Contemporary Clinical Trials 43 (2015) 142–154

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Contemporary Clinical Trials

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Investigating within-day and longitudinal effects of maternal stress onchildren's physical activity, dietary intake, and body composition:Protocol for the MATCH study

Genevieve F. Dunton ⁎, Yue Liao, Eldin Dzubur, AdamM. Leventhal, Jimi Huh, Tara Gruenewald, GaylaMargolin,Carol Koprowski, Eleanor Tate, Stephen IntilleDepartment of Preventive Medicine, University of Southern California, 2001 N. Soto Street, 3rd floor, Rm 302E, MC 9239, Los Angeles, CA 90033-9045, USA

⁎ Corresponding author.E-mail address: dunton@usc.edu (G.F. Dunton).

http://dx.doi.org/10.1016/j.cct.2015.05.0071551-7144/© 2015 Elsevier Inc. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 4 December 2014Received in revised form 11 May 2015Accepted 13 May 2015Available online 15 May 2015

Keywords:ObesityPsychosocial stressPhysical activityDietary intakeChildren

Parental stress is an understudied factor that may compromise parenting practices related to children's dietaryintake, physical activity, and obesity. However, studies examining these associations have been subject tomethodological limitations, including cross-sectional designs, retrospectivemeasures, a lack of stress biomarkers,and the tendency to overlookmomentary etiologic processes occurringwithin each day. This paper describes therecruitment, data collection, and data analytic protocols for the MATCH (Mothers And Their Children's Health)study, a longitudinal investigation using novel real-time data capture strategies to examinewithin-day associationsof maternal stress with children's physical activity and dietary intake, and how these effects contribute to children'sobesity risk. In theMATCH study, 200mothers and their 8 to 12 year-old children are participating in 6 semi-annualassessment waves across 3 years. At each wave, measures for mother–child dyads include: (a) real-time EcologicalMomentary Assessment (EMA) of self-reported daily psychosocial stressors (e.g., work at a job, family demands),feeling stressed, perceived stress, parenting practices, dietary intake, and physical activity with time and locationstamps; (b) diurnal salivary cortisol patterns, accelerometer-monitored physical activity, and 24-hour dietaryrecalls; (c) retrospective questionnaires of sociodemographic, cultural, family, and neighborhood covariates; and(d) height, weight, and waist circumference. Putative within-day and longitudinal effects of maternal stress onchildren's dietary intake, physical activity, and body composition will be tested through multilevel modeling andlatent growth curve models, respectively. The results will inform interventions that help mothers reducethe negative effects of stress on weight-related parenting practices and children's obesity risk.

© 2015 Elsevier Inc. All rights reserved.

1. Introduction

The prevalence of childhood overweight and obesity has increaseddramatically over the past thirty years [1], and both of these conditionsare associatedwith serious health risks from childhood onward includingmetabolic and cardiovascular disorders [2–5]. Parents are thought tohave a significant influence over the energy balance-related behaviorsof their children, including physical activity and dietary intake [6–8].However, results from family-focused obesity trials emphasizing parentaleducation and skills training have been limited and inconsistent [9,10].Parental stress is an understudied, yet theoretically-relevant, factor thatmay compromise effective family functioning, emotional dynamics, andpractices related to health — all of which, in turn, may increase risk ofoverweight and obesity in children. As maternal employment rateshave risen dramatically in the past few decades [11], the struggle tobalance work and family demands can elevate psychological stress

[12,13], which may lead to heightened obesity risk [14,15]. To date,only a few known studies have directly examined the relation betweenparental stress and obesity risk in children. Koch et al. [16] found thatparent-reported stressful life events, worries, and overall stress wereassociated with greater risk of obesity in children. Stenhammar et al.[17] found that maternal but not paternal reports of family stresswere related to increased risk of overweight in young children. Moenset al. [18] found that families with overweight children experiencemore parenting stress. In addition, Lytle et al. [19] found that parentalstress was positively related to children's BMI z-score for overweightparents only.

Research in this area is limited by cross-sectional research designs,retrospective measures, the failure to assess stress biomarkers such ascortisol, and a lack of measurement of children's dietary intake andphysical activity. Past studies focus on how parents' usual or averagelevels of stress—retrospectively summarized over the past few weeks—relate to children's obesity risk [16,17,20]. However, the effects of parentalstress on children's behaviorsmay operate on a shorter time frame. Levelsof parental stress may vary across the day, and this within-day variation

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in parental stress may be associated with within-day variation inchildren's activity and food intake. For instance, amothermay be stressedwhen coming home from work at 4 pm on a given day, which compro-mises her ability to prepare a healthy dinner for her children or encouragethem to be physically active. The failure to account for within-dayvariation by previous studies in this area is akin to committing anecological fallacy in epidemiological research [21]. These gaps haveleft a relatively unrefined picture of the mechanisms underlying,amplifying, and buffering the parental stress-child obesity link, andhave hampered the translation of findings into successful parent-focused programs to prevent and treat child obesity.

To address these limitations, the Mothers And Their Children'sHealth (MATCH) study is testingwhether the putative effects of parentalstress on children's physical activity and behaviors operate throughwithin-day processes that contribute to children's long-term obesityrisk over time (see Fig. 1). The within-day component of the modelproposes that elevated maternal stressful states at any given point inthe day (a) compromise subsequent weight-related parenting behaviors(i.e., limiting, monitoring, modeling, encouragement of children'sphysical activity and dietary intake) and (b) elevate children's stressfulstates, which in turn, will both lead to less healthy dietary intake andphysical activity practices by children at a subsequent point in the day.The model suggests that these within-day effects may be moderatedby social-ecological factors including sociodemographics, culture, familycharacteristics, and neighborhood context (e.g., food insecurity, familyrules, acculturation, and access to parks and fast food restaurants).Preliminary results from the pilot component and recruitment progressto date will be presented to address issues of feasibility, compliance,and user satisfaction.

2. Methods

2.1. Design overview

The current study uses a longitudinal, observational, dyadic,case-crossover design [22,23] in a sample of mother–child pairs. Incase-crossover designs, a dyad serves as their own control to assess thewithin-day effects of immediate antecedents on a repeatedly-measureddependent variable [22]. A total of 200 mothers and their 8 to12 year-old children (N = 400 total) are participating in 6 semi-annualassessment waves across 3 years. The study protocol was approved bythe Institutional Review Board at the University of Southern California.

