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Influence of Pokémon Go on Physical Activity: Study and Implications
Tim Althoff, Ryen W. White, Eric Horvitz
TL;DR
Insufficient physical activity remains common despite its health importance, and the effectiveness of widely adopted movement-based mobile games has been uncertain. This study combines search queries with wearable step data to examine Pokémon Go’s effects over three months. It reports increased activity over 30 days, including a 1,473-step daily gain among particularly engaged users and an estimated 144 billion added US steps.
Problem
Physical inactivity is widespread, while Pokémon Go’s ability to increase walking and reach less-active populations had not been determined.
Method
The study combines search-engine queries and Microsoft Band step data for 31,793 users, identifying 1,420 Pokémon Go players and comparing them with controls.
Results
Pokémon Go significantly increased physical activity over 30 days; particularly engaged users added 1,473 steps per day or 26%, and the estimated US total was 144 billion steps.
Takeaways & Limitations
The game increased activity across age, gender, weight-status, and prior-activity groups and appears able to complement traditional interventions while reaching low-activity populations.
Takeaways & Limitations
The study population was not a random US sample, gameplay and engagement were inferred from search queries, and follow-up was limited.
Abstract
from arXiv · showhide
Physical activity helps people maintain a healthy weight and reduces the risk for several chronic diseases. Although this knowledge is widely recognized, adults and children in many countries around the world do not get recommended amounts of physical activity. While many interventions are found to be ineffective at increasing physical activity or reaching inactive populations, there have been anecdotal reports of increased physical activity due to novel mobile games that embed game play in the physical world. The most recent and salient example of such a game is Pokémon Go, which has reportedly reached tens of millions of users in the US and worldwide. We study the effect of Pokémon Go on physical activity through a combination of signals from large-scale corpora of wearable sensor data and search engine logs for 32 thousand users over a period of three months. Pokémon Go players are identified through search engine queries and activity is measured through accelerometry. We find that Pokémon Go leads to significant increases in physical activity over a period of 30 days, with particularly engaged users (i.e., those making multiple search queries for details about game usage) increasing their activity by 1473 steps a day on average, a more than 25% increase compared to their prior activity level ($p<10^{-15}$). In the short time span of the study, we estimate that Pokémon Go has added a total of 144 billion steps to US physical activity. Furthermore, Pokémon Go has been able to increase physical activity across men and women of all ages, weight status, and prior activity levels showing this form of game leads to increases in physical activity with significant implications for public health. We find that Pokémon Go is able to reach low activity populations while all four leading mobile health apps studied in this work largely draw from an already very active population.
1. INTRODUCTION
Physical inactivity remains a major health and public-health burden, while Pokémon Go offers a widely adopted game-based approach whose effectiveness for increasing walking is unresolved. The study examines whether this phenomenon can stimulate physical activity at societal scale and across populations.
- Motivation: Only 21% of US adults meet official physical activity guidelines, while less than 30% of US high school students achieve 60 minutes daily.Physical inactivity is also associated with substantial worldwide mortality and economic burden.
- Motivation: Pokémon Go combines augmented reality with the real world and requires players to move physically, reaching 25 million active US users and 40 million worldwide.Its broad adoption motivates viewing it as a potential societal-scale physical-activity intervention.
- Present Work: The study analyzes wearable sensor data and search-engine query logs for 31,793 users over three months to measure Pokémon Go’s effect on physical activity.It identifies 1,420 Pokémon Go users, compares them with controls and other mobile health apps, and estimates public-health impact.
- Research Questions: The research asks whether Pokémon Go increases activity, how long effects persist, who is reached, how it compares with health apps, and its US-wide impact.The study frames mobile games as possible complements rather than replacements for established physical-activity interventions.
2. METHODS
The study combines search-query evidence of Pokémon Go use with wearable step measurements to compare activity changes around game uptake. It also uses demographic and query examples to support user identification and analysis.
- Data Sources: Search-engine queries mentioning “pokemon” identify likely Pokémon Go players, while Microsoft Band daily steps measure physical activity.The analysis compares activity before and after strong evidence that a user started playing.
- Study Population: The main population contains 31,793 US Microsoft users who linked wearable and online-activity data, including 1,420 high-confidence Pokémon Go players.Changes are compared with a random control sample of 50,000 US Microsoft Band users, using self-reported demographics.
- User Identification: Table 1 provides representative experiential and non-experiential Pokémon Go queries used in the user-identification procedure.Examples include Pokémon attribute terminology and desktop-play references.
2.1 Identifying Pokémon Go Users Through Search Queries
Pokémon Go players are identified from experiential search queries that indicate likely gameplay rather than general interest. The first such query serves as an operational estimate of when gameplay began.
- Query Annotation: 454 frequent unique queries mentioning “pokemon” were manually annotated as experiential or non-experiential based on whether they indicated likely gameplay.Experiential queries target specific aspects of game play rather than news coverage or general interest.
- Player Identification: 1,420 of 25,446 querying users, or 5.6%, issued an experiential Pokémon Go query, closely matching an independent 5.9% estimate of regular US users.The method uses each user’s first experiential query as t0, a proxy for starting gameplay.
