; thomas sequist, md, mph ; daniel slater, md€¦ · department of medicine, brigham and women’s...
TRANSCRIPT
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Do Weight Management Programs Involving Health Coaches Improve Body Mass Index and Parent Empowerment for Children with Obesity or Who Are Overweight?
Elsie M. Taveras, MD, MPH1,2; Richard Marshall, MD3; Mona Sharifi, MD, MPH4; Earlene Avalon, PhD5; Lauren Fiechtner, MD, MPH1,6; Christine Horan, MPH1; Monica Gerber, MPH1; John Orav, PhD7; Sarah N.
Price, MPH1; Thomas Sequist, MD, MPH8; Daniel Slater, MD3
1 Division of General Academic Pediatrics, Department of Pediatrics, Massachusetts General Hospital for Children, Boston, Massachusetts 2 Department of Nutrition, Harvard School of Public Health; Boston, Massachusetts 3 Department of Pediatrics, Harvard Vanguard Medical Associates, Boston, Massachusetts 4 Section of General Pediatrics, Department of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 5 Boston Children’s Hospital, Boston, Massachusetts 6 Division of Gastroenterology and Nutrition, Department of Pediatrics, Mass General Hospital for Children, Boston, Massachusetts 7 Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts 8 Partners HealthCare System, Inc., Boston, Massachusetts
Original Project Title: Improving Childhood Obesity Outcomes: Testing Best Practices of Positive OutliersPCORI Project ID: IH-1304-6739 HSRProj: HSRP20143558ClinicalTrials.gov: NCT02124460
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To cite this document, please use: Taveras E, MD, Marshall R, Sharifi M, et al. 2018. Do Weight Management Programs Involving Health Coaches Improve Body Mass Index and Parent Empowerment for Children with Obesity or Who Are Overweight? Washington, DC: Patient-Centered Outcomes Research Institute (PCORI). https://doi.org/10.25302/3.2018.IH.13046739
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Table of Contents
ABSTRACT ................................................................................................................................................. 2
BACKGROUND .......................................................................................................................................... 3
ROLE OF THE PATIENT AND STAKEHOLDER ............................................................................................................... 4
METHODS .................................................................................................................................................. 6
Study Overview ......................................................................................................................................................... 6
Eligibility and Recruitment ........................................................................................................................................ 6
Enrollment ................................................................................................................................................................. 9
Blinding ...................................................................................................................................................................... 7
Intervention ............................................................................................................................................................... 7
Outcome Measures ................................................................................................................................................... 9
Statistical Analysis ................................................................................................................................................... 10
RESULTS ................................................................................................................................................. 10
Baseline Characteristics ........................................................................................................................................... 10
Body Mass Index Outcomes .................................................................................................................................... 13
Family-centered Outcomes ..................................................................................................................................... 13
Intervention Feasibility, Acceptability, and Unintended Consequences ................................................................. 18
DISCUSSION ........................................................................................................................................... 18
CONCLUSION ......................................................................................................................................... 21
REFERENCES .......................................................................................................................................... 22
APPENDIX .............................................................................................................................................. 26
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Abstract
Background: Novel approaches to care delivery that leverage clinical and community resources could
improve body mass index (BMI) and family-centered outcomes.
Objective: To examine the extent to which 2 clinical–community interventions improved child BMI z-
score, parent sense of empowerment, and child health-related quality of life (HR-QoL).
Methods: We conducted a 2-arm, randomized controlled trial from June 2014 to June 2016 with
measures at baseline and 1 year. We enrolled 721 children ages 2-12 years with a BMI ≥ 85th percentile
from 6 primary care practices of Atrius Health. Children were randomized to 1 of 2 arms: (1) enhanced
primary care (eg, electronic alerts and clinical decision support tools for pediatric weight management,
educational materials, a Neighborhood Resource Guide, and monthly text messages promoting behavior
change) or (2) enhanced primary care plus contextually tailored, individual health coaching (twice-
weekly text messages and 6 telephone or video contacts) to support behavior change and link families
to community resources. In intent-to-treat analyses, we used multiple imputation and linear repeated
measures models to examine 1-year changes in age- and sex-specific BMI z-score, HR-QoL as measured
by the Pediatric Quality of Life Inventory(PedsQL) version 4.0, and parent sense of empowerment.
Results: At 1 year, we obtained BMI z-scores from 664 children (92%) and parent-reported outcomes
from 657 parents (91%). Baseline mean (SD) child age was 8.0 (3.0) years; 35% were white, 33% black,
22% Hispanic, and 10% other; 44% lived in households with annual incomes < $50,000. We observed
significant improvements in BMI z-score in both the enhanced primary care group (–0.06 units; 95% CI:
–0.10, –0.02) and the enhanced primary care plus coaching group (–0.09 units; 95% CI: –0.13, –0.05)
but no statistically significant difference between the 2 arms (–0.02 units; 95% CI: –0.08, 0.03). Both
intervention arms also significantly improved parent sense of empowerment. Parents in the enhanced
primary care plus coaching group, but not in the enhanced care group, reported improvements in their
child’s HR-QoL (1.53 units; 95% CI: 0.51, 2.56). However, there were no significant differences between
the arms in either family-centered outcome.
Conclusions: Two interventions that included high-quality clinical care for obesity and linkages to
community resources resulted in improved family-centered outcomes for childhood obesity and
improvements in child BMI from baseline to 1 year, but there were no significant differences between
the 2 interventions.
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BACKGROUND
Childhood overweight and obesity place a significant burden on morbidity and quality of life. As
of 2012, in the United States, the prevalence of childhood overweight and obesity appears to have
plateaued and may even be decreasing among 2- to 5-year old children.1 Yet, overall prevalence remains
at historically high levels and substantial racial/ethnic disparities continue to persist, and, in the case of
Hispanic children, may even be widening.2-4 Health care system–based interventions to reduce obesity
have been somewhat effective but are often of limited effectiveness due to myriad social and
environmental barriers that impede improvement in obesity-related behaviors.5,6
An important but often overlooked aspect of interventions to improve obesity and reduce
disparities is the careful consideration of the socioenvironmental context in which decisions related to
health behaviors are being made—and in which behavior change is expected to occur. Neighborhood
socioeconomic characteristics and built-environment factors including food and physical activity can
significantly influence health behaviors and may contribute to disparities in childhood obesity.7-11
Understanding community levels of poverty and unemployment as well as individual, health-related
social problems could assist health practitioners in providing targeted resources and selecting referrals
to local health and social service agencies.12 Sophisticated geographic information systems (GIS)
analyses can provide community-level data on access to food establishments that could assist in meal-
planning discussions with families. GIS methods and community mapping could also provide information
about recreational spaces and playgrounds as well as transportation availability that might influence
parents’ decision making regarding their child’s physical activity options. Thus, characterizing the
environment can assist in developing a tailored clinical–community intervention that could be adapted
to an individual’s environment and needs.
