TheEffectofAdherencetoDietaryTrackingonWeightLoss:Using...

Research ArticleThe Effect of Adherence to Dietary Tracking onWeight Loss: UsingHLM toModel Weight Loss over Time

John Spencer Ingels, Ranjita Misra, Jonathan Stewart, Brandon Lucke-Wold, andSamantha Shawley-Brzoska

West Virginia University, Morgantown, WV, USA

Correspondence should be addressed to Ranjita Misra; [email protected]

Received 2 May 2017; Revised 23 June 2017; Accepted 16 July 2017; Published 9 August 2017

Academic Editor: Andrea Scaramuzza

Copyright © 2017 John Spencer Ingels et al. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

The role of dietary tracking on weight loss remains unexplored despite being part of multiple diabetes and weight managementprograms. Hence, participants of the Diabetes Prevention and Management (DPM) program (12 months, 22 sessions) trackedtheir food intake for the duration of the study. A scatterplot of days tracked versus total weight loss revealed a nonlinearrelationship. Hence, the number of possible tracking days was divided to create the 3 groups of participants: rare trackers (<33%total days tracked), inconsistent trackers (33–66% total days tracked), and consistent trackers (>66% total days tracked). Aftercontrolling for initial body mass index, hemoglobin A1c, and gender, only consistent trackers had significant weight loss (−9.99pounds), following a linear relationship with consistent loss throughout the year. In addition, the weight loss trend for the rareand inconsistent trackers followed a nonlinear path, with the holidays slowing weight loss and the onset of summer increasingweight loss. These results show the importance of frequent dietary tracking for consistent long-term weight loss success.

1. Introduction

Type 2 diabetes (T2DM) affected over 29 million adultsacross the United States in 2015 and is projected to rise toover 35 million adults by 2040 if steps are not taken to slowor reverse this trend [1]. The health burden of diabetes isextensive: it is the leading cause of adult blindness, kidneyfailure, and nontraumatic amputations [2]. The impact ofdiabetes is strongly felt in West Virginia, where, in 2014,12% of the population were diagnosed with T2DM versus9.1% nationally in the U.S. [3]. Economic burden is high asindividuals with T2DM are estimated to have medical costs2.3 times greater than someone without this chronic condi-tion. This is primarily due to associated comorbidities andcomplications [4]. The growing prevalence of diabetes inWV has led to excessive health and economic burdens forthe state that require cost-effective interventions to manageand prevent T2DM.

Several evidence-based programs show that interventionsto encourage lifestyle modification are effective in reducing

risk for T2DM and improving glycemic control. The U.S.Diabetes Prevention Program (DPP) is a benchmark pro-gram that aimed to assess the impact of lifestyle change tomanage prediabetes [5]. The project assessed the effective-ness of the lifestyle intervention (24 weeks, 16 sessions) ascompared with a medication (metformin) and placebo con-trol group. The program effectively lowered the incidenceof T2DM over the 3 years of study by 58 percent in the life-style intervention versus 31 percent in the metformin group.The results suggest that targeted behavior change loweredrisk of T2DM and improved participants’ health. Similareffectiveness of a lifestyle intervention was found in theSydney Diabetes Prevention Program [6], the Finnish Diabe-tes Prevention Study [7], and the Da Qing IGT and DiabetesStudy [8], with each showing the usefulness for managing orpreventing T2DM.

Dietary modifications are a common feature of lifestyleinterventions; participants are encouraged to track their die-tary intake through food journals and logs. However, it isunclear of what is the effect of increased dietary tracking on

HindawiJournal of Diabetes ResearchVolume 2017, Article ID 6951495, 8 pageshttps://doi.org/10.1155/2017/6951495

https://doi.org/10.1155/2017/6951495

health outcomes. Participants in lifestyle management pro-grams have cited the importance of self-monitoring dietaryintake and physical activity to their success [9], yet limitedstudies have examined the benefits of increased self-monitoring. Participants in a 12-week weight loss programwho tracked with their preferred method (e.g., pen and paperversus web-based service) were more adherent to trackingbut did not significantly differ in weight loss [10]. A secondstudy had participants track food intake with pen and paper,a memo pad on their phone, or with calorie tracking applica-tion on their phone [11]. Those who tracked with theapplication tracked significantly more days, but again, no dif-ference in weight loss was noted between groups [11]. Whileneither study showed that better adherence to dietary trackingled to an increase in weight loss, the small sample sizes andlack of accounting of preprogram weight were limitations.However, in a 6-month study controlling for participantdemographics, preprogram weight, program attendance,and physical activity for 1685 participants found increasedadherence to dietary tracking-predicted weight loss [12].Additional positive results were noted among participantswho consistently adhered to dietary tracking with self-monitoring booklets over 8 weeks [13] and 12 weeks [14];they lost more weight than those who were inconsistentin their tracking. While participants in lifestyle manage-ment programs report the importance of tracking inrelation to improved outcomes, the research to date hasshown mixed results.

