Perspectives on Technology Use in the Context of Caregiving for Persons With Dementia: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Examining ways to support persons with dementia and their caregivers to help minimize the disease's impact on individuals, families, and society is critical. One emerging avenue for support is technology (eg, smartphones and smart homes). OBJECTIVE: Given the increasing presence of technology in caregiving, it is pertinent to appreciate whether and how technology can be most useful to a care partner's everyday life. This study aims to further understand care partner technology use, attitudes, and the potential role of off-the-shelf technologies (eg, smartphones and smart homes) in supporting caregiving from the perspective of care partners for persons with dementia. METHODS: We conducted a telephone cross-sectional survey using random digit dialing with 67 self-identified care partners of persons with dementia across one Canadian province. Participants were asked about attitudes toward technology, barriers to and facilitators for technology use, technology use with caregiving, and demographic information. Eight open-ended questions were analyzed using content analysis; 2 closed-ended questions about comfort with and helpfulness of technology (rated on a scale of 1 to 10) were analyzed with frequencies. From these data, an in-depth semistructured interview was created, and 10 (15%) randomly sampled care partners from the initial collection of 67 care partners were interviewed approximately 1 year later, with responses analyzed using content analysis. RESULTS: Frequency analysis rated on a scale of 1 to 10 suggested that care partners were comfortable with technology (wearable technology mean 7.94, SD 2.02; smart home technology mean 6.94, SD 2.09), although they rated the helpfulness of technology less strongly (mean 5.02, SD 2.85). Qualitatively, care partners described using technology for functional tasks and some caregiving. Barriers to technology use included cost, lack of knowledge, security or privacy concerns, and undesirable features of technology. Facilitators included access to support and the presence of desirable features. Some care partners described merging technology with caregiving and reported subsequent benefits. Others stated that technology could not be adopted for caregiving due to the degree of impairment, fear of negative consequences for the person living with dementia, or due to incongruity with the caregiving philosophy. Furthermore, care partners noted that their technology use either increased or was unchanged as they moved through the COVID-19 pandemic. CONCLUSIONS: The 2 analyses were conducted separately, but there was notable overlap in the data, suggesting temporal stability of identified content. Both analyses suggested care partners' relative comfort with technology and its use, but other care partners noted concerns about integrating technology and caregiving. Care partners' reports of increased technology use throughout the COVID-19 pandemic may also suggest that the pandemic impacted their perceptions of the usefulness of technology, being influenced by the requirements of their reality. Future investigations should examine how to support care partners in adopting relevant technology.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".