Increasing Physical Activity With Mobile Devices: A Meta-Analysis

Original Paper

Increasing Physical Activity With Mobile Devices: A Meta-Analysis

Jason Fanning^*, BS; Sean P Mullen^*, PhD; Edward McAuley^*, PhD

Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
^*all authors contributed equally

Corresponding Author:
Jason Fanning, BS

Department of Kinesiology and Community Health
University of Illinois at Urbana-Champaign
Freer Hall
906 S. Goodwin Avenue
Urbana, IL, 61801
United States
Phone: 1 217 300 5306
Fax: 1 217 333 3124
Email:

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ABSTRACT

Background: Regular physical activity has established physical and mental health benefits; however, merely one quarter of the U.S. adult population meets national physical activity recommendations. In an effort to engage individuals who do not meet these guidelines, researchers have utilized popular emerging technologies, including mobile devices (ie, personal digital assistants [PDAs], mobile phones). This study is the first to synthesize current research focused on the use of mobile devices for increasing physical activity.
Objective: To conduct a meta-analysis of research utilizing mobile devices to influence physical activity behavior. The aims of this review were to: (1) examine the efficacy of mobile devices in the physical activity setting, (2) explore and discuss implementation of device features across studies, and (3) make recommendations for future intervention development.
Methods: We searched electronic databases (PubMed, PsychINFO, SCOPUS) and identified publications through reference lists and requests to experts in the field of mobile health. Studies were included that provided original data and aimed to influence physical activity through dissemination or collection of intervention materials with a mobile device. Data were extracted to calculate effect sizes for individual studies, as were study descriptives. A random effects meta-analysis was conducted using the Comprehensive Meta-Analysis software suite. Study quality was assessed using the quality of execution portion of the Guide to Community Preventative Services data extraction form.
Results: Four studies were of ?good? quality and seven of ?fair? quality. In total, 1351 individuals participated in 11 unique studies from which 18 effects were extracted and synthesized, yielding an overall weight mean effect size of g = 0.54 (95% CI = 0.17 to 0.91, P = .01).
Conclusions: Research utilizing mobile devices is gaining in popularity, and this study suggests that this platform is an effective means for influencing physical activity behavior. Our focus must be on the best possible use of these tools to measure and understand behavior. Therefore, theoretically grounded behavior change interventions that recognize and act on the potential of smartphone technology could provide investigators with an effective tool for increasing physical activity.

(J Med Internet Res 2012;14(6):e161)
doi:10.2196/jmir.2171

KEYWORDS

Behavior change, exercise, meta-analysis, mobile phone, physical activity, review

It is well documented that regular physical activity is associated with reduced morbidity and mortality attributable to such diseases as cardiovascular disease, diabetes, and some cancers [1-4]. Unfortunately, participation rates have remained dismally low in spite of significant scientific endeavors to enhance participation, reduce attrition, and increase maintenance of this important health behavior. In 2007, less than half of all adults in the United States achieved recommended levels of physical activity [5]. Furthermore, past research suggests that among those beginning a new exercise program, 50% will drop out within six months [6]. The advent of new and ubiquitous technologies offers a potential solution to increasing the effectiveness of and adherence to physical activity interventions. One such technology is the mobile telephone, which has shown promise as a delivery mechanism for health behavior interventions. These devices have become a more pervasive part of society with usage rates increasing exponentially. For example, in the United States some 83% of adults own mobile phones, while in younger populations (ie, 18-24 year olds) as many as 95% own mobile phones [7]. In the United States [8], Australia [9], and Western Europe [10], activated mobile phones outnumber citizens.

Declining cost and enhanced versatility in features are likely contributors to the rapid increase in mobile phone usage, and short message service (SMS, ie, text-messaging) has become an almost universal way to engage in brief conversations and convey short messages. In the United States, 73% of all adult cell phone users send daily text messages, at an average rate of 39.1 per day. Among younger Americans, a staggering 97% of mobile phone users send text messages at an average rate of 87.7 messages per day [7]. In total, nearly 2.1 trillion messages were sent in 2010 [8]. In the late 2000s, the growth of feature phones (ie, devices capable of basic voice and multimedia functions) was supplanted by surging growth in the smartphone market. These devices originally combined the computing power of handheld computers with mobile communication features serving primarily adult professionals. As of May 2011, 35% of all mobile consumers in the U.S. owned smartphones, and rates were higher in ethnic minorities [7]. Smartphones are equipped with advanced technological features that distinguish them from the feature phone. Typically they are capable of sending and receiving information via the Internet, connecting to local wireless networks and Bluetooth devices, utilizing global positioning system (GPS) data and allowing users to download countless mobile applications straight to their device from just about any location.