This study will extend the current research on the impactof adherence to dietary tracking on weight loss to a 12-monthcommunity-based Diabetes Prevention and Management(DPM) program. Participants were required to track dietaryintake over the project period which provided a large numberof data points (22 sessions) to examine patterns betweentracking and weight loss. The aim of this study is to deter-mine the impact dietary tracking has on weight loss successover the course of a 1-year program. No research to datehas tracked weight loss over the course of the year; thus, asecond aim will be to use Hierarchical Linear Modeling(HLM) [15] to control for clustering and to determine whatmodel (e.g., linear, quadratic, and cubic) best fits the partici-pants’ weight loss progress over the course of the year.

2. Materials and Methods

2.1. DPM Program Design and Implementation. The 22-session educational program is a modified DPP that useseducational material from the DPP [5] and the AmericanAssociation of Diabetes Educators 7 Self-Care Behaviors(AADE7) [16]. The DPM program was implemented at twochurches inWest Virginia, one in Morgantown and the otherin Charleston. The educational sessions covered a variety oftopics related to weight loss, exercise, nutrition, diabetesprevention, and management, as well as behavioral changes(e.g., stress management and negative thinking). The sessionswere conducted with the entire group at each location weeklyfor the first 3 months, biweekly for the second 2 months, andmonthly for the final 7 months.

The group sessions were participatory and interactiveover 60 minutes, rather than didactic. Session activities fos-tered problem solving, group interactions and social support,skill development such as reading food labels, calculatingcalories and fat, and setting achievable goals for each week.Many sessions included guided physical activity, food dem-onstrations, or model meals. The intervention was tailoredto the participants’ preferences and readiness to change withcareful attention to cultural appropriateness for the targetpopulations. Each session program encouraged participantsto set and develop reasonable short-term goals and behav-ioral action plans toward dietary modifications and moderatephysical activity of 150 minutes per week. Food trackingbooklets and pedometers were provided to monitor theirdietary and physical activity levels.

Participants were encouraged to do the following: (1)maintain daily food journals and physical activity records;(2) reduce portion sizes; (3) reduce foods high in calories,fat, and simple sugar; (4) increase consumption of fruits, veg-etables, and low-fat dairy products; and (5) weigh themselvesfrequently and at least weekly. To promote accountability,participants weighed in at the beginning of each sessionand reported their minutes of physical activity and the num-ber of daily diet records kept each week. Tracking of dietaryintake included total calories and fat grams (using a 2016CalorieKing book [17] provided to them) as well as a physicalactivity using step count from a pedometer provided to theparticipants each week. Participants turned in their foodtracking books at each educational session and received feed-back and individualized encouragement to improve lifestylebehaviors; they also received new blank booklets at eachsession to use for the following week.

Participants were assigned with a health coach (HC) whoworked one on one with them throughout the program toidentify threats to their plans and goals and to developand review coping strategies. HCs contacted their partici-pants each week to check in on progress, answer ques-tions, and provide support. Check-ins were conducted viaphone, text message, or email depending on the preferencesof the participant.

2.2. Participant Recruitment. Participants were recruitedfrom the two communities through the following: advertisingin print media, local email lists, sending recruitment mate-rials to be distributed to all eligible participants visiting aFederally Qualified Health Center and free clinic, and postingflyers and brochures in public places such as community bul-letin boards (physical and electronic), physician’s offices,grocery stores and restaurants, churches, and word of mouth.A prescreening was conducted at a convenient location atseveral times over the course of a few weeks prior to the pro-gram start date, in order to review the program objectives,assess if they met the criteria for participation (i.e., have type2 diabetes or prediabetes diagnosis), and answer questionsabout the requirements for participation in the program.