Fig. 1.Model of within-day and long-term effects of matern

2.2. Participants

Participants include ethnically-diverse mothers and children livingin the greater Los Angeles metropolitan area. Children are currentlybeing recruited from public elementary schools based on the followinginclusion criteria: (1) child is in the 4th or 5th grade, (2) ≥ 50% of child'scustody resideswith themother, and (3) bothmother and child are ableto read English or Spanish. Exclusion criteria for mother or child are:(1) currently taking medications for thyroid function or psychologicalconditions such as depression, anxiety, mood disorders, and ADHD(including psychotropic medications, antidepressants, and stimulants),(2) health issues that limit physical activity, (3) enrolled in specialeducation programs (3) currently using oral or inhalant corticosteroidsfor asthma, (4) pregnancy, (5) child classified as underweight by a BMIpercentile b 5% adjusted for sex and age, which is approximatelyequivalent to a z-score of −2.0 for BMI and (6) mothers who workmore than two weekday evenings (between the hours of 5–9 pm)per week or more than 8 h on any weekend day. Inclusion and exclu-sion criteria are assessed by research staff during the phone screen-ing process. The race/ethnicity breakdown is expected to be 61%Hispanic, 17% African-American, 14% White, 3% Asian/Pacific Is-lander, and 6% other. Based on norms in recruitment schools, ap-proximately 68% of students are expected to be eligible for free orreduced price meals.

Several issueswere considered in establishing inclusion and exclusioncriteria. This study will focus on children ages 8–12 years old at baselinebecause this period, known as late adiposity rebound, shows rapidlyaccelerating BMI and increased risk for obesity-related disorders thatstarts in childhood and may continue across the life course [24–26].Although fathers play an increasing role in children's health, this studyfocuses onmothers becausewomen in dual-earner couples report takinggreater responsibility for child care than their male partners [27,28].Requiring that at least 50% child custody resides with the mother willincreasemonitoring time spent together during the 7 days of assessmentin naturalistic settings. Due to budgetary restraints, the EMA and paper-and-pencil survey materials are only available in English and Spanish.Individuals who use psychotropic or corticosteroid medication, or arepregnant are excluded because these substances and conditions mayinterfere with salivary cortisol secretion, making these data uninterpret-able. Children enrolled in special education programs will be excludedgiven the potential for reduced understanding of the assent andquestionnaire process.

al stress on children's eating, activity, and obesity risk.

Table 1Measures to assess model constructs.

Model Construct Measures

Independentvariable

Maternal stress (M) EMA and paperquestionnaireSalivary cortisol

Mediator Weight-related parentingpractices (M)

EMA

Mediator Child stress (C) EMASalivary cortisol

Moderators Sociodemographic, cultural, family Paper questionnaire

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2.2.1. Recruitment and trackingMothers and children are recruited through informational flyers

and in-person research staff visits to public elementary schools andcommunityevents. The longitudinal trackingplan involves: (1)obtaininghome and cell phone numbers, e-mail addresses, and contacts of friends,parents, and relatives; (2) using Facebook and online tracking services(e.g., PeopleFinder.com) to locate families; and (3) sending studynewsletters and birthday/holiday cards. Home data collections willbe arranged for families who move out of the district in subsequentyears.

environment factors (M)Moderators Neighborhood context (M and C) Location monitoring/GISDependentvariable

Physical activity and sedentarybehavior (C)

EMA and paperquestionnaireAccelerometer

Dependentvariable

Dietary intake (C) EMA24-Hour dietary recall

Dependentvariable

Obesity risk (C) BMI z-scoreWaist circumference

Note: (M)mother completes assessment; (C) child completes assessment; (EMA) EcologicalMomentary Assessment; (GIS) Geographic Information Systems.

Table 2EMA and salivary cortisol daily measurement schedule.

Weekend days Weekdays

Waking Saliva SalivaWaking (+30 min) Saliva Saliva7:00 am–8:00 am EMA No assessment9:00 am–10:00 am EMA No assessment11:00 am–12:00 pm EMA No assessment1:00 pm–2:00 pm EMA No assessment3:00 pm–4:00 pm EMA EMA3:30 pm–4:30 pm Saliva Saliva5:00 pm–6:00 pm EMA EMA7:00 pm–8:00 pm EMA EMA9:00 pm–9:30 pm (mothers only) EMA EMABedtime Saliva Saliva

Note: no assessments were conducted during school hours onweekdays becausemothersand their children are typically not together during this time.

2.3. Procedures

Each parent–child pair is on their own assessment schedule,depending on time of enrollment. Assessments take place during thefall (mid-Aug. through mid-Dec.) and spring (Jan. through May) toavoid data collection during the summer months and winter holidaywhenmother and childrenmay have unusual patterns of physical activityand dietary intake. To limit equipment costs and reduce staff burden,assessments are performed on a rolling basis over the 10–12 weeks inthe fall and spring (approximately 15–20 mother–child pairs per week).Interested families are called by phone to be screened for eligibility andscheduled for the first assessment, which includes in-person parentalconsent and child assent.

At each assessment wave, participants attend a 90-minute datacollection session held on a weekday evening at a local school orrecreation center. During these sessions, they complete the paper-and-pencil surveys and anthropometric assessments, and they receivemobile phone, saliva, and accelerometer instructions. Over the next7 days, mothers and children proceed through a daily EMA and salivameasurement schedule. Assessments take place in the natural environ-ment, and participants are asked to proceed with their daily routines asnormal. Each member of the dyad receives random EMA prompts across7 days. Bothmothers and children give 4 salivary cortisol samples per dayon thefirst 4 of the dayswith EMAprompting (including 2weekdays and2 weekend days to represent different patterns of stress that may occuron these days). On 2 of the 4 saliva collection days, (one weekday andone weekend day) children also complete 24-hour diet recalls by phoneinterview. Salivary cortisol and 24-hour dietary recall assessments willonly be made on a subset of the total days of monitoring within eachwave due to the costs of and potential participant burden introduced bythese measures. Children wear an accelerometer across all 7 days. Thus,we have 2 days per wave with overlapping data to test within-dayhypotheses about the relationships of maternal and child stress (fromEMAand cortisol), parenting practices (fromEMA), and children's dietaryintake (from the 24-hour recall). Furthermore, we have 4 days perwave with overlapping data to test within-day hypotheses aboutthe relationships of maternal stress (from EMA and cortisol), parentingpractices (from EMA), and children's physical activity (from EMA andaccelerometer). On all 7 days or each wave, we have overlapping datato test relationships about EMA-reported stress, dietary intake, andphysical activity.

Research staff members contact families by phone twice during themonitoringweek to encourage compliance and address technical issues.Participants return the equipment and saliva samples, and receivecompensation during a 30-minute follow-up session at the end of the7 days. Data uploading and device resetting take place on site for immedi-ate turn-around to a new sub-cohort that same evening. Additionalequipment is available for distribution in case of no-shows among thosescheduled to return equipment. Mother–child dyads are given $200 foreach complete assessment wave. If the dyad completes less than 70% ofthe prompted EMA surveys or saliva collections, has fewer than 4 daysof valid accelerometer data (N10 h per day), or fewer than one 24-hourdietary recall; both members of the dyad are asked to redo that datacollection wave.