Behavioral scientists have begun to realize the potential of mobile devices to understand multiple health behaviors, and meta-analyses have supported the efficacy of mobile technology for influencing behaviors including diabetes management [11] and smoking cessation [12]. It is clear that some of the unique qualities of these devices would be attractive features for physical activity interventions allowing scientists to: collect objective and self-report measures of activity in real time; provide feedback and support at the point of decision; provide interactive, immersive, and individualized content that is automatically generated; and deliver materials on a device that is already carried by the individual [13]. A number of reviews exist examining the use of technologies that offer similar benefits to mobile devices and their effects on health behaviors. For example, Goode et al [14] reviewed telephone-based interventions for influencing physical activity and dietary behaviors. Multiple researchers have examined aspects of Internet-delivered interventions [15,16]. Others have examined the use of mobile technology to aid in disease prevention and management [17], as well as the influence of SMS technology on various health behaviors (eg, smoking cessation, diabetes self-management, asthma self-management; [18]). To date, however, no meta-analysis has been conducted that examines the efficacy of mobile devices for changing physical activity behavior. Given the increase in research applications of such devices, we believe that a comprehensive analysis of the influence of mobile devices on physical activity behavior would afford future researchers a foundation for guiding subsequent interventions. Herein, we present a meta-analysis of interventions that have utilized mobile devices (ie, PDA or mobile telephones) to influence physical activity behavior.

Search Strategy

An extensive search of online electronic databases (PsychINFO, PubMed, Scopus) was conducted between August 15, 2011, and July 3, 2012, in which we sought articles published since the year 2000. The following search string was utilized across the three databases: ((mobile phone) OR (cell phone) OR PDA OR SMS OR (text message)) AND ((physical activity) OR exercise).

Inclusion and Exclusion Criteria

For inclusion in this analysis, studies were required to be published or in press, in the English language, and to incorporate mobile technologies in the collection or dissemination of intervention materials meant to positively influence physical activity behavior. This included data collection or conveyance of intervention information via SMS, as well as implementation of native mobile device software or hardware. Studies were required to include a comparison group and to provide original data sufficient for calculating Cohen?s d effect sizes (ie, baseline and follow-up means and baseline standard deviation). These criteria are intentionally broad, as relatively few relevant studies have been published.

Articles that described proof-of-concept trials, conference proceedings, or review articles were excluded from this analysis. Outcomes from studies that were not explicitly related to physical activity were also excluded, as were studies in which participants interacted with the mobile component less than one time per week.

Review Procedure

Study selection was conducted in four phases (see Figure 1). During the initial stage, all citations from each database query were imported into a central citation manager (EndNote X5 [19]), which facilitated removal of duplicates. Next, the first two authors searched titles for publications that referenced physical activity and a mobile device, removing those that definitively did not match inclusionary criteria. In the following stage, both reviewers examined abstracts of the remaining articles, further screening out articles that did not meet criteria. During the final stage, full-text citations were reviewed to make sure that all criteria were met, and study descriptives were extracted and tabulated. When study length was reported in months rather than weeks, a four-week month was assumed. Following this review, search results were compared between the two authors, with the third author acting as arbiter to any inconsistencies. When physical activity outcomes were reported but were not sufficient to calculate effect sizes, study authors were contacted to determine means and standard deviations at all time-points (n = 7). In the event that this information could not be obtained, the effect was excluded from analysis (n = 3 [20-22]). Reference lists of included articles and relevant reviews were searched for additional articles, and direct requests were made to experts in the area of mobile health for additional studies in review or in press.

Source: http://www.jmir.org/2012/6/e161/

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