scholarly journals Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial

10.2196/19216 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e19216
Author(s):  
Laura J Damschroder ◽  
Lorraine R Buis ◽  
Felicia A McCant ◽  
Hyungjin Myra Kim ◽  
Richard Evans ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Controlled Trial ◽  
Patient Activation ◽  
Health Coaching ◽  
Mobile App ◽  
Physical Conditioning ◽  
Healthy Weight ◽  
Mhealth Intervention ◽  
Us Veterans

Background Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed Stay Strong, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (Stay Strong+Coaching) would improve physical activity compared to Stay Strong alone. Objective The goal of this study is to compare 12-month outcomes from Stay Strong alone versus Stay Strong+Coaching. Methods Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the Stay Strong app alone (n=179) or Stay Strong+Coaching (n=178); both programs lasted 12 months. Personalized coaching components for Stay Strong+Coaching comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation. Results The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (P<.001) with no between-group differences at 6 months (P=.82) or 12 months (P=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for Stay Strong and 61.8% (110/178) for Stay Strong+Coaching. Average baseline weight for participants in Stay Strong and Stay Strong+Coaching was 214 lbs and 198 lbs, respectively, with no difference at baseline (P=.54) or at 6 months (P=.28) or 12 months (P=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm. Conclusions Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial’s 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up. Trial Registration ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 International Registered Report Identifier (IRRID) RR2-10.2196/12526

scholarly journals Effect of Adding Telephone-Based Brief Coaching to an mHealth App (Stay Strong) for Promoting Physical Activity Among Veterans: Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Laura J Damschroder ◽  
Lorraine R Buis ◽  
Felicia A McCant ◽  
Hyungjin Myra Kim ◽  
Richard Evans ◽  
...  
Keyword(s):  
Physical Activity ◽  
Missing Data ◽  
Controlled Trial ◽  
Patient Activation ◽  
Health Coaching ◽  
Mobile App ◽  
Physical Conditioning ◽  
Healthy Weight ◽  
Mhealth Intervention ◽  
Us Veterans

BACKGROUND Though maintaining physical conditioning and a healthy weight are requirements of active military duty, many US veterans lose conditioning and rapidly gain weight after discharge from active duty service. Mobile health (mHealth) interventions using wearable devices are appealing to users and can be effective especially with personalized coaching support. We developed <i>Stay Strong</i>, a mobile app tailored to US veterans, to promote physical activity using a wrist-worn physical activity tracker, a Bluetooth-enabled scale, and an app-based dashboard. We tested whether adding personalized coaching components (<i>Stay Strong+Coaching</i>) would improve physical activity compared to <i>Stay Strong</i> alone. OBJECTIVE The goal of this study is to compare 12-month outcomes from <i>Stay Strong</i> alone versus <i>Stay Strong+Coaching.</i> METHODS Participants (n=357) were recruited from a national random sample of US veterans of recent wars and randomly assigned to the <i>Stay Strong</i> app alone (n=179) or <i>Stay Strong+Coaching</i> (n=178); both programs lasted 12 months. Personalized coaching components for <i>Stay Strong+Coaching</i> comprised of automated in-app motivational messages (3 per week), telephone-based human health coaching (up to 3 calls), and personalized weekly goal setting. All aspects of the enrollment process and program delivery were accomplished virtually for both groups, except for the telephone-based coaching. The primary outcome was change in physical activity at 12 months postbaseline, measured by average weekly Active Minutes, captured by the Fitbit Charge 2 device. Secondary outcomes included changes in step counts, weight, and patient activation. RESULTS The average age of participants was 39.8 (SD 8.7) years, and 25.2% (90/357) were female. Active Minutes decreased from baseline to 12 months for both groups (<i>P</i>&lt;.001) with no between-group differences at 6 months (<i>P</i>=.82) or 12 months (<i>P</i>=.98). However, at 12 months, many participants in both groups did not record Active Minutes, leading to missing data in 67.0% (120/179) for <i>Stay Strong</i> and 61.8% (110/178) for <i>Stay Strong+Coaching</i>. Average baseline weight for participants in <i>Stay Stron</i>g and <i>Stay Strong+Coaching</i> was 214 lbs and 198 lbs, respectively, with no difference at baseline (<i>P</i>=.54) or at 6 months (<i>P</i>=.28) or 12 months (<i>P</i>=.18) postbaseline based on administrative weights, which had lower rates of missing data. Changes in the number of steps recorded and patient activation also did not differ by arm. CONCLUSIONS Adding personalized health coaching comprised of in-app automated messages, up to 3 coaching calls, plus automated weekly personalized goals, did not improve levels of physical activity compared to using a smartphone app alone. Physical activity in both groups decreased over time. Sustaining long-term adherence and engagement in this mHealth intervention proved difficult; approximately two-thirds of the trial’s 357 participants failed to sync their Fitbit device at 12 months and, thus, were lost to follow-up. CLINICALTRIAL ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 INTERNATIONAL REGISTERED REPORT RR2-10.2196/12526