2.4. Measures

Table 1 lists the sources of the independent variables, mediators,moderators, and dependent variables used to test the conceptualmodel. Measures are collected from mothers and children during eachof the 6 assessment waves. All questionnaire items are available inEnglish and Spanish (mothers only). Table 2 shows the schedule forEMA and salivary cortisol assessment on weekend days and weekdays.

2.5. Ecological Momentary Assessment

EMA data are collected through a custom software application (app)for smartphones running the Android operating system (Google USA,Inc.) EMA data from smartphones is wirelessly uploaded after eachentry and stored on an internet-accessible server, where investigatorscan monitor compliance. Mothers and children who own Androidsmartphones download the EMA app at intake and complete the EMAsurveys directly from their personal phones. Participants withouta compatible mobile phone are loaned a MotoG (Motorola, USA)smartphone without a data plan and are instructed to connect to theirhome wireless Internet. If a wireless connection is not available athome, EMA data are downloaded directly from the phone when it isreturned to the researchers at the end of the data collection wave. Uponbeing prompted by the app with chimes and/or vibration, participantsare instructed to stop their current activity and complete a short EMAsurvey on the touch screen of the phone. This process requires about2–3 min. If no entry is made, the application emits up to two reminder

Table 3MATCH Ecological Momentary Assessment (EMA) items (mother).

Variable (subscale) Item Response options Format Timing Frequency

(A) Positive and negative affect Right before the phone went off, how (HAPPY,FRUSTRATED/ANGRY, STRESSED, CALM/RELAXED, SADDEPRESSED) were you feeling?

Not at allA littleQuite a bitExtremely

Separate screen for eachmood item

Every prompt 100%

(B) Perceived stress 1. How certain do you feel that you can deal with all thethings that you have to do RIGHT NOW?2. How confident do you feel about your ability to handle allof the demands on you RIGHT NOW?

Not at allA littleQuite a bitExtremely

Separate screen for eachitem

Every prompt 100%

(C) Stressful events Since waking up this morning (Over the last 2 HOURS)which of these things caused you stress? (check all)

YesNo

Every prompt 100%

(D) Daily hassles/stressors Since waking up this morning (Over the last 2 HOURS),which of these things have you done? (check all)

Work at homeWork at a jobDemands made by your familyTension with a coworkerTension with a spouseTension with your childrenSomething elseNone of these things

Every prompt 100%

(E.1) Eating and activity behavior Since waking up this morning (Over the last 2 HOURS),which of these things have you done? (check all)

TV, VIDEOS or VIDEO GAMESEXERCISE or SPORTSEaten CHIPS or FRIESEaten PASTRIES or SWEETSEaten FAST FOODEaten FRUITS or VEGETABLESDrank SODA or ENERGY DRINKS (not counting diet)None of these things

Every prompt 100%

(E.2) Parental modeling Was ANYONE with you when you were (watching TV,VIDEOS or VIDEO GAMES; Doing EXERCISE OR SPORTS,Eating CHIPS or FRIES, Eating PASTRIES or SWEETS, EatingFAST FOOD, Eating FRUITS or VEGETABLES, Drinking SODAor SOFT DRINKS)? (check all_

No (alone)My childSpouse/romantic partnerOther

Every prompt 100% followup sequence tooccur for each responseselected in Section E.1

(F) Time spent with child Since waking up this morning (Over the last 2 HOURS),have you spent timeWITH YOUR CHILD (together in thesame location)?

No (note: skip to Section N)Yes

Every prompt 100%

(G.1) Permission — sedentary behavior Since waking up this morning (Over the last 2 HOURS),has your child ASKED to watch TV or VIDEOS or playVIDEO GAMES?

Yes, and I allowed it (go to G.2)Yes, and my spouse/partner allowed it (go to G.2)Yes, but I/we did NOT allow it (skip G.2)No, but did so WITHOUT my permission (go to G.2)No, has not asked (skip G.2)

Every Prompt 60%

(G.2) Limiting — sedentary behavior Since waking up this morning (Over the last 2 HOURS),have you tried to LIMIT your child's TV or VIDEO ORVIDEO GAME time?

NoYes

Every prompt 60%

(H.1) Encouraging— physical activity Since waking up this morning (Over the last 2 HOURS),have you ENCOURAGED your child to BE PHYSICALLYACTIVE?

NoYes

Every prompt 60%

(H.2) Encouraging— physical activity Since waking up this morning (Over the last 2 HOURS), haveyou TAKEN your child to a place to BE PHYSICALLY ACTIVE?

NoYes

Every prompt 60%

(I.1) Permission — junk food intake Since waking up this morning (Over the last 2 hours), hasyour CHILD asked to eat any CHIPS, FRIES, PASTRIES,SWEETS, OR CANDY?

Yes, and I allowed it (go to I.2)Yes, and my spouse/partner allowed it (go to I.2)Yes, but I/we did NOT allow it (skip I.2)No, but did so WITHOUT my permission (go to I.2)

Every prompt 60%

(continued on next page)

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Table 3 (continued)

Variable (subscale) Item Response options Format Timing Frequency

No, has not asked (skip I.2)(I.2) Limiting — junk food intake Since waking up this morning (Over the last 2 hours), have

you tried to LIMIT the amount of CHIPS, FRIES, PASTRIES,SWEETS, OR CANDY your child ate?

Every prompt 60%

(J.1) Permission— fast food intake Since waking up this morning (Over the last 2 HOURS), hasyour CHILD asked to eat at a FAST FOOD restaurant

Yes, and I allowed it (go to J.2)Yes, and my spouse/partner allowed it (go to J.2)Yes, but I/we did NOT allow it (skip J.2)No, but did so WITHOUT my permission (go to J.2)No, has not asked (skip to J.2)

Every prompt 60%

(J.2) Limiting — fast food intake Since waking up this morning (Over the last 2 HOURS), didyou try to CONTROL what type of food your child ordered atthe FAST FOOD restaurant?

NoYes

Every prompt 60%

(K.1) Encouragement— fruit andvegetable intake

Since waking up this morning (Over the last 2 HOURS),have you ENCOURAGED your child to eat any FRESHFRUITS OR VEGETABLES?

NoYes

Every prompt 60%

(K.2) Preparation — fruit and vegetableintake

Since waking up this morning (Over the last 2 HOURS),have you COOKED OR PREPARED any FRESH FRUITS ORVEGETABLES for your child to eat?

NoYes

Every prompt 60%

(L.1) Permission — soda intake Since waking up this morning (Over the last 2 HOURS),has your CHILD asked to have diet or regular SODA, SOFTDRINKS, OR SPORTS/ENERGY DRINKS?

