Building Community Capacity for Disability Prevention for Minority Elders
Phase: NA
Conditions: Depression, Anxiety, Physical Disability
Sponsor: Massachusetts General Hospital

1 
 
 
Building Community Capacity for Disability Prevention for Minority Elders 
NCT02317432  
April 2, 2021 
 
R01AG046149 
PI: Margarita Alegria 
 
 
Brief Study Protocol and Methods 
 
 

2 
 
Descriptive methods 
The Positive Minds component (adapted from the CERED manualized intervention) is a psychosocial 
intervention that utilizes psychoeducation and cognitive behavioral therapy (CBT) to treat symptoms of 
depression and anxiety, including noticing and overcoming unhelpful thoughts. It also presents a range of 
mindfulness practices, communication techniques, and behavioral activation, by engaging the participant 
in pleasant activities, and encouraging supportive relationships.  Sessions are tailored to the participant’s 
needs using a collaborative approach. The 10-session intervention is culturally adapted for use among a 
wide group of minority elders and conducted in the participant’s language of preference (English, Spanish, 
Cantonese or Mandarin). Community Health Workers used motivational enhancement strategies to engage 
the participants. The 10 one-hour sessions were completed over a 6-month period.  Every other week the 
CHWs evaluated participants with the PHQ-9 and GAD-7 and, if necessary, the 5-item Paykel suicide 
questionnaire, both for participant safety and to ensure the participant’s mental health was not deteriorating 
to the point where immediate intervention or referral was necessary. As part of an emergency protocol, we 
connected participants to an emergency responder as required. This symptom assessment was administered 
for both intervention and enhanced usual care groups.  
Participants were simultaneously offered the Strong Bodies exercise sessions. Group sessions with an 
Exercise Trainer (3-5 elders per group) were provided 3 times per week for 12-14 weeks, for a total of 36 
sessions. The functional disability prevention component of the Strong Bodies intervention emphasizes 
building resistance or power training. The intervention consists of a series of exercises that are conducted 
while wearing a weighted vest. Resistance progresses in 2% body weight increments and weights were 
distributed evenly within the vests.  
To decrease barriers to engagement in the combined intervention, we designed the program to be 
deliverable both in person and over the phone. Study participants assigned to receive the Strong Bodies 
intervention were offered an instructional video of the exercises for at-home use. If participants were unable 
to attend the small-group exercise sessions in-person 3 times per week for 12 weeks, they received training 
from their Exercise Trainer to use the instructional video and perform the exercises at home. The video was 
intended to supplement the in-person classes and provide an opportunity for participants to engage in 
exercises when they are unable to attend the class in person. To this end, we discussed important 
considerations for phone delivery during the training of study clinicians. For instance, we discussed the 
need to ask participants whether they were in a private place with no distractions, the importance of vocal 
intonation, referring to the participant manual, and verbal affirmations to convey empathy. 
Community Health Workers (CHWs) deliver the Positive Minds component of the intervention. We drew 
on the Bureau of Labor Statistics model of CHWs who “implement programs in the community that 
promote, maintain, and improve individual and community health” (Standard Occupational Classification 
21-1094 Community Health Workers. 2017; https://www.bls.gov/oes/2017/may/oes211094.htm) 
 
CHWs were selected and trained from each collaborating community-based organization (CBO) and the 
Disparities Research Unit. Each CBO had a designated site leader who oversaw the intervention process 
locally. Part of the Site Leaders’ role was to identify staff – either within the organization or identified 
externally – who would carry out the CHW position. Our recruitment criteria included identifying 
individuals with work experience that was relevant to the intervention; who were bilingual/bicultural; and 
who exhibited interpersonal skills in communication, trust, warmth and rapport-building.   
 
We devoted significant time to training the CHWs to offer the clinical intervention at the study sites. Over 
a four-month period, they were trained on the core elements of the intervention and on understanding the 
skills and objectives of the intervention. CHWs began with a 2-day training including didactic instruction 
and role play. The training included two intensive days of workshops and practice sessions, including 

3 
 
attention to research objectives, ethics in research design, and participant privacy and confidentiality 
(standard HIPAA training). We included procedures recommended by Sterling-Turner and colleagues (1) 
involving modeling, role playing, rehearsal, and feedback associated with higher levels of treatment fidelity, 
treatment effectiveness and successful implementation. The second phase of training included role-play of 
all 10 sessions with two practice participants and then two pilot participants. These sessions were audio 
recorded, and supervisors reviewed audio-recordings and offered feedback. CHWs were certified to begin 
seeing patients from the clinical trial once they could administer the intervention with at least 75% fidelity 
in the pilot cases.  
 
