Impact of age and comorbid heart failure on the utility of smart voice-assistant devices
Bibliographic record
Abstract
Abstract Aims The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyse the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and results Subgroup analysis (HF vs. caregivers and younger vs. older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, the median age was 51 (34–65) years and 21 (40%) were HF patients. The HF subgroup showed a significantly lower percentage of agreement compared with caregivers (95% vs. 99%, P = 0.03), and both the HF and older subgroups tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use compared with caregivers and younger individuals. Conclusion This subgroup analysis highlights important differences in the performance of a voice-assistant–based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Study Registration ClinicalTrials.gov Identifier: NCT04508972.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".