Cited by 82Mobile Health and mHealth ApplicationsNutrition, Health and Food BehaviorNutritional Studies and Diet

Using Hand-Held Computer Technologies to Improve Dietary Intake

Atienza, Audie A., King, Abby C., Oliveira, Brian M., Ahn, David K., Gardner, Christopher D.·2008·American Journal of Preventive Medicine

Abstract

Background Portable hand-held information technology offers much promise not only in assessing dietary intake in the real world, but also in providing dietary feedback to individuals. However, stringent research designs have not been employed to examine whether it can be effective in modifying dietary behaviors. The purpose of this pilot study was to evaluate the efficacy of a hand-held computer (i.e., personal digital assistant [PDA]) for increasing vegetable and whole-grain intake over 8 weeks in mid-life and older adults, using a randomized study design.Methods Twenty-seven healthy adults aged > or =50 were randomized and completed the 8-week study. Intervention participants received an instructional session and a PDA programmed to monitor their vegetable and whole-grain intake levels twice per day and to provide daily individualized feedback, goal-setting, and support. Controls received standard, age-appropriate, written nutritional education materials. Dietary intake was assessed via the Block Food Frequency Questionnaire at baseline and 8 weeks.Results Relative to controls, intervention participants reported significantly greater increases in vegetable servings (1.5-2.5 servings/day; p=0.02), as well as a trend toward greater intake of dietary fiber from grains (3.7-4.5 servings/day; p=0.10).Conclusions This study's findings provide preliminary evidence that using portable hand-held technology to provide daily individualized feedback on dietary behavior in the real world can increase the dietary intake of healthy food groups.

Integrity signals

Registry

Crossref · 10.1016/j.amepre.2008.01.034

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

The citation lists haven’t been fetched yet

References 39 · Cited by 82

Authors
Atienza, A. A., King, A. C., Oliveira, B. M., Ahn, D. K., & Gardner, C. D.
Affiliations
  • National Institutes of Health (US)
  • Stanford Medicine (US)
Corresponding author
Atienza, Audie A.
Venue
American Journal of Preventive Medicine
Publisher
Elsevier BV
Published
2008-06
Volume
34
Issue
6
Pages
514-518
ISSN
0749-3797 (ISSN-L)
OpenAlex
W2083713577
Cited by
82
References
39
Citations by year
2025: 1 · 2023: 2 · 2022: 1 · 2021: 10 · 2020: 4 · 2019: 4 · 2018: 6 · 2017: 8 · 2016: 8 · 2015: 8 · 2014: 3 · 2013: 6 · 2012: 7
FWCI
8.99
Type
journal article
Language
en
Open access
closed
Locations
  • American Journal of Preventive Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • U.S. Public Health Service (5T32 H107034)
  • National Heart, Lung, and Blood Institute (T32HL007034)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
Keywords
personal digital assistants, dietary behavior change, vegetable consumption, whole grain intake, dietary fiber, older adults, middle-aged adults, goal setting, nutrition education
MeSH terms
Diet, Female, Humans, Male, Middle Aged, Pilot Projects, Socioeconomic Factors, Body Mass Index, Computers, Handheld

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review