Another underused approach to obesity management and disparities reduction is to identify
innovative strategies from positive outliers (also referred to in the literature as positive deviants).
Positive outliers are defined as individuals who have succeeded where many others have not to change
their health behaviors, reduce their body mass index (BMI), and develop resilience in the context of
adverse built and social environments.13,14 The premise of this positive outlier approach is that solutions
to problems that face a community often exist within that community, and that these successful
members of the community possess strategies that can be generalized and promoted to improve the
outcomes of others. Such individuals could help guide intervention development for other families in
their same neighborhoods who have struggled with behavior change.
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We designed the Connect for Health randomized controlled trial to leverage clinical and
community resources to improve obesity and family-centered outcomes. The intervention was built
upon practices of positive outlier families as well as strategies recommended by a diverse group of
stakeholders representing parents, children, pediatric clinicians, and community/public health
providers.15,16 We hypothesized that children randomized to the intervention arm to receive enhanced
primary care plus a contextually tailored intervention delivered by trained health coaches would have
greater reductions in BMI z-score compared with children randomized to a control group to receive
enhanced primary care plus nontailored health coaching. In this report, we summarize the main
outcomes of Connect for Health.
ROLE OF THE PATIENT AND STAKEHOLDER
Stakeholder Engagement
The design and implementation of Connect for Health was informed by a diverse group of
stakeholders representing clinicians, community and public health providers, parents, and children.
Members of the research and clinical team have long-standing collaborative relationships from
developing previous obesity interventions,17 and thus a key activity in preparation for this study was to
build a strong network of cross-collaboration among stakeholders who had not yet worked with each
other.
Using established methods on engagement18 and the Patient-Centered Outcome Research
Institute’s (PCORI’s) Methodology Standards,19 we worked with our clinician team to identify positive
outlier children and families who would be interested in participating in focus group discussions about
their experiences.15,16 Several children and parents from these focus groups were already participating
as ongoing members of our Parent and Youth Advisory Boards. Leaders and staff of youth-serving
community-based organizations, including the YMCA, provided critical input on the ideas and activities
we ultimately implemented. Based on principles of engagement, the research team delineated the role
of stakeholder participants as well as discussed, the relevance and importance of the work being
developed and the ways in which input from all stakeholders would be utilized. Keys to successful
implementation, incorporation of study activities into practice, and sustainability of project activities
rely on engagement of stakeholders at several critical points before and during the intervention. The
research and stakeholder group met on a monthly basis after the start of the study to discuss barriers
and facilitators to implementation.
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Parent and Youth Advisory Boards
Patient advisory committees have been increasingly used in health care systems to gather
patient-centered input on a variety of health care processes and to accelerate improvement.20 Engaging
patients as active participants in designing and conducting health care research is increasingly
recognized as critical to ensuring that the research questions posed and outcomes measured are
relevant and important to patients.21-23 In this study, we convened Parent and Youth Advisory Boards to
inform the study development. The Parent Board consisted of 11 parents whose children received care
in the participating pediatric practices and was led by a parent whose children received their care at 1 of
the practices. The Youth Board consisted of their children, who were between 10 and 12 years old. We
recruited board members from among focus group and interview participants in our formative work
with positive outlier children and their parents. Board members provided in-depth information about
their own successful strategies that could be generalized and promoted to improve the outcomes of
other families, advised the research team on the perspectives of potential trial participants, and
provided feedback on specific components of the study. The Board met in person 1 to 2 times in each
study year and communicated as needed more regularly to discuss study methods and implementation,
including recruitment, informed consent, outcome measures, data collection, incentives, retention, and
intervention design. Parents and youth on the advisory boards received a modest stipend for their
participation.
Learning From Positive Outliers
Prior to the start of the study, we conducted 5 focus groups with parents (n = 41) and 4 focus
groups with children (n = 21) who were identified from their electronic health record to have reduced or
maintained their BMI despite having had a history of a BMI ≥ 95th percentile. We have previously
published the methods and results of this formative work.12,13 Parents reported several practices that
facilitated their ability to help their child improve his or her BMI. Among them were (1) making changes
as a family rather than solely for the child; (2) implementing limits and rules around snacking, screen
time, and activity, and maintaining consistency around those rules; (3) being involved in the decision
making with their health care providers about their child’s weight management; (4) using more
immediate rather than long-term outcomes of weight management to motivate change; and (5)
maximally leveraging community resources to support behavior change. Children emphasized the value
of positive support from their family and the importance of their peer relationships in motivating their
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behavior change. Parents and children outlined a number of outcomes that mattered most to them
related to weight management, including not being bullied or teased, feeling good about oneself, fitting
into age-appropriate clothing, and being able to keep up with other children during a physical activity.
As a result, our intervention included an emphasis on social and emotional wellness. The feedback we
received specifically informed our outcome measures, the neighborhood resources we included in our
intervention, the youth-serving community-based organizations that we partnered with, and our
approach to coaching (eg, shared decision making and family-centered care were key training topics for
the health coaches).
METHODS
Study Overview
Connect for Health was conducted within 6 pediatric practices of Harvard Vanguard Medical
Associates (HVMA), a multispecialty group practice in eastern Massachusetts with many years of
experience with an electronic health record system and advanced team-based care model. The Connect
for Health study design, eligibility, and recruitment have been described in detail elsewhere.24 We
randomly assigned patients to 1 of 2 arms: (1) enhanced primary care or (2) enhanced primary care plus
contextually tailored, individual health coaching. The enhanced primary care group served as the control
arm even though these patients received some intervention previously incorporated into standard
practice at HVMA. The primary outcomes included improvements in child BMI z-score in addition to
family-centered outcomes that matter to parents and children, including pediatric health-related quality
of life and parental resource empowerment. All study activities were approved by the Institutional
Review Board at Partners Health Care System. The Connect for Health trial has been recorded in the
clinicaltrials.gov national registry of randomized trials.
