Background: This study compared the efficacy of two multi-component m-health interventions with a wait-list control group on body weight (primary outcome), and secondary outcomes of cardiovascular risk factors, lifestyle behaviours, and mental health. Methods: Three-arm randomised controlled trial (Enhanced: physical activity, diet, sleep, Traditional: physical activity, diet, Control) with assessments conducted at baseline, 6 and 12 months. Participants (n = 116) were overweight or obese adults aged 19–65 (M = 44.5 [SD = 10.5]). The 6-month intervention was delivered via a smartphone app providing educational materials, goal-setting, self-monitoring and feedback, and also included one face-to-face dietary consultation, a Fitbit and scales. The trial was prospectively registered and conducted between May 2017 and September 2018. Group differences on primary and secondary outcomes were examined between the Pooled Intervention groups (Pooled Intervention = Enhanced and Traditional) and Control groups, and then between Enhanced and Traditional groups. Results: Nineteen participants (16.4%) formally withdrew from the trial. Compared with the Control group, average body weight of the Pooled Intervention group did not differ at 6 (between-group difference = −0.92, (95% CI −3.33, 1.48)) or 12 months (0.00, (95% CI −2.62, 2.62)). Compared with the Control group, the Pooled Intervention group significantly increased resistance training (OR = 7.83, (95% CI 1.08, 56.63)) and reduced energy intake at 6 months (−1037.03, (−2028.84, −45.22)), and improved insomnia symptoms at 12 months (−2.59, (−4.79, −0.39)). Compared with the Traditional group, the Enhanced group had increased waist circumferences (2.69, (0.20, 5.18)) and sedentary time at 6 months (105.66, (30.83, 180.48)), and improved bed time variability at 12 months (−1.08, (−1.86, −0.29)). No other significant differences were observed between groups. Conclusions: Relative to Controls, the Pooled Intervention groups did not differ on body weight but improved resistance training, and reduced energy intake and insomnia symptom severity. No additional weight loss was apparent when targeting improvements in physical activity, diet and sleep in combination compared with physical activity and diet.

Many adults are insufficiently physically active, have prolonged sedentary behaviour and report poor sleep. These behaviours can be improved by interventions that include education, goal setting, self-monitoring, and feedback strategies. Few interventions have explicitly targeted these behaviours simultaneously or examined the relative efficacy of different self-monitoring methods. This study aims to compare the efficacy of two self-monitoring methods in an app-based multi-behaviour intervention to improve objectively measured physical activity, sedentary, and sleep behaviours, in a 9 week 2–arm randomised trial. Participants will be adults (n = 64) who report being physically inactive, sitting >8 h/day and frequent insufficient sleep (≥14 days out of last 30). The “Balanced” intervention is delivered via a smartphone ‘app’, and includes education materials (guidelines, strategies to promote change in behaviour), goal setting, self-monitoring and feedback support. Participants will be randomly allocated to either a device-entered or user-entered self-monitoring method. The device-entered group will be provided with a activity tracker to self-monitor behaviours. The user-entered group will recall and manually record behaviours. Assessments will be conducted at 0, 3, 6, and 9 weeks. Physical activity, sedentary behaviour and sleep-wake behaviours will be measured using the wrist worn Geneactiv accelerometer. Linear mixed models will be used to examine differences between groups and over time using an alpha of 0.01. This study will evaluate an app-based multi-behavioural intervention to improve physical activity, sedentary behaviour and sleep; and the relative efficacy of two different approaches to self-monitoring these behaviours. Outcomes will provide information to inform future interventions and self-monitoring targeting these behaviours. ACTRN12615000182594 (Australian New Zealand Clinical Trials Registry. Registry URL: www.anzctr.org.au ; registered prospectively on 25 February 2015).

Social media campaigns provide broad-reach and convenience for promoting freely-available health programs. However, their effectiveness and subsequent engagement of new users is unknown. This study aimed to assess the reach and new member registration rates resulting from a dedicated 10,000 Steps social media campaign (SMC) and to compare program engagement and time to non-usage attrition of new users from the SMC with other users. SMC reach (using Facebook, Instagram, and display advertisements engagement metrics), new-user numbers, engagement (usage of the website and its features), and time to non-usage attrition were assessed using generalized linear regression, binary logistic regression, and Cox proportion hazards regression models. During the SMC, Instagram and display advertisement impressions, Facebook reach and new daily registrations were significantly higher compared with six weeks and one year prior. There were no between-group differences in the average usage of most website/program features. Risk of non-usage attrition was higher among new users from the SMC than new users from one year prior. The SMC was effective in promoting awareness of the 10,000 Steps program. Further research to identify long-term engagement strategies and the most effective combination of social media platforms for promotion of, and recruitment to, health programs is warranted.

