Behavior Research Methads. Instruments, & Computers1987, 19 (2), 215-223

SESSION IXUSING COMPUTERS IN APPLIED SETTINGS

Paula Goolkasian, Chair

Behavioral assessment of eating patterns andblood glucose in diabetes using the

Self-Monitoring Analysis System

DAVID G. SCHLUNDT and CRYSTAL BELLVanderbilt University, Nashville, Tennessee

The Self-Monitoring Analysis System is a set ofmicrocomputer programs that were developedto removeclinicaljudgment fromthe task ofbehavioral assessmentusing self-monitoring diaries.Theprogramis written in Turbo Pascaland runs on MS-DOS (I6-bit) or CP/M (8-bit) microcomputersystems.The program allowsthe user to customdesign data bases with hundreds ofbinary, cate­gorical, integral, and real variables. Full-screen data-entry forms can be custom designed, andrandom accessfiles allowfor flexibledata entry and editing. The program makes extensive utili­zation ofPascal's very flexibledata structure capabilities. Dynamicallocation ofmemoryallowsthe programto maximize its use ofavailableRAM. Data froma patient with diabetes is presentedas a case study illustrating the system's utility.

Microcomputer applications are becoming increasinglyimportant in clinical psychology. Such applications asautomated test scoring and interpretation, computerizedbehavioral interviews, and clinical management databasestakemany tedious tasksawayfromtheclinician. Themostexciting new applications are those that allow the clini­cianto performtasksthat werepreviously difficult or im­possibleto do. The on-line processing of psychophysio­logical data, for example, opens up new possibilities inthe area of biofeedback.

Behavioral medicine is an area of psychology in whichnew microcomputer applications are being developed.This paper presents one such application, the Self­Monitoring Analysis System,and describesits use in theassessment of eating patterns and blood glucose controlin diabetes.

SELF-MONITORING ANDBEHAVIORAL ASSESSMENT

Behavioral clinical psychologists are interested in theassessment and modification of patients' behavior in the

This research was supported in part by NIH Grant 5P60 AM20593.David G. Schlundt is with the Diabetes Research and Training Centerand the Department of Psychology, and Crystal Bell is with the Depart­ment of Psychology. Address requests for reprints or for informationabout the availability of SMAS to: David G. Schlundt, Department ofPsychology, 134 Wesley Hall, Vanderbilt University, Nashville,TN 37240.

natural environment. To further these aims, behavioralassessment wasdeveloped as an alternativeto traditionalpersonality assessment (Goldfried & Kent, 1972). In tradi­tionalpersonality assessment, behavioris treatedas a signof the individual's underlying personality. In behavioralassessment, behavior is treated as a sample of how theindividual behaves under specific stimulus conditions.Methods of behavioral assessment, therefore, involve ob­taining samples of behaviorunderstimulus conditions thatare as closeto real life as possible(Goldfried& Linehan,1977).

A numberof methods havebeen developed for obtain­ing behavior samples for assessment purposes. Thesemethods include behavior checklists (Barlow, Abel, &Blanchard, 1977), behavioral interviews (Linehan, 1977),role playing tests (Freedman, Rosenthal, Donahoe,Schlundt, & McFall, 1978), directobservation of behavior(Dodge, Schlundt, Schocken, & Delugach, 1984), situa­tionalinventories (Levenson & Gottman, 1978), andself­monitoring (Nelson, 1977).Self-monitoring is a particu­larly promisingtechniquein which the subject keeps anongoing recordor diaryof behavioras it occursin every­day situations. Self-monitoring often not only involveskeeping track of target behaviors-such as social inter­actions (Twentyman & McFall, 1975),eating(Mahoney,1974),and smoking (McFall, 1970)-but also allowsthesystematic collection of data about the antecedents andconsequences of behavior. Collectionof information onantecedents, behaviors, and consequences allows the psy-

215 Copyright 1987 Psychonomic Society, Inc.

216 SCHLUNDT AND BELL

chologist to perform a functional analysis of behavior inwhich hypotheses about the environmental events that con­trol the behavior are generated (Schlundt, 1985).

When clients keep a self-monitoring diary, a largeamount of data is often generated. Typically, cliniciansreview the records and use clinical judgment to identifypatterns and draw inferences about functional relationshipsamong antecedents, behaviors, and consequences.Although the clinical review of self-monitoring recordsprovides data that might not otherwise be obtained, clini­cal judgment is known to be subject to inaccuracies andbiases (Meehl, 1954). Many studies across a diverse rangeof domains have shown that statistical prediction is typi­cally more accurate and reliable than clinical judgment(Sawyer, 1966).