We used a five-stage approach to structure our cultural and linguistic adaptation process (2). This included 
information gathering, preliminary adaptation and translation, preliminary testing, further adaptation, 
refinement, and a final adaptation trial. We used our brief, evidence-based CBT intervention, CERED (3) 
as the starting point for the study.  
 
Latino, Chinese and African American study staff conducted a review of the intervention for fit among 
elders. We held focus groups to ask diverse elders specifically about their beliefs in mental illness, disability 
prevention and willingness to complete specific tasks.  Study staff summarized the feedback received, 
which included: 1) lack of awareness of need for treatment; 2) stigma; 3) interest in traditional treatments 
or prayer rather than CBT, and the 4) need for a balanced authoritative and caring role for the CHW. We 
then modified the manual and workbook materials to address this feedback.  
 
For linguistic adaptation, bilingual study staff undertook a first translation of the manuals and materials.  
These were thoroughly reviewed and edited by a supervising staff member. In the case of our Chinese-
language materials, we also contracted out with a professional translation service to fine tune the content. 
We then convened a multicultural committee to test translation and back translation protocols of 
intervention materials.  
 
As the pilot cases and then formal trial began, we incorporated edits needed to reflect the materials at hand 
most accurately.  We used weekly supervision conducted in diverse languages and discussion in bi-weekly 
meetings to identify materials and components that needed adaptation by language or for one or more 
cultural group. We conducted weekly discussions among the supervisors by teleconference where they 
could also raise concerns, enabling us to linguistically adapt the manual and materials. 
 

4 
 
Analytical methods  
 
Satisfaction with the PMSB treatment was measured using six questions from the 12-month follow-up 
assessment. Three of these questions asked about satisfaction with the Positive Minds sessions, and three 
about satisfaction with the Strong Bodies sessions. A sample item from these six questions was “How 
satisfied or unsatisfied were you with the care you received from the Exercise Trainer?” Response options 
were 1 (very unsatisfied), 2 (not that satisfied), 3 (somewhat satisfied), and 4 (very satisfied). 
 
We first analyzed the means, variances, skewness/kurtosis, and minimum and maximum values for the 
satisfaction questions. Although participants could choose among four options ranging from 1 (very 
unsatisfied) to 4 (very satisfied), the distributions were skewed, suggesting that the primary decision among 
participants was whether they were very satisfied with the sessions. To account for these extreme 
distributions, we recoded the questions to be binary (with values 1, 2 and 3 coded as zero, and 4 coded as 
one) and created a single satisfaction measure as the mean of these six binary variables (scale ranges from 
0 to 1), such that higher values represent a higher proportion of participants who were very satisfied. 
 
We carried out descriptive analysis to examine patterns of missing data and how they were related to sample 
baseline characteristics. We defined two types of missing data: complete missing (if a specific follow-up 
assessment was missing because of participant withdrawal or lost-to-follow-up) and partial missing (when 
follow-up assessments were conducted but variables were missing due to “refused to answer” or “don’t 
know” responses). Because most data were complete missing, we examined baseline characteristics 
between participants who completed all follow-up assessments versus those who missed at least one. 
 
Although missing a follow-up assessment did not seem to be related to participants’ demographic 
characteristics, to account for missing data we used multiple imputation methods in Stata version 15.1 via 
the mi impute chained command (5). Multiple imputation was carried out in three steps. First, we imputed 
the missing data for all the variables considered, creating 20 imputed datasets each one consisting of 307 
participants with three follow-up assessments per participant. In the second step we ran analysis on each 
individually imputed data set. Finally, we aggregated the individual estimates to obtain final estimates and 
adjusted standard errors for the uncertainty due to imputation (6). 
 
