A Social Networking and Gamified App to Increase Physical Activity: Cluster RCT

Edney, Sarah; Olds, Tim; Ryan, Jillian; Vandelanotte, Corneel; Plotnikoff, Ronald C.; Curtis, Rachel; Maher, Carol

doi:10.1016/j.amepre.2019.09.009

Cited by 70 publications

(92 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Participant clusters allocated to the waitlist control group were instructed to go about their usual daily activities and received access to the Active Team program at the end of the study. Full details of the study's main findings are published elsewhere [36]. Briefly, from baseline to postintervention (3-month assessment), objectively measured MVPA increased by an average of 11 (SD 329) minutes/ week in the socially-enhanced intervention group and increased by an average of 3 (SD 316) minutes/week in the waitlist control group (non-significant difference between groups).…”

Section: Intervention Detailsmentioning

confidence: 99%

“…Minimum wear criteria were used to determine valid accelerometer data for inclusion in data analyses: participants must have worn the accelerometer for at least 10 waking hours, on four or more days, including one weekend day. Participants returning incomplete data were asked to wear the GENEActiv accelerometer again up to two more times [36]. Accelerometer files were processed with 60 s epochs and the Esliger cutpoints [38] were used to define MVPA as any activity above 645 counts per minute.…”

Section: Physical Activity (Objective)mentioning

confidence: 99%

See 1 more Smart Citation

Examining social-cognitive theory constructs as mediators of behaviour change in the active team smartphone physical activity program: a mediation analysis

et al. 2021

Self Cite

View full text Add to dashboard Cite

Background Regular engagement in physical activity has well-established physical and psychological health benefits. Despite this, over a quarter of the global adult population is insufficiently physically active. Physical activity interventions grounded in behaviour change theory, such as the social-cognitive theory, are widely considered to be more effective than non-theoretical approaches. Such interventions set out to intervene on the ultimate outcome (physical activity), but also influence intermediate factors (social-cognitive theory constructs) which in turn, are believed to influence physical activity behaviour. The primary aim of the study was to use mediation analysis to examine whether changes in the social-cognitive theory and related constructs, in particular self-efficacy, outcome expectations, intentions, barriers and goal setting, mediated the effects of a smartphone-based social networking physical activity intervention. Methods Mediation analyses were conducted using the PROCESS Macro in SPSS to (i) calculate the regression coefficients for the effect of the independent variable (group allocation) on the hypothesised mediators (social-cognitive theory constructs), (ii) calculate the regression coefficient for the effect of the hypothesised mediators (social-cognitive theory constructs) on the dependent variable (objectively measured physical activity or self-report physical activity), independent of group assignment and (iii) determine the total, direct and indirect intervention effects. Results Data from 243 participants were included in the mediation analysis. There was no evidence of mediation for change in objectively measured MVPA or self-reported MVPA. Conclusions There was no conclusive evidence that any of the social-cognitive theory constructs mediated the relationship between an app-based intervention and change in physical activity. Ongoing efforts to develop and understand components that make physical activity app-based interventions effective are recommended. Trial registration This trial was registered with the Australian and New Zealand Clinical Trial Registry (ACTRN12617000113358, date of registration 23 January, 2017).

show abstract

Section: Intervention Detailsmentioning

confidence: 99%

Section: Physical Activity (Objective)mentioning

confidence: 99%

Examining social-cognitive theory constructs as mediators of behaviour change in the active team smartphone physical activity program: a mediation analysis

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ten obesity trials reported multicomponent effects on health outcomes ("multicomponent" means that patients received other intervention [s] in addition to PGHD device [s], making it difficult to attribute improvements to the devices themselves). Therefore, we do not discuss each study in detail, but rather describe general aspects of the studies and results and refer the reader to Appendix Table C-8. Six 24,[47][48][49][50][51] of the 10 studies were at high risk of bias, and the other four [52][53][54][55][56][57] were at moderate risk of bias. Typical issues involved nonisolated effects, unclear randomization and allocation concealment, and unblinded outcome assessors.…”

Section: Multicomponent Effects On Health Outcomes: Obesitymentioning

confidence: 99%

“…Edney et al (2020) [53][54][55] Waitlist: No intervention. Self-monitoring: Received a wrist-worn pedometer (Zencro TW 64S) and measured daily steps, and had a daily goal as seen in an app called Active Team.…”