2.3. Food Tracking Booklets. The tracking was completed bypen and paper with booklets that contained columns fordescribing food, type and amount, and listing fat, calorie,

2 Journal of Diabetes Research

and carbohydrate content. There was also a section for track-ing exercise at the bottom of each day. HCs reviewed andprovided written feedback to their participants’ food jour-nals. The feedback focused on recognizing positive changes,providing general encouragement, and discussing additionalinformation on modifications of their current dietary habitsso that they make healthier choices on what to eat and notto eat. These journals, with feedback, were returned to theparticipants at the subsequent session.

Participants were encouraged to refer to nutrition labelswhen available and were provided a copy of The CalorieKing,Fat & Carbohydrate Counter 2016 [17] in order to look upcalorie, fat, and carbohydrate contents of their food. Aresearch assistant scanned each tracking booklet, which wasused to calculate the total days tracked by week for partici-pants throughout the program period. In order to qualify ashaving tracked for the day, the food journal needed to haveat least one meal tracked (e.g., breakfast, lunch, or dinner).The final data contained the number of days a participanttracked of the dietary information by each week and overthe course of the program.

2.4. Measures. Participants were weighed at each session (in aprivate room for confidentiality) by a trained HC. In addi-tion, waist circumference, lipid profile (total cholesterol,LDL, HDL, and triglyceride), and HbA1c were measured atbaseline, midprogram or 6 months, and 12 months. At base-line, participant’s height was measured for calculating theirbody mass index (BMI) score. Demographic informationand the participants’ self-reported diabetes status (prediabe-tes or diabetes) were also collected.

3. Plan of Analysis

The data was analyzed using Hierarchical Linear Modeling(HLM) software [15]. Model building was done in threesteps: (1) building the best equation to model weight change

over time (e.g., linear, quadratic, and cubic), (2) building thebest model with control variables, and (3) building the bestmodel with tracking variables. The 1st aim of the study, todetermine the impact of dietary tracking on weight success,was answered with the results from step 3. The 2nd aim,to determine what model best fits the participants’ weightloss progress over time, will be answered with the resultsfrom step 1.

Weight loss across the 22 sessions was used as a repeatedmeasure at level 1. Weight loss progress was graphed over the22 sessions (49 weeks) of the program (see Figure 1), andweek 1 weight loss was removed from level 1 and added asa control at level 2 in order to better model weight changeover time. Time was centered at the last session of the pro-gram in order to create meaningful intercept that representedthe average weight change between week 2 and week 49, aftercontrolling for week 1 weight change, BMI, HbA1c, and gen-der. Centering time at the end point created a negative timevariable (e.g., week 49= 0, week 47=−2, and week 40=−9),so a negative intercept represented weight loss over thecourse of the program. In order to capture a possible non-linear relationship in weight change over polynomial time,terms were calculated and tested (e.g., quadratic, cubic,and quartic).

Control variables included BMI, HbA1c, and gender atlevel 2 to determine the effect tracking had on weight overtime. A scatterplot of total days tracked and total weight lossindicated a nonlinear relationship (Figure 2). To best capturethis relationship between tracking and weight loss, threetracking groups were created: rare, inconsistent, and consis-tent trackers. Participants were split into groups with thosethat tracked less than 33% of total days possible (raretrackers, n = 25), 33–66% (inconsistent trackers, n = 5), orover 66% (consistent trackers, n = 15). Tracking groups weredummy coded twice, with rare tracking as the referent groupand inconsistent and consistent tracking groups as the twofocal groups. Gender was used as a control variable in the

11-Oct

18-Oct

25-Oct

1-Nov

8-Nov

15-Nov

22-Nov

6-Dec

13-Dec

20-Dec

3-Jan

10-Jan

31-Jan

21-Feb

13-Mar

17-Apr15-May

12-Jun

10-Jul14-Aug

11-Sep

‒2.5

‒2

‒1.5

‒1

‒0.5

0

0.5

1

Figure 1: Bar chart showing average weight loss in pounds across participants at each session.

3Journal of Diabetes Research

model andwas effect coded as it is a categorical predictor in theregressionmodel. BMI andHbA1cwere each centered at theirgrand mean. Therefore, the intercept would represent theaverage weight loss for participants in the rare tracking groupafter controlling for gender, HbA1c, and BMI. The time coef-ficients represent the rare tracking group weight loss slopeover time after controlling for gender, HbA1c, and BMI.

