Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach
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Abstract
Background: Although mobile technologies such as smartphone apps are promising means for motivating people to adopt a healthier lifestyle (mHealth apps), previous studies have shown low adoption and continued use rates. Developing the means to address this issue requires further understanding of mHealth app nonusers and adoption processes. This study utilized a stage model approach based on the Precaution Adoption Process Model (PAPM), which proposes that people pass through qualitatively different motivational stages when adopting a behavior. Objective: To establish a better understanding of between-stage transitions during app adoption, this study aimed to investigate the adoption process of nutrition and fitness app usage, and the sociodemographic and behavioral characteristics and decision-making style preferences of people at different adoption stages. Methods: Participants (N=1236) were recruited onsite within the cohort study Konstanz Life Study. Use of mobile devices and nutrition and fitness apps, 5 behavior adoption stages of using nutrition and fitness apps, preference for intuition and deliberation in eating decision-making (E-PID), healthy eating style, sociodemographic variables, and body mass index (BMI) were assessed. Results: Analysis of the 5 behavior adoption stages showed that stage 1 (“unengaged”) was the most prevalent motivational stage for both nutrition and fitness app use, with half of the participants stating that they had never thought about using a nutrition app (52.41%, 533/1017), whereas less than one-third stated they had never thought about using a fitness app (29.25%, 301/1029). “Unengaged” nonusers (stage 1) showed a higher preference for an intuitive decision-making style when making eating decisions, whereas those who were already “acting” (stage 4) showed a greater preference for a deliberative decision-making style (F4,1012=21.83, P<.001). Furthermore, participants differed widely in their readiness to adopt nutrition and fitness apps, ranging from having “decided to” but not yet begun to act (stage 2; nutrition: 6.88%, 70/1017; fitness: 9.23%, 95/1029) to being “disengaged” following previous adoption (stage 5; nutrition: 13.77%, 140/1017; fitness: 15.06%, 155/1029). Conclusions: Using a behavior stage model approach to describe the process of adopting nutrition and fitness apps revealed motivational stage differences between nonusers (being “unengaged,” having “decided not to act,” having “decided to act,” and being “disengaged”), which might contribute to a better understanding of the process of adopting mHealth apps and thus inform the future development of digital interventions. This study highlights that new user groups might be better reached by apps designed to address a more intuitive decision-making style.
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Research has shown that non-intention to change behavior predicts non-engagement for nutrition and fitness apps among the general population.
“Research has indeed shown that this is true for the adoption of nutrition and fitness apps among the general population [48].”
From Use and Effect of Embodied Conversational Agents for Improving Eating Behavior and Decreasing Loneliness Among Community-Dwelling Older Adults: Randomized Controlled Trial (2022) · cites this paper as [48]
The Results and Discussion describe nonusers who had not considered using an app or had decided not to use one, supporting a link between intentions about app adoption and nonuse. They do not show that lack of intention to change eating or physical-activity behavior predicts non-engagement. The Discussion notes that some people use these apps without intending to change their behavior. (LLM, conf=0.85)
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- Authors
- König, L. M., Sproesser, G., Schupp, H. T., & Renner, B.
- Affiliations
- University of Konstanz (DE)
- Corresponding author
- König, Laura M
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2018-03-13
- Volume
- 6
- Issue
- 3
- Pages
- e55
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC5871740
- OpenAlex
- W2793273676
- Cited by
- 98
- References
- 91
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 13 · 2023: 18 · 2022: 12 · 2021: 14 · 2020: 11 · 2019: 7 · 2018: 1
- FWCI
- 12.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ERef Bayreuth (University of Bayreuth) (submittedVersion)
- KOPS (University of Konstanz) (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (01EL1420A)
- Universität Konstanz
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- mobile health apps, nutrition apps, fitness apps, app adoption, Precaution Adoption Process Model, body mass index, decision-making preferences, health behavior change
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 ›
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