Each imputation was carried out using the chained equations method (7). In what follows, we refer to 
variables with missing data as incomplete variables and those with non-missing data as complete variables. 
Each incomplete variable 𝑥𝑗 was specified as a conditional function (𝑔𝑗) given the set of all other variables 
used in the imputation, comprising both incomplete (𝑥1, … , 𝑥𝑚) and complete variables (𝑍). We fitted the 
conditional model 𝑔𝑗 to generate the predicted values of 𝑥𝑗 using an iterative method. Specifically, each 
incomplete variable 𝑥𝑗 was iteratively estimated and in each iteration the variable 𝑥𝑗
𝑡 was then updated to 
𝑥𝑗
𝑡+1 based on the conditional model. This updated variable was then used in the estimations of the other 
variables, following the conditional model specification 
 
𝑥𝑗
𝑡+1~𝑔𝑗 (𝑥𝑗│𝑥1
𝑡+1, … , 𝑥𝑗−1
𝑡+1, 𝑥𝑗+1
𝑡
, … 𝑍, 𝜙𝑗), 
 
for 𝑗∈{1 … 𝑚}, where 𝜙𝑗 were parameters of the conditional model 𝑔𝑗 . These steps were repeated for all 
variables 𝑥1 , … , 𝑥𝑚 and, after an initial burn-in phase, the procedure was stopped once convergence was 
reached. 
 
The variables used for imputation included main outcome variables and their baseline measures, participant 
socio-demographics, clinical characteristics, and study design variables, such as site and intervention 
indicators, as well as dummy indicators for each follow-up assessment. 
 

5 
 
We used interval regressions to incorporate the theoretical bounds of the clinical outcome variables to 
increase the efficiency and accuracy of the imputation procedure (e.g., we restricted the imputed values of 
PHQ-9 to lie within the 0 to 27 intervals). We used logistic regression models to impute binary variables, 
and linear regression models to impute continuous variables. 
 
For Analysis, first, we compared distributions of baseline characteristics between participants who received 
the PMSB intervention and participants in the usual care group, to assess the balance of the observed 
covariates. For each follow-up assessment, we compared the baseline covariates to detect any significant 
baseline differences between those who completed assessment versus those who did not. 
 
Second, to assess the effect of the intervention, we conducted an intent-to-treat (ITT) analysis, using a 
multilevel, multivariate regression model to assess changes in outcome variables over time in the treatment 
and control groups. To account for the nature of longitudinal data, the ITT analysis was carried out by 
fitting multilevel mixed-effects models to allow for valid variance calculation and statistical inference. The 
multilevel models computed included random effects at both participant- and site-level to account for 
within-participant and within-site correlations, as well as robust clustered standard errors to account for 
within-site correlations due to participants nesting within same site. Letting 𝑌𝑖𝑡𝑗 denote the outcome 
measured at time 𝑡 after the baseline for participant 𝑖 at site 𝑗, we estimated the following model: 
 
(1)    𝑌𝑖𝑡𝑗= 𝛽0𝑖+ 𝛽0𝑗+ 𝛽1𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖+ 𝛽2𝑇𝑖𝑚𝑒𝑡+ 𝛽3𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒𝑡+ 𝛽4 (𝑇𝑖𝑚𝑒𝑡−𝑡∗)
+ 𝛽5𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× (𝑇𝑖𝑚𝑒𝑡−𝑡∗) + 𝛽6𝑋𝑖+ 𝜀𝑖𝑡𝑗 
 
where the participant-specific random intercept can be written as (𝛽0𝑖= 𝛼00 + 𝜔0𝑖) and the site-specific 
random intercept can be written as (𝛽0𝑗= 𝛾00 + 𝜖0𝑗). We fitted only linear models as all our outcomes 
were continuous. 
 
