User Engagement and Abandonment of mHealth: A Cross-Sectional Survey

Mustafa, Abdulsalam Salihu; Ali, Nor’ashikin; Dhillon, Jaspaljeet Singh; Alkawsi, Gamal; Baashar, Yahia

doi:10.3390/healthcare10020221

Cited by 70 publications

(40 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Importantly, our findings suggest that the effectiveness of the program appeared independent of prior wearable tracker experience, at least in the short term. However, the novelty of the eHealth dashboard might have had a positive impact on behavior [ 62 ]. Participants with no prior eHealth experience reported increases in weekly walking during the 8-week intervention, whereas no changes were observed in those with prior eHealth experience.…”

Section: Discussionmentioning

confidence: 99%

“…Participants volunteered for the program and therefore may reflect a highly motivated and healthy population, limiting generalizability. Desires to maintain or improve fitness, weight, and quality of life are motivating factors associated with the use of fitness applications [ 62 ]. Moreover, the effectiveness of the VPS program for improving walking and sitting may have been amplified due to participants having more time or seeking opportunities to undertake physical activity during the COVID-19 pandemic public health restrictions.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Effectiveness of an 8-Week Physical Activity Intervention Involving Wearable Activity Trackers and an eHealth App: Mixed Methods Study

McCormack¹,

Petersen²,

Ghoneim³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

Background Health-promotion interventions incorporating wearable technology or eHealth apps can encourage participants to self-monitor and modify their physical activity and sedentary behavior. In 2020, a Calgary (Alberta, Canada) recreational facility developed and implemented a health-promotion intervention (Vivo Play Scientist program) that provided a commercially available wearable activity tracker and a customized eHealth dashboard to participants free of cost. Objective The aim of this study was to independently evaluate the effectiveness of the Vivo Play Scientist program for modifying physical activity and sedentary behavior during the initial 8 weeks of the piloted intervention. Methods Our concurrent mixed methods study included a single-arm repeated-measures quasiexperiment and semistructured interviews. Among the 318 eligible participants (≥18 years of age) registered for the program, 87 completed three self-administered online surveys (baseline, T0; 4 weeks, T1; and 8 weeks, T2). The survey captured physical activity, sedentary behavior, use of wearable technology and eHealth apps, and sociodemographic characteristics. Twenty-three participants were recruited using maximal-variation sampling and completed telephone-administered semistructured interviews regarding their program experiences. Self-reported physical activity and sedentary behavior outcomes were statistically compared among the three time points using Friedman tests. Thematic analysis was used to analyze the interview data. Results The mean age of participants was 39.8 (SD 7.4) years and 75% (65/87) were women. Approximately half of all participants had previously used wearable technology (40/87, 46%) or an eHealth app (43/87, 49%) prior to the intervention. On average, participants reported wearing the activity tracker (Garmin Vivofit4) for 6.4 (SD 1.7) days in the past week at T1 and for 6.0 (SD 2.2) days in the past week at T2. On average, participants reported using the dashboard for 1.6 (SD 2.1) days in the past week at T1 and for 1.0 (SD 1.8) day in the past week at T2. The mean time spent walking at 8 weeks was significantly higher compared with that at baseline (T0 180.34 vs T2 253.79 minutes/week, P=.005), with no significant differences for other physical activity outcomes. Compared to that at baseline, the mean time spent sitting was significantly lower at 4 weeks (T0 334.26 vs T1 260.46 minutes/day, P<.001) and 8 weeks (T0 334.26 vs T2 267.13 minutes/day, P<.001). Significant differences in physical activity and sitting between time points were found among subgroups based on the household composition, history of wearable technology use, and history of eHealth app use. Participants described how wearing the Vivofit4 device was beneficial in helping them to modify physical activity and sedentary behavior. The social support, as a result of multiple members of the same household participating in the program, motivated changes in physical activity. Participants experienced improvements in their mental, physical, and social health. Conclusions Providing individuals with free-of-cost commercially available wearable technology and an eHealth app has the potential to support increases in physical activity and reduce sedentary behavior in the short term, even under COVID-19 public health restrictions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Effectiveness of an 8-Week Physical Activity Intervention Involving Wearable Activity Trackers and an eHealth App: Mixed Methods Study

McCormack¹,

Petersen²,

Ghoneim³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…In our study, participants were older, more overweight, and had T2D, which could decrease the validity compared to populations with fewer overweight participants and with no chronic illnesses. Age could also impact the validity, because studies [ 30 , 31 ] show that younger populations have a higher usage of health apps and therefore are more prone to higher user engagement. Furthermore, we only chose self-reported weights 1-21 days prior to each clinical follow-up, with 65.3% (68/104) and 75% (78/104) of the participants self-reporting their weights within 7 and 13 days, respectively ( Multimedia Appendix 2 ).…”

Section: Discussionmentioning

confidence: 99%

Agreement Between Clinically Measured Weight and Self-reported Weight Among Patients With Type 2 Diabetes Through an mHealth Lifestyle Coaching Program in Denmark: Secondary Analysis of a Randomized Controlled Trial

