“Aging Means to Me… That I Feel Lonely More Often”? An Experimental Study on the Effects of Age Simulation Regarding Views on Aging

The relationship between sedentary behavior and depression in older adults: A systematic review and meta-analysis

BACKGROUND

The association between sedentary behavior and depression in older adults has been reported in several studies; however, study results on the relationship between the different types of sedentary behavior and depression are not uniform. The purpose of this study was to quantitatively review the association between depression and total sedentary behavior, mentally active sedentary behavior, and passive sedentary behavior.

METHODS

We systematically searched for observational studies on the association between sedentary behavior and depression in older adults using PubMed, Embase, and Web of Science databases. A random effects model was used to combine odds ratios (ORs) and 95 % confidence intervals (CIs). In addition, we performed subgroup and sensitivity analyses to explore potential sources of heterogeneity.

RESULTS

Five longitudinal and 10 cross-sectional studies with a total of 144,161 participants were included in the meta-analysis. The OR of total sedentary behavior associated with depression was 1.49 (95 % CI 1.24-1.79). The combined OR value of mentally active sedentary behavior and depression was 0.82 (95 % CI 0.69-0.97), and no association was found between passive sedentary behavior and the risk of depression.

LIMITATIONS

We were unable to find dose-response relationships between the different types of sedentary behavior and depression because there were too few studies with raw data to analyze.

CONCLUSION

Total sedentary behavior may increase the risk of depression in older adults, whereas mentally active sedentary behavior is associated with a lower risk of depression. Differentiating between the types of sedentary behavior can inform interventions to prevent or ameliorate depression in older adults.

Aging in 10 Minutes: Do Age Simulation Suits Mimic Physical Decline in Old Age? Comparing Experimental Data with Established Reference Data

INTRODUCTION

Age simulation suits are increasingly used in health care education. However, empirical evidence that quantifies the simulated performance losses in established geriatric tests and compares those declines with reference data of older adults is scarce.

METHODS

In a standardized lab setting, we compared performance of N = 61 participants (46 middle-aged, 15 young adults) with and without age simulation suit, for example in the Timed Up and Go Test (+dual task), Short Physical Performance Battery, grip strength, and 30-Second-Chair- Standing Test. Additionally, we compared the results with suit to established reference values of older adults in different age groups.

RESULTS

Reduced performance was observed in both groups when wearing the suit, yet to different degrees dependent on the assessment and user age. For one, larger declines were observed in more challenging and complex tasks across age groups. In addition, comparisons with reference values revealed age-differential "instant aging" effects.

DISCUSSION

A simulated "fourth age," where frailty and impairments are accumulating, was not reached in the majority of assessments, especially not among younger participants. In conclusion, existing age simulation suits may have some educational and empathy potential, but so far, they fail in simulating the age period with most serious functional loss.

Aging Suit and Motor Performance in Young Adults: Effects Depend on Why the Suit is Worn!

By 2030, one in six people worldwide will be at least 60 years old, with associated sensorimotor changes that complicate daily activities. Aging suits simulate these conditions for younger individuals to enhance their empathy and understanding of older adults' challenges. Research on aging suits has recently increased, with evidence that these suits impair younger adults' motor performance, but a greater understanding of the motor changes induced by aging suits and to identify any factors that may modify them more research is needed. In this research, we explored how the presentation of the suit and the wearer's physical activity level affected motor performance. We divided 95 younger adults into two groups: (a) one informed about the suit's aging properties (aging suit group); and (b) one in which participants were told that it was a posture training suit (posture training group). Each group was further divided into low and high physical activity levels by a median-split method. Participants completed two fine motor tasks (i.e., placing and building bricks) and two gross motor tasks (i.e., heel-to-toe walking and figure-of-eight walking/running) with and without the suit. The aging suit significantly affected motor performance on all tasks, but participants aware that it was an aging suit performed worse on the figure-of-eight walking/running task than participants who thought it was a posture training suit. In addition, for the same motor task, the groups engaged in low physical activity were more affected by wearing the suit than those engaged in high physical activity. Thus, how aging suits are presented and the general activity level of wearers significantly influenced the aging suits' effects on motor performance. The findings from this study can guide future researchers in validating the use of these suits in larger studies.

"If this is what it means to be old…": a mixed methods study on the effects of age simulation on views on aging and perceptions of age-related impairments

Age simulation suits are a promising tool to increase empathy and to promote positive attitudes toward older adults. However, studies have largely focused on (young) healthcare professionals, are probably biased by social desirability, and have not addressed participants' views of the aging process triggered by the simulation. The current work combines two studies addressing effects of aging suits on both general and personal views on aging among heterogeneous samples, and exploring spontaneous associations during the simulation. In study 1, N = 165 adults (M = 37.1 years, SD = 15.4, range 18-74 years) answered questionnaires containing general views regarding older adults ("old people are…") as well as personal perceptions ("aging means to me…") before and after wearing an aging suit. In study 2, young adults (N = 22; M = 24.8 years, SD = 4.3, range 20-38 years) and middle-aged adults (N = 41; M = 60.8 years, SD = 6.9, range 40-75 years) carried out established geriatric assessments with and without aging suit, and spontaneous impressions on the instant aging experience were recorded. Findings indicated negative shifts in both general and personal views on aging measures in both age groups (d = .30 to d = .44). Analyses of qualitative data resulted in seven main themes, e.g., "strain/coordination", "future me", "empathy/insight". Group comparisons revealed higher frequencies of future-self related thoughts among middle-aged adults, whereas younger adults mentioned predominantly physical effects of the suit. In conclusion, applying age simulation suits might evoke unintended negative views on aging. In comparison with young adults, middle-aged adults showed broader reflections including thoughts related to emotions, future-self, and potential struggles of older people.

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Background

Advances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.

Objectives

The aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?

Materials and methods

A systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011-2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.

Results

Searches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan-Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.

Conclusion

There is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.

Implications

In a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.