Describing the Process of Adopting Nutrition and Fitness Apps: Behavior Stage Model Approach

Abstract

BackgroundAlthough 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.ObjectiveTo 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.MethodsParticipants (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.ResultsAnalysis 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).ConclusionsUsing 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.

Training Dieters to Eat Intuitively with a Mobile Application

“…While there are many apps available to help decrease caloric intake, most are time consuming due to the need to log in everything consumed on a daily basis; a feature that may decrease their popularity and usage (König, Sproesser, Schupp, & Renner, 2018).…”

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“…These apps are seen as an inclusive and scalable support system for healthy food [7]. However, the prevalence of such apps remains limited and many suffer from self-selection bias [27] and low rates of retention [36]. Several factors appear to limit the impact of such technology-based interventions.…”

“…carrying bags or products [12,36]. Third, studies show that current diet-related mHealth are usually primarily retained by users with an above average interest in nutrition [28], an interest that is particularly not often prevalent amongst sociodemographic segments prone to diet-related diseases [13,27].…”

An App to Improve Eating Habits of Adolescents and Young Adults (Challenge to Go): Systematic Development of a Theory-Based and Target Group–Adapted Mobile App Intervention (Preprint)

“…These include health apps in various areas, such as nutrition, fitness, wellness, diagnostics or therapy [25], but systematic studies in the area of mHealth are scarce [26][27][28], and studies also highlight missing user acceptance of nutrition apps, for which the relatively high usage effort might be a reason [29,30]. Rohde et al concluded that app usage in the long-term is influenced by user-and app-related acceptance factors.…”