THE SELF-MONITORINGANALYSIS SYSTEM

The Self-Monitoring Analysis System (Schlundt, 1985,1986, in press) is a microcomputer-based set of programsthat was developed to remove clinical judgment from theanalysis of self-monitoring data in behavioral assessment.The Self-Monitoring Analysis System Version 3.0(SMAS-3.0) consists of three microcomputer programswritten in Turbo Pascal (Borland International, 1985) thatrun on CP/M and MS-DOS microcomputers. CP/M is oneof the most popular operating systems in use on 8-bitmicrocomputers, such as Kaypro ITs and IVs, Osbornes,and TRS-80s. MS-DOS is a widely used operating sys­tem for 16-bit microcomputers (IBM PC and compati­bles). When Borland International makes a compiler avail­able for 32-bit microcomputer systems (e.g., Macintosh),SMAS can be made available on these systems as well.Three major programs make up the SMAS system:SETUP, ENTER, and SUMMARY.

SETUP allows the user to design a data base that cor­responds to the information collected by subjects in self­monitoring diaries. It enables the user to create hundredsof binary, nominal, integral, and real variables and al­lows the user to define and name the categories of binaryand nominal variables. SETUP also allows the user greatflexibility in the design of data entry screens. SETUPresults in the creation of a file family definition that servesas a template for the creation of patient data bases.

ENTER enables the user to create, maintain, andmodify self-monitoring data bases. Each patient's data isentered into a separate file that is identified by a uniquethree-letter data extension. ENTER implements a full­screen data entry routine and allows for random accessof records in the data base for editing purposes. It alsoperforms checks on values entered for each variable toguard against common data entry errors.

SUMMARY takes a file of data analysis commands andproduces statistical analyses of patient data bases as itsoutput. The commands that were implemented are thosethat produce results that will enable the clinician to iden-

Table 1Commands Available in the SUMMARY program

Corrunand Action

CATPLOT Plots the probability distribution of a categoricalor binary variable.

INTCAT Plots the mean values of an integer or real vari­able for each category of a categorical or binaryvariable and performs a one-way analysis ofvariance.

CROSSTAB Produces a two-way contingency table that dis­plays the association between two categorical orbinary variables.

CORREL Measures the association between two integer orreal variables using Pearson correlationcoefficient.

MULTI Performs log-linear contingency table analysis andmultivariate information analysis on three to sevencategorical or binary variables.

EXCLUDE Used to selectively exclude observations from ananalysis.

LAG Creates time-lagged variables for use in sequentialanalysis.

BREAK Used in sequential analysis applications to detectbreaks in behavior sequences.

QSCORE Used to create new variables that are linear com­binations of old variables.

REVERSE Rescales integer variables. This is useful in scor­ing rating scales.

PLOTVARS Generates a plot of the value of each of a user­specified set of variables for each record in thedata file.

LIST Generates a line printer listing of the data base.

DISPLAY Allows the user to select variables for output to adisk file in ASCII format. This allows SMAS databases to be used by other computer programs.

UPDATE Merges newly created variables with old variablesinto a new file family.

tify behavioral patterns without having to rely on clinicaljudgment. Table 1 presents the available commands andtheir results. SUMMARY is written to allow for flexiblerepetitive processing of the members of a file family; thatis, a single run of SUMMARY can produce individualanalyses of self-monitoring data for up to 25 patients. Autility program, MERGE, allows data from any numberof patients to be pooled for group analysis.

The statistical procedures implemented in SUMMARYprovide a general set of routines for analyzing observa­tional data (e.g., Schlundt, 1982). When behavior is ob­served over time. the individual behavioral observationsoften do not meet the statistical assumption of indepen­dent random sampling (see Schlundt & Donahoe, 1983).To overcome the problem of nonindependence, specialsequential data analysis techniques must be used (Chat­field & Lemon, 1970). SUMMARY contains facilities forperforming sequential analysis of the interaction of be­havior and environmental events over time using Mar­kov chains (Schlundt, 1985).

Programming Problems and Their Solutionin the Development of SMAS

In developing any complex computer program, a se­ries of decisions mustbemadein order to generatecom­putercodethatwillaccomplish thedesiredobjectives. Toillustrate this process and to give insight into the innerworkings of SMAS, we present a numberof these deci­sions and their solutions.