4. Results

4.1. Descriptive Statistics. As part of a larger study, assessingthe effectiveness of lifestyle modification on health outcomes,this study included participants enrolled in 2015-2016 for theDPM program in West Virginia. Sixty-six participants com-pleted the baseline clinical, anthropometric, and behavioralscreening. However, 21 participants were excluded due toexcessive missing data resulting with 45 participants(M=13, F= 32, average age= 61.2± 10.7 years) in the finalmodel. Participants attended an average of 15 (out of the22) intervention sessions, and three-fourths attended ≥16sessions (i.e., at least 75% of the sessions). However, partici-pants who missed one or more session had the opportunityto view the recorded session via a closed YouTube channelcreated for only program participants by site. Twenty-fourparticipants self-reported being diagnosed with diabetes,and the rest reported they had prediabetes (confirmedby HbA1c values). Average total weight loss was 5.6pounds (SD = 12 0), average week 1 weight loss was 1.7pounds (SD = 2 5), and average days tracked was 94.9days (SD = 110 5). See Table 1 for a baseline descriptivedata by rare, inconsistent, and consistent trackers.

4.2. Correlations. Bivariate Pearson’s correlations showed nosignificant association between baseline weight, BMI, gender,HbA1c, diabetes status, total tracking days, and total weightloss. Week one weight loss (r = 0 37, p < 0 01) and consistenttracking (r = −0 28, p = 0 02) were significantly correlatedwith total weight loss. Since none of the anthropometricmeasurements were statistically significant and due toour small sample size, they were not included in the mul-tivariate model.

4.3. Level 1 Model. A cubic model was the best fit for the dataas compared to the quadratic model X2 (1, N = 2) = 14.13,p < 0 001. A quartic model could not be run due to overlapin the variance explained between the cubic and quarticterms. To examine the within- and between-participantvariability, the intraclass correlation coefficient (ICC) wascalculated. ICC showed 64% between-participant variabilityexisted in total weight change, while 36% within-participantvariability existed for weight change over time. In addition,we checked the assumption of normality in error using ahistogram of the residual variance at level 1 and level 2, whichshowed the variance to be normally distributed.

4.4. Level 2 Model. A control model, using week 1 weight loss,BMI, HbA1c, and gender to predict both the intercept andthe slopes at level 2, provided the best fit to the data. Thesevariables failed to significantly predict final weight loss orweight change over time, but adding the tracking variablesresulted in a statistically significant improvement in the con-trol model X2 (8, N = 2) = 19.35, p = 0 01. The final level 2model explained 15.31% of the variance between partici-pants’ final weight loss, as compared to the control model.

4.5. Predicting Final Weight Change. Consistent tracking wasa statistically significant predictor of average weight changeover the course of the program (β06 = −7 59, p = 0 04), whileinconsistent tracking (β05 = 4 97, p = 0 34) and rare tracking(β00 = −2 40, p = 0 30) were not associated with statisticallysignificant weight loss. After controlling for gender, BMI,week 1 weight loss, and HbA1c, only those participantswho consistently tracked their diet lost a statistically signifi-cant amount of weight, that is, an average of 10 pounds.

4.6. Changes in Weight over Time. Figure 3 shows the patternof weight loss over time by participants over the 49-week pro-gram period, controlling for other variables in the model.Rare and inconsistent trackers lost weight initially but didnot sustain it over time. However, with the progress of time,the quadratic coefficient increased at a rate faster than that ofthe linear coefficient in the model suggesting that these twogroups of participants will regain their initial weight loss.Not until the 33rd week or the 8th month of the program isthe cubic time coefficient large enough to overcome thequadratic term’s weight gain prediction. The weight gaincorresponds with participants going through the holidays(Thanksgiving through the New Year), and the weight lossin the 33rd week corresponds with the beginning of summer(June). However, consistent dietary trackers could sustainweight loss over time (see Figure 3). In other words, theseindividuals did not experience the cyclical fluctuations and

20

10

0

‒10

‒20

‒30

‒400 100 200 300 400

Tota

l wei

ght l

oss

Total days tracked

RareInconsistent

ConsistentTracking groups

Figure 2: A scatterplot of total days tracked by total weight loss inpounds per participant.


maintained a consistent weight loss over time as the cubiccoefficient minimized the effect of the quadratic coefficient.Hence, consistent trackers’ weight loss more closely resem-bles the weight loss predicted by the initial linear time coeffi-cient of losing approximately 2/3 of a pound per week. SeeTable 2 for full reporting of all coefficients across the null,control, and final models.

