A Group-Based Walking Study Using WeChat to Enhance Physical Activity Among Older Adults: The Role of Social Engagement
Phase: NA
Conditions: Inactivity, Sedentary Behavior
Sponsor: Brandeis University

 
 
 
A Group-Based Walking Study to Enhance Physical Activity Among Older Adults: The 
Role of Social Engagement 
 
Study Protocol and Statistical Analysis Plan 
NCT03803085 
 
 
Document Date: 7/22/2019 
 
 

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WeChat Study Protocol 
Scientific background 
There is compelling evidence that an active lifestyle has broad benefits for 
cognitive, physical, and psychological health among older adults (Cockerham, Wolfe, & 
Bauldry, 2020; Matz-Costa et al., 2016). Physical activity can delay or prevent many 
chronic diseases, including heart disease, type 2 diabetes, dementia, and some cancers 
(Booth et al., 2012). Active older adults have a reduced risk of moderate or severe 
physical limitations, are less likely to suffer from falls and fracturing bones, and are more 
likely to remain mobile and independent (Kohl et al., 2012; McPhee et al., 2016). Being 
physically and socially active can also improve mental health and delay dementia and 
cognitive decline (Holt-Lunstad et al., 2015; Liu & Lachman, 2019). According to the 
2016 report from Centers for Disease Control and Prevention (CDC), non-
institutionalized adults ages 50 years and older account for $860 billion in health care 
costs each year, despite the fact that 4 in 5 of the most costly chronic conditions for this 
age group can be prevented or managed with physical activity (National Center for 
Health Statistics, 2017).   
CDC researchers examined patterns of inactivity using data from the 2014 
Behavioral Risk Factor Surveillance System for all 50 states and the District of Columbia 
(D.C.) and found 31 million Americans (28 percent) age 50 years and older were 
inactive, defined as no physical activity outside of work during the past month (Watsonet 
al., 2016). Trends in physical activity consistently show that activity levels progressively 
decrease with age with the prevalence of inactivity being highest among adults aged 65 or 
greater. Current estimates indicate that 66% of adults over 75 do not engage in any 

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regular physical activity. Moreover, evidence suggests that 50% of sedentary adults have 
no plan of starting an exercise program (Watson et al., 2016).  
 
In recent years, a number of researchers have conducted studies to evaluate the 
potential of using pedometers as health interventions (see Foster et al., 2010; Sullivan & 
Lachman, 2017). Foster and colleagues (2010) used pedometers and a specifically made 
app, StepMatron, on Facebook to compare the effects of social and non-social motivation 
on physical activity within a friend group in the same work place. The ten participants in 
the social group could see their step counts compared to their friends in a competitive 
format. The social group could also communicate with each other through the page. The 
nonsocial group could only see their own step counts and had no communication through 
the app with other participants. There was a significantly higher number of steps in the 
social group compared to the non-social group. The conversations of the participants in 
the social group indicated that the competitive social aspect among the friends influenced 
the number of steps.  In a review of the evidence for increasing physical activity among 
sedentary adults using fitness technology, Sullivan and Lachman (2017) indicated that 
fitness technology, including trackers and smartphone applications, have become 
increasingly popular for measuring and encouraging physical activity. Overall, fitness 
technology has the potential to significantly impact public health, research, and policies. 
Behavior change techniques such as goal setting, feedback, rewards, and social factors 
are often included in fitness technology; however, it is not clear which components are 
most effective. Sullivan and Lachman (2017) discussed additional cognitive behavioral 
strategies not typically included in fitness technology devices or apps, such as action 
planning, restructuring negative attitudes, enhancing environmental conditions, and 

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identifying other barriers to regular physical activity, that were promising for engaging 
inactive populations. Our study adds to the discussion and considers social engagement 
as a possible mechanism for increasing physical activity among older adults using fitness 
technology.  
The use of fitness technology services could have beneficial effects on chronic 
disease management in late life, including positive changes in health perception, social 
functioning, and mental health (Lindeman et al., 2020). Fitness technology is regarded as 
a vital means for maintaining and enhancing older people’s quality of life (Khosravi et 
al., 2016; Mitzner et al., 2018). In addition, previous research also indicated that 
information communication technologies could provide a link between older adults and 
their informal and formal networks. Technologies allow older adults to transcend spatial 
barriers and provide them a greater sense of connection to other people (Clarke et al., 
2009; Schlomann et al., 2020). Exploring the role of social factors in increasing physical 
activity using technology is a promising direction (Zhang et al., 2016).   
Objectives 
This paper reports on the use of WeChat to improve physical and social activity 
among community dwelling older adults. WeChat is a free mobile text and voice 
messaging communication service. This pilot study explored the efficacy of one function 
of the app WeChat: a group-based pedometer function WeRun. There are previous 
empirical studies conducted on WeRun with younger adults; for example, Gui and 
colleagues (2017) conducted a qualitative study of 32 users of WeRun. Their findings 
indicated that sharing fitness data with pre-existing social networks motivated users to 
continue self-tracking their steps and enhanced their existing social relationships. 

