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Abstract

Physical inactivity is a health problem that affects people worldwide and has been identified as the fourth largest risk factor for overall mortality (contributing to 6% of deaths globally). Many researchers have tried to increase physical activity levels through traditional methods without much success. Thus, many researchers are turning to mobile technology as an emerging method for changing health behaviours. This systematic review sought to summarise and update the existing scientific literature on increasing physical activity through mobile device interventions, taking into account the methodological quality of the studies. The articles were identified by searching the PubMed, SCOPUS and SPORTDiscus databases for studies published between January 2003 and December 2013. Studies investigating efforts to increase physical activity through mobile phone or even personal digital assistant interventions were included. The search results allowed the inclusion of 11 studies that gave rise to 12 publications. Six of the articles included in this review reported significant increases in physical activity levels. The number of studies using mobile devices for interventions has increased exponentially in the last few years, but future investigations with better methodological quality are needed to draw stronger conclusions regarding how to increase physical activity through mobile device interventions.

Keywords

cellular phone mobile application mobile phone personal digital assistant physical activity short message service text message

Introduction

The positive effects of physical activity (PA) on health and wellness are widely established and documented for all ages. These effects include an improvement in cardiovascular disease, diabetes, osteoporosis and some cancers.^1–4 However, despite widespread messages regarding the beneficial impacts of modest amounts of PA, 6 per cent of all deaths globally are attributable to physical inactivity;⁵ in fact, physical inactivity is the fourth leading risk factor for global mortality. This evidence supports the conclusion that physical inactivity is one of the most important public health problems of the 21st century.⁶ Rates of physical inactivity are growing rapidly in all parts of the world, which is a phenomenon that has attracted the attention of the international healthcare community. The positive effects of PA on people’s health have been described in numerous reviews,^7,8 and a variety of behavioural modification programmes have been developed with the intention of reducing rates of physical inactivity.

Although there are currently PA interventions that target behaviourally sedentary healthy adults at reasonable costs,⁹ new tools to reduce costs and increase the effectiveness of interventions are emerging, such as mobile phones and personal digital assistants (PDAs). In particular, the field of telehealth (eHealth) has seen growth as a paradigm involving the concepts of health and the use of technology as tools in health services. In this context and thanks to advances in communications technology, a related new concept has arisen: mHealth (mobile health), a component of eHealth.¹⁰ The World Health Organization (WHO) defines mHealth as a ‘medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, PDAs, and other wireless devices’.¹¹ The technological capabilities of mobile technologies are continuing to advance at a rapid pace. Current technological capabilities allow for low-cost interventions.¹²

At present, there are over 5 billion mobile phones in the world, of which 1.08 billion are smartphones. Approximately 80 per cent of all the people in the world have a mobile phone.¹³ Mobile phones have the potential to improve population health due to their multiple functions. Many interventions have used mobile technology to engage and motivate healthy behaviours, such as the treatment of diabetes through text messages, smoking cessation, controlling blood pressure and diet control.^14–17 Another emerging approach in the primary prevention of disease is encouraging increased PA through mobile device interventions.

We have found only one review that specifically focused on increasing PA in the general population through the use of mobile devices.¹⁸ This earlier review requires updating because it did not provide a detailed review of interventions via mobile devices (e.g. accurate descriptions of the intervention and study groups and detailed descriptions of the study characteristics). Our review adds a qualitative assessment of the included studies using a previously validated checklist and also provides four new articles; moreover, it includes a detailed study protocol, a literature search for suitable studies based on inclusion criteria and an analysis plan.^19,20 The aim of this systematic review was to identify, retrieve, critically appraise and synthesise the existing scientific literature on PA mobile device interventions. The research question was ‘Can these mobile device interventions increase PA?’

Methods

Data sources and search strategy

The electronic databases searched were PubMed, SCOPUS and SPORTDiscus. These databases were searched for studies conducted between January 2003 and December 2013. The search was limited to English and Spanish language publications. We started the literature search in May 2013 and conducted updates until December 2013. We searched for relevant studies using a combination of three different categories. For these three categories, all relevant keyword variations were used, including both keyword variations in the controlled vocabularies of the various databases and free textword variations in these concepts. The first category included key words that focused on the type of intervention and included (1) ‘text messaging’ (MeSH); ‘cellular phone’ (MeSH); ‘telephone’ (textword); ‘mobile device’ (textword) and ‘SMS’ (textword). The second category included the intended outcome of the intervention and included (2) ‘PA’ (MeSH); ‘exercise’ (MeSH); ‘physical fitness’ (MeSH) and ‘sports’ (MeSH). The third category was in regard to the type of study design and included (3) ‘intervention’ (textword); ‘program’ (textword) and ‘application’ (textword). This search strategy was used for all the consulted databases, taking into account the differences of the various controlled vocabularies. We also systematically searched the reference lists of the included studies.

Inclusion criteria and exclusion criteria

To be included in the review, an article had to meet the following criteria:

Reviewed and published in English or Spanish.