Yes, and I allowed it (go to L.2)Yes, and my spouse/partner allowed it (go to L.2)Yes, but I/we did NOT allow it (skip L.2)No, but did so WITHOUT my permission (go to L.2)No, has not asked (skip L.2)

Every prompt 60%

(L.2) Limiting — soda intake Since waking up this morning (Over the last 2 HOURS),have you tried to LIMIT the amount of diet or regularSODA, SOFT DRINKS, OR SPORTS/ENERGY DRINKS yourchild drank?

NoYes

Every prompt 60%

(M) Family rules Since waking up this morning (Over the last 2 HOURS),which have happened? (check all)

Eaten a meal together as a family.Child watched TV/videos while eating.Child ate a meal in the car.Let my child watch TV/videos as a rewardGave my child food as a reward

Every prompt 60%

(N) Time use Since waking up this morning (Over the last 2 HOURS),which have you done? (check all)

Errands/shoppingTook children to lessons/classes/activitiesHousework/chores/cookingWork for a jobTook care of an infant/toddlerNone of these

Every prompt 100%

(O) Social context Who were you with just before the phone went off?(choose all that apply)

SpouseYour child (in this study)Your child(ren) (not in this study)Other family members (nephews, cousins, aunts)Friend(s)CoworkersOther types of acquaintancesPeople you don't knowI was alone

Every prompt 100%

(P) Perceived barriers (eating) Thinking about today, did any of the following thingsmake it difficult to cook/prepare healthy food or snacksfor your child? (choose all)

Not enough TIMEFeeling TOO TIREDFeeling TOO STRESSEDNone of the above

9—9:30 pm prompt only 100%

(Q) Perceived barriers (physical activity) Thinking about today, did any of the following thingsmake it difficult to take your child to a place to exercise?(choose all)

Not enough TIMEFeeling TOO TIREDFeeling TOO STRESSEDNone of the above

Not enough TIMEFeeling TOO TIREDFeeling TOO STRESSEDNone of the above

9—9:30 pm prompt only 100%

(R) Sick day/illness Were you sick or ill today? NoYes

9—9:30 pm prompt only 100%

(S) Time off from work Did you miss work or take time off from work today? NoYes

9—9:30 pm prompt only 100%

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signals at 3-min intervals. After this point, the EMA program becomesinaccessible until the next recording opportunity. Participants areinstructed to ignore signals that occur during an incompatible activity(e.g., driving, sleeping, bathing). During the pilot study, motherscompleted 8 EMA surveys per day (on weekdays and weekdays).However, the number of weekday EMA prompts for mothers wasreduced to limit potential participant burden. In the current protocol,EMA surveys are prompted 7 times per day on weekend days and 3times per day on weekdays (during non-school time) (see Table 1).EMA prompts are linked between mothers and children, so that bothoccurwithin a 1-hourwindow.Mothers are randomly prompted duringthefirst half of eachwindow and children during the secondhalf of eachwindow to prevent any contamination effects from completing surveysat the same time. Mothers also complete an additional unpaired lateevening EMA survey prompt each day. Soliciting 7 or more EMA entriesper day is acceptable for EMA studies with children and adults [29–32].

EMA items assess perceived stress, stressful events, exposure tostressors, positive and negative affect, weight-related parentingpractices formothers only, dietary intake andphysical activity behaviors,and social contexts (see Tables 3 and 4). Sample screen shots are shownin Fig. 2. Perceived stress at the currentmoment is assessed using 2 items(i.e., mothers-ability to handle demands, deal with things; children-ability to manage things, things are working out) from the PerceivedStress Scale (PSS) [33] Whether any stressful events have occurred inthe past 2 h is assessed with a yes/no response. In mothers, exposure tostressors over the past 2 h is assessed using items adapted from thedaily hassles scale by Bolger and colleagues [34] addressing work,home, and family domains. For children, exposure to stressors over thepast 2 h is measured using items modified from a scale developed by

Table 4MATCH Ecological Momentary Assessment (EMA) items (child).

Variable Items

(A) Positive and negative affect Right before the phone went off, how (HAPPY, JOYFUL,STRESSED, MAD, SAD) were you feeling?

(B) Perceived stress Right before the phone went off, how (HAPPY, JOYFUL,STRESSED, MAD, SAD) were you feeling?

(C) Stressful events Since waking up this morning (Over the last 2 HOURS),has anything STRESSFUL happened to you?

(D) Daily hassles/stressors Since waking up this morning (Over the last 2 HOURS),which of these things caused you stress? (check all)

(E) Eating and activity behavior Since waking up this morning (Over the last 2 HOURS),which of these things caused you stress? (check all)

(F) Social context Who were you with just before the phone went off?(choose all that apply)

(G) Sick day/illness Were you sick or ill today?

(H) Absent from school Were you absent from or did you miss school today?

Parfenoff and colleagues [35] addressingpeer, family, school, and generaldomains. Mothers are also asked whether they have spent any timetogether with their child over the past 2 h. If so, then the EMA app followsa branching sequence of up to 12 items assessingweight-related parentingpractices (e.g., encouragement, monitoring, limiting), taken from theParenting Strategies for Eating and Activity Scale (PEAS).

EMA items assessing physical activity and dietary intake ask whetherover the past 2 hmothers and children have engaged in any screen time(i.e., TV/videos/video games) or exercise/sports, and/or consumed fruit/vegetables, pastries/sweets, soda/energy drinks, chips/fries, and fastfood. For each of these items that is endorsed, mothers and childrenreceive a follow-up question assessing who (if anyone) was withthem while they were doing it (e.g., mother, siblings, friends) to assessparental modeling (another weight-related parenting practice). TheEMAmeasures also assess potential covariates related to stress, parentingpractices, physical activity and dietary intake—including positive andnegative affect [36–39], and other time demands (e.g., ran errands, wentshopping, took children to lessons/classes/activities, did housework/chores/cooking, worked for a job, or took care of an infant/toddler). Formothers, the last EMA survey of each day additionally asks aboutperceived barriers to cooking and preparing healthy food for the familyand taking children to a place to be physically active (e.g., not enoughtime, feeling too tired/stressed, being ill that day, or taking time off ormissing work that day).