scholarly journals Understanding the Effect of Adding Automated and Human Coaching to a Mobile Health Physical Activity App for Afghanistan and Iraq Veterans: Protocol for a Randomized Controlled Trial of the Stay Strong Intervention (Preprint)

2018 ◽  
Author(s):  
Lorraine R Buis ◽  
Felicia A McCant ◽  
Jennifer M Gierisch ◽  
Lori A Bastian ◽  
Eugene Z Oddone ◽  
...  
Keyword(s):  
Physical Activity ◽  
Randomized Controlled Trial ◽  
Mobile Health ◽  
Controlled Trial ◽  
Health Coaching ◽  
Mobile App ◽  
Activity Monitoring ◽  
Smartphone App ◽  
Randomized Controlled ◽  
Over Time

BACKGROUND Although maintaining a healthy weight and physical conditioning are requirements of active military duty, many US veterans rapidly gain weight and lose conditioning when they separate from active-duty service. Mobile health (mHealth) interventions that incorporate wearables for activity monitoring have become common, but it is unclear how to optimize engagement over time. Personalized health coaching, either through tailored automated messaging or by individual health coaches, has the potential to increase the efficacy of mHealth programs. In an attempt to preserve conditioning and ward off weight gain, we developed Stay Strong, a mobile app that is tailored to veterans of recent conflicts and tracks physical activity monitored by Fitbit Charge 2 devices and weight measured on a Bluetooth-enabled scale. OBJECTIVE The goal of this study is to determine the effect of activity monitoring plus health coaching compared with activity monitoring alone. METHODS In this randomized controlled trial, with Stay Strong, a mobile app designed specifically for veterans, we plan to enroll 350 veterans to engage in an mHealth lifestyle intervention that combines the use of a wearable physical activity tracker and a Bluetooth-enabled weight scale. The Stay Strong app displays physical activity and weight data trends over time. Enrolled participants are randomized to receive the Stay Strong app (active comparator arm) or Stay Strong + Coaching, an enhanced version of the program that adds coaching features (automated tailored messaging with weekly physical activity goals and up to 3 telephone calls with a health coach—intervention arm) for 1 year. Our primary outcome is change in physical activity at 12 months, with weight, pain, patient activation, and depression serving as secondary outcome measures. All processes related to recruitment, eligibility screening, informed consent, Health Insurance Portability and Accountability Act authorization, baseline assessment, randomization, the bulk of intervention delivery, and outcome assessment will be accomplished via the internet or smartphone app. RESULTS The study recruitment began in September 2017, and data collection is expected to conclude in 2019. A total of 465 participants consented to participate and 357 (357/465, 77%) provided baseline levels of physical activity and were randomized to 1 of the 2 interventions. CONCLUSIONS This novel randomized controlled trial will provide much-needed findings about whether the addition of telephone-based human coaching and other automated supportive-coaching features will improve physical activity compared with using a smartphone app linked to a wearable device alone. CLINICALTRIAL ClinicalTrials.gov NCT02360293; https://clinicaltrials.gov/ct2/show/NCT02360293 (Archived by WebCite at http://www.webcitation.org/75KQeIFwh) INTERNATIONAL REGISTERED REPOR DERR1-10.2196/12526

scholarly journals Estimating Measurement Error of the Patient Activation Measure for Respondents with Partially Missing Data

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ariel Linden
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Controlled Trial ◽  
Patient Activation ◽  
Percentage Error ◽  
Missing Responses ◽  
Improve Measurement ◽  
Patient Activation Measure ◽  
Minimum Score ◽  
Missing Items

