Analysis of Training Behavior in Users of a Fitness App: Cross-Sectional Study

TL;DR

This study explores how users of a fitness app engage with their training, finding that factors like gender, motivation, and goals influence retention and behavior differently.

Contribution

The study identifies distinct factors affecting retention, training frequency, and adherence in a fitness app, emphasizing the need to treat them separately.

Findings

Males, subscribers, and users with intrinsic motivation showed higher retention in the fitness app.

Training frequency varied by sex, body type, and activity level, but not by fitness goals or perceived difficulty.

Adherence was higher among thin users compared to midbuilt users, but not influenced by motivation type.

Abstract

Mobile health (mHealth) apps are increasingly being used to promote physical activity (PA) and can support exercise uptake and maintenance. Despite their potential, these tools face high dropout rates and inconsistent adherence, posing a significant challenge. Understanding how users engage with fitness apps is essential for improving user experience and health outcomes.

This study aims to analyze user behavior patterns in the Mammoth Hunters (MH) fitness app (Mammoth Hunters SL), focusing on retention (days from registration to user’s last recorded training session), average weekly training frequency, and adherence (alignment between planned and actual training). We examined how these outcomes are influenced by sociodemographic, motivational, and other variables.

This cross-sectional study involved 2771 Mammoth Hunters app users. In a subsample (n=289), training data were complemented by motivational data acquired through online surveying via an ad-hoc scale (internal consistency >0.83) based on the self-determination theory (SDT). Descriptive statistics and nonparametric tests (Kruskal-Wallis, Dunn post-hoc, and Spearman correlation) were used to assess correlation between sociodemographic, motivation, and training behavior variables.

Mean retention (days) was significantly higher among males than females (135 vs 109, respectively; P<.01), users in the subscription vs free plan (154 vs 81; P<.001), active or very active individuals vs inactive, midbuilt vs thin body types (132 vs 120; P=.001), and those with slightly lower BMI. Users pursuing antiaging or muscle gain goals showed longer retention than those aiming to lose weight (gain: 132, antiaging: 128, lose weight: 116; P<.001). Average weekly frequency (sessions per week) of training was statistically significantly different by sex (male: 1.9 vs female: 1.8; P=.04), body type (thin: 1.96 vs mid: 1.77; P=.04), activity level (very active: 2.05 vs inactive: 1.83; P=.04), and motivation type (extrinsic introjected motivation correlated positively: r=0.17; P<.05), but did not correlate with perceived difficulty or fitness goals. Adherence, defined as actual vs targeted training frequency, was only significantly different among body types, with thin users showing higher adherence than the midbuilt group (57% vs 52.1%; P=.02). Intrinsic motivation showed a positive correlation with retention (r=0.19; P=.002), as did identified motivation (r=0.12; P<.05).

This study shows that retention is influenced by demographic factors, with males, subscribers, previously active, midbuilds, those aiming to gain muscle, and individuals with autonomous types (ie, intrinsic and identified) of motivation displaying greater long-term participation. These findings provide valuable preliminary insight into the complexities of exercise training behavior in apps. They suggest that training frequency, retention, and adherence do not respond to the same factors. App developers, researchers, and trainers should assess these variables separately and develop strategies accordingly.

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