Used mobile devices such as phones or PDAs as an intervention to increase PA.

PA was an outcome.

Published between January 2003 and December 2013.

The reference lists of the selected articles were checked for additional eligible articles using the same inclusion criteria. The criteria for exclusion from the review were as follows:

The article focused on a goal other than increasing or promoting PA.

The study did not use mobile devices to deliver the intervention.

The reviews did not meet pre-defined criteria for methodological quality.

The research participants were diseased.

The articles that consisted of proof-of-concept trials, conference proceedings or review articles were also excluded from this analysis. Outcomes from studies that were not explicitly related to PA were also excluded.

Study selection

The process used to identify and select the articles for review is shown in Figure 1. In total, 422 articles (228 identified via PubMed, 160 identified via Scopus and 34 identified via SPORTDiscus) were found, of which 128 were duplicates. During the review, all citations from each database were imported into a bibliographic management program (RefWorks 2.0),²¹ which facilitated the removal of duplicates. In the first stage, the reviewers searched titles for publications that referenced mobile phone interventions that sought to affect PA, and based on this review of titles, 198 publications did not meet the inclusion criteria, with the majority of these (183) being excluded because they did not include a PA mobile device intervention. In the following stage, the reviewers examined the abstracts of the remaining 96 publications. Forty-six of these abstracts were excluded because they did not match the inclusion criteria; the majority of those articles did not include a PA mobile device intervention. In the final stage, 50 full-text citations were reviewed to ensure that all the criteria were met. Thirty-eight of the 50 full-text citations did not meet the inclusion criteria. After completely reviewing the corresponding full-text articles, the total number of accepted articles was reduced to 12. The remaining 38 studies were excluded because their interventions were not predominantly delivered by mobile phone (n = 22), they included no reporting of PA outcomes (n = 10), they consisted of a review article (n = 1) or they consisted of protocol studies (n = 5).

Figure 1.

Flow chart outlining the article selection process for the present review.

Data extraction

Data extraction was completed by three reviewers (A.M., J.V.-C. and P.P.) for accuracy. The full-text articles were extracted and examined using a methodology quality checklist that was guided by items from an available quality assessment tool.²² Information was extracted regarding study characteristics (year, author, study design, country, number of participants, age of participants and gender of participants), targeted health behaviours (PA, weight loss or other) and characteristics of the intervention (duration, description of contents, methods, statistics and the intervention and control groups). Whenever possible, the pre-test and post-test results of any PA measurements were also included. The reviewers were blinded to the authors or journals involved when they were extracting the data. The screening for eligible articles and the data extraction from the selected articles were performed independently.

Study quality assessment

The article quality was rated independently by three authors (A.M., J.V.-C. and P.P.) who separately scored the methodological quality of the included articles using a validated 26-point checklist.²² The methodological quality of each study was assessed based on four dimensions (e.g. reporting (10 items), which assessed whether the information provided in the article was sufficient to allow a reader to make an unbiased assessment of the findings of the study; external validity (3 items), which addressed the extent to which the findings from the study could be generalised to the population from which the study subjects were derived; bias (7 items), which addressed biases in the measurement of the intervention and outcome and confounding (6 items), which addressed bias in the selection of study subjects). The maximum score a study could receive was 27, with higher scores indicating higher quality. The answers were scored as 0 or 1, except for one item in the reporting subscale, which was scored as 0 to 2. Items were rated ‘1’ if the requested information was present in the article and the criteria were met. Items were rated ‘0’ if the requested information was not present in the article and the criteria were not met. Items were also rated ‘0’ if insufficient information was provided.

A parallel assessment was conducted regarding the quality criteria for applying PA interventions and mHealth interventions (Table 2). These criteria were derived according to review literature on PA assessments in general and on evaluation methods for behavioural intervention technologies.^23,24 All criteria were scored as ‘yes’, ‘no’ or ‘unable to determine’.

Results

Methodological quality assessment

The full methodological quality scoring of the included studies is presented in Table 1. The mean ± standard deviation (SD) Downs and Black score was 16.08 ± 1.44 (range: 14–19). The mean ± SD of the percentage of the analysed studies was 61.66 ± 4.69. One study met 19 criteria,²⁵ implying a higher methodological quality, while the other studies were rated with various scores.^26–36 Regarding the reporting dimension, three studies were rated with 9 points, indicating the highest score,^28,33,34 and only two studies^25,34 reported the adverse events that may have been a consequence of the intervention. Additionally, four studies^25,27,31,32 did not report the number of participants lost to follow-up. Regarding external validity, one study²⁶ obtained the maximum score, and two studies^31,34 received 0 points. Regarding internal validity, one study³¹ scored 6 points, four studies^30,33–35 scored 4 points, five studies^{25,27,28,32,36} scored 3 points and two studies^26,29 scored 2 points. Only two studies^31,35 attempted to blind the study subjects to the interventions received. One study³¹ blinded those individuals responsible for measuring the main outcome of the intervention. Regarding confounding, one study³⁶ scored 5 points, six studies^{27,28,32–35} scored 4 points, four studies^25,29–31 scored 3 points and one study²⁶ scored 1 point. Furthermore, in 11 studies,^{25–31,33–36} it was impossible to determine whether the randomised intervention was concealed from both the patients and the healthcare staff until the final recruitment.