Eligibility and Recruitment
Eligibility for Connect for Health included (1) child age 2.0-12.9 years old, (2) child BMI ≥ 85th
percentile for age and sex, and (3) family was not planning to leave HVMA within the study time frame.
Recruitment began in June 2014 and ended March 2015; follow-up ended for all participants in March
2016. At the time of a visit with a child between the ages of 2 and 12 years with a BMI > 85th percentile,
HVMA clinicians received a BestPractice® alert in the electronic health record that contained a link to
electronically refer the patient to the Connect for Health study (Appendix A). After receiving the referral,
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study staff sent each family a letter providing more information about the study and inviting the family
to participate. Research assistants then called parents to establish eligibility, explain the study, obtain
verbal consent, and complete a telephone survey. We then randomized participants and mailed parents
an enrollment letter informing them of their child’s intervention group.
Randomization
We randomized study participants from each of the participating pediatric practices in the order
they were enrolled, organizing the lists into blocks of 4 in order to maintain balance between the 2 study
arms in each of the practices. The lists were generated by the study biostatistician (JO) and maintained
by the study project manager (CH). Allocation concealment was preserved by central allocation by the
study program manager. Only the study project manager (CH) and study biostatistician (JO) had access
to the randomization lists—the study project manager to allocate participants to the proper
intervention and the study biostatistician to provide blinded reports of study progress and safety data.
Blinding
All study participants, referring pediatricians, and research staff performing assessments were
blinded to specific study hypotheses and to intervention assignment. Health coaches delivering the
intervention were not blinded but only worked with participants in the intervention arm.
Intervention
All pediatric primary care providers received a computerized clinical decision support (CDS) alert
during primary care visits identifying 2- to 12-year-old children with a BMI ≥ 85th percentile and 2
additional CDS tools to assist in the management and follow-up of children with overweight or
obesity.17,24,25 Clinicians also provided parents a comprehensive set of educational materials focusing on
specified behavioral targets to support self-guided behavior change. Both the CDS tools and educational
materials were based on the STAR trial and have been shown to be effective in improving child BMI.17
The materials focused primarily on decreases in screen time and sugar-sweetened beverages, improving
diet quality, increases in moderate and vigorous physical activity, and improvement of sleep duration
and quality. Based on our qualitative work with positive outlier families and feedback from our Parent
and Youth Advisory Boards, we also developed materials to promote social and emotional wellness.
Enhanced Primary Care (Control)
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Participants randomized to the enhanced primary care group were exposed to the clinical best
practices described above. In addition, participants received monthly text messages that contained links
to publicly available resources to support behavior change (eg, links to the Let’s Move! program).26
Participants also received a Neighborhood Resource Guide listing places that support healthy living in
their community and surrounding areas.
Enhanced Primary Care Plus Health Coaching
In the enhanced primary care plus coaching arm, families received individualized health
coaching tailored to their socioenvironmental context. Three trained health coaches contacted families
every other month for 1 year via telephone, videoconference (using Vidyo®, a HIPAA-compliant,
password-protected software offered for free to families), or in-person visits, according to parent
preference. These contacts were approximately 15 to 20 minutes in duration. Prior to the start of the
intervention, the 2 health coaches received training from a senior health educator (S.P.) in behavior
change theory and counseling, childhood obesity risk factors and management, providing family-
centered care,27 motivational interviewing,28,29 and shared decision making.30,31 Details of the health
coach training and quality assurance have been previously described.24 Families also received twice-
weekly text messages or emails in addition to monthly mailings.
Health coaches used a motivational interviewing style of counseling and shared decision-making
techniques30,31 to provide family-centered care in addressing childhood obesity risk factors and
management. At each contact, health coaches used an online community resource map developed for
the study32 to identify resources within each family’s community that could support behavior change,
including nutritional support (i.e., local farmers markets, supermarkets); places for physical activity (i.e.,
Boys and Girls Clubs, swimming pools, recreation centers, ice rinks, and YMCAs); and social support
services (i.e., Supplemental Nutrition Program for Women, Infants, and Children offices). In addition,
through a partnership with the YMCA, health coaches offered families a 1-month free family
membership to area YMCAs to encourage physical activity and community connections. Families were
also offered the opportunity to take part in 1 of 12 healthy grocery shopping programs throughout the
course of the intervention led by a community partner, Cooking Matters®, empowering families to cook
healthy meals and to learn ways to shop and eat well on a limited budget.
To engage parents and children in setting behavior change goals, health coaches used a
behavior change decision aid tool developed by our study team that helped families identify outcomes
that mattered most to them and potential motivators for engaging in behavior change. The tool
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facilitated shared decision making to determine parents’ goals and set the agenda for health coaching.
Following each contact, the health coach mailed the family a packet that included issues of a healthy
cooking magazine for children, handouts for parents about behavior change goals, and materials
designed specifically for children. Intervention materials for children were developmentally appropriate.
Parents also received interactive text messages twice weekly during the 1-year period. These messages
were based on the well-received text message campaign implemented in the STAR trial.33
Outcome Measures
We obtained height and weight from children’s electronic health record at baseline and at 1
year. In routine practice standardized across all 6 study sites, medical assistants measured children’s
weight, without shoes, using electronic, calibrated scales, and height using a stadiometer. We calculated
BMI, age- and sex-specific BMI z-scores, and BMI categories (BMI < 85th, BMI 85th through < 95th, BMI
> 95th through < 120% of 95th percentile, and BMI ≥ 120% of the 95th percentile).34
Parent-reported outcomes were assessed using telephone surveys at baseline and at 1 year.
Parents reported their child’s health-related quality of life using the 4 subscales of the PedsQL-4.0
(physical, emotional, social, and school).35,36 We also assessed parents’ sense of empowerment
(hereafter referred to as parental resource empowerment) using the child weight management subscale
of the Parent Resource Empowerment Scale.25 The 5 items in the scale assessed parents’ perceived
knowledge of resources, ability to access resources, comfort with accessing resources, knowledge of
how to find resources, and ability to acquire resources related to child weight management. For each
question, parents responded “strongly disagree,” “disagree,” “agree,” or “strongly agree,” which were
worth 1 to 4 points, respectively. Items were averaged to create a summary parental resource
empowerment score (range = 1-4). Cronbach’s α for this score was 0.87.37
Other Measures
Using questionnaires at baseline and at 1 year, we obtained each child’s race/ethnicity as well as
each parent’s educational attainment and height and weight, from which we calculated BMI. We also
assessed annual household income. To assess the feasibility of the study and parents’ acceptance of and
satisfaction with the intervention components, we asked parents to rate how satisfied they were with
several aspects of the program, including the text messages and information on community resources.
