Selection, Optimization, and Compensation Strategies Used by Older Adults to Live Well With Technology: Qualitative Study
Bibliographic record
Abstract
Background: With rapid digitalization, technologies are increasingly integrated as part of our everyday lives and are becoming increasingly essential for individuals to participate in society. Technology presents opportunities to support healthy aging. Examples include digital health monitoring and opportunities to maintain social connectedness through online platforms. However, the processes in which older adults select and integrate technologies into their daily lives have not been well examined. objectives: This study uses the Selection, Optimization, and Compensation (SOC) model to understand how older adults integrate technology into their everyday lives to live well. The two key research questions are as follows: (1) How do older adults describe their technology use and their choices, analyzed with respect to SOC processes? (2) How do older adults perceive that technology is a part of living well? Methods: A descriptive qualitative study was conducted. Purposive sampling was used to recruit older adults who were aged 55 years and older, were living in the community, spoke and understood English, and resided in Australia. Five in-person focus groups comprising 20 older adults were conducted. Data were analyzed using hybrid inductive and deductive reflexive thematic analyses, based on the SOC model. Results: All participants resided in Brisbane, Queensland. Older adults selected technology intentionally to enhance different aspects of their daily lives. Perceived "involuntary" selection of technology could lead to feelings of resentment or frustration. Optimization strategies included self-monitoring, integrating technology into daily routines, digital literacy and proficiency, and problem-solving skills. Compensatory strategies included choosing alternative technology that suited participants' abilities or skills and seeking support through informal or formal avenues. Conclusions: These findings provide important considerations for technology developers to design technology in collaboration with older adults to ensure that they align with needs and preferences. Digital literacy is not sufficient to empower older adults to use technology; when empowering older adults to use technology, service providers should also consider facilitating other intrinsic and extrinsic resources and skills.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".