5. Discussion

Tracking is an important component of success in lifestylechange programs [9], but there is limited research on the

effect of dietary tracking adherence on weight loss [10–14].The DPM program tracked participants’ weight over 49weeks, at each of the 22 sessions, which allowed the examina-tion of the role of dietary tracking on weight loss over time.The results showed that individuals who completed over228 days of food logs to track their dietary intake (out of apossible 343) lost a significant amount of weight, as com-pared with those who tracked less than 228 days. Further-more, participants who tracked between 114 and 228 daysdid not lose a significant amount of weight. In terms of pre-dicting weight loss, individuals who consistently track 5 ormore days a week were successful in losing and sustaining

‒12

‒10

‒8

‒6

‒4

‒2

0

2

4

6

1 5 9 13 17 21 25 29 33 36 41 45 49

Weight change by tracking group

RarelyInconsistent

ConsistentNull⁎

Figure 3: Predicted weight loss, in pounds, over time across tracking groups, from week 2 to week 49, holding all other variables constant.Inconsistent curve is not statistically different from the rare curve. Week 9 is the thanksgiving week and the beginning of the holidayseason, and weight begins to climb. Week 33 is the beginning of summer (June), and we see weight start to drop again. However,consistent trackers do not experience these changes and sustain steady weight loss throughout the year. ∗Nonsignificant.

Table 1: Descriptive statistics across the 3 tracking groups: rare (<33% of days tracked, n = 25), inconsistent (33–66%, n = 5), and consistent(>66%, n = 15).

Rare Inconsistent Consistent

Gender M= 7, F = 18 M=1, F = 4 M=5, F = 10

College graduate 17 2 3

Income Missing = 1 Missing = 1

<25k 4 1 3

25–50k 7 0 9

50–75k 5 1 2

75–100k 6 0 0

>100k 3 2 0

Mean days tracked (s.d.) 43.2 (25.2) 166.6 (30.2) 294.9 (42.0)

Mean baseline weight in pounds (s.d.) 225.8 (44.6) 205.5 (71.9) 202.3 (52.6)

Mean weight loss in pounds (s.d.) −4.6 (7.7) −4.1 (16.1) −8.5 (12.9)

Mean baseline HbA1c (s.d.) 6.97 (1.32) 6.86 (2.22) 6.65 (1.21)

Mean baseline BMI (s.d.) 36.4 (6.3) 34.9 (9.4) 34.2 (7.6)

M=males; F = females; s.d. = standard deviation.


weight loss over the course of the year. Our results concurwith research by Boutelle and Kirschenbaum [13] who foundthat consistent trackers lost more weight in an 8-week study.Our results extend these findings by showing consistenttracking continues to be beneficial over the course of a 1-year program, highlighting the benefit of consistent trackingas a safe and sustainable effort to improve long-term weightloss success.

While studies present equivocal benefit in weight out-comes with increased adherence to tracking, an 8-week studyby Wharton et al. [11] found that those who tracked withmobile phone application tracked more days than thosewho tracked with either pen and paper or the memo padon their phone, yet weight loss did not vary between thegroups. However, those in the pen and paper and memogroups received nutritional counseling prior to the start ofthe program and received weekly emails to encourage healthy

eating. The application group received no dietary advice out-side the information from the nutritional software on totalcalories and macronutrient consumption. Hence, it is possi-ble that the weekly emails or the nutritional counseling priorto the start of the program may have hidden the effect-increased adherence to dietary tracking of participants inthe mobile phone application group on their final weightchange. The results from the current study provide emergingevidence on the impact of increased adherence to dietarytracking on weight loss due to the standardized assessmentof all participants in the program.

Another (similar) study did not find a difference inweight change among active duty overweight military mem-bers (BMI≥ 25 kg/m2) who tracked with their preferredmethod (phone, pen and paper, or web-based application)versus their nonpreferred method [10]. Each participantwas provided with the same nutritional feedback and support

Table 2: Comparison of model parameters (standard error) across the null, control, and final models on final weight outcome in pounds.