Table 1.

Methodological quality of studies.

Reference	Reporting (11)	External validity (3)	Bias (7)	Confounding (6)	Total score (27)	%
Conroy et al.²⁵ (2011)	7	2	3	3	15	58
Cheung et al.²⁶ (2008)	8	3	2	1	14	54
David et al.²⁷ (2012)	7	2	3	4	16	62
Fjeldsoe et al.²⁸ (2010)	9	2	3	4	19	69
Hurling et al.²⁹ (2007)	8	2	2	3	15	58
Kim and Glanz³⁰ (2013)	8	1	4	3	16	62
Kirwan et al.³¹ (2012)	6	0	6	3	15	58
Prestwich et al.³² (2009)	6	2	3	4	15	58
Prestwich et al.³³ (2010)	9	1	4	4	18	69
Shapiro et al.³⁴ (2008)	9	0	4	4	17	65
Sirriyeh et al.³⁵ (2010)	7	1	4	4	16	62
Turner-McGrievy and Tate³⁶ (2011)	8	1	3	5	17	65

Regarding the quality criteria that apply to PA interventions and mHealth interventions, the results showed that 12 studies were adapted to the characteristics of the participants,^25–36 and four articles^25,29,34,36 provided feedback to the participants throughout the given intervention (Table 2). Ten studies used one or more theoretical models to compose the information transmitted to the intervention group.^{25,27,29,32–36} These models were the Social Cognitive Theory,^27,34,36 the Protection Motivation Theory,^32,33 the Transtheorical Model,^26,27 the Theory of Planned Behaviour,^29,35 the Goal Setting Theory²⁷ and the Problem Solving Theory.²⁷ Two of the included studies used a combination of methods to assess PA.^29,30 Eight articles^25–31,36 used support tools to deliver the interventions (e.g. paper diaries,²⁵ pedometers,^{26,27,30,31,34} accelerometers,²⁹ interactive voice response,²⁷ face-to face sessions,²⁸ mobile applications³¹ and podcasts²⁶). Two studies^30,31 used a pedometer and a validated questionnaire to assess PA measures. All the studies^25–36 used objective methods, such as accelerometers, pedometers or questionnaires, to assess the outcomes.

Table 2.

Characteristics of intervention, process and outcome of studies.

Study authors	Adapted interventions to subject characteristics	Feedback on progress	Based on theoretical guidelines	Support tools	Combination of PA assessment measures	Objective PA assessment methods
Conroy et al.²⁵ (2011)	Yes	Yes	No	Yes	No	Yes
Cheung et al.²⁶ (2008)	Yes	No	Yes	Yes	No	Yes
David et al.²⁷ (2012)	Yes	No	Yes	Yes	No	Yes
Fjeldsoe et al.²⁸ (2010)	Yes	No	Yes	Yes	No	Yes
Hurling et al.²⁹ (2007)	Yes	Yes	Yes	Yes	No	Yes
Kim and Glanz³⁰ (2013)	Yes	No	No	Yes	Yes	Yes
Kirwan et al.³¹ (2012)	Yes	No	No	Yes	No	Yes
Prestwich et al.³² (2009)	Yes	No	Yes	No	No	Yes
Prestwich et al.³³ (2010)	Yes	No	Yes	No	No	Yes
Shapiro et al.³⁴ (2008)	Yes	Yes	Yes	No	No	Yes
Sirriyeh et al.³⁵ (2010)	Yes	No	Yes	No	No	Yes
Turner-McGrievy and Tate³⁶ (2011)	Yes	Yes	Yes	Yes	No	Yes

PA: physical activity.

Review characteristics

Characteristics of the study populations and selected studies

The characteristics of the selected studies and study populations are described in Table 3. Five of the 12 studies were conducted in the United States,^{25,27,30,34,36} four were conducted in the United Kingdom,^29,32,33,35 two were conducted in Australia^28,31 and one was conducted in Hong Kong.²⁶ The resulting articles were published over a 7-year period from 2007 to 2013. The durations of the interventions ranged from 2 to 24 weeks (mean ± SD: 10.25 ± 7.23 weeks), with two studies describing interventions lasting for 6 months.^25,36 In all studies, the intervention itself was conducted throughout the entire length of the given study. In terms of the targeted health behaviour, all the interventions were focused on PA because that was an inclusion criterion for our review, but four interventions^27,30,31,33 were specifically programmes to promote walking, and two studies^25,36 focused on interventions targeting weight loss behaviours.

Table 3.

Study characteristics.