Poor health behaviors are prevalent in shift-workers, but few multiple health-behavior interventions consider their unique needs. This study aimed to (1) evaluate the feasibility and acceptability of an existing app-based intervention to improve physical activity, diet, and sleep quality in a shift-worker population, (2) estimate intervention effect in a four-week pilot randomized controlled trial (RCT) (ACTRN12618001785291). Shift-workers (18–65 years old) were randomized to intervention (n = 20) or wait-list (n = 20) groups. Outcomes included recruitment, engagement, attrition, usefulness ratings, System Usability Scale (SUS), qualitative interviews, and estimation of treatment effect (minutes of physical activity, diet quality, and sleep quality) using mixed model analysis. Recruitment took one week. App-use at week four was 55% (11/20), 85% (34/40) completed the four-week follow-up questionnaire, and 20% (4/20) of the intervention group completed the qualitative interview. The intervention was rated as slightly to moderately useful by 76.9% (10/13) of participants on a five-point scale. The SUS score was 62.7 (12.7) out of 100. Diet quality improved for the intervention (4.5 points; 95% confidence interval (CI) = 0.1, 8.9; p = 0.047) vs. the wait-list group, but not physical activity or sleep quality. Qualitative interviews found that a more tailored intervention, more accessible information, and interactive features were desired. The intervention was feasible in terms of recruitment, but modifications to increase engagement are needed.

This three-arm randomised controlled trial evaluated whether (1) a multi-component weight loss intervention targeting diet, physical activity (PA), and sleep was effective at improving dietary intake over six months and 12 months, compared with a control, and (2) the enhanced diet, PA, and sleep intervention was more effective at improving dietary intake than the traditional diet and PA intervention. A total of 116 adults (70% female, 44.5 years, BMI 31.7 kg/m²) were randomised to either traditional diet and PA intervention; enhanced diet, PA, and sleep intervention; or wait-list control. To examine between-group differences, intervention groups were pooled and compared with the control. Then, the two intervention groups were compared. At six months, the pooled intervention group consumed 1011 fewer kilojoules/day (95% CI −1922, −101), less sodium (−313.2 mg/day; 95% CI −591.3, −35.0), and higher %EI from fruit (+2.1%EI; 95% CI 0.1, 4.1) than the controls. There were no differences in intake between the enhanced and traditional groups at six months. At 12 months, the pooled intervention and control groups reported no significant differences. However, compared to the traditional group, the enhanced reported higher %EI from nutrient-dense foods (+7.4%EI; 95% CI 1.3, 13.5) and protein (+2.4%EI; 95% CI 0.1, 4.6), and reduced %EI from fried/takeaway foods (−3.6%EI; 95% CI −6.5, −0.7), baked sweet products (−2.0%EI; 95% CI −3.6, −0.4), and packaged snacks (−1.1%EI; 95% CI −2.2, −0.3). This weight loss intervention reduced total energy and sodium intakes as well as increased fruit intake in adults at six months. The enhanced intervention group reported improved dietary intake relative to the traditional group at 12 months.

Background: Engagement is positively associated with the effectiveness of digital health interventions. It is unclear whether tracking devices that automatically synchronize data (eg, Fitbit) produce different engagement levels compared with manually entering data. Objective: This study examines how different step logging methods in the freely available 10,000 Steps physical activity program differ according to age and gender and are associated with program engagement. Methods: A subsample of users (n=22,142) of the free 10,000 Steps physical activity program were classified into one of the following user groups based on the step-logging method: Website Only (14,617/22,142, 66.01%), App Only (2100/22,142, 9.48%), Fitbit Only (1705/22,142, 7.7%), Web and App (2057/22,142, 9.29%), and Fitbit Combination (combination of web, app, and Fitbit; 1663/22,142, 7.51%). Generalized linear regression and binary logistic regression were used to examine differences between user groups’ engagement and participation parameters. The time to nonusage attrition was assessed using Cox proportional hazards regression. Results: App Only users were significantly younger and Fitbit user groups had higher proportions of women compared with other groups. The following outcomes were significant and relative to the Website Only group. The App Only group had fewer website sessions (odds ratio [OR] −6.9, 95% CI −7.6 to −6.2), whereas the Fitbit Only (OR 10.6, 95% CI 8.8-12.3), Web and App (OR 1.5, 95% CI 0.4-2.6), and Fitbit Combination (OR 8.0; 95% CI 6.2-9.7) groups had more sessions. The App Only (OR −0.7, 95% CI −0.9 to −0.4) and Fitbit Only (OR −0.5, 95% CI −0.7 to −0.2) groups spent fewer minutes on the website per session, whereas the Fitbit Combination group (OR 0.2, 95% CI 0.0-0.5) spent more minutes. All groups, except the Fitbit Combination group, viewed fewer website pages per session. The mean daily step count was lower for the App Only (OR −201.9, 95% CI −387.7 to −116.0) and Fitbit Only (OR −492.9, 95% CI −679.9 to −305.8) groups but higher for the Web and App group (OR 258.0, 95% CI 76.9-439.2). The Fitbit Only (OR 5.0, 95% CI 3.4-6.6), Web and App (OR 7.2, 95% CI 5.9-8.6), and Fitbit Combination (OR 15.6, 95% CI 13.7-17.5) groups logged a greater number of step entries. The App Only group was less likely (OR 0.65, 95% CI 0.46-0.94) and other groups were more likely to participate in Challenges. The mean time to nonusage attrition was 35 (SD 26) days and was lower than average in the Website Only and App Only groups and higher than average in the Web and App and Fitbit Combination groups. Conclusions: Using a Fitbit in combination with the 10,000 Steps app or website enhanced engagement with a real-world physical activity program. Integrating tracking devices that synchronize data automatically into real-world physical activity interventions is one strategy for improving engagement.