Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial
Advances in web and mobile technologies have created efficiencies relating to collection, analysis and interpretation of dietary intake data. This study compared the impact of two levels of nutrition support: (1) low personalization, comprising a web-based personalized nutrition feedback report gene...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Nutrients |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6643/12/11/3334 |
| _version_ | 1797549376628850688 |
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| author | Megan E. Rollo Rebecca L. Haslam Clare E. Collins |
| author_facet | Megan E. Rollo Rebecca L. Haslam Clare E. Collins |
| author_sort | Megan E. Rollo |
| collection | DOAJ |
| description | Advances in web and mobile technologies have created efficiencies relating to collection, analysis and interpretation of dietary intake data. This study compared the impact of two levels of nutrition support: (1) low personalization, comprising a web-based personalized nutrition feedback report generated using the Australian Eating Survey<sup>®</sup> (AES) food frequency questionnaire data; and (2) high personalization, involving structured video calls with a dietitian using the AES report plus dietary self-monitoring with text message feedback. Intake was measured at baseline and 12 weeks using the AES and diet quality using the Australian Recommended Food Score (ARFS). Fifty participants (aged 39.2 ± 12.5 years; Body Mass Index 26.4 ± 6.0 kg/m<sup>2</sup>; 86.0% female) completed baseline measures. Significant (<i>p</i> < 0.05) between-group differences in dietary changes favored the high personalization group for total ARFS (5.6 points (95% CI 1.3 to 10.0)) and ARFS sub-scales of meat (0.9 points (0.4 to 1.6)), vegetarian alternatives (0.8 points (0.1 to 1.4)), and dairy (1.3 points (0.3 to 2.3)). Additional significant changes in favor of the high personalization group occurred for proportion of energy intake derived from energy-dense, nutrient-poor foods (−7.2% (−13.8% to −0.5%)) and takeaway foods sub-group (−3.4% (−6.5% to 0.3%). Significant within-group changes were observed for 12 dietary variables in the high personalization group vs. one variable for low personalization. A higher level of personalized support combining the AES report with one-on-one dietitian video calls and dietary self-monitoring resulted in greater dietary change compared to the AES report alone. These findings suggest nutrition-related web and mobile technologies in combination with personalized dietitian delivered advice have a greater impact compared to when used alone. |
| first_indexed | 2024-03-10T15:13:49Z |
| format | Article |
| id | doaj.art-58629e66fb5e4cfc8481c579f5b35f8a |
| institution | Directory Open Access Journal |
| issn | 2072-6643 |
| language | English |
| last_indexed | 2024-03-10T15:13:49Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nutrients |
| spelling | doaj.art-58629e66fb5e4cfc8481c579f5b35f8a2023-11-20T19:04:15ZengMDPI AGNutrients2072-66432020-10-011211333410.3390/nu12113334Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized TrialMegan E. Rollo0Rebecca L. Haslam1Clare E. Collins2Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, AustraliaPriority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, AustraliaPriority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Callaghan, NSW 2308, AustraliaAdvances in web and mobile technologies have created efficiencies relating to collection, analysis and interpretation of dietary intake data. This study compared the impact of two levels of nutrition support: (1) low personalization, comprising a web-based personalized nutrition feedback report generated using the Australian Eating Survey<sup>®</sup> (AES) food frequency questionnaire data; and (2) high personalization, involving structured video calls with a dietitian using the AES report plus dietary self-monitoring with text message feedback. Intake was measured at baseline and 12 weeks using the AES and diet quality using the Australian Recommended Food Score (ARFS). Fifty participants (aged 39.2 ± 12.5 years; Body Mass Index 26.4 ± 6.0 kg/m<sup>2</sup>; 86.0% female) completed baseline measures. Significant (<i>p</i> < 0.05) between-group differences in dietary changes favored the high personalization group for total ARFS (5.6 points (95% CI 1.3 to 10.0)) and ARFS sub-scales of meat (0.9 points (0.4 to 1.6)), vegetarian alternatives (0.8 points (0.1 to 1.4)), and dairy (1.3 points (0.3 to 2.3)). Additional significant changes in favor of the high personalization group occurred for proportion of energy intake derived from energy-dense, nutrient-poor foods (−7.2% (−13.8% to −0.5%)) and takeaway foods sub-group (−3.4% (−6.5% to 0.3%). Significant within-group changes were observed for 12 dietary variables in the high personalization group vs. one variable for low personalization. A higher level of personalized support combining the AES report with one-on-one dietitian video calls and dietary self-monitoring resulted in greater dietary change compared to the AES report alone. These findings suggest nutrition-related web and mobile technologies in combination with personalized dietitian delivered advice have a greater impact compared to when used alone.https://www.mdpi.com/2072-6643/12/11/3334behavioral nutrition interventiondigital healthpersonalized nutritiontelehealth |
| spellingShingle | Megan E. Rollo Rebecca L. Haslam Clare E. Collins Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial Nutrients behavioral nutrition intervention digital health personalized nutrition telehealth |
| title | Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial |
| title_full | Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial |
| title_fullStr | Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial |
| title_full_unstemmed | Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial |
| title_short | Impact on Dietary Intake of Two Levels of Technology-Assisted Personalized Nutrition: A Randomized Trial |
| title_sort | impact on dietary intake of two levels of technology assisted personalized nutrition a randomized trial |
| topic | behavioral nutrition intervention digital health personalized nutrition telehealth |
| url | https://www.mdpi.com/2072-6643/12/11/3334 |
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