Improving a Mobile Telepresence Robot for People With Alzheimer Disease and Related Dementias: Semistructured Interviews With Stakeholders
Bibliographic record
Abstract
BACKGROUND: By 2050, nearly 13 million Americans will have Alzheimer disease and related dementias (ADRD), with most of those with ADRD or mild cognitive impairment (MCI) receiving home care. Mobile telepresence robots may allow persons with MCI or ADRD to remain living independently at home and ease the burden of caregiving. The goal of this study was to identify how an existing mobile telepresence robot can be enhanced to support at-home care of people with MCI or ADRD through key stakeholder input. OBJECTIVE: The specific aims were to assess what applications should be integrated into the robot to further support the independence of individuals with MCI or ADRD and understand stakeholders' overall opinions about the robot. METHODS: We conducted in-person interviews with 21 stakeholders, including 6 people aged >50 years with MCI or ADRD living in the community, 9 family caregivers of people with MCI or ADRD, and 6 clinicians who work with the ADRD population. Interview questions about the robot focused on technology use, design and functionality, future applications to incorporate, and overall opinions. We conducted a thematic analysis of the data obtained and assessed the patterns within and across stakeholder groups using a matrix analysis technique. RESULTS: Overall, most stakeholders across groups felt positively about the robot's ability to support individuals with MCI or ADRD and decrease caregiver burden. Most ADRD stakeholders felt that the greatest benefits would be receiving help in emergency cases and having fewer in-person visits to the doctor's office. Caregivers and clinicians also noted that remote video communication with their family members using the robot was valuable. Adding voice commands and 1-touch lifesaving or help buttons to the robot were the top suggestions offered by the stakeholders. The 4 types of applications that were suggested included health-related alerts; reminders; smart-home-related applications; and social, entertainment, or well-being applications. Stakeholders across groups liked the robot's mobility, size, interactive connection, and communication abilities. However, stakeholders raised concerns about their physical stability and size for individuals living in smaller, cluttered spaces; screen quality for those with visual impairments; and privacy or data security. CONCLUSIONS: Although stakeholders generally expressed positive opinions about the robot, additional adaptations were suggested to strengthen functionality. Adding applications and making improvements to the design may help mitigate concerns and better support individuals with ADRD to live independently in the community. As the number of individuals living with ADRD in the United States increases, mobile telepresence robots are a promising way to support them and their caregivers. Engaging all 3 stakeholder groups in the development of these robots is a critical first step in ensuring that the technology matches their needs. Integrating the feedback obtained from our stakeholders and evaluating their effectiveness will be important next steps in adapting telepresence robots.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".