Section: Creel Et Al (2016) 45mentioning

confidence: 99%

Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes

Treadwell¹,

Reston²,

Rouse³

et al. 2021

View full text Add to dashboard Cite

Background. Automated-entry consumer devices that collect and transmit patient-generated health data (PGHD) are being evaluated as potential tools to aid in the management of chronic diseases. The need exists to evaluate the evidence regarding consumer PGHD technologies, particularly for devices that have not gone through Food and Drug Administration evaluation. Purpose. To summarize the research related to automated-entry consumer health technologies that provide PGHD for the prevention or management of 11 chronic diseases. Methods. The project scope was determined through discussions with Key Informants. We searched MEDLINE and EMBASE (via EMBASE.com), In-Process MEDLINE and PubMed unique content (via PubMed.gov), and the Cochrane Database of Systematic Reviews for systematic reviews or controlled trials. We also searched ClinicalTrials.gov for ongoing studies. We assessed risk of bias and extracted data on health outcomes, surrogate outcomes, usability, sustainability, cost-effectiveness outcomes (quantifying the tradeoffs between health effects and cost), process outcomes, and other characteristics related to PGHD technologies. For isolated effects on health outcomes, we classified the results in one of four categories: (1) likely no effect, (2) unclear, (3) possible positive effect, or (4) likely positive effect. When we categorized the data as “unclear” based solely on health outcomes, we then examined and classified surrogate outcomes for that particular clinical condition. Findings. We identified 114 unique studies that met inclusion criteria. The largest number of studies addressed patients with hypertension (51 studies) and obesity (43 studies). Eighty-four trials used a single PGHD device, 23 used 2 PGHD devices, and the other 7 used 3 or more PGHD devices. Pedometers, blood pressure (BP) monitors, and scales were commonly used in the same studies. Overall, we found a “possible positive effect” of PGHD interventions on health outcomes for coronary artery disease, heart failure, and asthma. For obesity, we rated the health outcomes as unclear, and the surrogate outcomes (body mass index/weight) as likely no effect. For hypertension, we rated the health outcomes as unclear, and the surrogate outcomes (systolic BP/diastolic BP) as possible positive effect. For cardiac arrhythmias or conduction abnormalities we rated the health outcomes as unclear and the surrogate outcome (time to arrhythmia detection) as likely positive effect. The findings were “unclear” regarding PGHD interventions for diabetes prevention, sleep apnea, stroke, Parkinson’s disease, and chronic obstructive pulmonary disease. Most studies did not report harms related to PGHD interventions; the relatively few harms reported were minor and transient, with event rates usually comparable to harms in the control groups. Few studies reported cost-effectiveness analyses, and only for PGHD interventions for hypertension, coronary artery disease, and chronic obstructive pulmonary disease; the findings were variable across different chronic conditions and devices. Patient adherence to PGHD interventions was highly variable across studies, but patient acceptance/satisfaction and usability was generally fair to good. However, device engineers independently evaluated consumer wearable and handheld BP monitors and considered the user experience to be poor, while their assessment of smartphone-based electrocardiogram monitors found the user experience to be good. Student volunteers involved in device usability testing of the Weight Watchers Online app found it well-designed and relatively easy to use. Implications. Multiple randomized controlled trials (RCTs) have evaluated some PGHD technologies (e.g., pedometers, scales, BP monitors), particularly for obesity and hypertension, but health outcomes were generally underreported. We found evidence suggesting a possible positive effect of PGHD interventions on health outcomes for four chronic conditions. Lack of reporting of health outcomes and insufficient statistical power to assess these outcomes were the main reasons for “unclear” ratings. The majority of studies on PGHD technologies still focus on non-health-related outcomes. Future RCTs should focus on measurement of health outcomes. Furthermore, future RCTs should be designed to isolate the effect of the PGHD intervention from other components in a multicomponent intervention.

show abstract

“…Four studies [38,43,44] were at high risk of bias for the 'blinding of outcome assessment' item because they measured PA using only self-reports. Six studies [33,37,43,44,49] were at high risk of bias for the 'incomplete outcome data' item because they reported high drop-out and did not included intention-to-threat analyses. Six studies [39,42,43,45,49] were rated at unclear risk for the 'selective outcome reporting' item because they have not been pre-registered or published in a protocol-study.…”

Section: Risk Of Biasmentioning

confidence: 99%

Does gamification improve physical activity? A systematic review and meta-analysis

Mazéas¹,

Duclos²,

Pereira³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background. The use of gamification to change behaviors and promote physical activity is a promising avenue to tackle the global physical inactivity pandemic and the current prevalence of chronic diseases. However, we do not have yet evidence of the effectiveness of gamified interventions with the existence of mixed results in the literature.Objective. This systematic review and meta-analysis aimed to evaluate the effectiveness of gamified interventions and their healthcare potential by testing the scalability and sustainability of their influence on physical activity (PA) and sedentary behaviors (SB).Methods. Five electronic databases (Pubmed, EMBASE, Scopus, Web of Science and CENTRAL) were searched for randomized controlled trials (RCTs) published since 2010. Eligibility criteria were based on components of PICO (participants, interventions, comparators, and outcomes). Studies were included when they used gamified interventions in daily life with an active or inactive control group, and when they assessed a PA or SB outcome. Studies were excluded when the intervention was based on an active video game using a gaming console. We conducted meta-analyses using a random effects model approach. Sensitivity analyses, influence analyses and publication bias analyses were performed to examine the robustness of our results.Results. The main meta-analysis performed on 16 studies and 2407 participants, revealed a small to medium summary effect of gamified interventions on PA behavior (g = 0.42 (95%CI [0.14, 0.69]). No statistical difference was found between different subgroups (adults vs adolescents, healthy participants vs adults with chronic diseases) and no interaction effects with moderators like age, gender or BMI, suggesting good scalability of gamified interventions. Moreover, the effect appears better on the step count (MD = +1609.56 steps per day (95%CI [372.39, 2846.73]) than on moderate-to-vigorous PA (g = 0.31 (95%CI [-0.19, 0.80]). The long-term effect (measured with follow-up averaging 14 weeks after the end of the intervention) is more volatile with a very small to small effect (g = 0.15, [0.07, 0.23]). Conclusions. This meta-analysis confirms that gamified interventions are promising to promote PA in various publics. However, if the effect persists after the end of the program, suggesting that it is not just a novelty effect due to the playful nature of gamification, it seems volatile with a small long-term effect. The integration of gamification in more global healthcare interventions could be a way to address this low sustainability. Future rigorous trials are needed to explore these perspectives.

show abstract

A Social Networking and Gamified App to Increase Physical Activity: Cluster RCT

Cited by 70 publications

References 60 publications

Examining social-cognitive theory constructs as mediators of behaviour change in the active team smartphone physical activity program: a mediation analysis

Examining social-cognitive theory constructs as mediators of behaviour change in the active team smartphone physical activity program: a mediation analysis

Automated-Entry Patient-Generated Health Data for Chronic Conditions: The Evidence on Health Outcomes

Does gamification improve physical activity? A systematic review and meta-analysis

Contact Info

Product

Resources

About