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However, we are not aware of any empirical studies conducted using WeRun with older 
adults. 
The objectives of this study are to explore: a) whether the participants in the 
treatment group would show greater increases and higher maintenance of daily steps as 
well as greater increases in level of social engagement and exercise self-efficacy in 
comparison to the participants in the control group who were not given the treatment; and 
b) whether social engagement would be a significant mediator, that is a mechanism of 
behavior change.  
Design 
The study was approved by the University’s Institutional Review Board. The 
clinical trial was registered at the US National Institutes of Health (ClinicalTrials.gov). 
After the participants were deemed eligible to participate in the study, participants were 
randomly assigned to one of two conditions: treatment or control. During the 4-week 
study, participants were blinded to condition. Data collection was also blinded to the 
experimenters, as the survey and physical activity data were collected through online 
platforms (Qualtrics and WeRun, respectively).  
All participants completed a survey with demographic questions and the self-
report outcome measures at pretest. They were asked to use WeRun to track their own 
daily steps for one week, prior to being assigned to groups. This provided pretest physical 
activity data and helped participants become familiar with WeChat and syncing the data 
with their phone. After 4 weeks of using the application, all participants completed the 
same self-report measures to assess changes in the outcomes of interest. They also 
completed a follow-up assessment 1 month after the post-test for the same measures.  

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During the intervention, participants’ daily walking steps were recorded by 
WeRun, and the data were logged by the research assistant every day. The research 
assistant also recorded each participants’ daily ranking and number of social interactions 
in the treatment condition. Messages were sent by email and WeChat messages to the 
participants to remind them to carry their phone with them as often as possible during the 
day and use the application every day. The trained research assistant explained how to 
use WeChat, WeRun to all participants. Participants in both conditions were asked to use 
the application every day. A message was sent to remind them to use the application if 
they had not done so for three consecutive days. 
  
Treatment condition: Participants used WeChat for 4 weeks, during which time 
the steps data were recorded by the WeRun function and downloaded by the research 
assistants. The participants in the treatment condition could interact with the other group 
members by sending text messages which were documented by the research assistants. 
The researchers sent out instructions to check step counts on Monday mornings during 
the 4-week intervention, for example  “Dear all, this is Week 1! please remember to take 
your phone with you as often as possible during the day and check your step count in 
WeRun by the end of the day. Please also share your number of steps with your group 
members. Thank you!” The daily walking steps of the participants in this group were 
displayed to the group and the participant with the highest daily steps in the group was 
indicated as the champion each day. 
  
Control condition: Participants only saw their own daily walking steps using 
WeChat. They did not see other group members’ daily steps and did not interact or 
compare walking steps with other group members. The participants received messages on 

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Monday mornings during the 4-week intervention to remind them to take their phones as 
often as possible during the day. 
Methods 
Participants   
Eligibility. Inclusion criteria specified that participants must be 60 or older and fit 
enough to walk for at least 20 minutes at a time. They also needed to have a smartphone, 
with either an Android or iPhone system with Internet access.  A power analysis (using 
G*Power 3.1) for the study and analysis design indicated that a total of 56 participants 
(randomly assigned across two groups) would provide adequate power (> 80%) to detect 
medium-sized effects (ρ = 0.3) at p < .05. We recruited 60 participants in the northeast 
U.S. by posting recruiting posters in community public posting boards, including public 
libraries, grocery stores and shopping malls. Participants were given two 25-dollar gift 
cards (one at the pretest and one after the 4-week intervention) as compensation for their 
participation.  
Sample. As shown in the consort diagram in Figure S1, of the 241 adults who 
filled out our eligibility questionnaire online, 38 were not eligible, 15 gave up as they had 
trouble downloading the app, 4 did not meet the age requirement, 4 indicating 
participating in a walking intervention would be a potential health risk, and 3 could not 
participate due to travel. One hundred and forty-three potential participants who were 
eligible did not respond when we notified them that they were eligible. Recruitment took 
place from October 2018 to March 2019, with primary data collection completed in 
February 2019, and follow-up data collection completed in March 2019. Those who were 

