Large-Scale Mobile Fitness App Usage Analysis for Smart Health

Chen, Xinlei; Zhu, Zheqi; Chen, Min; Li, Yong

doi:10.1109/mcom.2018.1700807

Cited by 25 publications

(9 citation statements)

References 8 publications

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“…Some researchers have indicated links between app usage and life status [168], [169]. Frey et al [168] are the first to recognize that a user's current life stage has a significant impact on their app adoptions.…”

Section: Table IX the Big-five Personality Framework And Examples Ofmentioning

confidence: 99%

“…For example, when a person's life stage changes from without children to with children, the use of travel and entertainment apps drops dramatically. Chen et al [169] used fitness app data to examine people's workout activities and discover their exercise styles. They found that people in the central business district (CBD) walk or jog more than those in rural areas and that people with longer mobility spans exercise less.…”

Section: Table IX the Big-five Personality Framework And Examples Ofmentioning

confidence: 99%

“…Most existing studies are based on biased populations. The users involved in [34], [43], [69], [78], [169], [187] are all Chinese. The participants in [39], [101], [216] all live in the Lake Geneva region in Switzerland.…”

Section: A Challenge In Data 1) Data Collectionmentioning

confidence: 99%

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Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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As smartphones have become indispensable personal devices, the number of smartphone users has increased dramatically over the last decade. These personal devices, which are supported by a variety of smartphone apps, allow people to access Internet services in a convenient and ubiquitous manner. App developers and service providers can collect fine-grained app usage traces, revealing connections between users, apps, and smartphones. We present a comprehensive review of the most recent research on smartphone app usage analysis in this survey. Our survey summarizes advanced technologies and key patterns in smartphone app usage behaviors, all of which have significant implications for all relevant stakeholders, including academia and industry. We begin by describing four data collection methods: surveys, monitoring apps, network operators, and app stores, as well as nine publicly available app usage datasets. We then systematically summarize the related studies of app usage analysis in three domains: app domain, user domain, and smartphone domain. We make a detailed taxonomy of the problem studied, the datasets used, the methods used, and the significant results obtained in each domain. Finally, we discuss future directions in this exciting field by highlighting research challenges.

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Section: Table IX the Big-five Personality Framework And Examples Ofmentioning

confidence: 99%

Section: Table IX the Big-five Personality Framework And Examples Ofmentioning

confidence: 99%

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Smartphone App Usage Analysis: Datasets, Methods, and Applications

Tong

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

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“…User behavior prediction is very important in the applications like smart health [22], smart home [20], new generation wireless communication [21,49] and etc. With the popularity of social networks, users leave a large volume of digital footprints online.…”

Section: User Behavior Predictionmentioning

confidence: 99%

Cap

Chen

Wang

et al. 2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Context-aware mobile application (App) usage prediction benefits a variety of applications such as precise bandwidth allocation, App launch acceleration, etc. Prior works have explored this topic through individual data profiles and contextual information. However, it is still a challenging problem because of the following three aspects: i. App usage behavior is usually influenced by multiple factors, especially temporal and spatial factors. ii. It is difficult to describe individuals' preferences, which are usually time-variant. iii. A single user's data is sparse on the spatial domain and only covers a limited number of locations. Prediction becomes more difficult when the user appears at a new location. This paper presents CAP, a context-aware App usage prediction algorithm that takes both contextual information (location & time) and attribution (App with type information) into consideration. We find that the relationships between App-location, App-time, and App-App type are essential to prediction and propose a heterogeneous graph embedding algorithm to map them into the common comparable latent space. In addition, we create a user profile for each user with App usage and trajectory history to describe the individual dynamic preference for personalized prediction. We evaluate the performance of our proposed CAP with two large-scale real-world datasets. Extensive evaluations demonstrate that CAP achieves 30% higher accuracy than a state-of-the-art method Personalized Ranking Metric Embedding (PRME) in terms of Accuracy@5. In terms of mean reciprocal rank (MRR), CAP achieves 1.5× higher than the straightforward baseline Sta and 2× higher than PRME. Our investigation enables a range of applications to benefit from such timely predictions, including network operators, service providers, and etc. CCS Concepts: • Information systems → Spatial-temporal systems; • Human-centered computing → Mobile phones; • Computing methodologies → Machine learning algorithms.

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“…Recent years have seen a rapidly growing body of research on promoting health and fitness through social interaction in online fitness communities [7,8,9]. Online fitness communities not only track and archive activities, but also adopt social networking features meant for increasing user engagement and retention [9,10].…”

Section: Introductionmentioning

confidence: 99%

"Friending" in Online Fitness Communities: Exploring Activity-Based Online Network Structure

Zeng¹,

Almquist²,

Spiro³

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Individuals are influenced by both direct and indirect interaction with their social contacts. While peer influence is known to affect health-related outcomes such as exercise, limited work has fully explored how social networks are structured to support (or inhibit) interaction that could lead to positive health behaviors. With the development of pervasive technology and rise of personal health and wellness tracking, increasing attention has been paid to promoting positive fitness behaviors through social interaction mechanisms in online fitness communities. This trend offers a unique opportunity to understand the opportunity structures for personal health and wellness support. Utilizing a large-scale behavioral trace dataset from the online fitness community Strava, we examine how the size of people's personal network is structured by demographics (e.g. gender and age) and an economic indicator (i.e. if they pay for a premium account). We employ stochastic process models to characterize the empirical network degree distributions in this population of fitness community members. We find that gender, age and account status are associated with distinct network structure. Results have implications in the analysis and the design of health interventions that make use of network relationships in online settings.

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Large-Scale Mobile Fitness App Usage Analysis for Smart Health

Cited by 25 publications

References 8 publications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Smartphone App Usage Analysis: Datasets, Methods, and Applications

Cap

"Friending" in Online Fitness Communities: Exploring Activity-Based Online Network Structure

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