	Country	Participants		Gender (% female)	Health behaviour	Length (weeks)	Age (mean ± SD; years)	Type of participants
		Participants randomised	Treat to analysis/participants with complete data
Conroy et al.²⁵ (2011)	United States	210 (IG-1: 72, IG-2: 68, IG-3: 70)	189 (IG-1: 61, IG-2: 64, IG-3: 64)	84	PA, weight loss	24	47.3 ± 8.8	Aged 18–59 years; BMI: 27–43 kg/m²
Cheung et al.²⁶ (2008)	Hong Kong	52 (IG: 38, CG: 14)	52 (IG: 38, CG: 14)	78.8	PA	6	IG: 38.9 ± 10.8 CG: 26.5 ± 1.9	School teachers who had a mobile phone
David et al.²⁷ (2012)	United States	71 (IG-1: 35, IG-2: 36)	39 (IG-1: 21, IG-2: 18)	100	PA, walking	12	57 ± 5	BMI: 25–40 kg/m²; post-menopausal status; access to a mobile phone during the intervention. Women were excluded if they were 75 years or older
Fjeldsoe et al.²⁸ (2010)	Australia	88 (IG: 45; CG: 43)	88 (IG: 45, CG: 43)	100	PA	12	30 ± 6	Women less than 12 months postpartum; not currently in the second or third trimesters of pregnancy; had possession of a mobile phone
Hurling et al.²⁹ (2007)	United Kingdom	77 (IG: 47, CG: 30)	77 (IG: 47, CG: 30)	66	PA	9	40.4 ± 7.6	Aged 30–55 years; BMI: 19–30 kg/m²; not vigorously active; Internet and email access; mobile phone user
Kim and Glanz³⁰ (2013)	United States	45 (IG: 30, CG: 15)	36 (IG: 26, CG: 10)	80.5	PA, walking	6	IG: 70.55 ± 7.5 CG: 69.31 ± 7.3	African-American, community-dwelling, aged 60–85 years
Kirwan et al.³¹ (2012)	Australia	200 (IG-1: 50, IG-2: 50)	200 (IG-1: 50, IG-2: 150)	48	PA, walking	12	IG-1: 39.3 ± 12.8 IG-2: 40.1 ± 12.1	Access to an iPhone or iPod touch for the duration of the study
Prestwich et al.³² (2009)	United Kingdom	–	154 (IG-1: 29, IG-2: 31, IG-3: 29, CG-1: 31, CG-2: 34)	58	PA	4	23.76 ± 4.64	Inclusion criteria: exercise <3 times/week, own a mobile phone, fluent in English, 18–40 years old
Prestwich et al.³³ (2010)	United Kingdom	149 (IG-1: 47, IG-2: 52, CG: 50)	134 (IG-1: 40, IG-2: 48, CG: 46)	64	PA, walking	4	23.44 ± 5.63	Inclusion criteria: exercise <3 times/week (including brisk walking), not have a medical condition that prevents the participant from walking briskly and own a cell phone
Shapiro et al.³⁴ (2008)	United States	58 (IG-1: 18, IG-2: 18, CG: 22)	31 (IG-1: 13, IG-2: 7, CG: 11)	62	PA	8	8.7 ± 2.3	Children of any weight, no major metabolic problems associated with obesity, ages 5–13 years, parent participation and fluency in English
Sirriyeh et al.³⁵ (2010)	United Kingdom	128(IG-1: 32, IG-2: 31, IG-3: 33, C: 32)	120 (IG-1: 31, IG-2: 30, IG-3: 31, CG: 28)	70	PA	2	17.3 ± 0.68	Inclusion criteria: 16–19 years old, in full-time further education and in possession of a personal mobile phone
Turner-McGrievy and Tate³⁶ (2011)	United States	96 (IG-1: 49, IG-2: 47)	86 (IG-1: 44, IG-2: 42)	75	PA, weight loss	24	IG-1: 43.2 ± 11.7 IG-2: 42.6 ± 10.7	Overweight and obese men and women (BMI: 25–45 kg/m², 18–60 years old) and own one of four types of Internet capable mobile devices: iPhone, iPod Touch, BlackBerry or an Android-based phone

SD: standard deviation; IG: intervention group; CG: control group; PA: physical activity; BMI: body mass index.

The bold values indicate the overall numbers of participants in each study.

The sample sizes of the studies ranged from 45 to 210 participants (mean ± SD: 110.66 ± 56.77), and the characteristics of the populations studied are described in Table 3. For two of the studies, the sample included only women,^27,28 and for all the interventions, a female predominance was generally observed in the samples (average female representation = 73.8%). The participant ages ranged from a mean of 70.55 years to a mean of 8.7 years.

