Bingocize: A Novel Mobile Application to Help Maintain or Improve Older Adults' Health, Function, and Cognition
Phase: NA
Conditions: Activities of Daily Living, Accidental Fall, Sarcopenia, Arteriosclerosis, Diabetes Mellitus, Type 2, Hypertension, Cognitive Decline, Executive Function, Cognitive Aging
Sponsor: Western Kentucky University

Official Title of Study:  
Bingocize: A Novel Mobile Application for Older Adult Health 
 
NCT Number:  
NCT03629912 
 
Date: 
10/11/2022 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
Background & Objectives 
 
Exercise interventions designed to improve physical and mental fitness have the 
potential to help reduce health care costs and maintain, or even improve, quality of life 
for older adults (Chodzko-Zajko et al., 2009).  For example, interventions that reduce 
chronic disease risk and severity can decrease lifetime Medicare costs by as much as 
60% (Rula et al., 2011).  Despite this potential, however, adherence and retention of 
older adults to health promoting programs continues to be a challenge.Many facilities 
serving older adults are searching for creative ways to engage older adults in health 
enhancing and rehabilitative activities. The purpose of our trial was to test the 
effectiveness of using a new mobile application (Bingocize®) to improve functional 
performance, knowledge of health topics, dietary habits, and aspects of fluid cognitive 
abilities.  
 
Design & Methods 
Participant characteristics, recruitment, and sample size. In our past 
research, we have successfully worked with several agencies in the area, including 
state of Kentucky Area Development Districts (ADDs) and NHE, Inc. (a company that 
coordinates services for dozens of senior center/assisted living/independent living 
facilities throughout our region). For the current study, we have secured letters of 
support from Community Action of Southern Kentucky (CASOKY), which is responsible 
for running senior centers in a 17-county region in our area. We also have a letter of 
support from NHE, Inc. who will provide access to recruit participants from multiple low-
income senior housing facilities. Both of these agencies will assist in recruiting 
participants from their facilities in Kentucky and Tennessee.   
Flyers, newsletters, electronic communication, direct contact, and word of mouth 
will be used to recruit volunteers. Males and females who are over 60 and over will be 
eligible to participate. The Physical Activity Readiness Questionnaire (PAR-Q) will be 
used to determine the participant’s health status based on responses to seven medical 
questions (Chodzko-Zajko et al., 2009). Participants answering “yes” to any of these 
questions will require a physician’s release prior to participation in the study. Fall risk will 
be assessed using the STEADI algorithm, the Falls Efficacy Scale (Tinetti et al., 1990), 
and the four meter walk test from the Short Physical Performance Battery (SPPB; 
Stevens & Phelan, 2013; Ward et al., 2015).  Diet/nutritional intake will be assessed 
using the Dietary Screener Questionnaire (DSQ; see Thompson, Midthune, Subar, 
McNeel, Berrigan, & Kipnis, 2005). Other criteria for inclusion in the research include: 
normal or corrected-normal vision, no history of severe neurological impairment, 
mobility (i.e., not wheel-chair bound), no history of colorblindness, English as their 
native language, and a minimum score of 17 on the tMMSE (telephone Mini-Mental 
Status Exam, scores of 17 or higher indicate no clinically significant dementia).  

