Mobility among older adults: Deconstructing the effects of motility and movement on wellbeing
Bibliographic record
Abstract
Daily mobility has been shown to contribute to the wellbeing of older adults, as it promotes healthy and independent living. However, very little is known about how the complex relationships between locations, geographic environments and daily mobility relate to wellbeing. In the current paper, we rely on the concept of ‘motility’– defined as potential mobility– and the concept of ‘movement’– defined as actual mobility– to take a step forwards in disentangling the relationship between mobility and wellbeing. We further examine how both motility and movement relate to two complementary definitions of wellbeing: hedonic wellbeing as a measurement of happiness, and eudaimonic wellbeing as the actualisation of an individual’s human potential. To investigate this relationship, we draw up a conceptual framework stressing pathways linking mobility to wellbeing, which we empirically test using structural equation modelling on a stratified sample of 470 older adults. We first quantitatively confirm that motility is defined by access, competences, appropriation and attitudes to modes of transportation. We then observe that motility has direct effects on eudaimonic wellbeing and, to a lesser extent, on hedonic wellbeing. Part of the motility effects on wellbeing are mediated by movement. Separating mobility into motility and movement stresses the independent and complementary role that potential and realised mobility play in shaping older adults’ wellbeing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".