All the studies reviewed included healthy subjects. Four studies focused on overweight or obese participants,^25,27,29,36 two studies included inactive or sedentary populations,^32,33 one study focused on post-menopausal women²⁷ and another study was centred on postpartum women.²⁸ One sample was composed of teachers,²⁶ and two samples were composed of college students.^34,35

Intervention outcomes

One type of outcome used indirect measures to assess PA^{25,28–33,35,36} (i.e. self-reported minutes of PA,²⁵ self-reported frequency of PA,^29,31–33 self-reported questionnaires^28,30,35,36); in the second type of outcome, PA was directly measured with accelerometers²⁹ or pedometers.^{26,27,30,31,34} The majority of the studies examined PA as a primary outcome.^{25–30,32,33,35} The main outcomes for most of the studies consisted of measures of both the intervention and control groups expressed as pre-test and post-test results, but five of the articles specified different outcomes.^{25,27,29,31,35} Of those five studies, two studies expressed their results as correlations between groups,^25,35 two only detailed post-test results^29,31 and one showed the pre–post change for both groups.²⁷ Only PA outcome parameters were included in this review, and the results are described in Table 4. The majority of the studies included one PA outcome parameter,^{25–27,29,31–36} although three studies reported two PA parameters.^28–30

Table 4.

Characteristics and results of included interventions.

