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Accepted for/Published in: JMIR Formative Research

Date Submitted: May 26, 2021
Date Accepted: Mar 17, 2022

The final, peer-reviewed published version of this preprint can be found here:

Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial

Okaniwa F, Yoshida H

Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial

JMIR Form Res 2022;6(6):e30630

DOI: 10.2196/30630

PMID: 35675107

PMCID: 9218879

Evaluation of Dietary Management Using AI and Human Interventions: A Non-Randomized Controlled Trial

  • Fusae Okaniwa; 
  • Hiroshi Yoshida

ABSTRACT

Background:

There has been an increase in personal health records with the increased use of wearable devices and smartphone applications to improve health. Traditional health promotion programs by human professionals have limitations in terms of cost and reach. Due to labor shortages and to save costs, there has been a growing emphasis in the medical field on building health guidance systems using artificial intelligence (AI). AI will replace advanced human tasks to some extent in the future. However, it is difficult to sustain behavioral change through technology alone at present.

Objective:

We examine the effectiveness of AI and human interventions to encourage dietary management behaviors. This study investigates if AI alone can effectively encourage healthy behaviors or human interventions are needed to achieve and sustain health-related behavioral change. In addition, we elucidate the conditions for maximizing the effect of AI on health improvement. We hypothesize that the combination of AI and human interventions will maximize their effectiveness.

Methods:

We conducted a three-month experiment by recruiting participants who were users of a smartphone diet management app. We recruited 102 participants and divided them into 3 groups. Treatment group (I) received text messages using the standard features of the app (AI-based text message intervention). Treatment group (II) received video messages from a companion in addition to the text messages (combined text message and human video message intervention by AI). The control group used the app to keep a dietary record, but no feedback was provided (no intervention). We examine the participants’ continuity and the effects on physical indicators.

Results:

The combined AI and video messaging had a lower dropout rate from the program compared to the control group, and the Cox proportional-hazards model estimate showed a hazard ratio of 0.078, which was statistically significant at the 5% level. Further, human intervention with AI and video messaging significantly reduced the body fat percentage of participants after 3 months compared to the control group, and the rate of reduction was greater in the group with more individualized intervention. The AI-based text messages affected body mass index but had no significant effect on body fat percentage.

Conclusions:

This experiment shows that it is challenging to sustain participants' healthy behavior with AI intervention alone. The results also suggest that even if the health information conveyed is the same, the information conveyed by humans and AI is more effective in improving health than the information sent by AI alone. The support received from the companion in the form of video messages may have promoted voluntary health behaviors. It is noteworthy that companions were competent even though they were non-experts. This means that person-to-person communication is crucial for health interventions.


 Citation

Please cite as:

Okaniwa F, Yoshida H

Evaluation of Dietary Management Using Artificial Intelligence and Human Interventions: Nonrandomized Controlled Trial

JMIR Form Res 2022;6(6):e30630

DOI: 10.2196/30630

PMID: 35675107

PMCID: 9218879

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