Self-tracking behaviour in physical activity: a systematic review of drivers and outcomes of fitness tracking
ABSTRACT
Advances in technologies (e.g. smartphones, wearables) have resulted in the concept of ‘self-tracking’, and the use of self-tracking technologies in physical activity (i.e. fitness tracking) is on the rise. click here for more information.
For example, many people track and monitor their fitness-related metrics (e.g. steps walked, distance ran, and calories burned) to change their behaviours or keep themselves active. Despite the widespread application of self-tracking in fitness, relatively little is known about its drivers and outcomes. To address this gap, the current paper provides an overview of the literature (empirical papers) on self-tracking with a focus on the drivers and outcomes of fitness tracking behaviour and offers four important contributions. First, it identifies 19 drivers of fitness tracking technology usage. Second, it discusses four main outcomes of fitness tracking behaviour. Third, by drawing on the existing studies conducted across various fitness tracking technologies (e.g. fitness trackers, apps) and user groups (e.g. patients, seniors, and females), it provides valuable insights that can be generalisable to other settings (e.g. other types of users and fitness tracking products). Finally, the current paper provides important practical implications and addresses avenues for future research.
KEYWORDS: Driversempirical studiesliterature reviewoutcomesphysical activityself-tracking
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1. Introduction
In recent years, the possibility of keeping records of everyday life has become remarkably easy (Jarrahi, Gafinowitz, and Shin 2018). Advances in technologies (e.g. smartphones, wearables) have made it possible for people to monitor and track almost every sphere of their lives (Ajana 2018). From daily activities such as walking, eating, and sleeping to mood and health, people now have access to more information about themselves than ever before (Etkin 2016). This phenomenon is referred to as self-tracking (or self-quantification) – using modern technologies to automatically track and collect personal information in numbers (Ajana 2018), and self-tracking is now a common practice in the life of many people (Epstein et al. 2016).
The increasing tendency for individuals to collect personal data was spotted in 2007, and since then the trend of self-tracking has grown steadily across the globe (Sjöklint, Constantiou, and Trier 2013). As self-tracking allows individuals to collect data about themselves automatically (or with less effort), it has been utilised in many different practices, such as fitness, healthcare, and medical care. Particularly, there has been a growing interest in the use of self-tracking technologies in physical activity (e.g. sports), namely fitness tracking, with an increasing amount of research devoted to the topic (e.g. Attig and Franke 2019; Canhoto and Arp 2017; Stiglbauer, Weber, and Batinic 2019). For example, a number of studies have explored the motivational and behavioural impacts of fitness tracking (e.g. Butryn et al. 2016; Pettinico and Milne 2017), while others have looked at the drivers (e.g. individual differences, product quality) of fitness tracking technology usage (e.g. Jarrahi, Gafinowitz, and Shin 2018; Schall Jr, Sesek, and Cavuoto 2018). Such research demonstrates various drivers and outcomes of fitness tracking.
Recent work such as that by Kalantari (2017) has provided a review of the literature on wearable technology adoption. Although such an investigation suggests several important factors (e.g. technology characteristics, individual characteristics) that can influence wearable technology usage, the drivers of self-tracking technology (in this case, fitness tracking technology) usage may not necessarily be the same, as there are several differences between wearable technologies and fitness tracking technologies. First, wearable technologies are electronic devices that can be comfortably worn or attached to the body of individuals (e.g. smartwatch, smart glasses) (Dehghani 2018), whereas fitness tracking technologies are devices (or apps) that can track individuals’ physical functions (e.g. steps, heart rate), such as Fitbit or Runkeeper (Chuah et al. 2016). Therefore, fitness tracking devices can be considered as one type of wearables, but not all wearables have the fitness tracking functionality. For example, devices such as head-mounted displays and smart glasses are wearables, but they often do not have fitness tracking features.
Second, some wearables do more than just fitness tracking. For example, a smartwatch may allow basic fitness tracking (e.g. step count), but fitness tracking is only one of the many features it has (e.g. calling, texting, gaming, and web browsing). Smartwatches thus are multi-functional devices going beyond fitness tracking (Chuah et al. 2016; Dehghani and Dangelico 2018). This means that people can use wearables for different purposes than fitness tracking itself. Therefore, although wearables are an important concept in the investigation of fitness tracking behaviour, care should be taken in generalising the findings on wearable technology to the domain of fitness tracking technology. Notably, the authors use the term ‘fitness tracker’ or ‘fitness tracking device’ in this paper instead of the commonly used term ‘activity tracker’, as not all activities can be considered as physical activity (e.g. eating, reading, or sleeping).
Other works such as that by Cheatham et al. (2018) and that by Almalki, Gray, and Martin-Sanchez (2016) have reviewed the literature regarding the effect of self-tracking technologies in medical sector (e.g. effect on patients’ health condition). However, limited attention has been paid to the effect of self-tracking, particularly fitness tracking, on other user outcomes (e.g. motivation, experience), especially among general population (e.g. regular users).
To the best of the authors’ knowledge, there has been no systematic review of the literature on fitness tracking behaviour. A synthesised summary of the earlier research thus can provide value for both academics and practitioners, as it would help identify the likely drivers and outcomes of fitness tracking behaviour. The aim of the current paper is therefore to provide a comprehensive review of a diverse range of contemporary literature that informs our understanding of the drivers and outcomes of fitness tracking behaviour.