Study	Description of intervention and study groups	Results
Conroy et al.²⁵ (2011)	The SMART was a clinical trial in which 210 adults were randomly and equally assigned to one of three arms: paper record, PDA with self-monitoring software and PDA with daily tailored feedback messages. The paper record group was given paper diaries to record all foods eaten, calories and fat grams consumed and minutes of PA. The PDA with self-monitoring software group was provided with a PDA that was installed with self-monitoring software that tracked energy and fat consumption and other data and recorded PA. The PDA with daily tailored feedback messages group was provided with the same intervention as the previous group but with more software on their PDAs to provide positive reinforcement	A higher mean rate of PA self-monitoring during 18 sessions was associated with both higher PA levels at 6 months (p = 0.29, p < 0.001) and greater increases in PA levels during the first 6 months of the study (p = 0.20, p < 0.0057). Similar trends were observed across all three intervention arms
Cheung et al.²⁶ (2008)	This study was a 6-week intervention that aimed to promote teachers’ PA levels during working hours. Thirty-eight teachers from three intervention schools received intervention prompts. The intervention content included sending messages through SMS about exercise benefits, distributing informational leaflets to the participants, putting up posters in the school environment and providing the participants with a pedometer. The CG was not given any prompts during the 6-week intervention	The results showed that there was a significant difference in the steps-at-work (t = 3.61, p < 0.001) between the intervention (mean steps-at-work = 1.35, SD = 2.78, n = 38) and control groups (mean steps-at-work = −1.72, SD = 2.55, n = 14). However, there was no significant difference in the steps-off-work (t = 1.11, p = 0.27; intervention mean steps-off-work = 4.09, SD = 11.07 and control groups mean steps-off-work = 0.38, SD = 9.22)
David et al.²⁷ (2012)	This study was a 12-week walking intervention administered through an interactive voice response system and mobile phones. Post-menopausal women (n = 71) were given a daily step goal, which they monitored using a pedometer. Each day, they answered an automated call from the IVR to their mobile phone and provided an assessment of their walking goals and mood. Every evening, they called the IVR to report their steps, answered a brief questionnaire and received a message with a helpful hint. The CG received the same intervention, but the individual did not identify herself as the coach	The difference in the 1-mil walk test between baseline and post-intervention (p < 0.001)
Fjeldsoe et al.²⁸ (2010)	MobileMums consisted of a theory-based PA intervention delivered to postnatal women primarily via mobile telephone SMS messaging. The 12-week intervention consisted of face-to face PA goal-setting consultation, a goal-setting magnet, three to five personally tailored SMS/week and a nominated support person who received two SMS messages per week. The CG participants did not receive any additional contact or resources during the trial period apart from reminder telephone calls from the research assistant to confirm the assessments	The IG increased PA frequency (p = 0.038) and walking for exercise frequency (p = 0.02) after the intervention
Hurling et al.²⁹ (2007)	This controlled trial was conducted in 77 healthy adults. The participants were randomised to the IG, which had access to the Internet and a mobile phone–based PA programme, or the CG, which lacked access and received no feedback. The CG only received verbal advice on recommended PA levels. The Internet, email and mobile phone behaviour change included a series of screens that helped test participants to identify their perceived barriers to PA and offered solutions. A weekly series of screens questioned the participants on their exercise level and provided a weekly schedule for planning PA sessions for which participants could choose to receive email and/or mobile phone reminders. The schedule included an automated assessor that provided feedback on the amount and type of PA being planned; both groups were issued a wrist-worn accelerometer to monitor their level of PA	The analysis showed that there were no differences between the groups for the initial levels of PA, whether measured by the accelerometer or IPAQ. There was no significant difference in the MET minutes per week, after adjusting for the baseline covariate, between the test group (mean = 12.0, SE = 3.1) and the control group (mean = 4.0, SE = 4.1), with p = 0.12 (95% CI for the difference = −2.3 to 18.3)
Kim and Glanz³⁰ (2013)	This study was a 6-week programme consisting of motivational text messaging. Both groups received a pedometer and a walking instructional manual. The IG received three motivational text messages a day, 3 days a week for 6 weeks. If the participants needed extra assistance, personal instructions for their mobile phones were given	The group that received motivational text messages had greater improvements in their step counts (679 vs 398; p < 0.05) and perceived activity levels (p < 0.05) than the group that did not receive text messages
Kirwan et al.³¹ (2012)	This case–control trial aimed to measure the potential of a newly developed smartphone application to improve health behaviours in existing members of a website-delivered PA programme (10,000 steps). The participants in the IG were emailed an attachment with the iStepLog, with instructions on how to install this application. During the 3-month intervention, these participants were able to log their steps by using either the iStepLog on their device or the 10,000 Steps website. The IG was free to use either technology as they preferred. After the intervention, the study participants were asked to complete a 10-item questionnaire concerning the usability and usefulness of the applications. The CG did not have access to the iStepLog	During the study period, the IG maintained their step count, but the matched group logged a significantly lower number of steps (mean 6274.73, SD 2106.11, p < 0.001). The intervention was not effective
Prestwich et al.³² (2009)	An experimental longitudinal design was used with exercise behaviour assessed at both baseline and follow-up at 4 weeks. The participants were randomly allocated to one of five conditions as explained below: - IG-1: had to read the PMT-based message and then form implementation intentions - IG-2: had to read the PMT-based motivational message and information that requested the participant to decide when they would prefer to receive SMS messages and had to think about the content of the SMS - IG-3: a combination of IG-1 + IG-2 in addition to allowing the participants to choose their own content for the SMS - CG-1: only had to complete measures of motivation - CG-2: had to read a motivational message designed to manipulate each component of PMT	The combined implementation intention/text-message system was more effective than either strategy alone in terms of increasing exercise. The PMT intervention did not significantly increase any of the PMT variables
Prestwich et al.³³ (2010)	This study consisted of promoting two theory-based interventions that incorporated implementation intentions and SMS reminders directed at one’s walking-related plans or goals. The participants were randomised to one of three groups: - IG-1: a combination of the CG + information that was ‘helpful to make very specific plans regarding how you will walk briskly five times per week and receive an SMS reminder of these plans’ - IG-2: a combination of the IG-1 + no reminders of these plans but contained information that would be helpful to receive reminders of their brisk walking goal - CG: had information on the current governmental guidelines for PA through a cell phone. Brisk walking was suggested	The IG-1 (p = 0.04) and IG-2 (p = 0.03) increased the number of days on which they met the PA daily guidelines
Shapiro et al.³⁴ (2008)	Children and parents participated in three group education sessions to encourage increasing PA and decreasing screen time and sugar-sweetened beverage consumption. All randomised children received a brief psycho educational intervention and were then randomly assigned to one of two groups. The content of each group was as follows: - IG-1: they were instructed to send two SMS messages per day to record the three behaviours daily and received automated SMS feedback - IG-2: they used self-monitoring forms to record the three behaviours daily for the parent and child and received weekly verbal feedback	The intervention was not effective
Sirriyeh et al.³⁵ (2010)	This study attempted to develop and pilot the feasibility and efficacy of a novel intervention using affective messages as a strategy to increase PA levels in adolescents. The participants were randomly assigned to one of three experimental conditions. Each member of the IG received one SMS message each day for the full 14-day period. The message content was intended to increase PA and provide effective and instrumental beliefs - IG-1: SMS messages included statements regarding the anticipatory affective gains associated with regular moderate and vigorous PA - IG-2: SMS messages included statements regarding the instrumental gains associated with regular moderate and vigorous PA - IG-3: IG-1 + IG-2 - CG: only received two SMS messages with a neutral content	Although the PA levels of those in the affective group were higher at time 2 than the instrumental group, due to the combined and control conditions, the post hoc test lacked the necessary power to identify any significant differences between the groups. The intervention was not effective
Turner-McGrievy and Tate³⁶ (2011)	This intervention aimed to examine whether a combination of podcasting, mobile support communication and mobile diet monitoring could assist people in promoting PA and weight loss. Adults were randomly assigned to podcast-only or podcast + mobile groups. Both groups received 2 podcasts per week for 3 months and 2 mini podcasts per week for 3–6 months. The podcasts contained a section on nutrition and PA information, an audio blog of a man or a woman trying to lose weight, a soap opera and a goal-setting activity: - IG-1: received a book with calorie and fat gram amounts of food to assist in monitoring their dietary intake - IG-2: were instructed to download a diet and PA monitoring application and a social networking site (Twitter)	The intervention groups did not differ in changes in energy expenditure, demonstrating that the addition of social networking support and mobile diet and PA monitoring did not enhance these outcomes beyond receiving the theory-based podcasts alone. There was no difference between the groups in self-reported PA (p = 0.79)

IG: intervention group; CG: control group; PA: physical activity; PDA: personal digital assistant; MET: metabolic equivalent; PMT: protection motivation theory; SMS: short message service; MVPA: moderate to vigorous physical activity; AWAS: Australian Women’s Activity Survey; IPAQ: International Physical Activity Questionnaire; SD: standard deviation; SE: standard error; CI: confidence interval; IVR: interactive voice response.