Programming language. Thechoice of whatprogram­ming language to use when developing a complex com­puter application has far-reaching consequences. SMASwentthrough twoearlier versions. Version 1.0 waswrit­ten in ffiM/System 23 BASIC. The problems with thischoice were that the program ran only on the ffiM­DATAMASTER (an 8-bit ffiM microcomputer thatprecededthe 16-bitffiM PC) and that BASIC, an inter­preted language, is very slow. Version 2.0 was rewrit­ten in C-BASIC (a semicompiled versionof BASIC thatoperateson CP/M and MS-DOS systems). This versionof SMAS was faster, but the program had serious flawsin its implementation that made editing data files verydifficult. Rather than fixtheseproblems, Version 3.0 wasrewritten in Turbo Pascal.

Pascal was chosen over BASIC for several reasons:(l) it is a true compiled language so that its executionspeed is as fastas possible, (2) it offersmuch greaterflexi­bility in creatingdata structuresthan does BASIC, (3) itallows for dynamic allocation of memory, and (4) it isa structured programming language that makes the jobof translating algorithms into understandable computercode much easier than does BASIC.

Turbo Pascal is an extremely easy to use softwarede­velopment system.It is a menu-driven systemconsistingof an editorwiththe samecommand set as WORDSTARfor creating and modifying programs, file and memorymanagement utilities, and the actualcompiler. Whenthecompilerencounters a syntaxerror, control is passed tothe editor, whichpositions the cursor at the error in theprogram. Whenan execution error occurs, thesystemlo­cates its source and passes control to the editor, whichpositions the cursor at the locationof the run-time error.These features make the development and debugging ofcomplex programs easy and quick.

Flexibility. One of the first decisions that had to bemade concerning the writing of SMAS was the degreeto whichthe program was to be flexible. Writing a pro­gram that takes data from a single diary and analyzes itwouldbe relatively trivial. Writinga program that takesmany diaries (some of which will be conceived of anddeveloped yearsin the future) requires a much morecom­plex program. The decision was made to create a veryflexible system that could handle a variety of differentkinds of diaries.

The solution to this problemwasthe programSETUP.SETUPcreatesa definition file thatcontains informationon the number of variables, the name or type of eachvari­able, and its location and field size on the data entryscreen. So thatchanges in definitions couldbe made, the

DIABETES SELF-MONITORING 217

definition fileis createdin ASCII format (words andnum­bers that can be edited and changed using a word­processing program).

Whendeveloping a program, it is difficult to anticipateall of itspossible usesin the future. For example, the pro­gram mustallocatespaceto store information abouteachvariable that is declared. Pascal lets the user definecon­stants at the beginning of the program that can be usedto allocate space when arrays and other data structuresare declared. For example,changing the numberof vari­ables that can be usedat one time simplyrequireschang­ing the valueof a singleconstant, MAXVARS, at the be­ginningof the program. Changing this constantchangesthe size of all of the arrays that are used to keep trackof the variables throughout the program.

Data entry. Since diaries generate large numbers ofobservations, much of the user's time will be spent en­tering data into the computer. In addition, experience inusingVersions 1.0and2.0 provedthatnodataentryoper­ator is infallible, making frequent changes to thedata basenecessary. It was decided that a separatedata entry anddataediting programwouldbe developed. Sincethispro­gram wouldbe used frequently, the goal was to make itas easy as possible to use. This led to the developmentof a set of Pascalprocedures implementing a full-screendata entry processor.

A full-screen processorpresentsthe user witha screencontaining a number of fields, witheachfield labeled witha variable name. The fields are blank when new data isbeing entered, and they containthe contents of the cur­rent record when the program is being used to edit datafiles. The cursor is initially placedin the first field. Theentry operatorbeginsto type numbers in the blankfieldsor makedesired changes. Cursorarrowsandcontrol keysallow editing functions within fields and the movementof the cursor from one field to the next.

Turbo Pascalhas a function that reads the last charac­ter typed at the keyboard. Thischaracter is thenprocessedusing the Pascal CASE statement. The CASE statementallows the user to specify a list of values or ranges ofvaluesand the kindsof actionsto be takendepending onthe value of the variable. For example, enteringa num­ber or letter causes the program to add that character tothe string it is building for the current data field, printsthe character on the screen, and then moves the cursorone space to the right to await entry of the next charac­ter. If the backarrow key is pressed, thecursor is movedone spaceto the left, andan internal pointeridentifies theplacein the currentstringthatcan be changed by the nextkeystroke. The fmal result is passing an array of enteredstrings back to the part of the program calling the dataentry procedure.

