Declared gamification elements in urban mobility apps of Latin American App Store storefronts and their association with user ratings

INTRODUCTION

Gamification is defined as the use of game design elements in contexts that are not games, with the aim of supporting motivation and engagement.(1,2) A scoping review of 280 research papers identified 15 game elements, among which points, badges and leaderboards were the most prevalent.(3) Elements are not interchangeable, since experimental work shows that different design elements produce different effects on motivation,(2) and taxonomies have been proposed to describe them in a consistent way.(4)

In transport and mobility, gamification has been used mainly to change travel behaviour. A systematic review of 49 papers found heterogeneity in objectives, methods and observed evidence, and no consensus on the effects of gamification in the field.(5) A mapping of the transport literature reported that goals and challenges, together with points, were the elements most commonly used.(6) Field studies and prototypes have combined tracking, feedback, challenges, rewards and rankings to promote sustainable modes, for example in smartphone apps for commuter cycling,(7,8) multimodal travel,(9-12) walking and public transport,(13-16) school travel(17) and urban lifestyles.(18) Reviews of persuasive technology and of mobility apps classify the strategies used in this field.(19,20)

Most of this evidence comes from interventions designed and deployed by researchers. Much less is known about what commercial apps already offer to their users. App stores allow such questions to be studied, because every listing declares the features of an app and displays a public rating. Content analyses of store listings have described gamification in health and fitness apps,(21,22) smoking cessation apps,(23) oral hygiene apps(24) and hypertension self-management apps,(25) and have related app features to user ratings.(26,27) Store data are also regional by construction, because users in different countries differ in how they select, price and rate apps.(28) To our knowledge, no equivalent description exists for urban mobility apps in Latin America, a region where public transport, ride-hailing and bike-sharing coexist and where the use of mobility apps is conditioned by digital inequality.(29-32)

Regional studies of mobility services in Latin America have examined ride-hailing adoption and its relation with public transit( 33-35) and satisfaction with bike-sharing,( 36) but they do not describe the app layer through which these services are used. Declared gamification could differ by mobility mode, because modes differ in how often users return to the app, and by country, because markets differ in competition and regulation. It could also be associated with higher ratings if game elements increase satisfaction and engagement,(37,38) although evidence from physical activity apps is mixed.( 39,40)

User ratings are the usual outcome in these analyses, because they are public, comparable across apps and used as a proxy of perceived value, although their validity as a measure of satisfaction is debated.( 22,26,41) The objective was to estimate the prevalence of declared gamification elements in urban mobility app listings of six Latin American App Store storefronts and to examine its variation by mobility mode and storefront and its association with user ratings. Four hypotheses were formulated. H1: the proportion of apps declaring gamification differs across mobility modes. H2: the proportion differs across storefronts. H3: apps declaring gamification have a different mean user rating from apps that do not. H4: the number of declared elements is associated with the mean rating.

METHODS

This was a cross-sectional content analysis of public app listings, reported in line with the STROBE items applicable to descriptive cross-sectional studies. The unit of analysis was the app, identified by its store identifier. For the comparison between storefronts, the unit was the listing, that is, an app in a given storefront. No human participants were involved and no personal data of users were collected; the data are metadata published by developers and aggregate ratings displayed by the store.

Listings were obtained from the public iTunes Search API( 42) for the storefronts of Brazil, Mexico, Colombia, Argentina, Chile and Peru, selected as six large storefronts of the region. The snapshot available from the public API was downloaded programmatically, without a login. For each storefront, 41 search terms in Spanish, Portuguese and English were submitted with the software entity and a limit of 200 results, giving 246 queries. Terms covered public transport (for example transporte público, horários de ônibus, metro), cycling and bike-sharing (bicicleta, ciclovía, ecobici), walking (caminar, caminhada), carpooling (carpool, carona, viajes compartidos), ride-hailing and taxi (taxi, mototaxi, ride hailing) and micromobility (monopatín, patinete, movilidad urbana). Because the API returns at most 200 results per query, the corpus is a search-based sample of listings and not a census of each storefront.

The 41 terms were grouped into six families, with 9 terms for public transport, 8 for cycling and bike-sharing, 5 for walking, 4 for carpooling, 7 for ride-hailing and taxi and 8 for micromobility and related urban mobility topics. Each term was queried separately in each storefront, and responses were stored without modification. Records of the same app returned by several queries within a storefront were merged, and the terms that returned each record were kept. Apps were identified by the store identifier, so that the same app listed in several storefronts could be linked. The release date reported by the API defined the release year, and the price and rating fields were those displayed in the storefront.

Listings were retained when the app was first released between 2013 and 2025, when its primary category was travel, navigation, utilities, lifestyle, business, productivity, social networking or maps, and when its name and description contained vocabulary of at least one mobility mode. A regional filter kept listings with evidence of a Latin American service area (a country or city name, a regional web domain) or, in its absence, a description in Spanish or Portuguese without references to places outside the region.

Mobility mode was assigned from a dictionary of terms in Spanish, Portuguese and English applied to the name (weighted three times) and to the first 400 characters of the description, and the mode with the highest score was retained. Modes were public transit, ride-hailing and taxi, cycling and bike-sharing, walking and carpooling. Two apps whose mode was clearly mis-assigned by the dictionary were reassigned to ride-hailing after reading.

