baseline. Results A total of 1096 participants were enrolled in the study. The mean age was 69.4 ± 5.8 years and 50.9% were male. The slowest gait speed group showed a significantly greater decline in the DSST scores over 10 years than the highest group (estimate = 0.28 and P  = 0.003), but not in the MMSE scores (estimate = 0.05 and P  = 0.078). The lowest handgrip strength group showed a significantly greater decline in the MMSE scores than the highest group (estimate = 0.06 and P  = 0.039) and in the DSST scores than the highest two quintiles (estimate = 0.20 and P  = 0.033 for the fourth quintile; estimate = 0.20 and P  = 0.040 for the highest quintile) over 10-year follow-up. Conclusions A slow gait speed could predict 10-year cognitive decline using DSST, and a low handgrip strength could predict 10-year cognitive decline using MMSE in addition to DSST. Thus both physical measures are lined to cognitive decline but there may be different mechanisms between brain and physical functions.

Evidence-based clinical guidelines have the potential to improve health care and health outcomes. Living guidelines methods provide an approach to ensuring guidelines are always up-to-date, maximizing this potential. However, to date, most work on living guidelines has been conducted in high income countries. The objective of this study is to explore the barriers and facilitators to the development, adaptation, and use of living guidelines among evidence-based guideline developers in low- and middle-income countries (LMICs). We used a descriptive qualitative study design. We employed purposive and snowball sampling techniques to recruit guideline developers from LMICs and World Health Organization offices to participate in online, semistructured interviews. Data were analysed using a thematic approach with NVivo 20 software. Ethics approval was granted by Monash University. We interviewed 18 participants from LMICs (Colombia, India, Iran, Indonesia, Argentina, and Malaysia) and WHO offices (including headquarters, regional offices, and country offices). Two main themes emerged, along with six associated subthemes. The main themes were as follows: (1) People in LMICs want living guidelines and (2) Resource limitations and their implications for living guidelines in LMICs. Our research identified that guideline developers in LMICs have a strong desire to both develop and use living guidelines, but are currently limited by important barriers. Initiatives to support development, adaptation, and use of living guidelines in LMICs may help overcome barriers and meet the need for living guidelines in LMICs. It is also essential to design strategies that overcome identified barriers to developing, adapting, and implementing living guidelines, such as a lack of resources, delays in updates, and limited accessibility. This study looked at how living (continuously updated) approaches can be used to develop, adapt, and use clinical guidelines in settings with limited resources, and explored the pros and cons of each. The findings revealed a strong need to develop and use living guidelines in low-resource settings despite challenges, such as resource scarcity, delays in updating, and limited access to these guidelines. Overall, the findings revealed that living guidelines were worthwhile in LMICs despite all of the related challenges. •Living guidelines methods provide an approach to ensuring that guidelines are always up-to-date.•Most work on living guidelines has been conducted in high income countries.•We explored barriers and facilitators to developing, adapting & using living guidelines in in LMICs.•Guideline developers in LMICs want to develop and use living guidelines but there are barriers.•Initiatives to overcome these barriers are need to support the need for living guidelines in LMICs.

Ambient assisted living (AAL) is a common name for various artificial intelligence (AI)-infused applications and platforms that support their users in need in multiple activities, from health to daily living. These systems use different approaches to learn about their users and make automated decisions, known as AI models, for personalizing their services and increasing outcomes. Given the numerous systems developed and deployed for people with different needs, health conditions, and dispositions toward the technology, it is critical to obtain clear and comprehensive insights concerning AI models used, along with their domains, technology, and concerns, to identify promising directions for future work. This study aimed to provide a scoping review of the literature on AI models in AAL. In particular, we analyzed specific AI models used in AАL systems, the target domains of the models, the technology using the models, and the major concerns from the end-user perspective. Our goal was to consolidate research on this topic and inform end users, health care professionals and providers, researchers, and practitioners in developing, deploying, and evaluating future intelligent AAL systems. This study was conducted as a scoping review to identify, analyze, and extract the relevant literature. It used a natural language processing toolkit to retrieve the article corpus for an efficient and comprehensive automated literature search. Relevant articles were then extracted from the corpus and analyzed manually. This review included 5 digital libraries: IEEE, PubMed, Springer, Elsevier, and MDPI. We included a total of 108 articles. The annual distribution of relevant articles showed a growing trend for all categories from January 2010 to July 2022. The AI models mainly used unsupervised and semisupervised approaches. The leading models are deep learning, natural language processing, instance-based learning, and clustering. Activity assistance and recognition were the most common target domains of the models. Ambient sensing, mobile technology, and robotic devices mainly implemented the models. Older adults were the primary beneficiaries, followed by patients and frail persons of various ages. Availability was a top beneficiary concern. This study presents the analytical evidence of AI models in AAL and their domains, technologies, beneficiaries, and concerns. Future research on intelligent AAL should involve health care professionals and caregivers as designers and users, comply with health-related regulations, improve transparency and privacy, integrate with health care technological infrastructure, explain their decisions to the users, and establish evaluation metrics and design guidelines. PROSPERO (International Prospective Register of Systematic Reviews) CRD42022347590; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022347590.