In the ITT analysis, individuals were assigned to the study arm to which they were randomized. Thus, 
𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖 is equal to 1 if participant 𝑖 is randomized to the intervention arm and 0 otherwise. 𝑇𝑖𝑚𝑒𝑡 
is a continuous measure of time capturing differences in months between assessment 𝑡 and assessment 𝑡−
1. Because the intervention ended by the time of the 6-month assessment, we centered the time variable at 
6-month follow-up in the following way: 𝑇𝑖𝑚𝑒𝑡 equals to -4 for 2-month follow-up, 0 for 6-month follow 
up, and 6 for 12-month follow-up. To model the pattern of outcome changes over time, our primary analysis 
employed linear spline models to divide the time axis into two segments, and within each segment consider 
piecewise linear trends (e.g., having a different time trend before and after the intervention ended). 
Specially, we denote 𝑡∗ to be the month when the intervention stopped, i.e., 6 months after baseline and 
(𝑇𝑖𝑚𝑒𝑡−𝑡∗)+  to be the post-intervention time trend, which equals to (𝑇𝑖𝑚𝑒𝑡−𝑡∗) if 𝑇𝑖𝑚𝑒𝑡> 𝑡∗ and 0 
otherwise. This choice of linear spline models allows for the ability to test whether the time trends differ 
before and after the intervention was finished and that treatment effect could attenuate over time once 
participants do not receive more intervention.  
 
Since the time variable is centered at 6-month follow-up, the beta coefficient on 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖 can be 
interpreted as the treatment effect on outcome levels evaluated at the end of the intervention. That is, testing 
for the significance of  𝛽1 tests the hypothesis that the treatment was more effective than enhanced usual 
care in improving the outcomes, as evaluated at 6 months after the baseline. The coefficient of intervention 
by time interaction 𝛽3 , tests whether the pattern of outcomes over time would be no different between 
treatment and enhanced usual care groups. Similarly,  𝛽5 tests whether changes in outcome responses over 
time were different between treatment and control groups after the intervention had ended. The constant 
term (𝛼00 + 𝛾00) in equation (1) represents the level of the outcome variables in the absence of the 
intervention (𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖= 0) at 6-month follow-up ( 𝑇𝑖𝑚𝑒𝑡= 0 and (𝑇𝑖𝑚𝑒𝑡−𝑡∗) = 0). The term 
𝜔0𝑖 denotes the participant-specific random effect, 𝜖0𝑗 the site-specific random effect, and 𝜀𝑖𝑡𝑗 denotes the 

6 
 
residual error term. 𝑋𝑖 includes the baseline measure of the outcomes to control for severity of the outcome 
at baseline. We also estimated the effect of the intervention six months after it ended, i.e., 12-months after 
baseline, using the time-by-intervention interactions. Given 𝑇𝑖𝑚𝑒𝑡= 6 for the 12-month follow-up and 
𝑡∗= 6 since PMSB lasted six months, the intervention effect evaluated 12 months after baseline is given 
by 𝛽1 + 𝛽2 × 6 + 𝛽3 × 6. 
 
In assessing the effectiveness of PMSB on physical function outcomes (SPPB (8), LLDFI and WHODAS 
2.0 (9)), 35 participants in the intervention condition were excluded from the analysis because they could 
not receive the Strong Bodies treatment due to lack of medical clearance (9 participants), death (2 
participants), medical condition (e.g., surgery, knee replacement; 5 participants), lack of access to facilities 
following the hurricanes in Florida and Puerto Rico (18 participants), or they had to be referred to treatment 
due to active suicidality (1 participant). Their control counterparts, according to the 2-person block 
randomization, were also excluded to rebalance the sample. We applied this exclusion because the lack of 
sessions in Strong Bodies was not related to the intervention but to these other external factors. In total, 70 
participants were excluded in all analyses where the outcomes were physical function measures. We 
confirmed that baseline characteristics between control and intervention participants remained balanced 
after this exclusion. 
 
We conducted four sets of sensitivity analyses. First, to account for multiple outcome assessment, we 
estimated a multivariate multilevel regression (10) to test treatment effects obtained from a joint model 
instead of modeling the outcomes separately. This strategy accounts for the correlation among outcomes 
and provides more powerful tests of treatment effects compared to more traditional approaches (e.g., 
Bonferroni adjustments or combining the outcomes into a composite measure)(11). To estimate this 
multivariate model, we created a long data set where each outcome (SPPB, LLFDI, WHODAS 2.0, HSCL-
25 and GAD-7) at each follow-up assessment (2-, 6-, and 12-months) was stacked for each individual, 
meaning a data set where each participant had 15-observations (five outcomes times three follow-up 
assessments). The values for each outcome were combined into a single outcome variable 𝑌ℎ𝑖𝑡𝑗, where ℎ 
indexes the outcome measure. Instead of estimating equation (1) separately for each outcome, we produced 
a single equation 
 
