Pilot Clinical Trial for a Mobile Health Intervention to Support Healthful Dietary Choices in Older Person
Phase: NA
Conditions: Frailty
Sponsor: University of Washington

 
 
 
 
 
 
 
 
 
 
Mobile Health Intervention to Support Healthful Diet  
 
 
 
NCT05236712 
 
 
December 15th, 2023 

STUDY PROTOCOL 
 
Objective  
 
We conducted a pilot randomized controlled trial (RCT) to assess the feasibility and preliminary 
efficacy of an innovative, theory-driven mobile intervention specifically designed for older adults 
with frailty, aimed at increasing Mediterranean food consumption. Feasibility was determined by 
the number of participants enrolled and retained. Primary efficacy outcomes included adherence 
to the Mediterranean diet score and insulin resistance measures. Secondary and theoretical 
mechanistic variables involved in behavioral change were food groups, social cognitive theory 
constructs, and anthropometric and functional measures. Usability and mobile app analytics 
provided deeper insights into participants' experiences and engagement with the app, identifying 
potential areas for improvement. 
 
Design  
 
This was a pilot randomized control trial (RCT) to test the feasibility and preliminary 
efficacy of a mobile intervention consisting of a patient-facing mobile app and a secure 
web-based administrative dashboard. Participants were randomly assigned to either the 
intervention group or the control group. Those randomized to the intervention received 
access to a mobile app, available for download by invitation only on the Apple App Store. 
Participants randomized to control received via email referral to National Institute on 
Aging (NIA) materials concerning healthy eating. 
 
Methods  
 
Participants 
Study participants were recruited between January and October 2022 through listserv 
emails, presentations, and flyers in retirement communities and other places frequented 
by older adults in Seattle, WA. Eligibility criteria included: a) being age 65 years and 
older, b) having difficulty with at least one of the following activities: performing heavy 
housework; performing light housework; walking outside alone; managing money/paying 
bills; shopping for personal items; preparing meals, suggestive of frailty. Exclusion criteria 
included: a) having difficulty with basic activities of daily living such as eating and getting 

out of bed, suggestive of overt disability, b) having a Memory Impairment Screen for 
Telephone (MIS-T) score of <3, suggestive of memory impairment or cognitive difficulties, 
c) having a 14-item Mediterranean Diet Adherence Screener (MEDAS) >8, suggestive of 
an optimal diet, d) severe hearing or visual impairment.  
 
Data Collection Procedures  
A study coordinator (SC) collected data during in-person assessments and remotely 
using standardized procedures at two time points. At the baseline in-person assessment, 
the SC administered clinical and demographic questionnaires, collected functional and 
anthropometric measures, and conducted a blood draw using dried blood spot collection 
methods ([DBS] described later). The second in-person visit occurred about twelve weeks 
later and included a similar battery of assessments and a recording of a semi-structured 
exit interview. Moreover, to better understand user engagement and interaction with the 
mobile health intervention, we collected and analyzed user log data. User log data was 
automatically captured and stored by the app's backend system, ensuring an accurate 
and comprehensive collection of user interactions. This data comprised a record of users' 
actions within the app, including the frequency and duration of use, features accessed, 
and progress through various intervention components. 
 
 
 

STATISTICAL ANALYSIS PLAN 
 
Descriptive statistics were first calculated to compare demographic characteristics 
between the intervention and the control groups. Outcomes measures were analyzed by 
calculating the within-group pretest–post-test change scores and then comparing the 
change scores between the intervention and the control groups using Mann–Whitney U 
tests. To estimate the magnitude of the intervention's impact compared to the control 
group, we used Hedges' g to measure effect size. Hedges' g is a variation of Cohen's d 
that corrects for biases that may occur in small sample sizes, making it a more 
appropriate choice for our study. Hedges' g takes the difference in means between the 
intervention and control groups and divides it by the pooled standard deviation while 
applying a correction factor to account for potential biases. A larger absolute value of 
Hedges' g indicates a greater effect size, with values of 0.2, 0.5, and 0.8 generally 
considered small, medium, and large effect sizes, respectively. An R package, “BootES” 
was used to calculate 95% bootstrap confidence intervals for the effect sizes. The 
"BootES" package is designed specifically for this purpose, providing a robust and 
efficient method for estimating confidence intervals based on resampling techniques. 
Finally, descriptive statistics and visual analytics were used to describe app quality and to 
analyze log data of user interactions.