A number of methodological considerations were made whendesigning the EMA protocol to balance the benefits of data richnesswith the drawbacks of potential participant burden and demandcharacteristics. Reduced-item EMA subscales are used instead ofthe full scales in order to limit survey fatigue. We also use a random

Response options Format Timing Frequency

Not at all A little Quite a bitExtremely

Separate screen foreach mood item

Every prompt 100%

NoYes

Separate screen foreach item

Every prompt 100%

YesNo

Every prompt 100%

Having a lot of homework to doNot doing well at somethingBeing teased by someoneArguing with someoneArguing with your parentsHaving too many things to doNone of these things

Every prompt 100%

TV, VIDEOS or VIDEO GAMESEXERCISE or SPORTSEaten CHIPS or FRIESEaten PASTRIES or SWEETSEaten FAST FOODEaten FRUITS or VEGETABLESDrank SODA or ENERGYDRINKS (not counting diet)None of these things

Every prompt 100%

MomDadSister(s) or brother(s)Other family members(cousins, uncles)Friend(s)ClassmatesPeople you don't knowI was alone

Every prompt 100%

YesNo

7:30–8 pmprompt only

100%

YesNo

7:30–8 pmprompt onweekdays only

100%

Perceived Stress Perceived Stress Stressful event Stressors Parenting-Eating

Perceived Stress Perceived Stress Stressful event Stressors Phy. Act and Eating

Child’s EMA

Mother’s EMA

Parenting-Phy. Act

Fig. 2. Sample screen images from mother' and child's Ecological Momentary Assessment (EMA) items.

Fig. 3. Sample data from mother–child dyad showing decline in cortisol concentrationacross the day linked with weekend Ecological Momentary Assessment (EMA) prompts.

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subscale inclusion strategy, so that only 60% ofweight-related parentingpractices items are included in each EMA survey to further reduceresponse burden. Also, EMA surveys are prompted at random timeswithin preset intervals (i.e., hybrid signal-interval contingent samplingschedule) to prevent anticipatory effects, such as pausing or changingcurrent behavior in anticipation of a survey prompt at a known time[40]. Despite the use of repeated measures, reactivity is generally lowwith EMA procedures [41]. Furthermore, we will combine EMA withmore widely validated measures of physical activity (i.e., accelerometer)and dietary intake (i.e., 24-hour recall) to minimize the weakness ofusing either instrument on its own.

2.5.1. Salivary cortisol assessmentsSalivary cortisol assessments provide an indicator of diurnal activity

of the hypothalamic–pituitary–adrenal (HPA) axis, a neuroendocrineaxis responsive to stress experience. Cortisol typically peaks shortlyafter waking in the morning and declines throughout the day.Slower rates of decline across the day are hypothesized to reflectgreater activation of the stress system. Saliva is collected with theSalivette device (Sarstedtf, Inc.), which is a small, cotton dentalsponge. Participants are asked to very gently chew and roll thesponge around their mouths for 2 min. This strategy has been usedin many daily experience studies with participant-administeredcollection in natural environments among children and adults[42–47]. Participants are automatically prompted by the mobilephone application to provide samples uponwaking (and before gettingout of bed), 30 min after waking, between 3:30–4:40 pm, and rightbefore bedtime (see Fig. 3). These 4 collection times permit assessmentof within-day effects of key components of the diurnal cortisol rhythm(i.e., morning awakening response, slope of decline across day, totalarea under the curve, as well as levels at specific times of the day). Toavoid contamination, saliva collections are made before breakfast anddinner. During the pilot study, participants were asked to record on apaper log the date, time, and whether any dietary eating, drinking

(not water), toothbrushing, smoking or exercising had occurred in theprior 30 min. To improve data quality and completeness, a changewas made to the protocol so that participants are currently asked towrite this information directly on the saliva tube itself. Saliva collectionswith reported eating, drinking (not water), toothbrushing, smoking orexercise in the previous 30minwill be excluded from statistical analyses.Participants store collected saliva samples in their home refrigerator assoon as possible after collection. At the end of the 7-day period, samplesare frozen at−80 °C until they canbe assayed in batch. Cortisol is assayedin duplicate with commercial chemiluminescence immunoassay (CLIA;IBL International, Hamburg, Germany), which has a lower detectionlimit of .005 μg/dL and intra- and inter-assay coefficients in the range of3.0–4.1% (IBL International).

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2.5.2. Accelerometer monitoringThe Actigraph, Inc. GT3X model accelerometer is used for measure-

ment of physical activity and sedentary behavior across the 7 days ofeach data collection wave. This device has been used extensively inlarge-scale studies of physical activity [48,49]. The Actigraph is wornon the right hip, attached to an adjustable belt, all times except sleeping,bathing, or swimming. The device is set to collect body movement datain activity counts units for each 30-sec epoch. Meterplus software(Santech, San Diego, CA) is used to identify periods of non-wear (N60continuous minutes of zero activity counts) and valid days (at least10 h of wear). Accelerometer recordings are time-stamped in order tobe linked with EMA and salivary cortisol data. Cut-points formoderate-to-vigorous physical activity (MVPA) and sedentary activity(SA) are consistent with studies of national surveillance data [50,51]using age-specific thresholds for children generated from the Freedsonprediction equation equivalent to 4 METs [51–56]. SA is be defined asb100 counts per minute [57,58].

2.5.3. Dietary assessmentDietary intakes for children are assessed using 24-hour dietary

recalls. The 24-hour dietary recalls will be collected using NutritionData System for Research (NDSR), a computer-based software applicationthat facilitates the collection of recalls in a standardized fashion [59].Dietary intake data gathered by interview is governed by a multiple-pass interviewapproach [60]. Five distinct passes providemultiple oppor-tunities for the participant to recall food intake. The first pass involvesobtaining from the participant a listing of all foods and beveragesconsumed in the previous 24 h. This listing is reviewed with theparticipant for completeness and correctness (second pass). Theinterviewer then collects detailed information about each reportedfood and beverage, including the amount consumed and method ofpreparation (third pass). In the optional fourth pass, the interviewerthen probes for commonly forgotten foods. Finally, the detailedinformation is reviewed for completeness and correctness (fifthpass). Interviews last up to 30 min each.

The 24-hour dietary recalls are collected from children withassistance from mothers. Trained research staff members conductthe 24-hour dietary recalls by phone\\a method shown to providevalid estimates of nutritional intake when compared with directobservation [61,62]. Children are asked to recall what they ate duringthe course of the most recent complete 24-hour period (midnight tomidnight). The dietary recall days include two days: one weekendday and one weekday. The child is the primary respondent but isassisted by the mother who will join the call via speaker phone orby a second phone to help answer any questions the child may notbe able to answer. Interviewers attempt to call up to 3 times between7 pm and 8 pm on the designated interview day. If participants cannotbe reached by 8 pm, another saliva collection day will be selected tocomplete the missed dietary assessment. Calls are attempted throughthe remainder of the 7-day monitoring period in order to obtain 2complete 24-hour dietary recalls per wave. Dietary intake data arerecorded per eating occasionwith a time stamp forwithin-day analyses.For ancillary analyses, usual daily dietary intake can also be calculatedby adding daily totals and diving by the number of days. Primaryoutcomes are: fat (g), total sugar (g), sweetened beverages (servings),and fruit and vegetables (cups), each adjusted for total energy intake(kcals).