(2)    𝑌ℎ𝑖𝑡𝑗= 𝛽0ℎ𝑖𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ+ 𝛽0ℎ𝑗𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ+ 𝛽1ℎ𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ
+ 𝛽2ℎ𝑇𝑖𝑚𝑒𝑡× 𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ+ 𝛽3ℎ𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒𝑡× 𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ
+ 𝛽4ℎ (𝑇𝑖𝑚𝑒𝑡−𝑡∗) × 𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ+ 𝛽5ℎ𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× (𝑇𝑖𝑚𝑒𝑡−𝑡∗) × 𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ
+ 𝛽6ℎ𝑋𝑖× 𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ+ 𝜀ℎ𝑖𝑡𝑗𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ 
 
where ℎ= 1 for SPPB, ℎ= 2 for LLFDI, ℎ= 3 for WHODAS 2.0, ℎ= 4 for HSCL-25, and ℎ= 5 for 
GAD-7. Since the time variable is centered at 6-month follow-up, the beta coefficients on 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖∗
𝑂𝑢𝑡𝑐𝑜𝑚𝑒ℎ, 𝛽1ℎ, can be interpreted as the treatment effect on outcome ℎ evaluated at the end of the 
intervention. After modeling the outcomes jointly, we conducted a joint global test to evaluate the null 
hypothesis that all treatment effects were zero, i.e., 𝛽11 = 𝛽12 = 𝛽13 = 𝛽14 = 𝛽15 = 0. This joint test 
considers the correlation among measures, which is likely to be different from zero in our context, e.g., 
participants with low SPPB scores are likely to also self-report lower LLFDI scores. 
 
Second, we examined whether our intent-to-treat results were robust to more traditional models that 
included only an indicator for intervention, three indicators of time (2-, 6- and 12-months) and their two-
way interactions using the following model: 
 
(3)    𝑌𝑖𝑡𝑗= 𝛽0𝑖+ 𝛽0𝑗+ 𝛽1𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖+ 𝛽21𝑇𝑖𝑚𝑒1 + 𝛽22𝑇𝑖𝑚𝑒2 + 𝛽23𝑇𝑖𝑚𝑒3
+ 𝛽31𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒1 + 𝛽32𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒2
+ 𝛽33𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒3 + 𝜀𝑖𝑡𝑗 

7 
 
 
where 𝑌𝑖𝑡𝑗 denotes the outcome measured at baseline, 2-, 6- and 12-months (𝑡= 0, 1, 2, and 3, respectively) 
for participant 𝑖 at site 𝑗, 𝑇𝑖𝑚𝑒1, 𝑇𝑖𝑚𝑒2, 𝑇𝑖𝑚𝑒3  are indicator variables for the 2-, 6- and 12-month follow-
ups, respectively (baseline is the reference period), and 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒1, 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖×
𝑇𝑖𝑚𝑒2, 𝐼𝑛𝑡𝑒𝑟𝑣𝑒𝑛𝑡𝑖𝑜𝑛𝑖× 𝑇𝑖𝑚𝑒3 are the two-way interactions between the intervention indicator and the 
time indicators. The coefficient 𝛽32 represents the effect of PMSB at the end of the intervention (6-months), 
while 𝛽33 represents the effect of PMSB six-months post-intervention (12-months). 
 
Our study sample was composed of a multicultural population across multiple clinical sites in three states 
and Puerto Rico. Intervention materials and protocols included translation and back translation of standard 
English measures. In a third sensitivity analysis, we tested whether our results were influenced by cultural 
differences in the intervention protocols, by examining if the intervention had a different effect based on 
race/ethnicity, language, or clinical site. To perform such test, besides the baseline measure of the outcome, 
𝑋𝑖 included either race/ethnicity, language or clinical site in equation (1), and we also included a test of 
whether the interaction between the intervention and any of these variables was statistically significant. 
Although evaluating the significance of the interaction effects is not a formal test that our results were not 
influenced by differences in the intervention protocols, observing a consistent pattern of significant 
interactions can provide some insight. For example, finding that the intervention was more effective for 
Latinos, for Spanish-speakers (80% of Latinos in our sample), and in sites were most participants received 
the intervention in Spanish, could indicate that the intervention was not culturally equivalent. 
 
