The Use of Technology Among Persons With Memory Concerns and Their Caregivers in the United States During the COVID-19 Pandemic: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Stay-at-home orders and other public health measures designed to mitigate the spread of COVID-19 have increased isolation among persons with memory concerns (PWMCs: individuals diagnosed with cognitive impairment or Alzheimer disease or related dementias). The pandemic has also exacerbated challenges for family members who care for PWMCs. Although technology has demonstrated the potential to improve the social connections and mental health of PWMCs and their family caregivers (CGs), previous research shows that older adults may be reluctant to adopt new technologies. OBJECTIVE: We aimed to understand why and how some PWMCs and their CGs altered their use of mainstream technology, such as smartphones and fitness trackers, and assistive technology to adapt to lifestyle changes (eg, increased isolation) during the COVID-19 pandemic. METHODS: Using data collected in 20 qualitative interviews from June to August 2020 with 20 PWMCs and family CG dyads, we assessed changes in and barriers to everyday technology use following the implementation of COVID-19 mitigation strategies in the United States. Zoom videoconferencing was utilized to conduct the interviews to protect the health of the participants who were primarily older adults. RESULTS: Using qualitative thematic analysis, we identified 3 themes that explained motivations for using technology during a pandemic: (1) maintaining social connections, (2) alleviating boredom, and (3) increasing CG respite. Results further revealed lingering barriers to PWMC and CG adoption of technologies, including: (1) PWMC dependence upon CGs, (2) low technological literacy, and (3) limitations of existing technology. CONCLUSIONS: This in-depth investigation suggests that technology can provide PWMCs with more independence and offer CGs relief from CG burden during periods of prolonged isolation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".