Data file layout. Anotherproblemencountered in de­veloping SMAS was how to store data in disk files in anefficient and accessible manner. Efficiency refersto stor­ing the most data in the least space, and accessibilitymeans enabling the user to accessany record in the databaseat random(this is opposed to the simplest file struc-

218 SCHLUNDT AND BELL

ture, which allows only sequential access). The majorproblem encountered in implementing these specificationswas created by the flexibility of the system. Since it isnot possible to know the number and types of variablesahead of time, it was not possible to determine the recordsize needed until run time. Random access files in Pascal(and most other languages) require specification of therecord size at compilation time. The ability to specify datastructures at run time rather than at compilation time isknown as dynamic allocation.

The dynamic allocation of the file structure was solvedby creating a random access file that consists of five­character strings. At run time, the number of five­character strings per record is calculated, and this valueis used to determine the starting location of each recordin the file. Each five-character string is used to store thevalues of five binary or categorical variables, or one in­tegral variable. Real numbers are stored in two consecu­tive five-character strings. This strategy allows categori­cal variables to have up to 128 values (the number ofdifferent ASCII characters). The program first packs allof the binary values into as few five-character blocks aspossible, then all of the categorical, then all of the integral,and finally all of the real values. This algorithm effec­tively implemented dynamic allocation of random accessdata file structure. The program ENTER can be used tocreate, extend, and edit these dynamically allocated ran­dom access files. The main disadvantage of using this al­gorithm is that the data cannot be edited using a wordprocessor so to read the data for use in other programs,special unpacking routines must be written. This is nota serious limitation, since the DISPLAY command of theSUMMARY program creates an ASCII file from a SMASdata base.

Dynamic allocation of RAM for data analysis. TheSUMMARY program reads the data from the speciallyformatted data file, stores it in random access memory(RAM), and allows the user to analyze this data using aseries of commands contained in a control file. Again,the flexibility of the program created special problems inallocating RAM space for data storage. Some data setsconsist of few variables with lots of observations, andsome contain lots of variables and fewer observations. Thetotal number of variables and observations is unknownat compilation time, thus requiring that either a very largefixed data table be allocated or that a dynamic allocationstrategy be used. Storage space is allocated for each vari­able in arrays of 256 slots that are linked together to forma list of data arrays. The slots are 1 byte for binary andcategorical variables, 2 bytes for integers, and 8 bytes forreal numbers. To maximize use of memory space, eightbinary variables are packed into each element of the ar­ray (one variable per bit). Thus the use of data packingand dynamic allocation of linked lists of data tables en­able SMAS to custom configure the allocation of RAMin a very efficient manner to accommodate many differ­ent data base configurations.

Expansion of program size. The bulk of the program­ming for SMAS involved developing a procedure for eachof the analysis commands implemented in the SUM­MARY program. This was facilitated by writing a com­mon set of data access routines that are used by all of theprocedures. However, as each new procedure was writ­ten, the total size of the program grew. One limitationof Turbo Pascal is that the compiled program can be nolarger than 64K. This problem was circumvented by usingTurbo Pascal's overlay processor. Blocks of memory areallocated at compilation time. The first block contains themain control routine of the program. The other blocksare called overlays, each of which may contain any num­ber of procedures. The amount of space allocated for theoverlay corresponds to the amount of space required bythe largest procedure. When a procedure in an overlayis called, it is read into memory from the disk. There isa separate disk file of compiled code for each of the over­lay blocks. The procedure remains in memory and is ex­ecuted directly the next time it is called. If a differentprocedure in the overlay block is called, it is read off thedisk replacing the previous procedure in memory. Fre­quently used routines are not overlaid, utility and memorymanagement routines are kept in separate overlays, andseveral overlays are allocated for command processorroutines.

This process of overlaying procedures allowed for thedevelopment of the many analytic procedures presentedin Table 1. The extension of SMAS by the addition ofnew commands is done by adding the new procedures toexisting overlay blocks. Thus the program is greatly ex­pandable without ever exceeding the 64K limitation.