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physically active (i.e., who had 7,000 - 10,000 daily steps or above everyday) were not 
included after the one-week baseline. A total of 60 adults were recruited and randomly 
assigned to either the control condition (n = 30) or the treatment condition (n = 30). They 
completed the study in one of three subgroups, with 20 participants randomly assigned 
into two groups (10 in treatment group, and 10 in control group) for each subgroup.  
Measures 
Covariates. Age, gender, education, race, and self-reported health were used as 
covariates in the analyses because of their expected relationship with the outcome 
measures. For physical health, the participants were asked “in general would you say 
your health is?”  The response options ranged from 1 (poor) to 5 (excellent) with a higher 
score indicating better physical health. 
The outcome measures were administered at the pretest, posttest and follow up 
using self-report questionnaires given before and at the completion of week 4 of the pilot 
study and at the 1 month follow up through an online platforms-Qualtrics.  
Daily step counts. Participants’ daily steps were recorded by using the WeRun 
function in WeChat by the end of each day during the 4-week intervention period and at 
the 1 month follow up. Mean daily steps for pretest week, post-test week and one month 
follow up week were calculated as the mean number of steps across the 7 days in that 
week of the study.  
Percentage change in mean weekly steps at posttest was calculated as the 
difference between the mean weekly steps at posttest and pretest divided by the mean 
weekly steps at pretest. The percentage change in mean weekly steps at the one month 

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follow up was calculated as the difference between the mean weekly steps at one month 
follow up and pretest divided by the mean weekly steps at pretest. 
Social engagement. We had two measures of social engagement. The 18-item 
version of the Lubben Social Network Scale (LSNS), was used to examine social contact 
and support as indicators of social engagement at the pretest and posttests (Lubben & 
Gironda, 2004). For the treatment group only, the number of participants’ daily text 
messages exchanged in the group discussion was used to measure social interaction. This 
version utilizes two subscales for friends and family and has the highest level of internal 
consistency (α= .82) of the three available versions of the LSNS (Lubben & Gironda, 
2004). The scale weighs size, closeness and frequency of social contacts. The sum of the 
12 equally weighed items is computed with scores ranging from 0 to 60. Lower scores 
indicate smaller networks, or lower levels of perceived social support. Items include 
‘How many relatives (friends) do you see or hear from at least once a month?’, ‘How 
many relatives (friends) do you feel you can talk to about private matters?’, and ‘How 
many relatives (friends) do you feel close to such that you could call on them for help?’  
Exercise self-efficacy.  Based on Bandura’s model (Bandura, 1990; Lachman et 
al., 2011; Resnick & Jenkins, 2000), exercise self-efficacy was measured using the Self-
Efficacy for Exercise (SEE) Scale, which was a 9-item instrument that focuses on self-
efficacy expectations related to the ability to continue exercising in the face of barriers to 
exercise. There was sufficient evidence of internal consistency (α = 0.92), and a squared 
multiple correlation coefficient using structural equation modeling provided further 
evidence of reliability (R2 ranged from 0.38 to 0.76). The sum of the nine items is 
computed with scores ranging from 0 to 90. Lower scores indicate lower levels of 

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exercise self-efficacy. Items include ‘the weather was bothering you’, “you were bored 
by the program or activity” and “you have to exercise alone”.  
Data Analysis Plan 
Statistical analyses of survey measures were done using SPSS. Descriptive 
information and correlations were computed for all study variables. Changes in daily 
steps, social engagement and exercise self-efficacy from the pretest to 4 weeks were 
examined. Repeated measures ANCOVA was used to test for effects of the treatment on 
mean daily step counts over the intervention period, exercise self-efficacy, and social 
engagement and to compare treatment and control groups at pretest, posttest, and follow-
up using a two condition by three occasions design. All post hoc analyses were 
performed using Bonferroni corrections controlling the covariates. We conducted a 
sensitivity analysis without the covariates and the results were the same. 
Conditional process modeling was applied using PROCESS in SPSS to test the 
predicted mediation model. Mediation analyses was based on 1000 bootstrapped samples 
using Hayes’ PROCESS Macro v3.3 (Hayes & Preacher, 2014), allowing for formal tests 
of the total, direct, and indirect effects of intervention on percentage of change of mean 
weekly steps from pre-test to post test. The predictor variable was condition, the mediator 
variable was change in social engagement, and the outcome was percent change in mean 
weekly steps from pre-test to post test.