To assess unintended consequences, we also asked parents if their participation in the program affected
their satisfaction with their child’s health care services HVMA.
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Statistical Analysis
We performed analysis from June 2016 to October 2016. We analyzed distributions of
participant characteristics across the 2 study arms using t-test and chi-square tests and found them to
be balanced at baseline (p > 0.05). We anticipated that 338 children per arm would complete the study,
which would allow us to detect to detect a difference between arms in the change in BMI as small as
0.24 and to detect a difference between arms in the change in PedsQL of 2.5 points, with 90% power
and 5% type I error. As suggested by the recent US Preventive Service Task Force Draft Evidence Review
of childhood obesity management, BMI z-score changes in the range of 0.15 to 0.20 are considered to
constitute a clinically important benefit for many children.38 We performed multiple imputations using
chained equations to impute missing outcomes for the approximately 8% of the 721 enrollees who did
not have BMI outcomes at 1-year follow-up. In intent-to-treat analyses, we assessed the effect of the
intervention on BMI z-score, PedsQL-4.0 summary score, and the Parent Resource Empowerment score
using generalized linear repeated measure models to account for clustering by study site and within
each participant over time. We used analogous logistic repeated measures models to model the effect
of the intervention on the odds of being in a lower BMI category at follow-up compared with baseline.
We adjusted all models for HVMA site. We implemented the models using the GLIMMIX and GENMOD
procedures in SAS, version 9.4 (Cary, NC: SAS Institute).
RESULTS
Baseline Characteristics
Primary care clinicians referred 1752 children from 6 pediatric practices to the Connect for
Health study over a 9-month recruitment period. We attempted to contact the parents of 1545 children
to assess eligibility. We enrolled 721 children in the study; 361 participants were randomized to the
enhanced primary care group, and 360 families were assigned to the enhanced primary care plus
coaching group (Figure 1). During the intervention period, 1 participant withdrew from the study, citing
a lack of time for the study activities. At 1 year, we obtained follow-up BMI data from 664 children
(92.0%) and parent survey data from 667 parents (92.5%). Table 1 shows the characteristics of the study
sample. Baseline mean (SD) age was 8.0 (3.0) years; 35.0% were white, 33.3% black, 21.8% Hispanic, and
9.9% other; 45.4% lived in households with annual incomes < $50,000. There were no significant
differences in any baseline characteristics between the patients randomized to each intervention arm.
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Table 1. Baseline Characteristics of Participants in the Connect for Health Study, Overall and by Intervention Assignment
Child Characteristics
Overall
(N = 721)
Enhanced
Primary Care
(N = 361)
Enhanced Primary
Care + Coaching (N
= 360)
p-
Value†
Mean (SD) or N (%)
Age, years (SD) 8.0 (3.0) 8.0 (3.0) 8.1 (3.0) 0.45
Gender, female 368 (51.0) 188 (52.1) 180 (50.0) 0.63
Race/ethnicity 0.22
Non-Hispanic white 252 (35.0) 134 (37.1) 118 (32.9)
Non-Hispanic black 240 (33.3) 110 (30.5) 130 (36.2)
Hispanic 157 (21.8) 85 (23.5) 72 (20.1)
Other 71 (–9.9) 32 (8.9) 39 (10.9)
Body mass index, kg/m2 (SD) 22.9 (4.8) 22.8 (4.6) 23.0 (4.9) 0.67
Body mass index, z-score (SD) 1.88 (0.5) 1.91 (0.5) 1.87 (0.5) 0.40
Body mass index category 0.65
85th to < 95th percentile 263 (36.5) 126 (34.9) 137 (38.1)
95th to < severe obesity* 303 (42.0) 157 (43.5) 146 (40.6)
Severe obesity 155 (21.5) 78 (21.6) 77 (21.4)
Parent and Household
Parent age, years (SD) 38.4 (7.2) 38.7 (7.3) 38.1 (7.1) 0.29
Parent body mass index category 0.90
BMI < 25 kg/m2 161 (22.8) 79 (22.6) 82 (23.1)
BMI 25 through 29 kg/m2 236 (33.5) 120 (34.3) 116 (32.7)
BMI ≥ 30 kg/m2 308 (43.7) 151 (43.1) 157 (44.2)
Parent educational
attainment, l ess than
college graduate
356 (49.3)
186 (51.5)
170 (47.2)
0.25
Annual household income,
< $50,000
327 (45.4)
150 (41.6)
177 (49.2)
0.07
* Severe obesity is defined as a body mass index ≥ 120% of the age- and sex-specific 95th percentile. † p-Values are derived from t-tests for continuous variables and chi-square tests for categorical variables.
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Body Mass Index Outcomes
Table 2 shows participants’ unadjusted and adjusted changes in BMI z-score and in BMI category
(ie, being in a lower category from baseline to 1-year follow-up). We observed significant changes in BMI
z-score in both the enhanced primary care group (–0.06 units; 95% CI: –0.10, –0.02) and the enhanced
primary care plus coaching group (–0.09 units; 95% CI: –0.13, –0.05). Although we observed slightly
more improvement in BMI z-score among the enhanced primary care plus coaching group compared
with the enhanced primary care group alone, there were no statistically significant differences between
the 2 intervention arms (–0.02 units; 95% CI: –0.08, 0.03).
At 1-year follow-up, we found that 9.3% of children in the enhanced primary group and 11.6% of
children in the enhanced primary care plus coaching group no longer had a BMI in the overweight or
obese range. Overall, we observed higher odds in both the enhanced primary care group (OR: 1.18; 95%
CI: 1.03, 1.35) and the enhanced primary care plus coaching group (1.23; 95% CI: 1.08, 1.40) of being in a
lower BMI category than participants were at baseline; however, there was no statistically significant
difference in odds between the 2 intervention arms (p = 0.70).
We conducted post hoc analyses to examine whether our observations of improved BMI z-score
in both intervention arms could be explained by an underlying temporal trend toward improvement.