In additional sensitivity analyses, we also examined whether our results changed after adjusting for baseline 
fitness (SPPB, LLFDI and WHODAS 2.0) or pharmacotherapy (GAD-7 and HSCL-25) service use, as well 
as whether the intervention was more effective among participants with moderate to severe baseline 
physical functioning (SPPB, LLFDI and WHODAS 2.0) or moderate to severe mental health symptoms 
(GAD-7 and HSCL-25). Baseline severity of physical functioning was measured with a dummy-coded 
variable equal one if participant’s baseline SPPB score was between 3 and 7 and equal zero otherwise. 
Baseline severity of mental health symptoms was measured with a dummy-coded variable equal one if 
participant’s baseline PHQ-9>=10 or GAD-7>=10 or GDS>=8 and equal zero otherwise. As before, 𝑋𝑖 
included either baseline service use or severity in equation (1), and we tested whether the interaction 
between the intervention and any of these variables was statistically significant. 
 
In secondary analyses, we used compliance (defined as number of treatment sessions received) as the 
independent variable of interest, categorized as compliance equal to zero (control group or intervention 
participants with no PM and no SB sessions), 1+ session (at least one PM or SB session without full 
compliance), and full compliance to the treatment protocol (10 PM and 25-36 SB sessions). While this 
analysis no longer relies on random assignment, it serves to provide confirmation of the results from the 
intent-to-treat analysis and provides further estimates of the magnitude of the intervention effects on the 
main outcomes evaluated at 6 and 12 months after the baseline. Because compliance made use of both 
number of PM and number of SB sessions, all compliance analyses excluded the 35 participants who did 
not receive the SB sessions and their 35 control counterparts. 
 
 
 
 

8 
 
 
References 
1. 
Sterling-Turner HE, Watson TS, Wildmon M, et al: Investigating the relationship between training 
type and treatment integrity. Sch Psychol Q 2001; 16:56 
2. 
Ramos Z,Alegría M: Cultural adaptation and health literacy refinement of a brief depression 
intervention for Latinos in a low-resource setting. Cultural Diversity and Ethnic Minority Psychology 
2014; 20:293 
3. 
Alegría M, Ludman E, Kafali EN, et al: Effectiveness of the Engagement and Counseling for Latinos 
(ECLA) intervention in low-income Latinos. Med Care 2014; 52:989-997 
4. 
Üstün TB, Chatterji S, Kostanjsek N, et al: Developing the World Health Organization disability 
assessment schedule 2.0. Bulletin of the World Health Organization 2010; 88:815-823 
5. 
StataCorp: Stata Statistical Software: Release 14, College Station, TX, StataCorp LP, 2015 
6. 
Rubin D: Multiple Imputation for Nonresponse in Surveys New York, John Wiley & Sons 1987 
7. 
Raghunathan TE, Lepkowski JM, Van Hoewyk J, et al: A multivariate technique for multiply 
imputing missing values using a sequence of regression models. Surv Methodol 2001; 27:85-96 
8. 
Simonsick EM, Guralnik JM, Ferrucci L, et al: A Short Physical Performance Battery Assessing 
Lower Extremity Function: Association With Self-Reported Disability and Prediction of Mortality 
and Nursing Home Admission. J Gerontol 1994; 49:M85-M94 
9. 
Federici S, Bracalenti M, Meloni F, et al: World Health Organization disability assessment schedule 
2.0: An international systematic review. Disabil Rehabil 2017; 39:2347-2380 
10. Baldwin SA, Imel ZE, Braithwaite SR, et al: Analyzing multiple outcomes in clinical research using 
multivariate multilevel models. J Consult Clin Psychol 2014; 82:920 