Declared gamification was defined as a listing statement that the app offers points, badges or achievements, rankings, challenges, or rewards linked to use (loyalty points, cashback, prizes or redeemable virtual coins). Generic mentions, such as a company mission, price tiers or discount coupons without a use-linked reward, were not counted. Because coding was done by a single AI coder without a second human coder, inter-coder agreement could not be estimated, and the coding is pending verification by the authors. An app was classified as gamified when it declared at least one element, and the number of elements (0 to 5) was recorded.

Other variables were release year (grouped as 2013-2016, 2017-2020 and 2021-2025), price (free or paid), the number of storefronts in which the app was listed, the average user rating and the number of ratings. For apps listed in several storefronts, the rating and rating count of the storefront with the largest count were used. Gamification was coded at the app level and assigned to all its listings.

No formal sample size calculation was made, because the number of apps was determined by the listings returned by the searches and by the eligibility criteria. Precision is therefore reported through confidence intervals, and the number of gamified apps, which determines the precision of every comparison, is stated with each result.

The search script, the dictionaries, the exclusion list with reasons and the adjudication of gamification candidates were kept as files, and the analysis script was run twice from the stored responses to confirm that all metrics were identical. Candidate texts for gamification and the list of excluded apps are available to the authors for verification. No retrieval dates are reported, because the data describe the snapshot available from the public API.

For the comparison of modes, the variable was the proportion of apps declaring any element, estimated with Wilson 95% confidence intervals. Independence between mode and gamification was tested with the Pearson statistic and a Monte Carlo permutation test (20000 permutations, fixed seed), because expected counts were small. Each mode was compared with all other modes with a sample odds ratio, a Woolf interval (0.5 added to empty cells) and Fisher exact p. Modes with fewer than 5 apps were described but not tested. The decision criterion was a permutation p below 0.05.

For the comparison of storefronts, the variable was the proportion of listings declaring any element. A logistic generalized estimating equation with independence working correlation and standard errors robust to clustering by app estimated odds ratios against Brazil, and a joint Wald test of the five coefficients was the decision criterion. A permutation test across apps, keeping each app's set of storefronts, was run as a check.

For ratings, the variable was the average user rating of apps with at least 5 ratings. The difference in means between gamified and other apps was reported with a percentile bootstrap interval (5000 resamples) and a permutation p, and an adjusted difference was estimated by linear regression with HC3 errors including the base-10 logarithm of the rating count. The model initially planned, with mode, release cohort and price, could not be estimated with so few gamified apps and was replaced by this parsimonious model. Thresholds of at least 1 and at least 25 ratings were used as sensitivity analyses. For the number of elements, Spearman correlation with a bootstrap interval and a permutation p was used. Four hypotheses were tested without correction for multiplicity, and all p values are exploratory. Analyses were done in Python with pandas, SciPy and statsmodels, with fixed seeds, and were reproducible across two runs.

RESULTS

The searches returned 23,217 listings of 7,325 distinct apps. Restricting to the release window left 17,211 listings, the category filter 11,018, the mode filter 7,180 and the regional filter 2,856. The eligibility review excluded 327 of 942 apps, because their service area was outside Latin America (158), they were not urban mobility services (101), they sold intercity tickets (36), they were driver-side apps (25) or they were delivery, air or freight apps (7). The final sample comprised 615 apps and 1,766 listings (Table 1).

Ride-hailing and taxi apps were the most numerous mode (271 apps), followed by public transit (224), cycling and bike-sharing (67) and carpooling (52). Only 1 app was classified as walking. Release years ranged from 2013 to 2025, 99.5% of apps were free, 46.7% were listed in more than one storefront and 28.3% had no ratings.

By release period, 1 gamified apps were first released in 2013-2016, 1 in 2017-2020 and 6 in 2021-2025. Their listings covered carpooling, public transit, ride-hailing and cycling apps, and none was a walking app. The number of listings by storefront was 405 in Brazil, 267 in Mexico, 269 in Colombia, 275 in Argentina, 263 in Chile and 287 in Peru. Apps with at least 5 ratings numbered 328, and the remaining apps had too few ratings for an average to be informative. Among the 8 gamified apps, 5 met this rating threshold.

H4. Number of declared elements

Among apps with at least 5 ratings, Spearman rho between the number of elements and the mean rating was 0.03 (95% CI, −0.09 to 0.14; permutation p = 0.571). Only 3 rated apps declared one element, 1 declared two and 1 declared three, so the association rested on 5 gamified apps in total.

DISCUSSION

Few urban mobility app listings in the six storefronts declared game elements, and these were concentrated in apps for sharing vehicles rather than spread across modes. Consequently, the first hypothesis was supported by the permutation test, whereas the second, third and fourth were not supported, and the estimates for the last two were too imprecise to exclude differences of up to about one point in either direction.

CONCLUSIONS

The prevalence of declared gamification in urban mobility apps of six Latin American storefronts was low, with a small fraction of apps declaring at least one element and rewards as the commonest element.

The proportion of gamified apps differed across mobility modes and was highest in carpooling apps.

The six storefronts did not differ detectably in the proportion of apps declaring gamification.

User ratings of apps declaring gamification did not differ detectably from those of the other apps, and the number of elements was not associated with ratings, with estimates compatible with differences of up to about one point in either direction.

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