We define a set of universal, irreducible and essential set of material conditions for achieving basic human wellbeing, along with indicators and quantitative thresholds, which can be operationalized for societies based on local customs and preferences. We draw support for this decent living standard (DLS) from different accounts of basic justice, including the capability approach and basic needs. The DLS goes beyond existing multidimensional poverty indicators by comprehensively addressing living conditions and the means of social participation. The DLS offers a normative basis to develop minimum wage and reference budgets, and to assess the environmental impacts, such as climate change, of eradicating poverty.

This study aims to extend the definition of quality of life (QoL) for adults with autism spectrum disorder (ASD, n = 180, ages 23–60) by: (1) characterizing the heterogeneity of normative outcomes (employment, independent living, social engagement) and objective QoL (physical health, neighborhood quality, family contact, mental health issues); and (2) identifying predictors of positive normative outcomes and good objective QoL. Findings of an exploratory latent class analysis identified three groups of adults with ASD–Greater Dependence, Good Physical and Mental Health, and Greater Independence. Findings indicate that better daily living skills, better executive function, and more maternal warmth are associated with assignment to better outcome groups. Findings have implications for interventions designed to enhance achievement of normative outcomes and objective QoL.

Assisted living residences have become prominent sites of long-term residential care for older adults with dementia. Estimates derived from national data indicate that seven out of ten residents in these residences have some form of cognitive impairment, with 29 percent having mild impairment, 23 percent moderate impairment, and 19 percent severe impairment. More than one-third of residents display behavioral symptoms, and of these, 57 percent have a medication prescribed for their symptoms. Only a minority of cognitively impaired residents reside in a dementia special care unit, where admission and discharge policies are more supportive of their needs. Policy-relevant recommendations from our study include the need to examine the use of psychotropic medications and cultures related to prescribing, better train assisted living staff to handle medications and provide nonpharmacological treatments, use best practices in caring for people with dementia, and promote consumer education regarding policies and practices in assisted living. Adapted from the source document.

Over the last decade, there has been considerable and increasing interest in the development of Active and Assisted Living (AAL) systems to support independent living. The demographic change towards an aging population has introduced new challenges to today’s society from both an economic and societal standpoint. AAL can provide an arrary of solutions for improving the quality of life of individuals, for allowing people to live healthier and independently for longer, for helping people with disabilities, and for supporting caregivers and medical staff. A vast amount of literature exists on this topic, so this paper aims to provide a survey of the research and skills related to AAL systems. A comprehensive analysis is presented that addresses the main trends towards the development of AAL systems both from technological and methodological points of view and highlights the main issues that are worthy of further investigation.

Background: Ambiguities and anomalies in the Activity of Daily Living (ADL) patterns indicate deviations from Wellness. The monitoring of lifestyles could facilitate remote physicians or caregivers to give insight into symptoms of the disease and provide health improvement advice to residents; Objective: This research work aims to apply lifestyle monitoring in an ambient assisted living (AAL) system by diagnosing conduct and distinguishing variation from the norm with the slightest conceivable fake alert. In pursuing this aim, the main objective is to fill the knowledge gap of two contextual observations (i.e., day and time) in the frequent behavior modeling for an individual in AAL. Each sensing category has its advantages and restrictions. Only a single type of sensing unit may not manage composite states in practice and lose the activity of daily living. To boost the efficiency of the system, we offer an exceptional sensor data fusion technique through different sensing modalities; Methods: As behaviors may also change according to other contextual observations, including seasonal, weather (or temperature), and social interaction, we propose the design of a novel activity learning model by adding behavioral observations, which we name as the Wellness indices analysis model; Results: The ground-truth data are collected from four elderly houses, including daily activities, with a sample size of three hundred days plus sensor activation. The investigation results validate the success of our method. The new feature set from sensor data fusion enhances the system accuracy to (98.17% ± 0.95) from (80.81% ± 0.68). The performance evaluation parameters of the proposed model for ADL recognition are recorded for the 14 selected activities. These parameters are Sensitivity (0.9852), Specificity (0.9988), Accuracy (0.9974), F1 score (0.9851), False Negative Rate (0.0130).