scholarly journals User Engagement and Attrition in an App-Based Physical Activity Intervention: Secondary Analysis of a Randomized Controlled Trial

10.2196/14645 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e14645 ◽  
Author(s):  
Sarah Edney ◽  
Jillian C Ryan ◽  
Tim Olds ◽  
Courtney Monroe ◽  
François Fraysse ◽  
...  
Keyword(s):  
Physical Activity ◽  
Mixed Models ◽  
Physical Activity Intervention ◽  
Controlled Trial ◽  
User Engagement ◽  
User Characteristics ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity ◽  
Super Users

Background The success of a mobile phone app in changing health behavior is thought to be contingent on engagement, commonly operationalized as frequency of use. Objective This subgroup analysis of the 2 intervention arms from a 3-group randomized controlled trial aimed to examine user engagement with a 100-day physical activity intervention delivered via an app. Rates of engagement, associations between user characteristics and engagement, and whether engagement was related to intervention efficacy were examined. Methods Engagement was captured in a real-time log of interactions by users randomized to either a gamified (n=141) or nongamified version of the same app (n=160). Physical activity was assessed via accelerometry and self-report at baseline and 3-month follow-up. Survival analysis was used to assess time to nonuse attrition. Mixed models examined associations between user characteristics and engagement (total app use). Characteristics of super users (top quartile of users) and regular users (lowest 3 quartiles) were compared using t tests and a chi-square analysis. Linear mixed models were used to assess whether being a super user was related to change in physical activity over time. Results Engagement was high. Attrition (30 days of nonuse) occurred in 32% and 39% of the gamified and basic groups, respectively, with no significant between-group differences in time to attrition (P=.17). Users with a body mass index (BMI) in the healthy range had higher total app use (mean 230.5, 95% CI 190.6-270.5; F2=8.67; P<.001), compared with users whose BMI was overweight or obese (mean 170.6, 95% CI 139.5-201.6; mean 132.9, 95% CI 104.8-161.0). Older users had higher total app use (mean 200.4, 95% CI 171.9-228.9; F1=6.385; P=.01) than younger users (mean 155.6, 95% CI 128.5-182.6). Super users were 4.6 years older (t297=3.6; P<.001) and less likely to have a BMI in the obese range (χ22=15.1; P<.001). At the 3-month follow-up, super users were completing 28.2 (95% CI 9.4-46.9) more minutes of objectively measured physical activity than regular users (F1,272=4.76; P=.03). Conclusions Total app use was high across the 100-day intervention period, and the inclusion of gamified features enhanced engagement. Participants who engaged the most saw significantly greater increases to their objectively measured physical activity over time, supporting the theory that intervention exposure is linked to efficacy. Further research is needed to determine whether these findings are replicated in other app-based interventions, including those experimentally evaluating engagement and those conducted in real-world settings. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12617000113358; https://www.anzctr.org.au/ACTRN12617000113358.aspx

scholarly journals User Engagement and Attrition in an App-Based Physical Activity Intervention: Secondary Analysis of a Randomized Controlled Trial (Preprint)

2019 ◽  
Author(s):  
Sarah Edney ◽  
Jillian C Ryan ◽  
Tim Olds ◽  
Courtney Monroe ◽  
François Fraysse ◽  
...  
Keyword(s):  
Physical Activity ◽  
Mixed Models ◽  
Physical Activity Intervention ◽  
Controlled Trial ◽  
User Engagement ◽  
User Characteristics ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity ◽  
Super Users