The patient activation measure (PAM) is an increasingly popular instrument used as the basis for interventions to improve patient engagement and as an outcome measure to assess intervention effect. However, a PAM score may be calculated when there are missing responses, which could lead to substantial measurement error. In this paper, measurement error is systematically estimated across the full possible range of missing items (one to twelve), using simulation in which populated items were randomly replaced with missing data for each of 1,138 complete surveys obtained in a randomized controlled trial. The PAM score was then calculated, followed by comparisons of overall simulated average mean, minimum, and maximum PAM scores to the true PAM score in order to assess the absolute percentage error (APE) for each comparison. With only one missing item, the average APE was 2.5% comparing the true PAM score to the simulated minimum score and 4.3% compared to the simulated maximum score. APEs increased with additional missing items, such that surveys with 12 missing items had average APEs of 29.7% (minimum) and 44.4% (maximum). Several suggestions and alternative approaches are offered that could be pursued to improve measurement accuracy when responses are missing.

Tweeting to Health

2016 ◽  
Vol 56 (1) ◽  
pp. 26-32 ◽  
Author(s):  
Arlene E. Chung ◽  
Asheley C. Skinner ◽  
Stephanie E. Hasty ◽  
Eliana M. Perrin
Keyword(s):  
Physical Activity ◽  
Vegetable Intake ◽  
Healthy Lifestyle ◽  
Lifestyle Changes ◽  
Descriptive Statistics ◽  
Healthy Weight ◽  
Physical Activity Intensity ◽  
Sugar Sweetened Beverage ◽  
Mhealth Intervention ◽  
Activity Intensity

We developed and pilot tested a mHealth intervention, “Tweeting to Health,” which used Fitbits, Twitter, and gamification to facilitate support for healthy lifestyle changes in overweight/obese (OW) and healthy weight (HW) young adults. Participants tracked activity and diet using Fitbits and used Twitter for messaging for 2 months. Physical activity, dietary intake, and Tweets were tracked and participants completed surveys at enrollment, 1 month, and 2 months. Descriptive statistics were used to examine steps/day, physical activity intensity, lifestyle changes, and total Tweets. Participants were on average 19 to 20 years old and had familiarity with Twitter. OW participants had on average 11 222 daily steps versus 11 686 (HW). One-day challenges were successful in increasing steps. Participants increased fruit/vegetable intake (92%) and decreased their sugar-sweetened beverage intake (67%). Compliance with daily Fitbit wear (99% of all days OW vs 73% HW) and daily dietary logging (82% OW vs 73% HW) and satisfaction was high.

Effect of an mHealth-Based Intervention on Excessive Gestational Weight Gain Prevention among Overweight and Obese Women: A Randomized Controlled Trial (Preprint)

2021 ◽  
Author(s):  
Hung Hui Chen ◽  
Ching-Fang Lee ◽  
Jian-Pei Huang ◽  
Li-Kang Chi ◽  
Yvonne Hsiung
Keyword(s):  
Physical Activity ◽  
Weight Gain ◽  
Controlled Trial ◽  
Intervention Group ◽  
Control Group ◽  
Obese Women ◽  
Third Trimester ◽  
Overweight Women ◽  
Mhealth Intervention ◽  
Excessive Gestational Weight Gain

