Technologies for health and wellness in later life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Having a healthy lifestyle is important for developing and maintaining optimal health across the lifespan and can positively contribute to quality of life and reduced care dependency in later life AGE-WELL has identified Healthy Lifestyles and Wellness as one of its eight Challenge Areas for research and innovation in the AgeTech sector. Technology may improve health and wellbeing by enabling individuals to track, monitor, and manage their health behaviours. Digital tools (AgeTech) may help older adults remain socially, mentally, and physically active in the face of age-related cognitive and physical decline, but also help promote longevity and improve long-term health. Older adults also represent a growing segment of the burgeoning digital wellness industry, with the US senior market projected to reach $900 million by 2022 (Consumer Technology Association, 2019). Given the digital health and wellness market's growth trajectory and evidenced potential for technology in promoting physical and mental wellbeing in later life, there is a need to understand the key emerging trends and opportunities for AgeTech. This poster provides an overview of initial work in the Challenge Area being led by a team at the STAR Institute at Simon Fraser University and outlines future directions for AGE-WELL's research and innovation agenda. Method An environmental scan The environmental scan draws on academic articles, grey literature, targeted organization websites, and internet searches, to identify digital products, projects, policies, and initiatives that promote the key domains of healthy aging: physical, social, cognitive, and mental wellbeing. Analysis of trends utilizes the PESTEL framework (political, economic, social, technological, environmental, and legal factors) to explore the forces driving or restricting innovation in the use of AgeTech to support healthy lifestyles and aging. Results and Discussion Preliminary results from the environmental scan demonstrate how technology can play an important role in supporting individuals to adopt and maintain healthy lifestyle behaviours and live an engaged and meaningful life. Current and emerging technologies address multiple health domains such as physical and social health outcomes. Despite increased demand from older adults for health and wellness technologies, there are limited age-specific solutions to support healthy aging. Although many commercially available products identified were not specifically designed for older adults, they incorporated features that may promote healthy lifestyles in later life. While there are an increasing number of health and wellness technologies aimed at the senior market, many of these digital solutions are targeted at those with reduced cognitive and physical functioning rather than the 'healthy old'. There is a need to prioritize AgeTech solutions which focus on this growing market. The results of the environmental scan will provide the basis for research and innovation activities in AgeTech for healthy lifestyles and wellness.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it