Mobile method of delivery

The technological methods used to apply the interventions ranged from PDA software²⁵ to text messages,^{26–30,32–35} mobile applications³¹ and podcasting.³⁶ Two studies^27,30 used the voice feature of mobile phones combined with short message service (SMS) messages. In one study,³¹ the mobile application used had the capability to record a participant’s daily steps. Built-in tracking software was integrated into the iStepLog³¹ application to allow the researchers to monitor how much time the participants spent using the application and how often they used the application to log their steps.

Intervention design

Table 4 provides a summary of all the studies included in this review. Of the included articles, eight were randomised control trials (RCTs),^{25,28–30,32,33,35,36} two were pilot studies,^26,34 one was a feasibility study²⁷ and one was a matched case–control trial.³¹

The effects of an intervention combining the Internet and SMS messages were observed in one study.²⁹ In that study,²⁹ messages were sent via e-mail in addition to the utilisation of other methodologies. Three studies^26,32,35 only used SMS messages to deliver the intervention. Four interventions^25,29,34,36 provided feedback to the participants; in three of those interventions,^29,34,36 the feedback messages were automated. There was considerable variability in the frequency of the messages. Of all the interventions that used SMS, only one²⁸ detailed the total number of messages, while the others reported either approximate ranges for the number of messages sent^26,27,30,34 or did not specify the number at all.^29,32,33 Three interventions sent an SMS message daily,^27,34,35 while the interventions for three other studies^26,28,30 delivered between three- and nine-weekly SMS messages. The contents of the messages varied significantly; the content described in most of the articles focused on how to overcome barriers,^26,29 how to achieve goals,^25,29,33 the benefits of PA,^26,35,36 the drawbacks of a sedentary life style,³⁴ reminder messages to engage in PA^33,34 and motivational messages.^27,28,30,32 Of the studies included in this review, three included a counsellor or coach who guided the intervention.^27,28,36 With respect to the effectiveness of the interventions, six studies^{25–28,30,33} reported increasing levels of PA, while the remaining studies reported that their interventions had no significant effects.

Discussion

There have been many systematic reviews of health behaviour interventions administered by mobile phones. These interventions were intended to affect weight loss,³⁷ smoking cessation³⁸ and HIV prevention behaviours.³⁹ This review aimed to summarise and update the existing literature on mobile device interventions for increasing PA. It is very important to find new ways to prescribe PA because levels of inactivity are high in both developed and developing countries. In rapidly growing large cities of the developing world, physical inactivity⁴⁰ is an even greater problem. However, mobile devices are becoming an important tool for conducting PA interventions. The technical capabilities of mobile devices vary widely, from voice response, text messaging, cameras, native applications, automated sensing, Internet access and wireless connectivity with other devices.⁴¹

In Spain,⁴² smartphone penetration has risen to 55 per cent of the population, and these smartphone owners are becoming increasingly reliant on their devices, with 72 per cent of smartphone owners accessing the Internet every day on their smartphones. Data⁴² suggest that people with smartphones have an average of 20 apps installed, including 8 apps that have been used in the last 30 days and 4 paid apps. Advances in mobile health technology and the adoption of smartphones mean that PA apps will be of vital importance. Apps have a tremendous potential to improve population health, are very efficient in facilitating communication and are a reference tool for quick and easy access. The ability to provide live feedback during PA, the ability to provide a system of rewards for goals achieved, the capability to not need an established Internet connection and the capability to track health information are some of the advantages of mobile applications. However, the requirement to have a specific platform because each native application only runs on one platform, the need to manually install updates, the high development cost and the maintenance cost of the applications are some of the disadvantages of native applications.

Few researchers have developed mobile applications to cause changes in PA behaviours; in our review, only Kirwan et al.³¹ used a mobile application to examine the effect of the smartphone application on self-monitoring and self-reported PA levels. The participants in the intervention group were able to log their steps using either the smartphone application or the 10,000 steps website; they also completed an online questionnaire assessing perceived usability and usefulness of the smartphone application. The intervention group was more likely to log steps daily compared to the control group. This study is important for understanding the use of mobile devices in health interventions because the major finding of the study was that the combination of a native application and a website was effective in promoting PA. This study may be the beginning of a new and emerging field of prescribing PA via native applications.