2.5.4. Anthropometric assessmentsHeight andweight are measured in duplicate using an electronically

calibrated digital scale (Tanita WB-110A) and professional stadiometer(PE-AIM-101) to the nearest 0.1 kg and 0.1 cm, respectively. Body massindex (BMI; kg/m2) and CDC age and gender-specific BMI z-scores aredetermined using EpiInfo 2005, Version 3.2 (CDC, Atlanta, GA). Waistcircumference is measured in triplicate and recorded to the nearest0.1.cm. Values for waist circumference will be referenced against

age- and gender-adjusted percentiles for children in the United States[63]. Alternative measures of body fat were considered (e.g., DXA) butwere ruled out due to feasibility and cost issues.

2.5.5. Retrospective questionnairesIn addition to EMAand cortisolmeasures of stress states, retrospective

measures of usual or chronic stress are used because they may captureunique elements of the stress construct that may contribute differentlyto long-term processes. Mothers complete two paper-and pencilretrospective measures of chronic stress: (1) perceived stress in thepast month (10-item Perceived Stress Scale) [64] and (2) stressfullife events in the past 6 months (13-item version of the Stressful LifeEvents Questionnaire) [65,66]. Mothers also complete measuresof sociodemographic, cultural, family, and neighborhood contextualfactors to test a potentialmoderators and confounders of the associationbetween maternal stress, dietary intake, and physical activity (seeFig. 1). Children complete a retrospective measure of usual or chronicstress (21-item Stress in Children scale [67]) and stressful events(40-item Child and Adolescent Survey of Experiences [68,69]). The 3Day Physical Activity Recall instrument [70–72] is also used in additionto the accelerometer because it can capture water-based sports andbicycling, which the accelerometer cannot.

2.6. Location monitoring and Geographic Information System (GIS) mapping

Objective neighborhood environmental context data will also betested as potential moderators and covariates of the associations be-tween maternal stress and children's dietary intake, physical activity,and obesity. Using smartphone location finding features (e.g., celltower triangulation, Wi-fi networks, and GPS), smartphones recordreal-time geographical location data. The EMA application wakes uponce every minute to search for and electronically record time and lati-tude/longitude data. Using GIS mapping with available datasets; accessto parks, green cover, open space, crime, traffic volume, fast food andhealthy food outlets will be assessed for each location coordinate. Addi-tionally, participants' home addresses will be geocoded. Surroundingeach home address, 1-km road network buffers will be created andneighborhood summary values for the above GIS-derived environmen-tal context indicators will be developed.

2.7. Data integration

EMA, accelerometer, cortisol, and 24-hour dietary recall data will beimported into Stata (version 13.1). Date- and time-stamps will be usedtomatch all records within eachmother–child pair to create a long datafile (i.e., each row represents an EMA prompting window). To examinewithin-day effects, maternal stress variables (EMA-reported and salivarycortisol) measured during any given prompting window Tn will bematched to (1)mediators: maternal parenting practices (EMA-reported)and child stress (EMA-reported and salivary cortisol) measured duringthe same prompting window Tn (concurrent effects) and subsequentwindow Tn + 1 (prospective effects); and (2) outcomes: child physicalactivity and dietary intake (EMA-reported) measured during the samewindow time Tn (concurrent effects) or subsequent windows Tn + 1andTn + 2 (prospective effects) (see Fig. 4). Maternal stress, parentingpractices, and child stress during any given EMA prompting or salivacollectionwindow Tnwill also bematched to children'sMVPA and SA(measured by accelerometer), and dietary intake (measured by 24-hourrecall) in the ±15 min, +30 min, +60 min, +90 min, +120 min,or +240 min (to capture concurrent and prospective effects). Day- andperson-level mean scores at each wave for indicators of stress, parentingpractices, physical activity, and dietary intakewill be added to the datasetto test intermediate and long-term effects.

Fig. 4.Within-day hypotheses for concurrent and time-lagged effectsmeasured through EcologicalMomentary Assessment (EMA). Note: Timen refers to any given EMAprompt. Timen + 1

refers to the next EMA prompt about 2 h after the EMA prompt at Timen. Timen + 2 refers to the subsequent EMA prompt about 4 h after the EMA prompt at Timen.

150 G.F. Dunton et al. / Contemporary Clinical Trials 43 (2015) 142–154

2.8. Statistical analyses

2.8.1. OverviewPrior to analysis, datawill be screened for distributional assumptions

(e.g., normality, outliers, multicollinearity) and subjected to arithmetictransformations to adjust for non-normally distributed data [73].Withinwaves, an analysis of missing salivary cortisol, accelerometer, and24-hour dietary intake recall data will be conducted [74,75]. If data aredetermined to be missing at random (MAR), missing EMA data will beimputed using REALCOM-IMPUTE, a multilevel multiple imputationsoftware that is available to researchers for use with MLwiN or Stata.REALCOM-IMPUTE allows for level-1 and level-2 explanatory variableswhen specifying an MI model and uses Markov Chain Monte Carlo(MCMC) estimation to impute missing values [76]. Attrition propensityscores will be computed based on the estimated probability of dropoutfor each participant and used to adjust the analyses [77]. Factor analyseswill examine the factor structures of measures. Principal componentsanalysis will create a stress index based on the EMA indicators(i.e., perceived stress, stressful events, and stressor exposure).

2.8.2. Within-day effectsTo test the within-day effects of maternal stress, a series of random-

effect regression models (RRM) (Eqs.1a and 2) and generalized linearmixed models (GLMM) (Eqs.1b and 2) will be conducted using SAS.RRM is used to analyze multilevel data (level-1: repeated measure,level-2: individual subject), allowing for varyingmeasurement schedules[78]. GLMM is used for non-normal dependent variables, accounting formultilevel data structure and incorporating random effects. Between-subject (BS, level-2) and within-subject (WS, level-1) versions of themain time-varying predictors will be generated. This approach permitsthe distinction between the level-1 and level-2 effect of a time-varyingpredictor [79].