Among 560 children with BMI z-scores available 1 year prior to baseline, at baseline, and at 1-year
follow-up, we found that BMI z-score was increasing in the year prior to enrollment in the enhanced
primary care group (0.23 units; 95% CI: 0.18, 0.29) and the enhanced primary care plus coaching group
(0.16 units; 95% CI: 0.11, 0.22) and then decreased in both groups in the year following enrollment
(Figure 2).
Family-centered Outcomes
Table 3 shows changes in participants’ health-related quality of life and in parental resource
empowerment during the 1-year intervention. Parents in the enhanced primary care plus coaching
group (1.53 units; 95% CI: 0.51, 2.56), but not in the enhanced care alone group (0.65 units; 95% CI: –
0.38, 1.67), reported improvements in their child’s health-related quality of life, but the clinical
significance of this magnitude of improvement is uncertain. Parental resource empowerment increased
in both intervention arms (Table 3); however, there were no statistically significant differences in either
family-centered outcome between the 2 intervention arms.
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Table 2. Changes in Body Mass Index Z-Score and Categories From Baseline to 1 Year, by Intervention Assignment (N = 721)
Body Mass Index Outcomes
Baseline 1-Year
Follow-up
Adjusted
Mean Change
P Adjusted
Difference**
P
Adjusted Mean (SD) β (95% CI)
BMI z-score, units
Enhanced primary care 1.91 (0.56) 1.85 (0.58) –0.06 (–0.10, –0.02) < .01 Reference
Enhanced primary care + coaching 1.87 (0.56) 1.79 (0.58) –0.09 (–0.13, –0.05) < .01 –0.02 (–0.08, 0.03) 0.39
Baseline
1-Year
Follow-up
Adjusted Odds of Being at
a Lower Category at
Follow-up
P Difference in ORs P
BMI Category, % % OR (95% CI)
Enhanced primary care
< 85th percentile 0 9.3
1.18 (1.03, 1.35)
0.02
Reference
85th to < 95th percentile 34.9 28.4
95th to < severe obesity 43.4 39.4
≥ Severe obesity 21.6 22.8
Enhanced primary care + coaching
< 85th percentile 0 11.6
1.23 (1.08, 1.40)
0.002
1.04 (0.86, 1.25)
0.70
85th to < 95th percentile 38.1 28.5
95th to < severe obesity 40.6 37.4
≥ Severe obesity 21.4 22.5
** Adjusted estimates from repeated measures model. Adjusted for pediatric practice site.
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Table 3. Changes in Pediatric Health-related Quality of Life and Parental Resource Empowerment From Baseline to 1 Year, by Intervention Assignment (N = 721)
Child Health-related
Quality of Life Outcomes*
Baseline 1-year
Follow-up
Adjusted
Mean Change
P Adjusted
Difference
P
Adjusted Mean (SD) β (95% CI)
PedsQL summary score, units
Enhanced primary care 86.0 (11.2) 86.6 (11.4) 0.65 (–0.38, 1.67) 0.21 Reference
Enhanced primary care + coaching 85.3 (11.2) 86.9 (11.4) 1.53 (0.51, 2.56) < .01 0.89 (–0.56, 2.33) 0.23
PedsQL psychosocial score,** units
Enhanced primary care 83.7 (12.7) 84.8 (13.2) 1.05 (–0.15, 2.25) 0.09 Reference
Enhanced primary care + coaching 83.1 (12.7) 84.9 (13.0) 1.80 (0.62, 2.98) < .01 0.75 (–0.93, 2.43) 0.38
PedsQL physical score, units
Enhanced primary care 90.3 (12.5) 90.0 (13.2) –0.29 (–1.61, 1.03) 0.67 Reference
Enhanced primary care + coaching 89.7 (12.8) 90.5 (13.3) 0.76 (–0.58, 2.09) 0.27 1.04 (–0.82, 2.91) 0.27
Parent-centered outcome
Parental resource empowerment, units
Enhanced primary care 2.9 (0.5) 3.1 (0.6) 0.29 (0.22, 0.35) < 0.01 Reference
Enhanced primary care + coaching 3.0 (0.5) 3.2 (0.6) 0.22 (0.15, 0.28) < 0.01 0.07 (–0.02, 0.16) 0.14
* All PedsQL scores have a possible range of 0 to 100. Higher scores indicate better health-related quality of life. ** To create the psychosocial health score (15 items), the mean is computed as the sum of the items divided by the number of items answered
in the Emotional, Social, and School Functioning Scale.
18
Table 4. Parents’ Perceptions of the Feasibility and Acceptability of the Connect for Health ( C 4 H )
Interventions
Parent Perception % of Respondents
Feasibility and Acceptability Measures in Both Intervention Arms (n = 657)
Parent very satisfied with overall experience in C4H program, %
Enhanced primary care 43
Enhanced primary care + health coaching 73
Increased satisfaction with care at HVMA*, %
Enhanced primary care 48
Enhanced primary care + health coaching 63
Would recommend C4H to friends and family, %
Enhanced primary care 80
Enhanced primary care + health coaching 93
Received text messages or emails from C4H, %
Enhanced primary care 91
Enhanced primary care + health coaching 100
Parent very satisfied with content of C4H text messages or emails, %
Enhanced primary care 53
Enhanced primary care + health coaching 72
Received information from C4H about resources in the community
Enhanced primary care 60
Enhanced primary care + health coaching 96
Parent very satisfied with information he or she received about resources in the community
Enhanced primary care 66
Enhanced primary care + health coaching 76
Feasibility and Acceptability Measures in Enhanced Primary Care + Health Coaching Arm (n = 325)
Met with health coach (in person or via phone or video) 96
Received materials via email or mail from health coach 98
Attend a Cooking Matters Healthy Shopping program 18
Tried a new YMCA membership 23
Received copies of Chop Chop magazine 87
Completed all 6 visits** 65
Participated in a video visit** 28
* Harvard Vanguard Medical Associates (HVMA) is the group practice where study participants received primary care.
** n = 360 (all participants who were randomized).
19
Intervention Feasibility, Acceptability, and Unintended Consequences
We aimed for participants in the enhanced primary care group to receive monthly text messages
and a Neighborhood Resource Guide. In follow-up interviews, 91% of parents reported that they
received the text messages and 53% were satisfied with their content. Although we mailed the
Neighborhood Resource Guide to 100% of participants, only 60% remembered receiving it, and of them,
66% reported being very satisfied with its content.