Imeraj¹,

Olesen²,

Laursen³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

Background Digital health interventions are increasingly used to handle and promote positive health behaviors. Clinical measures are often used, and a certain precision is essential for digital health interventions to have an effect. Only few studies have compared clinically measured weights with self-reported weights. No study has examined the validity of self-reported weight from a mobile app used in a tailored weight loss intervention. Objective The aim of this study was to analyze the agreement between clinically measured weight and self-reported weight collected from a mobile health lifestyle coaching program during a 12-month weight loss intervention for obese patients with and without type 2 diabetes. The secondary aim was to investigate the determinants for possible discrepancies between clinically measured and self-reported weights of these patients with different demographic and lifestyle characteristics and achievements of weight loss goals. Methods Weight registrations were collected from participants (N=104) in a Danish randomized controlled trial examining the effect of a digital lifestyle intervention on weight loss among obese patients with and without type 2 diabetes. Data were collected at baseline and after 6 and 12 months. Self-reported weight was measured at home and registered in the app. Results Self-reported body weight was lower than the weight measured in the clinic after 6 months by 1.03 kg (95% CI 1.01-1.05; P<.001) and after 12 months also by 1.03 kg (95% CI 0.99-1.04; P<.001). After 6 months, baseline weight and BMI were associated with a discrepancy of 0.03 kg (95% CI 0.01-0.04; P=.01) and 0.09 kg (95% CI 0.02-0.17; P=.02) per increment of 1 kg and 1 kg/m2, respectively, between clinically measured weight and self-reported weight. Weight change during the first 6 months was also associated with a difference of 0.1 kg (95% CI 0.04-0.01; P<.001) per kilogram of difference in weight between clinically measured weight and self-reported weight. Participants who did not achieve the 5% weight loss goal underestimated their weight by 0.79 kg (95% CI 0.34-1.23) at 6 months. After 12 months, only baseline weight was associated with a discrepancy of 0.03 kg (95% CI 0.01-0.05; P=.02) per increment of kilogram between clinically measured weight and self-reported weight. None of the other factors showed any significant discrepancy after 12 months. Conclusions Self-reported weight obtained from mobile health is a valid method for collecting anthropometric measurements. Trial Registration ClinicalTrials.gov NCT03788915; https://clinicaltrials.gov/ct2/show/NCT03788915

show abstract

“…Future studies should leverage the capability of mobile apps in collecting real-time usage data and utilize such data to conduct streak-and-break analysis. Then, identify critical time points (i.e., weeks before first break, or during first break to re-engage) and develop a bundle of engagement strategies to re-gain users' attention before they abandon the app (e.g., peer support, game-like and flashy features, feature upgrades, bonus incentives, new/fresh app aesthetics (53)(54)(55)(56).…”

Section: Nationalmentioning

confidence: 99%

Long-term usage of a commercial mHealth app: A “multiple-lives” perspective

Lau

Mitchell²,

Faulkner³

2022

Front. Public Health

View full text Add to dashboard Cite

BackgroundEmerging evidence suggests that individuals use mHealth apps in multiple disjointed ways in the real-world—individuals, for example, may engage, take breaks, and re-engage with these apps. To our knowledge, very few studies have adopted this ‘multiple-live’ perspective to analyze long-term usage of a physical activity (PA) app. This study aimed to examine the duration of use, as well as the frequency, length, and timing of streaks (uninterrupted periods of use) and breaks (uninterrupted periods of non-use) within a popular commercial PA app called Carrot Rewards over 12 months. We also examined sociodemographic correlates of usage.MethodThis retrospective observational study analyzed data from 41,207 Carrot Rewards users participating in the “Steps” walking program from June/July 2016 to June/July 2017. We measured four usage indicators: duration of use, frequency and length of streaks and breaks, time to first break, and time to resume second streak. We also extracted information regarding participants' age, gender, province, and proxy indicators of socioeconomic status derived from census data. We used descriptive statistics to summarize usage patterns, Kaplan-Meier curves to illustrate the time to first break and time to resume second streak. We used linear regressions and Cox Proportional Hazard regression models to examine sociodemographic correlates of usage.ResultsOver 60% of the participants used Carrot Rewards for ≥6 months and 29% used it for 12 months (mean = 32.59 ± 18.435 weeks). The frequency of streaks and breaks ranged from 1 to 9 (mean = 1.61 ± 1.04 times). The mean streak and break length were 20.22 ± 18.26 and 16.14 ± 15.74 weeks, respectively. The median time to first break was 18 weeks across gender groups and provinces; the median time for participants to resume the second streak was between 12 and 32 weeks. Being female, older, and living in a community with greater post-secondary education levels were associated with increased usage.ConclusionThis study provides empirical evidence that long-term mHealth app usage is possible. In this context, it was common for users to take breaks and re-engage with Carrot Rewards. When designing and evaluating PA apps, therefore, interventionists should consider the 'multiple-lives' perspective described here, as well as the impact of gender and age.

show abstract

User Engagement and Abandonment of mHealth: A Cross-Sectional Survey

Cited by 70 publications

References 40 publications

Effectiveness of an 8-Week Physical Activity Intervention Involving Wearable Activity Trackers and an eHealth App: Mixed Methods Study

Effectiveness of an 8-Week Physical Activity Intervention Involving Wearable Activity Trackers and an eHealth App: Mixed Methods Study

Agreement Between Clinically Measured Weight and Self-reported Weight Among Patients With Type 2 Diabetes Through an mHealth Lifestyle Coaching Program in Denmark: Secondary Analysis of a Randomized Controlled Trial

Long-term usage of a commercial mHealth app: A “multiple-lives” perspective

Contact Info

Product

Resources

About