BACKGROUND The success of a mobile phone app in changing health behavior is thought to be contingent on engagement, commonly operationalized as frequency of use. OBJECTIVE This subgroup analysis of the 2 intervention arms from a 3-group randomized controlled trial aimed to examine user engagement with a 100-day physical activity intervention delivered via an app. Rates of engagement, associations between user characteristics and engagement, and whether engagement was related to intervention efficacy were examined. METHODS Engagement was captured in a real-time log of interactions by users randomized to either a gamified (n=141) or nongamified version of the same app (n=160). Physical activity was assessed via accelerometry and self-report at baseline and 3-month follow-up. Survival analysis was used to assess time to nonuse attrition. Mixed models examined associations between user characteristics and engagement (total app use). Characteristics of super users (top quartile of users) and regular users (lowest 3 quartiles) were compared using <italic>t</italic> tests and a chi-square analysis. Linear mixed models were used to assess whether being a super user was related to change in physical activity over time. RESULTS Engagement was high. Attrition (30 days of nonuse) occurred in 32% and 39% of the gamified and basic groups, respectively, with no significant between-group differences in time to attrition (<italic>P</italic>=.17). Users with a body mass index (BMI) in the healthy range had higher total app use (mean 230.5, 95% CI 190.6-270.5; <italic>F</italic><sub>2</sub>=8.67; <italic>P</italic>&lt;.001), compared with users whose BMI was overweight or obese (mean 170.6, 95% CI 139.5-201.6; mean 132.9, 95% CI 104.8-161.0). Older users had higher total app use (mean 200.4, 95% CI 171.9-228.9; <italic>F</italic><sub>1</sub>=6.385; <italic>P</italic>=.01) than younger users (mean 155.6, 95% CI 128.5-182.6). Super users were 4.6 years older (t<sub>297</sub>=3.6; <italic>P</italic>&lt;.001) and less likely to have a BMI in the obese range (χ<sup>2</sup><sub>2</sub>=15.1; <italic>P</italic>&lt;.001). At the 3-month follow-up, super users were completing 28.2 (95% CI 9.4-46.9) more minutes of objectively measured physical activity than regular users (<italic>F</italic><sub>1,272</sub>=4.76; <italic>P</italic>=.03). CONCLUSIONS Total app use was high across the 100-day intervention period, and the inclusion of gamified features enhanced engagement. Participants who engaged the most saw significantly greater increases to their objectively measured physical activity over time, supporting the theory that intervention exposure is linked to efficacy. Further research is needed to determine whether these findings are replicated in other app-based interventions, including those experimentally evaluating engagement and those conducted in real-world settings.

Physical activity intervention using Fitbits in an introductory college health course

2016 ◽  
Vol 76 (3) ◽  
pp. 337-348 ◽  
Author(s):  
Aubrianne E Rote
Keyword(s):  
Physical Activity ◽  
College Students ◽  
Repeated Measures ◽  
Physical Activity Intervention ◽  
Financial Burden ◽  
Controlled Trial ◽  
Activity Monitor ◽  
Health Course ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity

Objective: This study took the form of an intervention examining change in physical activity and quality of experience among students in an introductory health course who were asked to wear a Fitbit activity monitor throughout the semester. Method: College students ( N = 56) took part in this controlled trial. Students enrolled in an introductory health course (Education + Fitbit; n = 24) were asked to purchase a Fitbit and wear it throughout the semester. This activity monitor purchase replaced the textbook requirement to reduce the financial burden for students. Change in objectively measured physical activity within this group was compared to students enrolled in a traditional introductory health course (Education Only; n = 14) and students enrolled in an introductory humanities course (Control; n = 18). To assess objectively measured physical activity, all participants wore a sealed pedometer for one week at the beginning and end of the semester. Students in the Education + Fitbit group also provided written feedback on their experience with the Fitbit. Results: A 2 × 3 repeated-measures analysis of variance (ANOVA) revealed a significant interaction between time and group, F(2, 53) = 3.957, p = .025. Post hoc analysis of this interaction indicated that students in the Education + Fitbit group significantly increased ( p = .014) objectively measured physical activity by 1,078 steps/day, whereas physical activity in Education Only and Control groups did not significantly change. Qualitative data demonstrated that student experiences with the Fitbit were resoundingly positive. Conclusion: Replacing a textbook requirement with requiring a commercially available activity monitor in an introductory health course may be an effective and enjoyable strategy to increase physical activity among US college students.

scholarly journals A 20-Year Follow-Up Study of Objectively Measured Physical Activity

2021 ◽  
Vol 18 (6) ◽  
pp. 3076
Author(s):  
Anders Raustorp ◽  
Andreas Fröberg
Keyword(s):  
Physical Activity ◽  
Early Adolescence ◽  
Total Sample ◽  
Short Time Span ◽  
Steps Per Day ◽  
Objectively Measured ◽  
Short Time ◽  
Objectively Measured Physical Activity

Background: The objectives of this study were to explore the effect of time, long-term tracking, and the proportion of objectively measured physical activity (PA) from early adolescence to the mid-thirties. Methods: PA was measured as mean steps per day (SPD) with pedometers during 2000 (T1), 2003 (T2), 2005 (T3), 2010 (T4), 2016 (T5) and 2020 (T6). Data from 64 participants (n = 32 males) were analysed from their early adolescence (T1) to their mid-thirties (T6). Results: SPD decreased in the total sample and among males and females (all, p < 0.001). Males took more mean SPD than females during T1 (p = 0.002), whereas females took more mean SPD during T2 (p = 0.009) and T6 (p = 0.008). Males’ mean SPD tracked between T1 and T2 (p = 0.021), T2 and T3 (p = 0.030), T3 and T4 (p = 0.015) and T4 and T5 (p = 0.003). Females’ mean SPD tracked between T3 and T4 (p = 0.024) and T5 and T6 (p < 0.001). In the total sample, more mean SPD were found on weekdays compared to weekend days at T3 (p = 0.017) and T5 (p < 0.001). Conclusions: SPD decreased between T1 and T6. Mean SPD tracked low-to-moderate in the short time span. From late adolescence to the mid-thirties, more mean SPD was observed during weekdays compared to weekend days.