For the enhanced primary care plus coaching group, we aimed to deliver biweekly text
messages, 6 contacts with a health coach, and tailored information about community resources from
the health coach. Participants were also invited to join their local YMCA (with a free 1-month
membership) and attend a Cooking Matters workshop at their health center. In follow-up interviews,
100% of participants reported receiving the text messages and 72% were very satisfied with their
content. Among the 360 participants in the enhanced primary plus health coaching group, 65%
completed all 6 visits with a health coach; 96% of participants reported receiving neighborhood resource
information and 76% were very satisfied with the information. Based on follow-up interviews, 81
parents (23%) reported joining their local YMCA and 64 parents (18%) reported attending 1 of the
Cooking Matters workshops.
Overall, 48% of participants in the enhanced primary care arm and 63% of participants in the
enhanced primary care plus health coaching arm reported that participation in Connect for Health
increased their satisfaction with their child’s health care services. Only 7 (1.1%) participants reported
that their participation in the Connect for Health program decreased their satisfaction with their child’s
health care services, and there were no differences across study arms.
DISCUSSION
Decisional Context
In this randomized controlled trial, we found that 2 interventions that delivered enhanced
primary care and leveraged clinical and community resources for childhood obesity support resulted in
modest improvements in child BMI z-score and greater resolution of elevated BMIs. While the
magnitude of reduction in BMI z-score was higher in the intervention group that additionally received
interactive, contextually tailored health coaching, the difference compared with the group exposed to
enhanced care alone was not statistically or clinically significant. Both interventions also led to improved
family-centered outcomes including child health-related quality of life and parental resource
empowerment; however, there were no statistically significant differences in either family-centered
20
outcome between the 2 intervention groups. Overall, the intervention components were feasible to
deliver, acceptable to parents, and did not have adverse effects on parent’s perceptions of their child’s
health care services.
Comparison of Intervention Groups
The Connect for Health study was designed with the hypothesis that the intervention group
receiving both enhanced primary care and health coaching tailored to children’s community resources
and social context would be more effective than the group receiving enhanced primary care alone. Yet,
our findings did not support this hypothesis, and there are several potential reasons. First, the enhanced
primary care group was not a typical “usual care” control group. The practices where we delivered the
study had already made several updates to their electronic health record to include clinical decision
support tools and to provide families with educational materials for self-guided behavior change
support. It would have been unethical to undo these practice changes once they were already
established and after evidence supported their effectiveness in improving child BMI.17 Second, based on
feedback from our Parent and Youth Advisory Boards, we made the decision to add content on social
and emotional wellness to existing parent educational materials and to provide passive information in
the form of a booklet on neighborhood resources. Both of these enhancements to the materials
available to the group that did not receive health coaching could have further strengthened the effects
of the control group on improving BMI. Third, it is possible that the number of contacts, frequency, or
content of the health coaching provided in the enhanced primary care plus coaching group was
insufficient to produce greater effects than that of the enhanced primary care group alone.
While our findings did not support the original hypothesis of a greater intervention effect among
the group that was individually coached, our findings do suggest that both intervention groups
experienced improved BMI. Without a traditional control group, our results could be attributed to
temporal trends or regression to the mean. Post hoc analyses of BMI changes prior to and after
enrollment in the trial suggest, however, that the temporal trend was for BMI z-score to continue
increasing after enrollment. Thus, our results are unlikely due to secular trends, but regression toward
the mean may still be a possibility.
Study Results in Context
The magnitudes of effect on BMI z-score in our study (e.g., –0.06 to –0.09 units) are similar and
only modestly higher than those previously summarized (–0.04 units) in a metaanalysis of brief
21
interventions in primary care.39 While interventions with these magnitudes of effect appear to have
interrupted the increasing BMI trends in our population, questions remain about their clinical
significance. There is currently a lack of direct evidence for any specific weight loss threshold that has
clinical significance.38 An expert panel in Germany has suggested that a BMI z-score reduction of 0.20
units is associated with clinically significant improvement.40 Other studies suggest that changes of 0.15
BMI z-score units led to more healthful cardio-metabolic profiles.38 As suggested by the recent US
Preventive Service Task Force Draft Evidence Review of childhood obesity management, regardless of
the threshold of clinical significance chosen, simply arresting gain in excess BMI likely constitutes a
clinically important benefit for many children.38
In addition to BMI, this study examined family-centered outcomes of importance to parents and
children and informed by a Parent Advisory Board and a Youth Advisory Board. We found that parent-
reported child health-related quality of life improved by 1.53 units among the enhanced primary care
plus coaching group and appeared to be driven by large improvements—comparably higher than
previous pediatric obesity trials39—in the psychosocial score of the PedsQL. These effects in the health
coaching group were not significantly greater than those of the enhanced primary care group. These
findings suggest that the educational content delivered in both intervention arms related to social and
emotional wellness, including content on stress reduction, positive thinking, and bullying, may have
driven the observed improvements in child quality of life. Both interventions also improved parents’
perception of empowerment related to their child’s weight management—a novel, family-centered
measure that has been shown to drive changes in food intake, physical activity, and screen-related
parenting among parents of children with obesity.41,42
Implementation
Given the positive temporal trends across both intervention groups but lack of superiority of the
enhanced primary care plus coaching group, pediatric primary practices may want to consider
implementing just the intervention components in the enhanced primary care group. These included
computerized, clinical decision support tools for primary care providers in the electronic health record, a
comprehensive set of educational materials focused on specified behavioral targets to support family
self-guided behavior change, text messages, and a list of neighborhood resources that support healthy
living in the community. Implementation of these components would require (1) clinician education and
academic detailing that could be delivered through a learning collaborative on the clinical decision
support tools and educational materials, (2) clinical informatics support to program the clinical decision
22
support tools into the electronic health records locally or centrally, (3) use of a platform to enroll
families in a text messaging campaign and deliver automated messages, and (4) development of an
inventory of local neighborhood resources that could support family behavior change.
Limitations and Generalizability
As in any study, this one is subject to potential limitations. First, as previously described, our
post hoc analyses showing an increasing trend in BMI z-score prior to intervention enrollment suggest
either that we were successful in reversing an upward trend or that our results reflect regression to the
mean; we are unable to rule out the possibility of the latter. Second, the study setting—a multisite
delivery system with a robust electronic health record—may not be representative of many smaller
pediatric practices in the United States and beyond. However, as a relatively large medical group, HVMA
is a typical primary care setting for many children. Moreover, meaningful use incentives are promoting
increases in electronic health record adoption in both large and small pediatric practices.43 Thus the
Connect for Health interventions are likely to generalize to more and more pediatric settings in the
future.