Null model Control model Final model

Intercept

Intercept2 −4.69 (1.75)a −4.58 (1.88)b −2.40 (2.29)Wk-1 loss −0.37 (0.65) 0.03 (0.62)

BMI_0 −0.39 (0.25) −0.45 (0.23)HbA1c_0 1.00 (1.28) 0.49 (1.20)

Gender 0.33 (3.83) 1.49 (3.56)

Inconsistent 4.97 (5.15)

Consistent −7.59 (3.60)b

Linear time slope

Intercept2 −0.53 (0.07)a −0.46 (0.08)a −0.61 (0.12)a

Wk-1 loss 0.01 (0.03) 0.003 (0.03)

BMI_0 −0.01 (0.01) −0.01 (0.01)HbA1c_0 −0.01 (0.05) −0.002 (0.05)Gender 0.30 (0.16) 0.28 (0.23)


Consistent 0.22 (0.16)

Quadratic time slope

Intercept2 −0.02 (0.003)a −0.02 (0.004)a −0.03 (0.01)a

Wk-1 loss 0.0004 (0.001) −0.0007 (0.001)BMI_0 0.0002 (0.0005) 0.0004 (0.0005)

HbA1c_0 −0.004 (0.002) −0.003 (0.002)Gender 0.007 (0.007) 0.004 (0.008)


Consistent 0.024 (0.007)a

Cubic time slope

Intercept2 −0.0002 (0.00005)a −0.0002 (0.00005)a −0.0003 (0.00007)a

Wk-1 loss −0.0000 (0.00002) −0.00002 (0.00002)BMI_0 0.00001 (0.00001) 0.00001 (0.00001)

HbA1c_0 −0.00006 (0.00003) −0.00005 (0.00003)Gender 0.00002 (0.0001) −0.00002 (0.0001)Inconsistent 0.0002 (0.0002)

Consistent 0.0003 (0.0001)a

ap < 0 01, bp < 0 05, (standard error).


through group classes, similar to the DPM program. How-ever, this 12-week study did not model weight change overtime, did not control for participant characteristics, and wascompleted with a group of military personnel who differfrom the general population, which may have minimizedthe ability to detect the impact of consistent tracking onweight change.

The current study found that a polynomial model pro-vided the best fit for the participant weight data as it createda curvilinear slope to best fit weight change over time. Asexpected, we detected greater weight loss for the majority ofthe participants during the first quarter of the program, asseen by the large effect of the linear time term, with this effectlessening over time. However, the quadratic time coefficientdecreased the rate of weight loss, while the cubic time coeffi-cient increased the rate of weight loss. Finally, the nonlineartrend of weight change was logical since the model with ses-sion dates revealed that the slowing in weight loss corre-sponded with the beginning of the holidays; similarly, theincrease in weight loss in the last quarter of the programwas associated with the beginning of summer months. Thisfinding concurs with a qualitative research study thatreported participants face more barriers to healthy behaviorsduring the holidays [18]. However, with the beginning ofsummer, weight loss becomes easier as the warm weatherallows participants to do outdoor activities, exercise, andshop for healthier food options.

Our results suggest that consistent tracking had a signif-icant impact on weight change over time. Tracking over228 days did not have an effect on the linear, or instanta-neous, rate of change. Early in the program weight changeof consistent trackers did not differ from rare or inconsistentdietary trackers. However, rare or inconsistent trackersgained weight during the holidays but the consistent trackers’rate of weight loss did not change as they sustained their rateof weight loss from the first quarter. Hence, consistent die-tary tracking seems to have a protective effect on participants’holiday eating challenges as they were able to survive the hol-iday parties and managed their holiday stress without addingextra pounds to their weight. Perhaps, consistent trackinghelped these participants stay on track with programmaticgoals and topics, such as planning healthy snacks and mealsduring the holiday season and being aware of fat, sugar,and calorie consumption. Thus, the rate of weight loss forconsistent trackers closely followed the initial rate of changeshown at the beginning of the program, as consistent track-ing cancelled out the effect of the quadratic and cubic terms.This indicates that consistent tracking predicted more stableweight loss over time. However, future research shouldexplore other factors that might impact weight loss to betterunderstand both the process by which consistent trackingimpacts weight loss as well as the outcome of sustainableweight loss. It is possible that consistent tracking allowedparticipants to be more mindful of their dietary habits andbe motivated to avoid high calorie, fat, and sugary items(e.g., eating a cookie) during the holiday season to avoidwriting it down in their tracking book. Additionally, theparticipants who consistently track may be more compliantand thus more likely to track and follow dietary

recommendations of the program. Hence, it is possible thatparticipant’s personal characteristics could impact trackingadherence and weight outcomes.