By systemising the findings and conclusions of existing studies on fitness tracking, the current paper makes four important contributions. First, the current paper adds to the literature on self-tracking behaviour by exploring and summarising the drivers of self-tracking behaviour in physical activity – fitness tracking. Second, along with the drivers, the current paper investigates the potential outcomes of fitness tracking behaviour, and the inclusion of both drivers and outcomes enables the development of an integrative framework of fitness tracking behaviour and suggests directions for future research. Third, by drawing on the existing studies on fitness tracking, which have been conducted across various fitness tracking technologies (e.g. armband, pedometer, and app) and user groups (e.g. patients, seniors, and students), the current paper provides valuable insights that can be generalisable to other settings (e.g. other types of users and fitness tracking products). Lastly, the current paper deepens the knowledge designers require to improve fitness tracking products and facilitate the use of these technologies (e.g. fitness trackers) among different individuals. The findings of the current paper also provide important insights for service providers (e.g. gyms, health centres) who are seeking to improve their users’ task motivation, health, or activity level in fitness.
The current paper is organised as follows. First, the authors discuss the research method used for the current review. Second, they present an overview of the drivers of fitness tracking technology usage. Third, the outcomes of fitness tracking behaviour are discussed, along with the roles of relevant moderating and mediating variables. Lastly, a summary and suggestions for future research are provided.
2. Literature review method
2.1. Search strategy
A systematic review of the literature was conducted using the following method. First, the authors identified two review questions: (1) what factors drive the use of fitness tracking technologies? and (2) how fitness tracking technologies affect users (e.g. physical and psychological outcomes)? Then, given these research questions, the authors used the following search strings in titles, keywords, and abstracts to search for relevant literature: ‘self track*’ OR ‘self quantif*’ OR ‘activity track*’ OR ‘fitness track*’. Other relevant search strings were also used to optimise the search results, e.g. ‘physical act*’ OR ‘fit*’ OR ‘act*’; ‘tech*’ OR ‘device*’ OR ‘wearable*” OR ‘pedometer’; ‘experiment*’ OR ‘survey*’ OR ‘interview*’ OR ‘field study*’ OR ‘field test*’ OR ‘trial*’ OR ‘focus group’ OR ‘empiric*’. A filter was then used to limit the results to only English-language peer-reviewed journal articles and conference proceedings to safeguard the quality and effectiveness of the review. Conference proceedings were included, as there have not been published many empirical papers answering the research questions of the current review. In addition, as the trend of self-tracking emerged in 2007 (Sjöklint, Constantiou, and Trier 2013), the authors initiated the search from (including) the year 2006. The search was conducted across five databases: Web of Science, EBSCO, Science Direct, Springer Link, and Google Scholar. The authors further searched the reference lists of the papers identified in the initial search. The results of the literature search are outlined in Appendix.
2.2. Inclusion and exclusion criteria
To select appropriate papers for inclusion in the current review, the authors read titles, abstracts, and findings of the searched papers and applied a number of inclusion criteria. First, selected papers had to include empirical evidence related to the drivers or outcomes of self-tracking behaviour. Second, selected papers had to investigate the drivers or outcomes of self-tracking behaviour specifically in the context of physical activity (i.e. fitness tracking). Lastly, selected papers had to have a clear focus on the fitness tracking feature of the focal technology or device (e.g. wearables or personal informatics), rather than other features such as gamification – a process of implementing game elements (e.g. points, badges, and leaderboards) (Huotari and Hamari 2017) – or a combination of different features as a whole. The reason is that with a multifaceted technology, it is difficult to determine whether fitness tracking is the specific component contributing to the use of fitness tracking technologies and the outcomes of fitness tracking.
In this round, exclusion criteria were as follows. First, the authors eliminated the papers focusing purely on describing the technical design or usage situation of fitness tracking technologies (e.g. which function users like). Second, the authors excluded the papers examining the reliability and validity of fitness tracking technologies. Third, they also eliminated the papers that provided limited evidence when investigating the outcomes of fitness tracking (e.g. lack of neutral control condition, lack of baseline measure, or confounded intervention). Lastly, the authors excluded the studies that implemented non-automatic tracking (e.g. manual logging of fitness data), as automatic tracking is one of the most important features of modern fitness tracking technologies.
2.3. Selection summary
The initial search produced 143 articles using five databases. Removing the duplicates left 118 papers for the analysis. After screening based on the literature selection criteria, a further 50 papers were excluded from the final synthesis (see Figure 1). By the end of the selection process, the authors identified 71 empirical papers as relevant for the current review. This number reflects the emergent nature of the topic. Of these, 53 were from peer-reviewed journals and 18 were from conference proceedings. 35 of the papers investigated the drivers of fitness tracking technology usage, while 53 papers examined the outcomes of fitness tracking. The timeframe of the selected papers ranged across a 13-year period from 2006 to 2019 (with a peak between 2014 and 2017), covering a variety of fitness tracking technologies (e.g. Fitbit, Nike +, pedometer, and apps) and user groups (e.g. patients, seniors, adults, and students). The majority of the papers investigated American participants, and the age range of these participants was 16–80 years. Notably, the process of mapping, consolidating and evaluating the literature in the selected field was repeated twice over the period of 6 months (i.e. April 2019 and October in 2019) to improve the overall review quality.