The research project has been reviewed and approved as a minimal risk project 
by WKU’s Institutional Review Board (IRB) to ensure there are no ethical issues, and an 
approved safety monitoring plan is in place. 
To calculate the needed sample size, we were conservative. Since physical 
outcome effect sizes due to the exercise intervention are generally larger than more 
distal cognitive outcome effect sizes, our calculations were based on examining 
cognitive outcome effect sizes (ηp
2) from interaction effects in our recently completed 
smaller study, as well as data from other published studies. With α=0.05 and power=0.8, 
(taking into account mean r among pre/post repeated measures and assumption of 
sphericity), the total sample size needed is 124. However, to adjust for the fact we are 
conducting a cluster randomized controlled trial (see Design section below; Murray, 
Varnell, & Blitstein, 2004) with an unknown intracluster coefficient, we will recruit a 
slightly larger sample - 160 - to better strengthen the overall validity of the study. This 
size is also comparable to other four-group exercise/cognition intervention studies 
published recently in the literature. (e.g., Loh et al., 2015).  
Design. We expect sixteen senior centers in western Kentucky and Tennessee 
will serve as sites for the groups in our study (see the Inclusion of Women & Minorities 
attachment for details). We list some of the senior centers we have existing 
relationships with in other parts of our application (Performance Site/Location 
document; see our Letters of Support from NHE, Inc., and CASOKY). For both study 
control and feasibility reasons, we require a facility to have eight to twelve (8-12) older 
adults who consent to participate. This study will combine both between-group 
(condition) and within-subject (pre/post) comparisons. For logistical reasons, data will 
be collected in “waves” (i.e., 4 senior centers during one 12-week period, then another 4 
senior centers during the next 12-week period, etc.).  
Using a pre-/post-test cluster randomized clinical trial (CRCT) experimental 
design with two factors (exercise and health education) at two levels (present and 
absent), senior centers will be randomly assigned to one of four conditions in the study, 
as shown in Table 1 below: (A) an experimental group that participates in Bingocize® 
(bingo, exercise, and health education) twice a week for 12 weeks, (B) a 
control/comparison group that participates in bingo+exercise only (the same exercises 
as the experimental group), (C) a control/comparison group that will participate in a 
bingo+health education program only (covering the same topics/materials as the 
experimental group), and (D) a control/comparison group that participates in bingo only. 
All four groups will use our Bingocize® app on tablets, as the app is designed to 
accommodate the usage of all of these methods. In addition to these between-group 
comparisons, within each individual we will take both pre- and post-intervention 
assessments of a variety of physical and cognitive outcome measures (described later). 
Table 1: Design Layout of Participant Groups 
 
Plays 
Bingo 
Using 
App? 
Exercises 
Using App? 
Health 
Education 
Using App? 
Group A (Experimental; Multimodal) 
Yes 
Yes 
Yes 
Group B (Exercise Control; Unimodal) 
Yes 
Yes 
No 

Group C (Health Education Control; 
Unimodal) 
Yes 
No 
Yes 
Group D (Bingo Control) 
Yes 
No 
No 
 
 
Statistical Analysis Plan 
Planned statistical analyses. Although we expect missing data to be fairly 
minimal (based on our prior studies), we will use missing value analysis (MVA) to 
analyze those data, determine whether they are missing at random, and handle as 
appropriate (see next section; Handling participant attrition). We will also test normality 
assumptions using skewness and kurtosis analyses. Baseline differences will be 
assessed using generalized linear mixed models (GLMMs). Mixed models allow for 
additional flexibility to control for random effects across clusters (senior centers) and 
correlated data at the patient level, while controlling for covariates. With regard to our 
major questions regarding the efficacy of the multimodal intervention, we will examine 
Time (Pre/Post) x Group effects (four groups described above), applying Bonferroni 
corrections for multiple comparisons as needed. If the interaction of time and group or 
their main effects are significant, then post-hoc pairwise comparisons will be conducted 
using Least Squared Difference.  Analyses will be conducted using lme4 in R. 
Handling participant attrition. In all longitudinal studies, attrition is inevitable. 
However, we expect drop-out rates to be quite low in our study. In our most recent 
study, session attendance in each group was 93% or higher. We plan to use 80% 
attendance as the minimum participation level for inclusion in this study. However, as 
mentioned above, we will be analyzing missing values to ensure there is no bias in the 
data; statistically speaking, when handling selective attrition (e.g., someone who drops 
out or fails to meet the attendance requirement) in our study, we must take into account 
its effects on both within-group differences (i.e., pre/post-test differences within each of 
our four groups) as well as its effects on between-group differences (i.e., comparisons 
between the different groups in our study). With regard to within-group attrition effects, 
the most common approach is to systematically compare the drop-outs to the retained 
participants, to identify any pre-existing differences (e.g., in demographic variables, or 
on pre-test performance). If there are not any differences, then obviously, no further 
analysis is needed. If differences are found between drop-outs and the retained 
participants, we will include those variables which differed as covariates in the 
subsequent statistical analyses. For example, if we found differences in a pre-existing 
condition between drop-outs and retained participants in our experimental (Bingocize®) 
group, we can include that pre-existing condition as a covariate in the pre/post-test 
analysis to determine whether it statistically accounts for any of the improvements that 
the retained participants may show from baseline to post-test.