Consistent tracking can also be viewed as a measure ofresilience or ability to stay on track during challenging times(like the holiday season). As shown in the graph (Figure 3),the weight loss over time for a participant who consistentlytrack followed a stable, negative linear trend. In contrast,weight loss over time for those who rarely or inconsistentlytrack was unstable, as they lost weight in the first and lastquarter of the program, while they gained weight duringthe middle, during the holiday seasons (Thanksgiving andChristmas), and during the winter months. The weight losspredicted during the second half of the program, that is,spring and summer months, may be explained by anincreased motivation to change dietary habits in order to fitinto beachwear and summer clothes for increased outdooractivities as the temperature warms up during this time ofthe year in West Virginia.

It is interesting to note that the rare trackers (i.e., hadfood journals for less than 114 days) and the inconsistenttrackers (i.e., those who had food journals and tracked theirdietary intake between 114 to 228 days of the year) did nothave significant or sustainable weight loss over the programperiod for the 12 months. Hence, successful behavioralinterventions should emphasize the benefits of consistentdietary tracking for participants, motivating individuals totrack for at least 5 days of each week for sustained and clin-ically significant weight loss. Inconsistent tracking of dietaryintake did not impact weight loss, and individuals did notdiffer in their overall weight loss from those who rarelytrack. This data concurs with what has been reported inprior research [12, 13].

6. Limitations

A primary limitation was that the DPM program was a pre-post, uncontrolled intervention study, with the majority ofparticipants who are non-Hispanic Whites (which are repre-sentative of the state). Furthermore, a quarter or 26% of par-ticipants had missing final weight information due to eithermissing the postintervention assessment or dropping outand were therefore excluded from the analysis. Another lim-itation was the use of self-reported adherence to diet. Hence,the model represents those who completed the program andthe dietary tracking and not all program participants. Thelow number of participants who inconsistently track mayhave limited the ability of the model to detect a statisticallysignificant effect over time. Treating tracking as a level 2 var-iable alone does not provide a complete evaluation of therelationship between tracking and weight loss over time; itinstead treats tracking as a static variable that does not varyweekly and over the program period. Hence, future researchcan enter number of days tracked as a time varying covariantin order to determine the effect of tracking on weight changethroughout the program. While dietary tracking shows par-ticipants’ level of commitment to tracking, weight loss isinfluenced by factors other than diet (such as physicalactivity, level of stress, medication management, and


comorbidities) that need to be included in future models.Finally, more research is needed on larger samples to confirmthis finding and to expand our understanding of the relation-ship between tracking and weight change over time.

7. Conclusions

In summary, dietary tracking was found to be an importantcomponent of successful weight loss, with those who trackedat least 5 days of each week showing significant and sustainedweight loss over time as compared to those who trackedfewer days or inconsistently during the program. Consistenttracking is a significant predictor of weight loss, resulting inan additional seven pounds of weight loss over the courseof the program suggesting the intervention successfullyachieved clinically and significant long-term weight loss inhigh-risk rural Appalachian adults with diabetes and predia-betes. In addition, a model of weight change over timerevealed that more weight was lost over the summer as com-pared with the holiday season. Despite potential challenges toeating healthy during holidays, those who consistentlymaintained their food journal and tracked their caloriesand fat intake did not experience an increase in weight overthe holidays, indicating that consistent tracking may act as aprotective factor to the challenges of following a healthy life-style during the holidays. Future research can test thishypothesis by looking at changes in dietary tracking overtime to determine if certain periods of tracking are criticalto weight loss success.

Conflicts of Interest

The authors declare that there is no conflict of interestregarding the publication of this paper.

References

[1] International Diabetes Federation, IDF Diabetes Atlas, Inter-national Diabetes Federation, Brussels, Belgium, 2015, http://www.diabetesatlas.org/.

[2] International Diabetes Federation, Complications of diabetes,International Diabetes Federation, 2016, http://www.idf.org/complications-diabetes.

[3] Center for Disease Control, “U.S. Diabetes SurveillanceSystem,” October 2016, http://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html.

[4] American Diabetes Association, “Economic costs of diabetesin the U.S. in 2012,” Diabetes Care, vol. 36, no. 4, pp. 1033–1046, 2013.

[5] W. C. Knowler, E. Barrett-Connor, S. E. Fowler et al., “Reduc-tion in the incidence of type 2 diabetes with lifestyle interven-tion or metformin,” The New England Journal of Medicine,vol. 346, no. 6, pp. 393–403, 2002.