scholarly journals Association between objectively measured physical activity and longitudinal changes in body composition in adolescents: the Tromsø study fit futures cohort

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e036991
Author(s):  
Nils Abel Aars ◽  
Sigurd Beldo ◽  
Bjarne Koster Jacobsen ◽  
Alexander Horsch ◽  
Bente Morseth ◽  
...  
Keyword(s):  
Physical Activity ◽  
High School ◽  
Body Composition ◽  
Vigorous Physical Activity ◽  
First Year ◽  
Upper Secondary ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity ◽  
Secondary High School

ObjectivesPhysical activity may be important in deterring the obesity epidemic. This study aimed to determine whether objectively measured physical activity in first year of upper secondary high school predicted changes in body composition over 2 years of follow-up in a cohort of Norwegian adolescents (n=431).DesignA longitudinal study of adolescents (mean age of 16 (SD 0.4) at baseline, 60.3% girls) participating in the Fit Futures studies 1 (2010–2011) and 2 (2012–2013).SettingAll eight upper secondary high schools in two municipalities in Northern Norway.ParticipantsStudents participating in both studies and under the age of 18 at baseline and with valid measurement of physical activity at baseline and body composition in both surveys.Primary and secondary outcomesChange in objectively measured body mass index and waist circumference and change in dual-energy X-ray absorptiometry measured fat mass index, lean mass index (LMI) and appendicular LMI (aLMI) between baseline and follow-up.ResultsAt baseline, boys had significantly higher physical activity volume (p=0.01) and spent on average of 6.4 (95% CI 2.1 to 10.6) more minutes in moderate-to-vigorous physical activity (MVPA) than girls (p<0.01). In girls, multivariate regression analyses showed that more sedentary time was negatively associated with changes in LMI (p<0.01) and aLMI (p<0.05), whereas more light activity had opposite effects on these measures (p<0.01 and p<0.05, respectively). No significant associations between measures of baseline physical activity and changes in body composition parameters were observed in boys.ConclusionsIn this cohort of Norwegian adolescents, sedentary and light physical activity was associated with changes in LMI and aLMI in girls, but not boys. Minutes spent in MVPA in first year of upper secondary high school was not associated with changes in measures of body composition in neither sex after 2 years.

Do Birds of a Feather Flock Together Within a Team-Based Physical Activity Intervention? A Social Network Analysis

2019 ◽  
Vol 16 (9) ◽  
pp. 745-751
Author(s):  
Sarah Edney ◽  
Tim Olds ◽  
Jillian Ryan ◽  
Ronald Plotnikoff ◽  
Corneel Vandelanotte ◽  
...  
Keyword(s):  
Physical Activity ◽  
Sex Education ◽  
Physical Activity Intervention ◽  
Education Level ◽  
Emotional States ◽  
Change Behavior ◽  
Risk Characteristics ◽  
Variability Index ◽  
Objectively Measured ◽  
Objectively Measured Physical Activity

Background: Homophily is the tendency to associate with friends similar to ourselves. This study explored the effects of homophily on team formation in a physical activity challenge in which “captains” signed up their Facebook friends to form teams. Methods: This study assessed whether participants (n = 430) were more similar to their teammates than to nonteammates with regard to age, sex, education level, body mass index, self-reported and objectively measured physical activity, and negative emotional states; and whether captains were more similar to their own teammates than to nonteammates. Variability indices were calculated for each team, and a hypothetical variability index, representing that which would result from randomly assembled teams, was also calculated. Results: Within-team variability was less than that for random teams for all outcomes except education level and depression, with differences (SDs) ranging from +0.15 (self-reported physical activity) to +0.47 (age) (P < .001 to P = .001). Captains were similar to their teammates except in regard to age, with captains being 2.6 years younger (P = .003). Conclusions: Results support hypotheses that self-selected teams are likely to contain individuals with similar characteristics, highlighting potential to leverage team-based health interventions to target specific populations by instructing individuals with risk characteristics to form teams to help change behavior.

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