Subpopulations Considerations
Our intervention did not appear to decrease the percentage of children with severe obesity.
Previous studies have suggested that the magnitude of decreases in net daily energy intake necessary
for children with severe obesity to achieve a healthy weight is considerably greater than is feasible with
the pediatric weight management that can be delivered in primary care–based interventions such as
Connect for Health.44,45 Our findings support the urgent recommendation for evidence-based, more
aggressive weight management approaches for children with severe obesity.45
CONCLUSION
Two interventions that included a package of high-quality clinical care for obesity and linkages to
community resources resulted in improved parent-reported outcomes for childhood obesity and
improvements in child BMI. While individualized health coaching led to improvements in health-related
quality of life, it did not have significantly greater effects on child BMI than did enhanced primary care
alone.
23
References
1. Ogden CL, Carroll MD, Lawman HG, et al. Trends in obesity prevalence among children and
adolescents in the United States, 1988-1994 through 2013-2014. JAMA. 2016;(315):2292-2299.
2. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United
States, 2011-2012. JAMA. 2014;(311):806-814.
3. Olds T, Maher C, Zumin S, et al. Evidence that the prevalence of childhood overweight is
plateauing: data from nine countries. IJPO. 2011;(6):342-360.
4. Taveras EM, Gillman MW, Kleinman K, Rich-Edwards JW, Rifas-Shiman SL. Racial/ethnic
differences in early-life risk factors for childhood obesity. Pediatrics. 2010;(125):686-695.
5. Dietz WH, Baur LA, Hall K, et al. Management of obesity: improvement of health-care training
and systems for prevention and care. Lancet. 2015; (9986):2521-2533.
6. Smedley BD, Syme SL, eds. Promoting Health: Intervention Strategies From Social and Behavioral
Research. Washington, DC: National Academies Press; 2000.
7. Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC. The built environment and
obesity. Epidemiol Rev. 2007;(29):129-143.
8. Fiechtner L, Block J, Duncan DT, et al. Proximity to supermarkets associated with higher body
mass index among overweight and obese preschool-age children. J Prev Med (Wilmington).
2013;(56):218-221.
9. Duncan DT, Sharifi M, Melly SJ, et al. Characteristics of walkable built environments and BMI z-
scores in children: evidence from a large electronic health record database. Environ Health Perspect.
2014;(122):1359-1365.
10. Christine PJ, Auchincloss AH, Bertoni AG, et al. Longitudinal associations between neighborhood
physical and social environments and incident type 2 diabetes mellitus: the Multi-ethnic Study of
Atherosclerosis (MESA). JAMA Internl Med. 2015; (8):1311-1320.
11. Sharifi M, Sequist TD, Rifas-Shiman SL, et al. The role of neighborhood characteristics and the
built environment in understanding racial/ethnic disparities in childhood obesity. J Prev Med
(Wilmington). 2016;(91):103-109.
12. Fleegler EW, Lieu TA, Wise PH, Muret-Wagstaff S. Families’ health-related social problems and
missed referral opportunities. Pediatrics. 2007;119:e1332-e1341.
13. Bradley EH, Curry LA, Ramanadhan S, Rowe L, Nembhard IM, Krumholz HM. Research in action:
using positive deviance to improve quality of health care. Implement Sci. 2009;(4):25.
24
14. Sharifi M, Marshall G, Marshall R, et al. Accelerating progress in reducing childhood obesity
disparities: exploring best practices of positive outliers. J Health Care Poor Underserved. 2013;(24):193-
199.
15. Sharifi M, Marshall G, Goldman R, et al. Exploring innovative approaches and patient-centered
outcomes from positive outliers in childhood obesity. Acad Pediatr. 2014;(14):646-655.
16. Sharifi M, Marshall G, Goldman RE, Cunningham C, Marshall R, Taveras EM. Engaging children in
the development of obesity interventions: exploring outcomes that matter most among obesity positive
outliers. Patient Educ Couns. 2015; (11):1393-1401
17. Taveras EM, Marshall R, Kleinman KP, et al. Comparative effectiveness of childhood obesity
interventions in pediatric primary care: a cluster-randomized clinical trial. JAMA Pediatr. 2015;(169):535-
542.
18. Guise JM, O’Haire C, McPheeters M, et al. A practice-based tool for engaging stakeholders in
future research: a synthesis of current practices. J Clin Epidemiol. 2013;(66):666-674.
19. PCORI Methodology Committee . The PCORI Methodology Report. Patient-Centered Outcomes
Reasearch Institute; 2013. Available from http://www.pcori.org/research-we-support/research-
methodology-standards.
20. Ponte PR, Conlin G, Conway JB, et al. Making patient-centered care come alive: achieving full
integration of the patient’s perspective. J Nurs Adm. 2003;(33):82-90.
21. Domecq JP, Prutsky G, Elraiyah T, et al. Patient engagement in research: a systematic review.
BMC Health Serv Res. 2014;(14):89.
22. Concannon TW, Fuster M, Saunders T, et al. A systematic review of stakeholder engagement in
comparative effectiveness and patient-centered outcomes research. J Gen Intern Med. 2014;(29):1692-
1701.
23. Frank L, Forsythe L, Ellis L, et al. Conceptual and practical foundations of patient engagement in
research at the Patient-centered Outcomes Research Institute. Qual Life Res. 2015;(5):1033-1041.
24. Taveras EM, Marshall R, Sharifi M, et al. Connect for Health: design of a clinical-community
childhood obesity intervention testing best practices of positive outliers. Contemp Clin Trials.
2015;(45):287-295.
25. Taveras EM, Marshall R, Horan CM, et al. Improving children’s obesity-related health care
quality: process outcomes of a cluster-randomized controlled trial. Obesity (Silver Spring). 2014;(22):27-
31.
25
26. Hale L, Do DP, Basurto-Davila R, et al. Does mental health history explain gender disparities in
insomnia symptoms among young adults? Sleep Med. 2009;(10):1118-1123.
27. Wells N, Bronheim S, Zyzanski S, Hoover C. Psychometric evaluation of a consumer-developed
family-centered care assessment tool. Matern Child Health J. 2015;(9):1899-909
28. Miller WR, Rollnick S. Motivational Interviewing: Preparing people for change (2nd Ed.). New
York, NY: Gulford Press;. 2002.