Application of SMAS in Behavioral MedicineSMAS has been used mainly for the assessment and

modification of eating behavior in bulimia, hypertension,and obesity. Schlundt, Johnson, and Jarrell (1985) ana­lyzed eating diaries of 8 bulimic and 20 obese patientsin order to identify situations that precipitate vomiting,binge eating, and overeating. They found that the environ­mental control of eating behavior was similar for obeseand bulimic subjects in that being alone at home in theevening and being in a negative mood was strongly as­sociated with binging and vomiting for the bulimic sam­ple and with overeating for the obese sample. In addi­tion, obese subjects were observed to have significantproblems with overeating and excess calorie intake in posi­tive social situations.

Schlundt, Johnson, and Jarrell (1986) used SMAS inconjunction with Markov chain analysis to study se­quences of eating episodes in 8 bulimic patients under­going behavior therapy. In addition to the high-risk situ­ations identified in the Schlundt et al. (1985) study, thesequential data analysis led to the identification of addi­tional behavior patterns. Self-induced vomiting was ob­served to be highly autocorrelated such that once vomit­ing occurs at one eating episode, the probability that

vomiting will occur at the subsequent episode is greatlyincreased. Repeated episodes of vomiting were muchmore likelyto occur whenboth mealswere eatenduringthe same time period, especially during the evening. Inaddition, skipping of meals, particularly of breakfast, wasassociated with a higherprobability of vomiting later inthe day.

SMAS is potentially useful for the study of anybehaviorthat can easilybe self-monitored over time. The processinvolves firstidentifying the targetbehaviors, theenviron­mental andbehavioral antecedents, andthe potential con­sequences. Thesevariables are thenusedto createa self­monitoring diary that subjects are instructed to keep. Afile family definition is thencreated, which translates thediary into a SMAS data base. The diaries are collected,entered into the computer, and SUMMARY is used togenerate analytical printouts. Theseprintouts canbe usedfor individual behavioral assessment, as a part of a be­havior therapy program, for research purposes, or forpro­gram evaluation.

Application of SMAS to BehavioralAssessment in Diabetes

Of the manychronic diseases that afflictpeople, dia­betes involves one of the most complex treatment regi­mens. Theeffective management of diabetes involves thecontrolof bloodglucose levelsthroughthe simultaneousadjustment of insulin injections, foodintake,andactivity(Unger, 1982). As withmostchronic diseases, poorcom­pliance withtreatment procedure is a serious problem indiabetes (Sackett, 1976).

In order to betterunderstand andpromote patient com­pliance, psychologists have begunto apply the methodsof behavioral medicine to the long-term management ofdiabetes (Fisher, Delamater, Bertelson, & Kirkley, 1982).Psychologists havebecome concerned withthe social andpsychological adjustment of patients with diabetes andwithhowthis affects their medical care (Mazze, Lucido,& Shamoon, 1984). The adventof home blood glucosemonitoring has led to the widespread use of self­monitoring techniques in diabetes care (Wing et al.,1985). In addition, the use of the self-monitored foodin­take technique is easilyapplied to the problemof assess­ing and modifying compliance with diabetic diets (Pateet al., 1986).

Table 2 presents an example of a self-monitoring di­ary that we have used with adults diagnosed as havinginsulin-dependent diabetes mellitus (lDDM). The formis duplicated andmade intoa booklet thatcontains enoughblankpages for recording a week's worth of meals. Pa­tients are instructed tocomplete a newpageforeachmealor snack eaten. The use of multiple-choice formats andrating scalesallows the form to be entered directly intothe computer withnojudgment or interpretation requiredon thepart of the operator. Responses on this diary sam­ple many of the environmental, behavioral, emotional,

DIABETES SELF-MONITORING 219

Table 2A Self-Monitoring Diary for Use with IDDM Patients

Time (a.m. p.m.) Date__ I__Day (Mo Tu We Th Fr Sa Su)Meal (Breakfast Mom-Snack Lunch Aft-8nack Supper Eve-Snack Other)

Place (Home Work Restaurant Social-Event Car Other)People (Family Friend Alone Other)Depressed (- - - N + + +) HappyAnxious (-- - N + ++) CalmStressed (- - - N + + +) No PressureTired (- - - N + + +) EnergeticHungry (-- - N + ++) FullAmount of food eaten (Undereat Just-right Overeat)Foods andBeverages Brand Name HowPrepared Amount Measured_______________ y_ N________________ y_ N________________ y_ N________________ y_ N________________ Y_ N________________ Y_ N________________ y_ N________________ y_ N_

Anysymptoms of lowbloodsugarsincelastmeal or snack(Yes No)Blood Glucose Reading: (Yes No) Valueof reading __Exercised sincelastmeal(Yes No) Ifyes then__ minutesInjection before meal (Yes No)

and physiological variables associated with each eatingepisode.