The use of commercial mobile applications to increase PA levels in adults remains questionable, and it has been proposed that the mobile apps should be peer-reviewed prior to being launched for commercial use.⁴³ A number of recent articles have investigated the potential dangers and safety of some clinical and health applications aimed at healthcare professionals or the general public. Yetisen and colleagues⁴⁴ provided an overview of the new Food and Drug Administration (FDA) regulations for mobile health application development by evaluating its impact from multiple perspectives and discussing its implications on the emerging markets. Cummings et al.⁴⁵ investigated some of the considerations surrounding mobile phones and the data these applications collect to help support consumers’ decision-making when using mobile phones and their associated applications. Mobile applications are likely to be a new source of information that enables patients to interact in a different way, but we must ensure that they are safe, useful and effective. If they work, we should use these applications, but as with any medical intervention, mobile applications require fair tests in the real world before we can know their effectiveness.⁴⁶ Relatively few studies exist on the effectiveness of mobile applications, and more research is needed to properly address the issue.⁴⁷ Future research should examine how commercial mobile applications for PA are being used by different groups of people to change health behaviours.⁴⁸

Most of the interventions^{25–28,30,32,34,35} included in this review used SMS messaging as a method in their research, and some of these interventions^25–28,30 reported an increase in PA among their participants. We found one article²⁵ that used PDAs to deliver the intervention; this study was a clinical PA and weight loss trial in which 210 overweight adults were randomised to one of three arms. The study adds to the small body of literature on PA self-monitoring and demonstrates how adding feedback to a convenient PDA-based self-monitoring device may affect both self-monitoring behaviour and PA and weight outcomes. The results suggested that providing participants with incremental real-time feedback during progress towards a weekly PA goal may be more helpful in achieving that goal compared to a more traditional form of feedback. Providing feedback in more immediate ways may help a participant adjust his or her PA behaviour in time to make better progress towards a weekly goal.

Therefore, combining a PA prescription with targeted SMS reminders, whether motivational or prescriptive, can be a useful tool for daily PA recommendations. However, the results of this review should not be generalised because the studies in this review differed in terms of the types of messages sent, the number of messages sent and the lengths and goals of the interventions. Furthermore, the sample sizes were generally small, and the participants had very different characteristics.

The research into special populations has also been limited. Only Shapiro et al.³⁴ enrolled children as participants, while Sirriyeh et al.³⁵ included adolescents as participants. A lack of sufficient evidence regarding the effectiveness of mobile device interventions for elderly people was also identified in this review because only Kim and Glanz³⁰ reported enrolling participants over 60 years of age. There are, however, many studies that have supported the physical and mental benefits of PA in individuals over 65 years of age.^49,50 Despite this evidence, many adults are not sufficiently active to achieve these benefits. SMS messaging and mobile applications may be useful to increase the PA levels of elderly people, but this line of research must be further pursued to confirm that possibility.

Strengths and limitations

There are strengths and limitations in our findings. The limited number of studies included in this review, the short duration of the interventions and the baseline differences between the groups complicate the ability to establish common criteria to prescribe PA through mobile devices. Future research should aim to improve these aspects and obtaining more conclusive data. At the initial point of the bibliographic search, we identified two common drawbacks: the existence of numerous study protocols^48,51,52 and the use of a mobile phone for various tasks (e.g. counting steps⁵³ and delivering PA questionnaires⁵⁴) within a PA intervention. The lack of information provided by the studies included in this review has complicated the analysis of their methodological quality. The most common methodological shortcomings were as follows: no descriptions of the participants’ characteristics, high numbers of subjects lost to follow-up and not enough information regarding the study design. We also found a lack of standardisation among the questionnaires used to measure PA in the articles included.

This systematic review provides four^27,30,32,36 interventions that were not included in the previous review. Three^27,30,32 of the four interventions reported significant results; in total, six interventions^{25–28,30,33} included in this review obtained significant results. The studies not included in the previous review provided new aspects to consider. For example, David et al.²⁷ conducted a feasibility study of a 12-week walking intervention administered through an interactive voice response system and mobile phones. The findings of the study added to the evidence that mobile phones can be used to deliver an effective and low-cost walking intervention. Kim and Glanz³⁰ examined a 6-week programme of motivational text messaging to increase PA among older participants. The added evidence was twofold: this was the first effective intervention conducted in the elderly through mobile phones; additionally, the study provided important data for low cost-effectiveness and larger interventions in PA promotion for minorities. Prestwich et al.³² encouraged people to receive text message reminders of their implementation intentions. The results suggested that implementation intentions and SMS reminders increased exercise frequency significantly. Turner-McGrievy and Tate³⁶ conducted a combination of podcasting, mobile support communication, mobile PA and diet to assist people in weight loss. The major contribution of this study has been the use of social media platforms such as Twitter to deliver the intervention. A major benefit of social media for health communication is the accessibility and widening access of health information to various population groups, regardless of age, education, race or ethnicity and locality, compared to traditional communication methods.⁵⁵

Conclusion

In conclusion, mobile devices are inexpensive tools that have become widely used. Mobile devices have emerged as an important tool for disease prevention and interventions affecting health behaviours. Future research should aim to obtain more conclusive data.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Increasing physical activity through mobile device interventions: A systematic review

Abstract

Keywords

Introduction

Methods

Data sources and search strategy

Inclusion criteria and exclusion criteria

Study selection

Data extraction

Study quality assessment

Results

Methodological quality assessment

Review characteristics

Characteristics of the study populations and selected studies

Intervention outcomes

Mobile method of delivery

Intervention design

Discussion

Strengths and limitations

Conclusion

Footnotes

Declaration of conflicting interests

Funding

References