Smartphone apps are a promising tool for delivering accessible and appealing physical activity interventions. Given the large growth of research in this field, there are now enough studies using the ..."gold standard" of experimental design-the randomized controlled trial design-and employing objective measurements of physical activity, to support a meta-analysis of these scientifically rigorous studies.
This systematic review and meta-analysis aimed to determine the effectiveness of smartphone apps for increasing objectively measured physical activity in adults.
A total of 7 electronic databases (EMBASE, EmCare, MEDLINE, Scopus, Sport Discus, The Cochrane Library, and Web of Science) were searched from 2007 to January 2018. Following the Population, Intervention, Comparator, Outcome and Study Design format, studies were eligible if they were randomized controlled trials involving adults, used a smartphone app as the primary or sole component of the physical activity intervention, used a no- or minimal-intervention control condition, and measured objective physical activity either in the form of moderate-to-vigorous physical activity minutes or steps. Study quality was assessed using a 25-item tool based on the Consolidated Standards of Reporting Trials checklist. A meta-analysis of study effects was conducted using a random effects model approach. Sensitivity analyses were conducted to examine whether intervention effectiveness differed on the basis of intervention length, target behavior (physical activity alone vs physical activity in combination with other health behaviors), or target population (general adult population vs specific health populations).
Following removal of duplicates, a total of 6170 studies were identified from the original database searches. Of these, 9 studies, involving a total of 1740 participants, met eligibility criteria. Of these, 6 studies could be included in a meta-analysis of the effects of physical activity apps on steps per day. In comparison with the control conditions, smartphone apps produced a nonsignificant (P=.19) increase in participants' average steps per day, with a mean difference of 476.75 steps per day (95% CI -229.57 to 1183.07) between groups. Sensitivity analyses suggested that physical activity programs with a duration of less than 3 months were more effective than apps evaluated across more than 3 months (P=.01), and that physical activity apps that targeted physical activity in isolation were more effective than apps that targeted physical activity in combination with diet (P=.04). Physical activity app effectiveness did not appear to differ on the basis of target population.
This meta-analysis provides modest evidence supporting the effectiveness of smartphone apps to increase physical activity. To date, apps have been most effective in the short term (eg, up to 3 months). Future research is needed to understand the time course of intervention effects and to investigate strategies to sustain intervention effects over time.
Participation in regular physical activity holds key benefits for cancer survivors, yet few cancer survivors meet physical activity recommendations. This study aimed to develop and pilot test a ...mHealth app referral service aimed at assisting cancer survivors to increase their physical activity. In particular, the study sought to examine feasibility and acceptability of the service and determine preliminary efficacy for physical activity behaviour change.
A systematic search identified potentially appropriate Apple (iOS) and Android mHealth apps. The apps were audited regarding the type of physical activity encouraged, evidence-based behavioural strategies and other characteristics, to help match apps to users' preferences and characteristics. A structured service was devised to deliver the apps and counselling, comprising two face-to-face appointments with a mid-week phone or email check-up. The mHealth app referral service was piloted using a pre-post design among 12 cancer survivors. Participants' feedback regarding the service's feasibility and acceptability was sought via purpose-designed questionnaire, and analysed using inductive thematic analysis and descriptive statistics. Change in physical activity was assessed using a valid and reliable self-report tool and analysed using paired t-tests. In line with recommendations for pilot studies, confidence intervals and effect sizes were reported to aid interpretation of clinical significance, with an alpha of 0.2 used to denote statistical significance.
Of 374 mHealth apps identified during the systematic search, 54 progressed to the audit (iOS = 27, Android = 27). The apps consistently scored well for aesthetics, engagement and functionality, and inconsistently for gamification, social and behaviour change features. Ten participants completed the pilot evaluation and provided positive feedback regarding the service's acceptability and feasibility. On average, participants increased their moderate-vigorous physical activity by 236 min per week (d = 0.73; 95% CI = -49 to 522; p = 0.09).
This study offered initial evidence that a mHealth app referral service for cancer survivors is feasible and acceptable and may increase physical activity levels. The large increase in physical activity is promising, but should be interpreted with caution given the small sample size and lack of control group. Further research is warranted on a larger scale to investigate generalisability, long-term compliance and application in clinical settings.
The popularity and reach of social media make it an ideal delivery platform for interventions targeting health behaviors, such as physical inactivity. Research has identified a dose-response ...relationship whereby greater engagement and exposure are positively associated with intervention effects, hence enhancing engagement will maximize the potential of these interventions.
This study examined the social media activity of successful commercial activity tracker brands to understand which creative elements (message content and design) they use in their communication to their audience, which social media platforms attract the most engagement, and which creative elements prompted the most engagement.
Posts (n=509) made by Fitbit and Garmin on Facebook, Twitter, and Instagram over a 3-month period were coded for the presence of creative elements. User engagement regarding the total number of likes, comments, or shares per post was recorded. Negative binomial regression analyses were used to identify creative elements associated with higher engagement.
Engagement on Instagram was 30-200 times higher than on Facebook, or Twitter. Fitbit and Garmin tended to use different creative elements from one another. A higher engagement was achieved by posts featuring an image of the product, highlighting new product features and with themes of self-improvement (P<.01).
Findings suggest that Instagram may be a particularly promising platform for delivering engaging health messaging. Health messages which incorporate inspirational imagery and focus on a tangible product appear to achieve the highest engagement. Fitbit and Garmin employed difference creative elements, which is likely to reflect differences in their target markets. This underscores the importance of market segmentation in health messaging campaigns.
To date, many online health behavior programs developed by researchers have not been translated at scale. To inform translational efforts, health researchers must work with marketing experts to ...design cost-effective marketing campaigns. It is important to understand the characteristics of end users of a given health promotion program and identify key market segments.
This study aimed to describe the characteristics of the adopters of Active Team, a gamified online social networking physical activity app, and identify potential market segments to inform future research translation efforts.
Participants (N=545) were Australian adults aged 18 to 65 years who responded to general advertisements to join a randomized controlled trial (RCT) evaluating the Active Team app. At baseline they provided demographic (age, sex, education, marital status, body mass index, location of residence, and country of birth), behavioral (sleep, assessed by the Pittsburgh Quality Sleep Index) and physical activity (assessed by the Active Australia Survey), psychographic information (health and well-being, assessed by the PERMA Positive Emotion, Engagement, Relationships, Meaning, Achievement Profile; depression, anxiety and stress, assessed by the Depression, Anxiety, and Stress Scale DASS-21; and quality of life, assessed by the 12-Item Short Form Health Survey SF-12). Descriptive analyses and a k-medoids cluster analysis were performed using the software R 3.3.0 (The R Foundation) to identify key characteristics of the sample.
Cluster analyses revealed four clusters: (1) younger inactive women with poor well-being (218/545), characterized by a higher score on the DASS-21, low mental component summary score on the SF-12, and relatively young age; (2) older, active women (153/545), characterized by a lower score on DASS-21, a higher overall score on the SF-12, and relatively older age; (3) young, active but stressed men (58/545) with a higher score on DASS-21 and higher activity levels; and (4) older, low active and obese men (30/545), characterized by a high body mass index and lower activity levels.
Understanding the characteristics of population segments attracted to a health promotion program will guide the development of cost-effective research translation campaigns.
Australian New Zealand Clinical Trial Registry ACTRN12617000113358; https://www.anzctr.org .au/Trial/Registration/TrialReview.aspx?id=371463.
RR2-10.1186/s12889-017-4882-7.
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