29. Rollnick S, Miller R, Butler CC. Motivational Interviewing in Health Care: Helping Patients Change
Behavior.NewYork, NY: Taylor & Francis Group; 2008.
30. Stacey D, Légaré F, Col NF, Bennett CL, Barry MJ, Eden KB, Holmes-Rovner M, Llewellyn-Thomas
H, Lyddiatt A, Thomson R, Trevena L, Wu JHC. Decision aids for people facing health treatment or
screening decisions. Cochrane Database of Systematic Reviews 2014, Issue 1. Art. No.: CD001431. DOI:
10.1002/14651858.CD001431.pub4.
31. Braddock CH III, Edwards KA, Hasenberg NM, Laidley TL, Levinson W. Informed decision making
in outpatient practice: time to get back to basics. JAMA. 1999;(282):2313-2320.
32. Fiechtner L, Sharifi M, Marshall R, et al. Use of geographic information systems and electronic
health records to integrate primary care with community assets to reduce childhood obesity. Oral
presentation at: The Obesity Society Annual Meeting; November 2014; Boston, MA.
33. Price S, Ferisin S, Sharifi M, et al. Development and implementation of an interactive text
messaging campaign to support behavior change in a childhood obesity randomized controlled trial. J
Health Commun. 2015;(20):843-850.
34. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth charts for the United States: Methods
and development. National Center for Health Statistics. Vital Health Stat 11(246). 2002.
35. Varni JW, Limbers CA, Burwinkle TM. Parent proxy-report of their children’s health-related
quality of life: an analysis of 13,878 parents’ reliability and validity across age subgroups using the
PedsQL 4.0 Generic Core Scales. Health Qual Life Outcomes. 2007;(5):2.
36. Varni JW, Limbers CA, Burwinkle TM. How young can children reliably and validly self-report
their health-related quality of life? an analysis of 8591 children across age subgroups with the PedsQL
4.0 Generic Core Scales. Health Qual Life Outcomes. 2007;(5):1.
37. Lim J, Davison KK, Jurkowski JM, et al. Correlates of resource empowerment among parents of
children with overweight or obesity. Child Obes. 2017; 13(1): 63-71.
26
38. O’Connor EA, Evans CV, Burda BU, Walsh ES, Eder M, Lozano P. Screening for Obesity and
Intervention for Weight Management in Children and Adolescents: A Systematic Evidence Review for the
U.S. Preventive Services Task Force. JAMA. 2017;317(23):2427-2444.
39. Sim LA, Lebow J, Wang Z, Koball A, Murad MH. Brief primary care obesity interventions: a meta-
analysis. Pediatrics. 2016;(138):4.
40. Wiegand S, Keller KM, Lob-Corzilius T, et al. Predicting weight loss and maintenance in
overweight/obese pediatric patients. Horm Res Paediatr. 2014;(82):380-387.
41. Davison KK, Jurkowski JM, Li K, Kranz S, Lawson HA. A childhood obesity intervention developed
by families for families: results from a pilot study. Int J Behav Nutr Phys Act. 2013;(10):3.
42. Jurkowski JM, Lawson HA, Green Mills LL, Wilner PG III, Davison KK. The empowerment of low-
income parents engaged in a childhood obesity intervention. Fam Community Health. 2014;(37):104-
118.
43. Boughn J. Centers for Medicare and Medicaid Services website. Medicaid EHR Incentive
Program: Making Meaningful Use Work for Pediatricians.
http://www.cms.gov/eHealth/ListServ_PediatriciansMedicaidMeaningfulUse.html. Accessed June 13,
2014.
44. Hall KD, Butte NF, Swinburn BA, Chow CC. Dynamics of childhood growth and obesity:
development and validation of a quantitative mathematical model. Lancet Diabetes Endocrinol.
2013;(1):97-105.
45. Dietz WH. Are we making progress in the prevention and control of childhood obesity? it all
depends on how you look at it. Obesity (Silver Spring). 2016;(24):991-992.
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Appendix A.
BestPractice® alert in the electronic health record that contained a link to electronically refer the
patient to the Connect for Health study.
28
Appendix B. Connect 4 Health Intervention Text Messages
Self-monitoring questions
C4H: How many hours did your child sleep last night? *Please text back your response* Thanks! C4H: How many hours or minutes did your child watch or use screen media yesterday? *Please text back your response* Thanks! C4H: How many hours or minutes did your child do physical activity yesterday? *Please text back your response* Thanks! C4H: How many sugary drinks did your child have yesterday? *Please text back your response* Thanks! C4H: In the past week, have you been concerned about your child feeling nervous, sad, or worried? Please text back yes or no. Thank you. C4H: About how many times in the last week did your child eat fast food? *Please text back your response*Thanks!
Feedback messages
C4H: Well-rested kids make for happier parents, more sleep makes for better moods. 2 yr olds need 13 hrs, 3-4 yr olds 11-13 hrs & 6-12 yr olds 10+ hrs of sleep. C4H: Your child’s doctor suggests less than 2 hours a day of screentime. This is time on TV, DVDs, movies, computer & video games. Handheld devices count too! C4H: There are 1,440 minutes in a day. Head outside and run around for 60 of them. You can split that time up through the day, or do it all at once. C4H: Aim for zero! Drinking 0 sugary drinks is best for kids. Give water when they are thirsty and a choice of water or lowfat white milk with meals. C4H: Make time to relax and breathe. With your child, take long, slow, deep breaths in while counting to 3. Then let the breath out slowly, counting to 3. C4H: Limiting fast food is good for everyone. If it’s a regular part of your family’s diet, think how to cut back on the times per month you eat it. C4H: Have you explored community resources that support your family’s healthy living? Check out the Y, farmers market & more! Ask your health coach for ideas. C4H: Frozen, fresh, canned? For fruits & veggies, they’re all good! Take 30 seconds and learn more: http://cookingmatters.org/tips/video-fresh-frozen-or-canned
29
Copyright© 2018 Massachusetts General Hospital. All Rights Reserved
Disclaimer:
The [views, statements, opinions] presented in this report are solely the responsibility of the author(s) and do not necessarily represent
the views of the Patient-Centered Outcomes Research Institute® (PCORI®), its Board of Governors or Methodology Committee.
Acknowledgement:
Research reported in this report was [partially] funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Award (IH-
1304-6739).