[6] P. Vita, M. Cardona-Morrell, A. Bauman et al., “Type 2 diabe-tes prevention in the community: 12-month outcomes fromthe Sydney diabetes prevention program,” Diabetes Researchand Clinical Practice, vol. 112, pp. 13–19, 2016.

[7] J. Lindström, A. Louheranta, M. Mannelin et al., “The FinnishDiabetes Prevention Study (DPS): lifestyle intervention and

3-year results on diet and physical activity,” Diabetes Care,vol. 26, no. 12, pp. 3230–3236, 2003.

[8] X. R. Pan, G. W. Li, Y. H. Hu et al., “Effects of diet and exercisein preventing NIDDM in people with impaired glucosetolerance: the Da Qing IGT and Diabetes Study,” DiabetesCare, vol. 20, no. 4, pp. 537–544, 1997.

[9] H. L. Stuckey, J. Boan, J. L. Kraschnewski, M. Miller-Day,E.B.Lehman, andC.N.Sciamanna, “Usingpositivedeviance fordetermining successful weight-control practices,” QualitativeHealth Research, vol. 21, no. 4, pp. 563–579, 2011.

[10] L. E. Shay, D. Seibert, D. Watts, T. Sbrocco, and C. Pagliara,“Adherence and weight loss outcomes associated with food-exercise diary preference in a military weight managementprogram,” Eating Behaviors, vol. 10, no. 4, pp. 220–227, 2009.

[11] C. M. Wharton, C. S. Johnston, B. K. Cunningham, andD. Sterner, “Dietary self-monitoring, but not dietary quality,improves with use of smartphone app technology in an 8-weekweight loss trial,” Journal of Nutrition Education and Behavior,vol. 46, no. 5, pp. 440–444, 2014.

[12] J. F. Hollis, C. M. Gullion, V. J. Stevens et al., “Weight lossduring the intensive intervention phase of the weight-lossmaintenance trial,” American Journal of Preventive Medicine,vol. 35, no. 2, pp. 118–126, 2008.

[13] K. N. Boutelle and D. S. Kirschenbaum, “Further supportfor consistent self-monitoring as a vital component of success-ful weight control,” Obesity Research, vol. 6, no. 3, pp. 219–224, 1998.

[14] C. G. Abildso, S. Zizzi, D. Gilleland, J. Thomas, and D. Bonner,“A mixed methods evaluation of a 12-week insurance-sponsored weight management program incorporatingcognitive-behavioral counseling,” Journal of Mixed MethodsResearch, vol. 4, no. 4, pp. 278–294, 2010.

[15] S. W. Raudenbush, A. S. Bryk, and R. Congdon, HLM 7.01for Windows, Scientific Software International, Inc., Skokie,IL, 2013.

[16] Anon, “AADE7 Self-Care Behaviors,” October 2016, https://www.diabeteseducator.org/patient-resources/aade7-self-care-behaviors.

[17] A. Borushek, The CalorieKing Calorie, Fat & CarbohydrateCounter, Family Health Publications, Costa Mesa, CA, USA,2014.

[18] E. M. Venditti, J. Wylie-Rosett, L. M. Delahanty, L. Mele,M.A.Hoskin, and S. L. Edelstein, “Short and long-term lifestylecoaching approaches used to address diverse participantbarriers toweight loss andphysical activity adherence,” Interna-tional Journal of Behavioral Nutrition and Physical Activity,vol. 11, no. 16, 2014.


http://www.diabetesatlas.org/

http://www.diabetesatlas.org/

http://www.idf.org/complications-diabetes

http://www.idf.org/complications-diabetes

http://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html

http://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html

https://www.diabeteseducator.org/patient-resources/aade7-self-care-behaviors



Submit your manuscripts athttps://www.hindawi.com

Stem CellsInternational

Hindawi Publishing Corporationhttp://www.hindawi.com Volume 2014


MEDIATORSINFLAMMATION

of


Behavioural Neurology

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

The Scientific World JournalHindawi Publishing Corporation http://www.hindawi.com Volume 2014

Immunology ResearchHindawi Publishing Corporationhttp://www.hindawi.com Volume 2014

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinson’s Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttp://www.hindawi.com

TheEffectofAdherencetoDietaryTrackingonWeightLoss:Using...

Documents

Transcript of TheEffectofAdherencetoDietaryTrackingonWeightLoss:Using...