Does Patient Preference for Mode of Intervention Delivery Impact Intervention Efficacy and Attrition?

2019 ◽  
Vol 34 (1) ◽  
pp. 63-66
Author(s):  
Ronald C. Plotnikoff ◽  
Fiona G. Stacey ◽  
Anna K. Jansson ◽  
Benjamin Ewald ◽  
Natalie A. Johnson ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Intervention Group ◽  
Step Count ◽  
Future Research ◽  
Study Group ◽  
Daily Step ◽  
Study Participation ◽  
Face To Face ◽  
Exercise Specialist ◽  
Daily Step Count

Purpose: To explore whether there was a difference in objectively measured physical activity and study participation between people who received their preferred study group allocation (matched) and those who did not receive their preferred study group (mismatched). Design: Secondary data from the NewCOACH randomized controlled trial. Setting: Insufficiently active patients in the primary care settings in Sydney and Newcastle, Australia. Participants: One hundred seventy-two adults aged 20 to 81 years. Intervention: Participants indicated their intervention preference at baseline for (1) five face-to-face visits with an exercise specialist, (2) one face-to-face visit and 4 telephone follow-ups with an exercise specialist, (3) written material, or (4) slight-to-no preference. Participants were then allocated to an intervention group and categorized as either “matched” or “mismatched” based on their indications. Participants who reported a slight-to-no preference was categorized as “matched.” Measures: Daily step count as measured by pedometers and study participation. Analysis: Mean differences between groups in daily step count at 3 and 12 months (multiple linear regression models) and study participation at baseline, 3 months, and 12 months (χ2 tests). Results: Preference for an intervention group prior to randomization did not significantly (all P’s > .05 using 95% confidence interval) impact step counts (differences of <600 steps/day between groups) or study participation. Conclusion: Future research should continue to address whether the strength of preferences influence study outcome and participation and whether the study preferences change over time.

Increasing Effectiveness of a Physical Activity Smartphone Intervention With Positive Suggestions: Randomized Controlled Trial. (Preprint)

2021 ◽  
Author(s):  
Aleksandrina Skvortsova ◽  
Talia Cohen Rodrigues ◽  
David de Buisonjé ◽  
Tobias Kowatsch ◽  
Prabhakaran Santhanam ◽  
...  
Keyword(s):  
Physical Activity ◽  
Physical Activity Intervention ◽  
Controlled Trial ◽  
Outcome Expectations ◽  
Positive Outcome ◽  
Step Count ◽  
Future Research ◽  
Control Group ◽  
Daily Step ◽  
Daily Step Count

BACKGROUND Electronic Health (eHealth) interventions have a potential to increase physical activity of their users. However, their effectiveness varies and they often have only short-lasting effects. One possible way to enhance their effectiveness, is increasing positive outcome expectations of the users by giving them positive suggestions regarding the effectiveness of the intervention. It has been shown that when individuals have positive expectations regarding various types of interventions, they tend to benefit from these interventions more. OBJECTIVE The main objective of this web-based study was to investigate whether positive suggestions can change the expectations of the participants regarding the effectiveness of a smartphone physical activity intervention and subsequently enhance the number of steps participants take during the intervention. Additionally, we studied if suggestions affect perceived app effectiveness, engagement with the app, self-reported vitality and fatigue of the participants. METHODS A 21-day physical fully automated activity intervention aimed at helping participants to walk more steps. The intervention was delivered via a smartphone-based application (app), that deliver specific tasks to participants (e.g., setting activity goals or looking for social support) and recorded daily step count of the participants. Participants were randomized to either a positive suggestions group (n = 69) or a control group (n = 64). Positive suggestions emphasizing the effectiveness of the intervention were implemented in an online flyer sent to the participants before the intervention. Suggestions were repeated on day 8 and 15 of the intervention via the app. RESULTS Participants significantly increased their daily step count from baseline compared to 21 days of the intervention (t (107) = -8.62, p < .001) regardless of the suggestions. Participants in the positive suggestions group had more positive expectations regarding the app (B= -1.61, SE= 0.47, p < 0.001) and higher expected engagement with the app (B= 3.80, SE= 0.63, p < .001) compared to the participants in the control group. No effect of suggestions on the step count (B = -22.05, SE = 334.90, p = .95), perceived effectiveness of the app (B= 0.78, SE= 0.69, p= 0.26), engagement with the app (B= 0.78, SE= 0.75, p= 0.29), and vitality (B= 0.01, SE= 0.11, p= 0.95) were found. Positive suggestions decreased the fatigue of participants during the three weeks of the intervention (B= 0.11, SE= 0.02, p< 0.001). CONCLUSIONS Even though the suggestions did not affect the number of daily steps, they increased the positive expectations of the participants and decreased their fatigue. These results indicate that adding positive suggestions to eHealth physical activity interventions might be a promising way to influence subjective, but not objective, outcomes of interventions. Future research should focus on finding ways to strengthen the suggestions as they have a potential to boost effectiveness of eHealth interventions. CLINICALTRIAL osf.io/cwjes