BACKGROUND Excessive gestational weight gain (GWG) is a public health concern since it can lead to adverse consequences and health problems for expecting mothers and their unborn infants. There is a need to evaluate the effects of a GWG management intervention to reduce the burden and risk among overweight and obese women during pregnancy. OBJECTIVE To explore the efficacy of a mobile health (mHealth) intervention to prevent excessive GWG, overweight and obese pregnant women were invited to use an app and wearable activity tracker (WAT). METHODS A randomized controlled trial with an experimental study design. Ninety-two pregnant women were recruited, and all overweight and obese participants from the two prenatal outpatient clinics in northern Taiwan had, at less than 17 weeks gestation, a prepregnancy body mass index (BMI) ≥ 25 kg/m2. These participants were randomly assigned (1:1) by a random number table; the experimental group received an mHealth-based program using the MyHealthyWeight (MHW) app and a WAT to wear during pregnancy. The control group received standard antenatal treatments without any mHealth-based elements. Two hospital follow-up visits were scheduled at 24-26 weeks in the second trimester and 34-36 weeks in the third trimester. Sociodemographic characteristics, pregnancy physical activity questionnaire (PPAQ), a self-efficacy questionnaire and body weight were measures of interest. A generalized estimating equation (GEE) was used to examine the trajectories and the intervention effect on GWG. RESULTS No difference in GWG was found between the intervention and control groups at baseline. The weight gain trajectory in the entire cohort of women with obesity exhibited a quadratic pattern; compared with the control group, a slight increase in the intervention group was found in the second trimester. Throughout the whole pregnancy, the mHealth intervention group had a significantly lower proportion of excessive GWG in total and weekly weight gain. In particular, obese women in the intervention group, compared with obese women in the control group, gained less weight (average difference of 8.76 kg) in the third trimester. The GEE model indicated that obese women who were aged 35 years, had prepregnancy exercise habits, had perceived self-efficacy of diet, and had more physical activity had lower GWG (p<.05). CONCLUSIONS The mHealth program has shown positive results in significantly managing GWG among obese and overweight women. Among obese women, the second semester trajectory of weight gain and the lower proportion of excessive GWG were more notable than those of overweight women. Although the intervention seems to be more effective among women with obesity, our results show the potential to prevent excessive GWG during pregnancy in both overweight and obese women. Guidance may be provided to health-care professionals who wish to promote healthy diet and physical activity behaviors. CLINICALTRIAL The protocol of the study was registered in ClinicalTrials. gov (NCT04553731).

scholarly journals Financial Incentives and Health Coaching to Improve Physical Activity Following Total Knee Replacement: A Randomized Controlled Trial

2018 ◽  
Vol 70 (5) ◽  
pp. 732-740 ◽  
Author(s):  
Elena Losina ◽  
Jamie E. Collins ◽  
Bhushan R. Deshpande ◽  
Savannah R. Smith ◽  
Griffin L. Michl ◽  
...  
Keyword(s):  
Physical Activity ◽  
Randomized Controlled Trial ◽  
Total Knee Replacement ◽  
Knee Replacement ◽  
Financial Incentives ◽  
Controlled Trial ◽  
Health Coaching ◽  
Randomized Controlled ◽  
Total Knee

scholarly journals Latent class growth modelling for the evaluation of intervention outcomes: example from a physical activity intervention

2021 ◽  
Author(s):  
Anna-Maria Lampousi ◽  
Jette Möller ◽  
Yajun Liang ◽  
Daniel Berglind ◽  
Yvonne Forsell
Keyword(s):  
Physical Activity ◽  
Latent Class ◽  
Intervention Studies ◽  
Physical Activity Intervention ◽  
Controlled Trial ◽  
Vigorous Physical Activity ◽  
Mobile App ◽  
Growth Modelling ◽  
Significant Difference ◽  
Latent Class Growth

AbstractIntervention studies often assume that changes in an outcome are homogenous across the population, however this assumption might not always hold. This article describes how latent class growth modelling (LCGM) can be performed in intervention studies, using an empirical example, and discusses the challenges and potential implications of this method. The analysis included 110 young adults with mobility disability that had participated in a parallel randomized controlled trial and received either a mobile app program (n = 55) or a supervised health program (n = 55) for 12 weeks. The primary outcome was accelerometer measured moderate to vigorous physical activity (MVPA) levels in min/day assessed at baseline, 6 weeks, 12 weeks, and 1-year post intervention. The mean change of MVPA from baseline to 1-year was estimated using paired t-test. LCGM was performed to determine the trajectories of MVPA. Logistic regression models were used to identify potential predictors of trajectories. There was no significant difference between baseline and 1-year MVPA levels (4.8 min/day, 95% CI: −1.4, 10.9). Four MVPA trajectories, ‘Normal/Decrease’, ‘Normal/Increase’, ‘Normal/Rapid increase’, and ‘High/Increase’, were identified through LCGM. Individuals with younger age and higher baseline MVPA were more likely to have increasing trajectories of MVPA. LCGM uncovered hidden trajectories of physical activity that were not represented by the average pattern. This approach could provide significant insights when included in intervention studies. For higher accuracy it is recommended to include larger sample sizes.

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