Level1 : yti ¼ β0i þ β1i MSWtið Þ þ⋯þ βkiXtiþ eti forcontinuousoutcomeð Þ ð1aÞ

Level1 : P yti xjð Þ ¼ e β0iþβ1i MSWtið Þþ⋯þβkiXtið Þ fordichotomousoutcomeð Þð1bÞ

Level‐2 : β0i ¼ γ00 þ γ01MSBi þ γ02Boyi þ⋯þ u0i

β1i ¼ γ10 þ γ11MSBi þ γ12Boyi þ⋯þ u1iβki ¼ γk0 þ γk1MSBi þ γk2Boyi þ⋯þ uki

ð2Þ

where the subscript i indicates individual and the subscript t indicatesoccasion or time with k time-varying covariates. Eqs. (1a) and (1b)respectively predict the probability of a child's behavior at Tn + 1

measured continuously and dichotomously. When the dependent

variable is binary, residual variance (“eti”) cannot be estimated forEq. (1b); rather it is assumed to be π2/3. Other level-1 predictors(i.e., βki X ti) might include relevant time-varying predictors, such asnegative affect or time of day. Relevant level-2 covariates will be includedin themodel such as child sex andmother BMI. The intercepts and slopes,βki, will be allowed to vary across subjects, uki.Wewill conduct sensitivityanalyses to determine the most appropriate temporal matching schemefor EMA data (concurrent versus prospective) and short-term windowsfor accelerometer and 24-hour dietary data (±15 min, +30 min,+60 min, +90 min, +120 min, +240 min) to examine within dayeffects. Additional analyseswill also examine day-level effects by enteringdaily averages for the proposed predictors and outcomes in the modelsdescribed above. In addition to the linear multilevel modeling approachpresented above, ancillary analyses will explore the use of Bayesianhierarchical statistical modeling approaches [80], which may beable to better account for uncertainty in sampling design, modelspecification, parameters of the specified model, and initial andboundary conditions in ecological data.

2.8.3. Mediation and moderationMediatorswill be included in themodels, and a product of coefficients

[81] will estimate the within-day indirect effect of maternal stress onchildren's physical activity through weight-related parenting practicesand children's stress in single path models and multiple path modelsinvolving both mediators. Confidence intervals and Sobel tests willbe used to determine the magnitude and statistical significance ofmediation effects [81,82].Modelswill also explore potentialmoderatorsthrough single-level interactions (e.g., access to fast food at level-1 bymaternal stress and level-1) and cross-level interaction interactions(e.g., parenting style at level-2 by maternal stress at level-1). Separatesets of models will be tested for each moderator. Exploratory analyseswill also examine whether these variables moderate the effects ofmaternal stress on the mediators and moderated mediation.

2.8.4. Long-term effectsLatent Growth Curve Models (LGCMs) in Mplus [83] will be used

to test the long-term effects of maternal stress on children's physicalactivity, dietary intake, and obesity outcomes. LGCMs estimate initialstatus (intercept) and growth (slope) factors by taking into accountthe multi-level data structure [84–86]. Model fit will be evaluatedwith Chi-square (χ2), Comparative Fit Index (CFI), and Root MeanSquare Error of Approximation (RMSEA). Parallel process LGCMswill test the effects of initial status (interceptstress) and change(slopestress) in maternal stress on the rate of change in children'sphysical activity, sedentary behaviors, dietary intake, BMI z-score,and waist circumference (slopeoutcomes) across waves 1–6 [87]. To

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reveal at which specific wave maternal stress has the greatest effecton the proposed child outcomes (e.g., physical activity, dietary intake,BMI z-score), a series of cross-lagged models will be developed toexamine autoregressive paths, as LGCM is unable to address this question[88]. Supplemental analyses will also consider whether the interceptstressand slopestress are related to slopeadoposity after controlling for slopes ofphysical activity and dietary intake, which may indicate the role ofnon-behavioral pathways linking stress and obesity [89–91]. Modelswill include the covariates of child sex, age, SES, and ethnicity; timespent together; and mothers' BMI. Other variables associated (p b .10)with outcomes will be included as covariates.

2.9. Sample size estimation

2.9.1. Within-day effectsThe number of level-1 data points (i.e., EMA prompts and saliva

assessments) is considered to be the unit of analysis for testing thewithin-day effects, assuming non-randomly varying slopes. Theestimated slopes range from .57 (r = .26, σx = .40 and σy = .88) to1.04 (r = .19, σx = .40 and σy = .96). The sample sizes required toachieve statistical power of .8 for this range of slopes were determinedusing G*Power (V3.0) software [92]. Linear bivariate regressions wereapplied with 5% of Type I error rate and two-sided tests. A sample of144 level-1 units will provide sufficient power to detect a slope of 0.5.To achieve this number at level-1, at least N = 36 mother–child dyads(level-2) would be needed to have sufficient power to detect thewithin-day effects during any given assessment wave after taking intoaccount planned (up to 40%) and unplanned (up to 30%) missing data.With randomly-varying slopes, the required level-1 sample size maybe upwardly adjusted based on ICCs [93]. However, the planned level-2sample size of 140 mother–child dyads (after attrition) is expected tobe sufficient to handle these adjustments.

2.9.2. Long-term effectsThe number of level-2 mother–child dyads is considered to be the

primary unit of analysis for tests of the long-term effects. Effect sizeswere calculated based upon previous studies [16–19], which havefound standardized regression coefficients |β| ranging from .07 to .66.Power was computed using 1000 Monte Carlo simulated data sets andreflects the proportion of effects from LGCMwith p b 0.05 [94]. A sampleof 140 (after taking into account up to 30% attrition) will provide .79power to detect an effect size as small as |β| = .15. Although this samplesize is modest, it surpasses the N = 100 that is preferred for growthcurve modeling, and statistical power is bolstered by the repeatedobservations over 6 waves [95].

3. Results to date

The goal of this paper was to describe the study protocol. The studyprocedures were piloted tested for feasibility and user acceptability in asample of 12 mother–child dyads. Results from the pilot test indicatedthat 75% of parents and children felt that the mobile phone EMA appwere easy or very easy to use, and 17% of mothers and 25% of childrenreported that responding to the EMA surveys required too much oftheir time. On average, mothers responded to 80% (range = 60%–95%)and children responded to 69% (range= 25%–100%) of the EMA surveyprompts. Mothers and children gave 100% and 95% (63%–100%) of thesaliva samples requested, respectively. Twenty-five percent of salivasamples given by mothers and 26% given by children did not have avalid date and time reported. Approximately 75% of the 24-hour dietaryrecalls interviews were completed during the 4 target days (Thurs–Sun),and 91% occurred sometime within the 7-day assessment period.Although dietary intake data collected outside the 7-day window cannotbe matched the EMA data for within-day analysis, it can be used toestimate usual dietary intake for ancillary analyses. Mothers andchildren had a mean of 5.17 (SD = 1.80) and 4.92 (SD = 1.16)

valid days of accelerometer wear, respectively. Ninety-two percentof mothers and children had at least 4 valid accelerometer days.None of the phones were lost, stolen, or broken during the pilotstudy. EMA compliance did not differ by time of day, day of theweek, or chronological day in the study. EMA, accelerometer, andsaliva compliance rates were not associated with age, sex, income,race/ethnicity, or BMI for mothers or children.