scholarly journals Feedback From Activity Trackers Improves Daily Step Count After Knee and Hip Arthroplasty: A Randomized Controlled Trial

2018 ◽  
Vol 33 (11) ◽  
pp. 3422-3428 ◽  
Author(s):  
Neill Van der Walt ◽  
Lucy J. Salmon ◽  
Benjamin Gooden ◽  
Matthew C. Lyons ◽  
Michael O'Sullivan ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Hip Arthroplasty ◽  
Controlled Trial ◽  
Step Count ◽  
Daily Step ◽  
Randomized Controlled ◽  
Activity Trackers ◽  
Daily Step Count ◽  
Knee And Hip Arthroplasty

scholarly journals Ambulation Orderlies and Recovery After Cardiac Surgery: A Pilot Randomized Controlled Trial

2017 ◽  
Vol 6 (3) ◽  
pp. 42-49 ◽  
Author(s):  
Quinn R. Pack ◽  
Erin A. Woodbury ◽  
Samuel Headley ◽  
Paul Visintainer ◽  
Richard Engelman ◽  
...  
Keyword(s):  
Functional Recovery ◽  
Barthel Index ◽  
Controlled Trial ◽  
Functional Independence ◽  
Step Count ◽  
Daily Step ◽  
Unique Identifier ◽  
Step Counts ◽  
Daily Step Count ◽  
The Impact

Background: One potential strategy to increasing physical activity after surgery is to use an ambulation orderly (AO), a dedicated employee who assures frequent patient walking. However, the impact of an AO on physical and functional recovery from surgery is unknown. Methods: We randomized postoperative cardiac surgical patients to receive either the AO or usual care. We measured average daily step count, changes in 6-min walk test (6MWT) distance, and changes in functional independence (Barthel Index). Our primary goal was to test protocols, measure variability in activity, and establish effect sizes. Results: Thirty-six patients were randomized (18 per group, 45% bypass surgery). Overall, patients exhibited significant recovery of physical function from baseline to discharge in the 6MWT (from 83 to 172 meters, p &lt; 0.001) and showed improvement in independent function (Barthel Index, 67 to 87, p &lt; 0.001). Moreover, each additional barrier to ambulation (supplemental oxygen, intravenous poles/fluid, walkers, urinary catheters, and chest tubes) reduced average daily step count by 330 steps/barrier, p = 0.04. However, the AO intervention resulted in only a small difference in average daily step counts (2718 versus 2541 steps/d, Cohen's d = 0.16, 608 patients needed for larger trial), which we attributed to several trial factors that likely weakened the AO intervention. Conclusion: In this pilot study, we observed significant in-hospital physical and functional recovery from surgery, but the addition of an AO made only marginal differences in daily step counts. Future studies should consider stepped-wedge or cluster trial designs to increase intervention effectiveness. Clinical Trials Registration: Clinicaltrials.gov unique identifier: NCT02375282.

Abstract P276: Activity Tracker Increases Daily Step Count Post-Cardiac Rehabilitation Compared to Placebo Device

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Erica Schorr ◽  
Hilton Dahl ◽  
Alicia Sarkinen ◽  
Rebecca Brown
Keyword(s):  
Cardiac Rehabilitation ◽  
Functional Capacity ◽  
Intervention Group ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracking ◽  
Activity Tracker ◽  
Daily Step Count ◽  
Tracking Devices

