The Association Between Logging Steps Using a Website, App, or Fitbit and Engaging With the 10,000 Steps Physical Activity Program: Observational Study

Increasing participation in resistance training using outdoor gyms: A study protocol for the ecofit type III hybrid effectiveness implementation trial

Background

In this paper we outline the protocol for an implementation-effectiveness trial of ecofit, a multi-component mHealth intervention aimed at increasing participation in resistance and aerobic physical activity using the outdoor built environment (i.e., outdoor gyms) and social support. We have previously demonstrated the efficacy and effectiveness of the ecofit program in insufficiently active people with (or at risk of) type 2 diabetes and community-dwelling adults, respectively. The objective of this trial is to compare the effects of two implementation support models (i.e., 'Low' versus 'Moderate') on the reach (primary outcome), uptake, dose received, impact and fidelity of the ecofit program.

Research design and methods

This hybrid type III implementation-effectiveness study will be evaluated using a two-arm randomized controlled trial, including 16 outdoor gym locations in two large regional municipalities in New South Wales, Australia. Outdoor gym locations will be pair-matched, based on an established socio-economic status consensus-based index (high versus low), and randomized to the 'Low' (i.e., ecofit app only) or 'Moderate' (i.e., ecofit app, face-to-face workout sessions and QR codes) implementation support group. The primary outcome of 'reach' will be measured using a modified version of the 'System for Observing Play and Recreation in Communities', capturing outdoor gym use amongst community members.

Conclusion

This implementation-effectiveness trial will evaluate the effects of different levels of implementation support on participation in resistance-focused physical activity using mHealth and outdoor gyms across the broader community. This may guide widespread dissemination for councils (municipalities) nation-wide wanting to promote outdoor gym usage.

Trial registry

This trial was preregistered with the Australian and New Zealand Clinical Trial Registry (ACTRN12624000261516).

mHealth application for improving treatment outcomes for patients with multidrug-resistant tuberculosis in Vietnam: an economic evaluation protocol for the V-SMART trial

INTRODUCTION

The Strengthen the Management of Multidrug-Resistant Tuberculosis in Vietnam (V-SMART) trial is a randomised controlled trial of using mobile health (mHealth) technologies to improve adherence to medications and management of adverse events (AEs) in people with multidrug-resistant tuberculosis (MDR-TB) undergoing treatment in Vietnam. This economic evaluation seeks to quantify the cost-effectiveness of this mHealth intervention from a healthcare provider and societal perspective.

METHODS AND ANALYSIS

The V-SMART trial will recruit 902 patients treated for MDR-TB across seven participating provinces in Vietnam. Participants in both intervention and control groups will receive standard community-based therapy for MDR-TB. Participants in the intervention group will also have a purpose-designed App installed on their smartphones to report AEs to health workers and to facilitate timely management of AEs. This economic evaluation will compare the costs and health outcomes between the intervention group (mHealth) and the control group (standard of care). Costs associated with delivering the intervention and health service utilisation will be recorded, as well as patient out-of-pocket costs. The health-related quality of life (HRQoL) of study participants will be captured using the 36-Item Short Form Survey (SF-36) questionnaire and used to calculate quality-adjusted life-years (QALYs). Incremental cost-effectiveness ratios (ICERs) will be based on the primary outcome (proportion of patients with treatment success after 24 months) and QALYs gained. Sensitivity analysis will be conducted to test the robustness of the ICERs. A budget impact analysis will be conducted from a payer perspective to provide an estimate of the total budget required to scale-up delivery of the intervention.

ETHICS AND DISSEMINATION

Ethical approval for the study was granted by the University of Sydney Human Research Ethics Committee (2019/676), the Scientific Committee of the Ministry of Science and Technology, Vietnam (08/QD-HDQL-NAFOSTED) and the Institutional Review Board of the National Lung Hospital, Vietnam (13/19/CT-HDDD). Study findings will be published in peer-reviewed journals and conference proceedings.

TRIAL REGISTRATION NUMBER

ACTRN12620000681954.

Exploring Variations in Sleep Perception: Comparative Study of Chatbot Sleep Logs and Fitbit Sleep Data

BACKGROUND

Patient-generated health data are important in the management of several diseases. Although there are limitations, information can be obtained using a wearable device and time-related information such as exercise time or sleep time can also be obtained. Fitbits can be used to acquire sleep onset, sleep offset, total sleep time (TST), and wakefulness after sleep onset (WASO) data, although there are limitations regarding the depth of sleep and satisfaction; therefore, the patient's subjective response is still important information that cannot be replaced by wearable devices.

OBJECTIVE

To effectively use patient-generated health data related to time such as sleep, it is first necessary to understand the characteristics of the time response recorded by the user. Therefore, the aim of this study was to analyze the characteristics of individuals' time perception in comparison with wearable data.

METHODS

Sleep data were acquired for 2 weeks using a Fitbit. Participants' sleep records were collected daily through chatbot conversations while wearing the Fitbit, and the two sets of data were statistically compared.