Participant recruitment and data collection for the first wave iscurrently ongoing. A total of 166 (out of 200) mother–child dyadshave been enrolled to date. A flow diagram showing participantprogress through the recruitment, screening, and enrollment processesis shown in Fig. 5. To date, a total of 453 mother–child dyads expressedinitial interest in participating in the study by retuning completed infor-mation sheets with contact information. Of these, 107 could not bereached by phone, 35 asked to be called back at a later time, 22 declinedto be involved in the study, and 289 mother–child dyads were screenedby phone for eligibility. Among those screened for eligible, 72 were noteligible (e.g., mother or child uses corticosteroid medication for asthmaor medication for Attention Deficit Hyperactivity Disorder, motherworks on nights or weekends, mother pregnant). Among the 217mother–child dyads eligible to participate, 13 have a pendingappointment, 33 did not show up for their initial data collectionappointment, and 5 declined to participate. To date, 166 mother–child dyads have been consented and enrolled in the study.

4. Discussion

Existing studies examining the relation between parental stress andchild obesity have used retrospective measures of stress, relied oncross-sectional study designs, and ignored within-day processes andintraindividual variation. These limitations have led to an unrefinedpicture of the mechanisms underlying, amplifying, and exacerbatingthe link between parental stress and children's obesity risk. To addressthese gaps, the MATCH study is testing a novel conceptual modelpurporting that the effects of parental stress on children's physicalactivity and dietary intake operate through within-day processes thatcontribute to children's long-term obesity risk in an accumulatedmanner over time. Applying a within-day approach permits arefined examination of psychosocial–behavioral transactions that affectchildhood obesity risk on a moment-by-moment basis as they actuallyoccur (e.g., increased maternal stress occurring at work may causepoorer parenting practices later that evening).

Overall, pilot study results generally support the feasibility andacceptability of the procedures andmeasures. However, a fewmodifica-tions to the study protocol were been made to reduce participantburden, and improve compliance and data quality. Pilot data indicatedthat 17% of mothers felt that responding to the EMA surveys requiredtoo much of their time. Therefore, a decision was made to eliminateEMA prompts for mothers that occurred during the day on weekdays(7 am–3 pm). This decision was based on the fact that children areat school during this time—therefore limiting the putative effectsof mothers' weight-related parenting behaviors (e.g., encouragingchildren's healthy dietary intake and physical activity, restrictingchildren's unhealthy dietary intake and sedentary activity). Children'sdietary intake and physical activity levels during the day on weekdaysare expected to bemore closely tied to school policies and programmingthan direct parental influence. Therefore, EMA prompting during thisperiod was eliminated from mothers to reduce potential participantburden. In addition, a changewasmade to the saliva collection protocol.Pilot data indicated that 25% of saliva samples given by mothers and26% given by children did not have a valid date and time reported.Therefore, we decided to ask participants to write this informationdirectly on the tube itself instead of on a separate paper log. It is expectedthat this change will improve data quality and completeness because itno longer requires participants to carry a separate log.

Completed Participant Interest Sheet

N= 453 mother-child dyads Unable to Contact n= 107 mother-child dyads

• Wrong/Disconnected phone number n=17 • Unanswered/ Unreturned Calls n=90

Screened for Eligibility n= 289 mother-child dyads

Ineligible n= 72 mother-child dyads

• Corticosteriod use n=6 • ADHD medication use n= 7 • Works on evenings/midnight n=5 • Pregnant n=5 • Autistic n=3 • Anti-Depressant use n= 7 • Thyroid medication use n=11 • Anxiety medication use = 2 • Not eligible age use n= 23 • Moved out of country/state n=3

Declined n= 22 mother-child dyads

• No longer interested n= 17 • No time to participate n= 5

Eligible n= 217 mother-child dyads

Missed Appointment n= 33 mother-child dyads

Appointment Scheduled n= 13 mother-child dyads

Not Screen for Eligibility n= 35 mother-child dyads

• Requested later call back n= 35

Consented and Enrolled n=166 mother-child dyads

Declined n= 5 mother-child dyads

Fig. 5. Flow diagram of participant progress through study recruitment, screening, and enrollment.

152 G.F. Dunton et al. / Contemporary Clinical Trials 43 (2015) 142–154

Priorwork in this area typically assesses stress and parenting practicesusing standard retrospective questionnaires,whichmaybeprone to recallerrors [96,97] and cannot capture intraindividual variability [98]. Thecurrent study addressed these methodological weaknesses throughEMA of mothers' and children's stress, and maternal parenting practicesduring the course of their daily lives integrated with accelerometry-based indices of physical activity and time-linked 24-hour dietaryrecalls. The real-time EMA methodology capitalizes upon recentadvances in mobile phone technology [99,100]. Mobile phones havebecome affordable, easy to use, and quite ubiquitous. An estimated68% of adults worldwide own a mobile phone [101], and they havebeen widely adopted across socioeconomic groups and in developingcountries [102,103]. Thus, the EMA application (“app”) and protocoldeveloped in the proposed studywill have the potential to be integratedinto existing or new large-scale cohort studies of mother–child dyads.Previous EMA-based studies of dietary intake and physical activityhave been conducted in children and adults separately [104,105].

However, this is the first known study to employ EMA in parents andchildren at the same time to examine the cross-over effects of parentalfactors on children's physical activity and dietary intake. It will enhanceknowledge of how EMA can be employed in parent–child dyads incombination with other types of real-time behavioral and biologicalmeasures (i.e., accelerometers and salivary cortisol assessments) in asynchronized fashion to capture short-term effects of parental factorson children's behavior. These advancements could have broad impacton advancing the ecological validity of methodologies for the fields ofdevelopmental psychology and health behavior research.

The current study will help to generate more definitive conclusionsabout the directionality of the association between maternal stressand child obesity. Through its hybrid design, itwill be able to differentiateeffects that primarily operate within-dyads from effects that operatebetween-dyads. It will also identify key mediating and moderatingmechanisms of these relationships that can form the basis of clinic- andcommunity-based interventions. Overall, results will determine the

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optimal timing for the incorporation of maternal stress reduction andbuffering strategies into family-focused campaigns and programs toprevent and treat childhood obesity. Given the increasing numbers ofmothersworking outside the home and risks of chronic health conditionselevated by childhood obesity, the results from this study could have abroad-spanning impact on public health.

Acknowledgments

Thisworkwas funded by theNational Heart, Lung, and Blood Institute(R01HL119255) and the American Cancer Society (118283-MRSGT-10-012-01-CPPB) and partially supported by theNational Institutes of HealthCancer Control and Epidemiology Research Training Grant (5T32 CA009492). Keito Kawabata and Cesar Aranguri assisted with participantrecruitment and data collection. Nnamdi Okeke, Ramesh Nayak, BojunPan, and DharamManiar assisted with software development.

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