Introduction: Despite optimal levels of physical activity (PA) among patients immediately post-cardiac rehabilitation, little is known about PA levels long-term. Further, interventions to maintain recommended PA levels and functional capacity achieved during cardiac rehabilitation are lacking. Objective: To test the effect of wearing a Garmin vÍvofit for 3 months post-cardiac rehabilitation on PA levels and functional capacity compared to a placebo device. Methods: Change in daily step count and 6-minute walk test (6MWT) were assessed over 3 months using the vÍvofit activity tracker in 35 patients (mean age 62±8 years; 83% male; 94% Caucasian) post-cardiac rehabilitation. Goal was 10,000 steps for all participants. Patients were randomized into the control or intervention group with control devices displaying a digital clock. VÍvofit step data were recorded continuously; the 6MWT was conducted at 0, 9, 12, and 15 weeks. Comparisons between the 2 groups were made using test of proportions, t-test, and logistic and linear regression. Results: Control and intervention groups were balanced with respect to age, gender, education, baseline step count, and body composition. Although all participants exhibited above average daily step counts (>8,000 steps at 3, 9, and 15 weeks); step counts for intervention group participants were higher at 3, 9, and 15 weeks (by 2,537 steps, 2,022 steps, and 1,545 steps, respectively). Intervention group participants (N=17) increased the distance covered during the 6MWT by 138 feet (sd=28), compared to a 48 foot (sd=18) improvement among control group participants (p=0.500); although not statistically significant, but perhaps clinically relevant. Conclusion: These data provide preliminary support for using wrist-worn activity tracking devices to continuously monitor and maintain PA levels post-cardiac rehabilitation. There is a need for larger trials testing the effectiveness of these devices with a more diverse sample over a longer period of time. Wrist worn activity tracking devices should be coupled with other components known to support long-term behavior change (e.g., social support and text messaging) to develop effective interventions for secondary cardiovascular disease prevention.

scholarly journals Work-related factors predict changes in physical activity among nurses participating in a web-based worksite intervention: A randomized controlled trial

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jennifer Brunet ◽  
Melissa Black ◽  
Heather E. Tulloch ◽  
Andrew L. Pipe ◽  
Robert D. Reid ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Mood States ◽  
Step Count ◽  
Time Estimate ◽  
Daily Step ◽  
Related Factors ◽  
Web Based ◽  
Work Related ◽  
Randomized Controlled ◽  
Daily Step Count

Abstract Background Despite the numerous benefits associated with physical activity (PA), most nurses are not active enough and few interventions have been developed to promote PA among nurses. A secondary analysis of raw data from a single-centre, three-arm parallel-group randomized controlled trial was conducted to assess whether work-related characteristics and general mood states predict changes in total weekly moderate-to-vigorous intensity PA (MVPA) and average daily step-count among nurses participating in a 6-week web-based worksite intervention. Methods Seventy nurses (meanage: 46.1 ± 11.2 years) were randomized to an individual-, friend-, or team-based PA challenge. Participants completed questionnaires pre- and post-intervention assessing work-related characteristics (i.e., shift schedule and length, number of hours worked per week, work role) and general mood states (i.e., tension, depression, anger, confusion, fatigue, vigour). Participants received a PA monitor to wear before and during the 6-week PA challenge, which was used to assess total weekly MVPA minutes and average daily step-count. Data were analyzed descriptively and using multilevel modeling for repeated measures. Results Change in total weekly MVPA minutes, but not change in average daily step-count, was predicted by shift schedule (rotating vs. fixed) by time (estimate = − 17.43, SE = 6.18, p = .006), and work role (clinical-only vs. other) by time (estimate = 18.98, SE = 6.51, p = .005). General mood states did not predict change in MVPA or change in average daily step-count. Conclusions Given that nurses who work rotating shifts and perform clinical work showed smaller improvements in MVPA, it may be necessary to consider work-related factors/barriers (e.g., time constraints, fatigue) and collaborate with nurses when designing and implementing MVPA interventions in the workplace. Trial registration ClinicalTrials.gov: NCT04524572. August 24, 2020. This trial was registered retrospectively. This study adheres to the CONSORT 2010 statement guidelines.

The translation of preschoolers’ physical activity guidelines into a daily step count target

2014 ◽  
Vol 33 (10) ◽  
pp. 1051-1057 ◽  
Author(s):  
Marieke De Craemer ◽  
Ellen De Decker ◽  
Ilse De Bourdeaudhuij ◽  
Maïté Verloigne ◽  
Yannis Manios ◽  
...  
Keyword(s):  
Physical Activity ◽  
Step Count ◽  
Daily Step ◽  
Physical Activity Guidelines ◽  
Daily Step Count

Abstract 11: mActive: A Blinded, Randomized mHealth Trial Supporting Digital Tracking and Smart Texting for Promotion of Physical Activity