RESULTS

In total, 736 people aged 30-59 years were recruited for this study, and the sleep data of 543 people who wore a Fitbit and responded to the chatbot for more than 7 days on the same day were analyzed. Research participants tended to respond to sleep-related times on the hour or in 30-minute increments, and each participant responded within the range of 60-90 minutes from the value measured by the Fitbit. On average for all participants, the chat responses and the Fitbit data were similar within a difference of approximately 15 minutes. Regarding sleep onset, the participant response was 8 minutes and 39 seconds (SD 58 minutes) later than that of the Fitbit data, whereas with respect to sleep offset, the response was 5 minutes and 38 seconds (SD 57 minutes) earlier. The participants' actual sleep time (AST) indicated in the chat was similar to that obtained by subtracting the WASO from the TST measured by the Fitbit. The AST was 13 minutes and 39 seconds (SD 87 minutes) longer than the time WASO was subtracted from the Fitbit TST. On days when the participants reported good sleep, they responded 19 (SD 90) minutes longer on the AST than the Fitbit data. However, for each sleep event, the probability that the participant's AST was within ±30 and ±60 minutes of the Fitbit TST-WASO was 50.7% and 74.3%, respectively.

CONCLUSIONS

The chatbot sleep response and Fitbit measured time were similar on average and the study participants had a slight tendency to perceive a relatively long sleep time if the quality of sleep was self-reported as good. However, on a participant-by-participant basis, it was difficult to predict participants' sleep duration responses with Fitbit data. Individual variations in sleep time perception significantly affect patient responses related to sleep, revealing the limitations of objective measures obtained through wearable devices.

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

Background

Emerging 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.

Method

This 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.

Results

Over 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.

Conclusion

This 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.

Understanding super engaged users in the 10,000 Steps online physical activity program: A qualitative study

Objective

Sustained engagement with Internet-based behavioural interventions is crucial to achieve successful behaviour change outcomes. As this has been problematic in many interventions, a lot of research has focused on participants with little or no engagement. However, few studies have attempted to understand users with continuous long-term engagement, the so called ‘super engaged users’, and why they keep on using programs when everybody else has long stopped. Therefore, the aim of this research was to qualitatively examine characteristics, usage profile and motivations of super engaged users in the 10,000 Steps program.

Methods

Twenty 10,000 Steps users (10 with more than 1 year of engagement, and 10 with more than 10 years of engagement) participated in semi-structured interviews, that were transcribed and thematically analysed.

Results

Participants were aged 60 years on average, with more than half being overweight/obese and/or suffering from chronic disease despite logging high step counts (219 million steps per participant on average) on the 10,000 Steps platform. Participants indicated that the reasons for sustained use were that engaging the program had become a habit, that the program kept them motivated, and that it was easy to use. Few participants had suggestions for improvement or expressed there were program elements they did not like. Uptake of program innovations (e.g., app-version, use of advanced activity tracker instead of pedometer) was modest among the super engaged users.

Conclusion

The findings from this study emphasise the need for digital health programs to incorporate features that will support the development of habits as soon as participants start to engage with the program. While a program’s usability, user-friendliness and acceptability are important to engage and retain new users, habit formation may be more important for sustained long-term engagement with the behaviour and the program.

The law of non-usage attrition in a technology-based behavioral intervention for black adults with poor cardiovascular health

Digital health innovations, such as telehealth and remote monitoring, have shown promise in addressing patient barriers to accessing evidence-based programs and providing a scalable path for tailored behavioral interventions that support self-management skills, knowledge acquisition and promotion of relevant behavioral change. However, significant attrition continues to plague internet-based studies, a result we believe can be attributed to characteristics of the intervention, or individual user characteristics. In this paper, we provide the first analysis of determinants of non usage attrition in a randomized control trial of a technology-based intervention for improving self-management behaviors among Black adults who face increased cardiovascular risk factors. We introduce a different way to measure nonusage attrition that considers usage over a specific period of time and estimate a cox proportional hazards model of the impact of intervention factors and participant demographics on the risk of a nonusage event. Our results indicated that not having a coach (compared to having a coach) decreases the risk of becoming an inactive user by 36% (HR = .63, P = 0.04). We also found that several demographic factors can influence Non-usage attrition: The risk of nonusage attrition amongst those who completed some college or technical school (HR = 2.91, P = 0.04) or graduated college (HR = 2.98, P = 0.047) is significantly higher when compared to participants who did not graduate high school. Finally, we found that the risk of nonsage attrition among participants with poor cardiovascular from "at-risk" neighborhoods with higher morbidity and mortality rates related to CVD is significantly higher when compared to participants from "resilient" neighborhoods (HR = 1.99, P = 0.03). Our results underscore the importance of understanding challenges to the use of mhealth technologies for cardiovascular health in underserved communities. Addressing these unique barriers is essential, because a lack of diffusion of digital health innovations exacerbates health disparities.