Can older people remember medication reminders presented using synthetic speech?

Wolters, Maria; Johnson, Christine; Campbell, Pauline; DePlacido, Christine; McKinstry, Brian

doi:10.1136/amiajnl-2014-002820

Cited by 11 publications

(13 citation statements)

References 41 publications

(35 reference statements)

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“…We use two sets of semantic fluency test data produced by English and Korean native speakers. The English data were collected from the studies Wolters et al (2014) and Iveson (2015), whereas the Korean data were collected by members of the NLP*CL lab, KAIST, South Korea for a study of acoustic and linguistic structure of category fluency data including Wolters et al (2016). We outline the details of each dataset below.…”

Section: Discussionmentioning

confidence: 99%

Automatic Scoring of Semantic Fluency

Kim

Wolters

et al. 2019

Front. Psychol.

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In neuropsychological assessment, semantic fluency is a widely accepted measure of executive function and access to semantic memory. While fluency scores are typically reported as the number of unique words produced, several alternative manual scoring methods have been proposed that provide additional insights into performance, such as clusters of semantically related items. Many automatic scoring methods yield metrics that are difficult to relate to the theories behind manual scoring methods, and most require manually-curated linguistic ontologies or large corpus infrastructure. In this paper, we propose a novel automatic scoring method based on Wikipedia, Backlink-VSM, which is easily adaptable to any of the 61 languages with more than 100k Wikipedia entries, can account for cultural differences in semantic relatedness, and covers a wide range of item categories. Our Backlink-VSM method combines relational knowledge as represented by links between Wikipedia entries ( Backlink model ) with a semantic proximity metric derived from distributional representations ( vector space model ; VSM). Backlink-VSM yields measures that approximate manual clustering and switching analyses, providing a straightforward link to the substantial literature that uses these metrics. We illustrate our approach with examples from two languages (English and Korean), and two commonly used categories of items (animals and fruits). For both Korean and English, we show that the measures generated by our automatic scoring procedure correlate well with manual annotations. We also successfully replicate findings that older adults produce significantly fewer switches compared to younger adults. Furthermore, our automatic scoring procedure outperforms the manual scoring method and a WordNet-based model in separating younger and older participants measured by binary classification accuracy for both English and Korean datasets. Our method also generalizes to a different category (fruit), demonstrating its adaptability.

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Section: Discussionmentioning

confidence: 99%

Automatic Scoring of Semantic Fluency

Kim

Wolters

et al. 2019

Front. Psychol.

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“…As some studies have demonstrated (Craig and Schroeder, 2017, 2019) perception improves when synthetic voices have better sound quality. Current technology has considerably improved in the past years, and synthetic voices now sound more intelligible and natural (Wolters et al, 2014). Some companies are now using Artificial Intelligence (A.I.)…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Due to artificiality, listeners need more cognitive resources to process synthetic voices, and therefore, the cognitive effort is greater than when processing human voices. This artificiality affects the recall process (Wolters et al, 2014). However, this extensive use of cognitive resources could be beneficial.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Synthetic versus human voices in audiobooks: The human emotional intimacy effect

Rodero

Lucas

2021

New Media & Society

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Human voices narrate most audiobooks, but the fast development of speech synthesis technology has enabled the possibility of using artificial voices. This raises the question of whether the listeners’ cognitive processing is the same when listening to a synthetic or a human voice telling a story. This research aims to compare the listeners’ perception, creation of mental images, narrative engagement, physiological response, and recognition of information when listening to stories conveyed by human and synthetic voices. The results showed that listeners enjoyed stories narrated by a human voice more than a synthetic one. Also, they created more mental images, were more engaged, paid more attention, had a more positive emotional response, and remembered more information. Speech synthesis has experienced considerable progress. However, there are still significant differences versus human voices, so that using them to narrate long stories, such as audiobooks do, is difficult.

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“…The success of reminder use has been variable, and the analysis of reminder use is based on a research methodology that relies on self-reported adherence as the primary factor for the validation of medication use [ 19 - 23 ]. Self-reported adherence can be an overestimated and biased account of medication adherence [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Voice Assistant Reminders and the Latency of Scheduled Medication Use in Older Adults With Pain: Descriptive Feasibility Study

Shade¹,

Rector²,

Kupzyk³

2021

JMIR Form Res

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Background Pain is difficult to manage in older adults. It has been recommended that pain management in older adults should include both nonpharmacologic and pharmacologic strategies. Unfortunately, nonadherence to pain medication is more prevalent than nonadherence to any other chronic disease treatment. Technology-based reminders have some benefit for medication adherence, but adherence behavior outcomes have mostly been verified by self-reports. Objective We aimed to describe objective medication adherence and the latency of medication use after a voice assistant reminder prompted participants to take pain medications for chronic pain. Methods A total of 15 older adults created a voice assistant reminder for taking scheduled pain medications. A subsample of 5 participants were randomly selected to participate in a feasibility study, in which a medication event monitoring system for pain medications was used to validate medication adherence as a health outcome. Data on the subsample’s self-assessed pain intensity, pain interference, concerns and necessity beliefs about pain medications, self-confidence in managing pain, and medication implementation adherence were analyzed. Results In the 5 participants who used the medication event monitoring system, the overall latency between voice assistant reminder deployment and the medication event (ie, medication bottle cap opening) was 55 minutes. The absolute latency (before or after the reminder) varied among the participants. The shortest average time taken to open the cap after the reminder was 17 minutes, and the longest was 4.5 hours. Of the 168 voice assistant reminders for scheduled pain medications, 25 (14.6%) resulted in the opening of MEMS caps within 5 minutes of the reminder, and 107 (63.7%) resulted in the opening of MEMS caps within 30 minutes of the reminder. Conclusions Voice assistant reminders may help cue patients to take scheduled medications, but the timing of medication use may vary. The timing of medication use may influence treatment effectiveness. Tracking the absolute latency time of medication use may be a helpful method for assessing medication adherence. Medication event monitoring may provide additional insight into medication implementation adherence during the implementation of mobile health interventions.

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Can older people remember medication reminders presented using synthetic speech?

Cited by 11 publications

References 41 publications

Automatic Scoring of Semantic Fluency

Automatic Scoring of Semantic Fluency

Synthetic versus human voices in audiobooks: The human emotional intimacy effect

Voice Assistant Reminders and the Latency of Scheduled Medication Use in Older Adults With Pain: Descriptive Feasibility Study

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