Technologies for health and wellness in later life
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,008 | 0,010 |
| Science ouverte | 0,000 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,028 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».