Article in Journal of nutrition in gerontology and geriatrics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.4field-weighted citation impact, top 9% of its field
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
3 · Its place in the literature
Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
10 authors at 2 institutions in 1 country.
Rahi R ShahDivision of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.ORCID 0000-0003-0732-7164
Claudia C DixonDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
Michael J FowlerDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
Tiffany M DriesseDivision of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Xiaohui LiangDepartment of Computer Science, University of Massachusetts Boston, Boston, Massachusetts, USA.ORCID 0000-0003-4064-2393
Caroline E SummerourDivision of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Danae C GrossDepartment of Nutrition, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.ORCID 0000-0003-3892-9408
Hillary B SpanglerDivision of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.ORCID 0000-0003-4027-4113
David H LynchDivision of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.ORCID 0000-0002-2512-9367
John A BatsisDivision of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.ORCID 0000-0002-0845-4416
University of North Carolina at Chapel Hill · USUniversity of Massachusetts Boston · US
Funding
Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
UNIV OF NORTH CAROLINA CLINICAL NUTRITION RESEARCH UNITP30DK056350 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Venkata Saroja Voruganti · 1999 to 2026
$31.6M
UNC-CH Summer Research Training in Aging for Medical StudentsT35AG038047 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JAN BUSBY-WHITEHEAD · 2010 to 2026
$1.4M
SCH: INT: Collaborative Research: Exploiting Voice Assistant Systems for Early Detection of Cognitive DeclineR01AG067416 · NIA · UNIVERSITY OF MASSACHUSETTS BOSTON · PI LIANG, XIAOHUI · 2019 to 2022
$1.2M
Mobile Health Obesity Wellness Intervention in Rural Older Adults_Res1K23AG051681 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BATSIS, JOHN A. · 2016 to 2022
Dietary assessments are important clinical tools used by Registered Dietitians (RDs). Current methods pose barriers to accurately assess the nutritional intake of older adults due to age-related increases in risk for cognitive decline and more complex health histories. Our qualitative study explored whether implementing Voice assistant systems (VAS) could improve current dietary recall from the perspective of 20 RDs. RDs believed the implementing VAS in dietary assessments of older adults could potentially improve patient accuracy in reporting food intake, recalling portion sizes, and increasing patient-provider efficiency during clinic visits. RDs reported that low technology literacy in older adults could be a barrier to implementation. Our study provides a better understanding of how VAS can better meet the needs of both older adults and RDs in managing and assessing dietary intake.
Indexed as
DieteticsNutritionistsAgedHumansDietary recalldietitianolder adultstechnologyvoice-assistant systems
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
Using Voice Assistant Systems to Improve Dietary Recall among Older Adults: Perspectives of Registered Dietitians. · full record | OpenQuestion