9 
 
11. Yoon FB, Fitzmaurice GM, Lipsitz SR, et al: Alternative methods for testing treatment effects on the 
basis of multiple outcomes: simulation and case study. Stat Med 2011; 30:1917-1932 
12. Borson S, Scanlan JM, Watanabe J, et al: Simplifying detection of cognitive impairment: comparison 
of the Mini‐Cog and Mini‐Mental State Examination in a multiethnic sample. Journal of the 
American Geriatrics Society 2005; 53:871-874 
13. Derogatis LR, Lipman RS, Rickels K, et al: The Hopkins Symptom Checklist (HSCL): A self-report 
symptom inventory. Behav Sci 1974; 19:1-15 
14. Gandek B, Ware JE, Aaronson NK, et al: Cross-validation of item selection and scoring for the SF-
12 Health Survey in nine countries: results from the IQOLA Project. J Clin Epidemiol 1998; 
51:1171-1178 
15. Hung DY, Lubetkin EI, Fahs MC, et al: Assessing the impact of behavioral risk factors and known-
groups validity of the SF-12 in a US Chinese immigrant population. Medical care 2009; 47:262 
16. Lam CLK, Eileen YY,Gandek B: Is the standard SF-12 health survey valid and equivalent for a 
Chinese population? Quality of life Research 2005; 14:539-547 
17. Matías-Carrelo LE, Chávez LM, Negrón G, et al: The Spanish translation and cultural adaptation of 
five mental health outcome measures. Culture, medicine and psychiatry 2003; 27:291-313 
18. Ware Jr JE, Kosinski M,Keller SD: A 12-Item Short-Form Health Survey: construction of scales and 
preliminary tests of reliability and validity. Medical care 1996; 34:220-233 
19. Wandersman A, Duffy J, Flaspohler P, et al: Bridging the Gap Between Prevention Research and 
Practice: The Interactive Systems Framework for Dissemination and Implementation. American 
Journal of Community Psychology 2008; 41:171-181 

10 
 
20. Weinberger M, Oddone EZ, Henderson WG, et al: Multisite randomized controlled trials in health 
services research: scientific challenges and operational issues. Medical Care 2001; 627-634 
21. Haley SM, Jette AM, Coster WJ, et al: Late Life Function and Disability Instrument: II. 
Development and evaluation of the function component. The Journals of Gerontology Series A: 
Biological Sciences and Medical Sciences 2002; 57:M217-M222 
22. Sayers SP, Jette AM, Haley SM, et al: Validation of the late‐life function and disability instrument. 
Journal of the American Geriatrics Society 2004; 52:1554-1559 
23. Denkinger MD, Igl W, Coll-Planas L, et al: Evaluation of the Short Form of the Late-Life Function 
and Disability Instrument in Geriatric Inpatients—Validity, Responsiveness, and Sensitivity to 
Change. J Am Geriatr Soc 2009; 57:309-314 
24. Abizanda P, López-Jiménez M, López-Torres J, et al: Validation of the Spanish Version of the Short-
Form Late-Life Function and Disability Instrument. J Am Geriatr Soc 2011; 59:893-899 
25. Beekman AT, Deeg DJ, van Tilburg T, et al: Major and minor depression in later life: a study of 
prevalence and risk factors. Journal of affective disorders 1995; 36:65-75 
26. Spitzer RL, Kroenke K, Williams JBW, et al: A Brief Measure for Assessing Generalized Anxiety 
Disorder: The GAD-7. Arch Intern Med 2006; 166:1092-1097 
27. Kroenke K, Spitzer RL,Williams JBW: The PHQ-9: Validity of a Brief Depression Severity 
Measure. J Gen Intern Med 2001; 16:606-613 
28. Chen TM, Huang FY, Chang C, et al: Using the PHQ-9 for depression screening and treatment 
monitoring for Chinese Americans in primary care. Psychiatric services 2006; 57:976-981 

11 
 
29. Paykel E, Myers J, Lindenthal J, et al: Suicidal feelings in the general population: a prevalence study. 
The British Journal of Psychiatry 1974; 124:460-469 
30. Chew LD, Bradley KA,Boyko EJ: Brief questions to identify patients with inadequate health literacy. 
Health (N Y) 2004; 11:12 
31. Horvath AO,Greenberg LS: Development and validation of the Working Alliance Inventory. Journal 
of counseling psychology 1989; 36:223 
32. Kim SC, Boren D,Solem SL: The Kim Alliance Scale: development and preliminary testing. Clinical 
Nursing Research 2001; 10:314-331 
33. Barnard J,Rubin DB: Miscellanea. Small-sample degrees of freedom with multiple imputation. 
Biometrika 1999; 86:948-955