Using Smart Speaker Technology for Health and Well-being in an Older Adult Population: Pre-Post Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Although smart speaker technology is poised to help improve the health and well-being of older adults by offering services such as music, medication reminders, and connection to others, more research is needed to determine how older adults from lower socioeconomic position (SEP) accept and use this technology. OBJECTIVE: This study aimed to investigate the feasibility of using smart speakers to improve the health and well-being of low-SEP older adults. METHODS: A total of 39 adults aged between 65 and 85 years who lived in a subsidized housing community were recruited to participate in a 3-month study. The participants had a smart speaker at their home and were given a brief orientation on its use. Over the course of the study, participants were given weekly check-in calls to help assist with any problems and newsletters with tips on how to use the speaker. Participants received a pretest and posttest to gauge comfort with technology, well-being, and perceptions and use of the speaker. The study staff also maintained detailed process notes of interactions with the participants over the course of the study, including a log of all issues reported. RESULTS: At the end of the study period, 38% (15/39) of the participants indicated using the speaker daily, and 38% (15/39) of the participants reported using it several times per week. In addition, 72% (28/39) of the participants indicated that they wanted to continue using the speaker after the end of the study. Most participants (24/39, 62%) indicated that the speaker was useful, and approximately half of the participants felt that the speaker gave them another voice to talk to (19/39, 49%) and connected them with the outside world (18/39, 46%). Although common uses were using the speaker for weather, music, and news, fewer participants reported using it for health-related questions. Despite the initial challenges participants experienced with framing questions to the speaker, additional explanations by the study staff addressed these issues in the early weeks of the study. CONCLUSIONS: The results of this study indicate that there is promise for smart speaker technology for low-SEP older adults, particularly to connect them to music, news, and reminders. Future studies will need to provide more upfront training on query formation as well as develop and promote more specific options for older adults, particularly in the area of health and well-being.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".