- Timing Assumption: The first experiential query can overestimate gameplay start time if users search several days after beginning, making estimated activity effects more conservative when effects are non-negative.The opposite timing error is considered less likely because experiential queries target specific gameplay aspects.
2.2 Measuring Physical Activity
Physical activity is measured as daily steps from wearable accelerometer and gyrometer data around each user’s first experiential query. The design uses matched controls and checks that tracking exposure does not explain observed differences.
- Activity Measurement: Daily steps are measured from the Microsoft Band’s three-axis accelerometer and gyrometer for 30 days before and after t0.Accelerometer-based measures are used instead of subjective surveys, which can overestimate activity.
- Activity Measurement: 792 identified Pokémon Go users tracked steps on at least one day before and after t0 and were included in the activity dataset.The tracking threshold had little effect because similar results appeared with stricter requirements.
- Control Design: Control users are randomly sampled US Microsoft wearable users, with matched t0 timing distributions to align observation periods.This accounts for shared temporal influences such as summer weather or vacation time.
- Illustrative Time Series: Two example users increased daily steps from below 5,000 before t0 to around 15,000 after their first experiential query.The examples illustrate the individual-level pattern examined across the broader study population.
- Measurement Check: Wear time remained effectively constant, with the ratio between Pokémon Go and control groups changing by less than one percent.This supports attributing step-count differences to activity rather than simply wearing the device longer.
2.3 Study Population Demographics
The identified Pokémon Go users were younger and less often female than average wearable-data users, while their overweight and obesity proportions resembled the control group. Their average activity level was below that of the control population, suggesting the game attracted less-active users.
- Pokémon Go users were younger and much less often female than the average user in the wearable dataset.
- The proportion of overweight and obese users was similar in the Pokémon Go and control groups.
- Pokémon Go users had lower average activity than the control group, indicating that the game attracted less-active users.
2.4 Measuring the Impact of Pokémon Go
The study measures activity changes around users’ first experiential Pokémon Go query, compares them with a control group and other health apps, and examines engagement, demographics, guideline attainment, and potential public-health effects.
- Randomly sampled reference points provide the control group’s comparison timing because control users have no experiential Pokémon Go queries.
- The analysis measures average daily steps from 30 days before to 30 days after each user’s first experiential query.Daily averages are measured separately for Pokémon Go and control users, with missing-step days excluded and Gaussian smoothing used for graph readability.
- The study tests whether greater Pokémon Go engagement, reflected by more experiential queries, corresponds to larger physical-activity increases.
- Individual effects are related to age, gender, BMI, and prior activity level to assess whether benefits are concentrated in particular user groups.
- The study compares Pokémon Go with four leading mobile health applications that represent the state of the art in consumer health apps.
- Public-health analyses estimate added US steps, changes in the share meeting activity guidelines, and potential life-expectancy effects.The guideline analysis uses approximately 8,000 daily steps as the equivalent threshold; the life-expectancy analysis assumes a sustained increase of 1,000 daily steps.
3. RESULTS
Pokémon Go was associated with increased physical activity, with larger effects among more engaged users and benefits observed across demographic groups. Compared with leading health apps, it attracted less-active users and produced larger activity increases, although activity declined after several weeks.
- Longitudinal Analysis: 192 daily steps was the average increase for users with at least one experiential query, while controls decreased by 50 daily steps.The increase occurred sharply around the first experiential query.
- Longitudinal Analysis: 1473 daily steps, or 26%, was the average increase over 30 days among users with at least ten experiential queries.Their activity rose from 5,756 to 7,229 daily steps, exceeding the control population by 13%.
- Longitudinal Analysis: Activity declined again about three to four weeks after the first experiential query, though highly engaged users remained above their starting level.The authors state that future work is needed to study long-term effects.
- Dose-Response Relationship: Activity increases scaled roughly linearly with experiential-query count, reaching 1473 daily steps, or 26%, for users with ten or more queries.The authors describe this pattern as a dose-response relationship between expressed interest and physical activity.
- Comparison to Existing Health Apps: Pokémon Go users were less active before play than users of four leading health apps but experienced larger activity increases.The comparison suggests that Pokémon Go attracted users who were not already very active.
- Public Health Impact: 144 billion steps were added to US physical activity during the first 30 days when the average 192-step increase was extrapolated to 25 million US users.For highly engaged users, the share meeting an 8,000-step guideline increased from 12.2% to 31.7%.
4. DISCUSSION
The study finds that Pokémon Go increased physical activity for about four weeks and reached users with low prior activity, while highlighting the need for sustained engagement and complementary interventions.
- Principal Results: Pokémon Go significantly increased physical activity at both group and individual levels over approximately four weeks.
- Principal Results: Highly engaged users increased activity by 1,479 steps per day, or 26%, after issuing ten game-related queries.
- Principal Results: The game reached people with low prior activity levels and overweight or obesity, unlike mobile health apps that largely drew from already active populations.
- Public Health Impact: Pokémon Go added an estimated 144 billion steps to US physical activity, and sustained engagement could measurably affect US life expectancy.
- Limitations and Implications: The study identifies sustained engagement and long-term behavior change as key challenges, and says activity-encouraging games should complement existing physical activity programs.
- Limitations and Implications: The evidence is limited by a non-random wearable-user sample, search-query proxies for gameplay and engagement, and a 30-day follow-up period.