Circulation ◽  
2015 ◽  
Vol 131 (suppl_1) ◽  
Author(s):  
Seth S Martin ◽  
David I Feldman ◽  
Roger S Blumenthal ◽  
Steven R Jones ◽  
Wendy S Post ◽  
...  
Keyword(s):  
Physical Activity ◽  
Phase I ◽  
Real Time ◽  
Phase Ii ◽  
The United States ◽  
Step Count ◽  
Daily Step ◽  
Baseline Activity ◽  
Daily Step Count ◽  
Digital Tracking

Introduction: The recent advent of smartphone-linked wearable pedometers offers a novel opportunity to promote physical activity using mobile health (mHealth) technology. Hypothesis: We hypothesized that digital activity tracking and smart (automated, real-time, personalized) texting would increase physical activity. Methods: mActive (NCT01917812) was a 5-week, blinded, sequentially-randomized, parallel group trial that enrolled patients at an academic preventive cardiovascular center in Baltimore, MD, USA from January 17 th to May 20 th , 2014. Eligible patients were 18-69 year old smartphone users who reported low leisure-time physical activity by a standardized survey. After establishing baseline activity during a 1-week blinded run-in, we randomized 2:1 to unblinded or blinded tracking in phase I (2 weeks), then randomized unblinded participants 1:1 to receive or not receive smart texts in phase II (2 weeks). Smart texts provided automated, personalized, real-time coaching 3 times/day towards a daily goal of 10,000 steps. The primary outcome was change in daily step count. Results: Forty-eight patients (22 women, 26 men) enrolled with a mean (SD) age of 58 (8) years, body mass index of 31 (6), and baseline daily step count of 9670 (4350). The phase I change in activity was non-significantly higher in unblinded participants versus blinded controls by 1024 steps/day (95% CI -580-2628, p=0.21). In phase II, smart text receiving participants increased their daily steps over those not receiving texts by 2534 (1318-3750, p<0.001) and over blinded controls by 3376 (1951-4801, p<0.001). The unblinded-texts group had the highest proportion attaining the 10,000 steps/day goal (p=0.02) (Figure). Conclusions: In present-day adult smartphone users receiving preventive cardiovascular care in the United States, a technologically-integrated mHealth strategy combining digital tracking with automated, personalized, real-time text message coaching resulted in a large short-term increase in physical activity.

scholarly journals Effectiveness of the Nutritional App “MyNutriCart” on Food Choices Related to Purchase and Dietary Behavior: A Pilot Randomized Controlled Trial

Nutrients ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1967 ◽  
Author(s):  
Cristina Palacios ◽  
Michelle Torres ◽  
Desiree López ◽  
Maria Trak-Fellermeier ◽  
Catherine Coccia ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Intervention Group ◽  
Smartphone Application ◽  
Analysis Of Covariance ◽  
Baseline Body Mass Index ◽  
Traditional Group ◽  
Face To Face ◽  
Cured Meats ◽  
Food Recalls ◽  
The Individual

Objective: To pilot test the effectiveness of “MyNutriCart”, a smartphone application (app) that generates healthy grocery lists, on diet and weight. Methods: A pilot randomized trial was conducted to test the efficacy of using the “MyNutriCart” app compared to one face-to-face counseling session (Traditional group) in Hispanic overweight and obese adults. Household food purchasing behavior, three 24-h food recalls, Tucker’s semi-quantitative food frequency questionnaire (FFQ), and weight were assessed at baseline and after 8 weeks. Statistical analyses included t tests, a Poisson regression model, and analysis of covariance (ANCOVA) using STATA. Results: 24 participants in the Traditional group and 27 in the App group completed the study. Most participants were women (>88%), with a mean age of 35.3 years, more than a high school education (>80%), a family composition of at least three members, and a mean baseline body mass index (BMI) of 34.5 kg/m2. There were significant improvements in household purchasing of vegetables and whole grains, in individual intakes of refined grains, healthy proteins, whole-fat dairies, legumes, 100% fruit juices, and sweets and snacks; and in the individual frequency of intake of fruits and cold cuts/cured meats within the intervention group (p < 0.05). However, no significant differences were found between groups. No changes were detected in weight. Conclusions: “MyNutriCart” app use led to significant improvements in food-related behaviors compared to baseline, with no significant differences when compared to the Traditional group. Cost and resource savings of using the app compared to face-to-face counseling may make it a good option for interventionists.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

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