To illustrate theuse of SMAS, dataare presented froma 17-year-old white female diagnosed as having IDDMwho was referred by her physician for dietary compli­ance counseling. This patient had steadily gained 18pounds over the past 3 years with no change in height.At the time she was seen, she was 62112 in. tall andweighed 146lbs. Her glycosolated hemoglobin (HbAlc)

was measured at 16.2 at the time of referral, indicatingpoor glycemic control (Trivelli, Ranney, & Lai, 1971).She had been prescribed a 1,015 Kcal diet abouta yearago by the clinicdietitian but experienced no success infollowing it and losingweight. Her dietprescription wasbased on the diabetic exchange system and consisted of245 calories for breakfast, 245 calories for lunch, 315calories for supper, andanevening snackof21O calories.

The patient was given the eating diary shown in Ta­ble 2 and was instructed to record all meals and snacksuntil her next appointment. She was told to follow herusual pattern so that an accurate picture of her currenteatinghabitscouldbe obtained. The patientkept the di­ary for 12daysbefore returning to the clinic. Eachfoodintakewascodedusing the diabetic exchange system andentered into the computer using SMAS. The number ofservings fromeachof the sixdiabetic foodgroups (meat,milk, bread, vegetables, fruit, and fat) at each meal wasenteredintothe database, alongwith information on thecontext of the eatingepisode. All but 2 of her 55 eatingepisodes were reported in sufficient detail to allow for

220 SCHLUNDT AND BELL

coding of the nutrient intake. UsingQSCORE, the foodgroup servings were used to estimate the grams of pro­tein, carbohydrate, andfat consumed at eachmeal. Totalcalories per meal was then computed from the macro­nutrient content.

Figure 1 presents the average calorie intakeby meal.NotethatINTCAT alsocomputes andprintsthefrequencyand standard deviation. The reported calorieintakes werequitediscrepant fromher prescribed mealplan. Her dailycalorieintake wasaveraging over2,200calories, withherevening snack aloneexceeding her prescription by 319%.

The percentof calories from carbohydrate, protein, andfat wascalculated fromthe SMAS printouts for eachmealand is presented in Table 3. This table indicates that thepatientwas taking in too muchfat and too few carbohy­dratesat all mealsexcepther afternoon snack. It is clearthat evening snacking was the most serious problem re­quiringattention. Oneof the intervention goals with thispatientwasto teachher to makedietarysubstitutions thatwouldhelpher reduceher overallfat intakeand increaseher intakeof complex carbohydrates. Anothergoal wasto help her make changes in her activity patterns in the

brkf 448.346Sdev 244.137freq 13.000

msnk 0.000Sdev 0.000freq 0.000

1unc 497.167Sdev 156.852freq 12.000

asnk 167.600Sdev 107.625freq 5.000

supr 477.625Sdev 155.934freq 12.000

esnk 669.727Sdev 232.752freq 11.000

othr 0.000Sdev 0.000freq 0.000

miss· 0.000Sdev 0.000freq 0.000

***************************************************************************SMA S - self-Monitoring Analysis SystemAuthor - ravid G. Schlundt, Rl.D. (version 3.0)

Date= 2/13/1987 Time: 13:15:32Analysis of dietary intake for a 17-year old m:M patient.8elf-'lOOnitored situations, food, blood glUcose, exercise, ard insulin in IDrMFile Family = a:eat Extension = lac********************************************************************************* I N TeA T ** ** I N TeA T ** ** I N TeA T **

Mean values of calories for each category of meal

0.000 100.000 200.000 300.000 400.000 500.000 600.000I****I****I****I****I****I****I****I****I****I****I****I****III""""'#############'##'#"#"'#""#####'#I##################'##'###'##########'##'#,#,#I#'#################'###############'#########IIIIII#######################'########'######'########'#I#############################'#'#########'####""I##'#########,###,'##,#"#"",##"""##",,,,,###II##############'##I###############'#1"####'######'#'#II#######'######'#"'#####"#'#,"#"##'#"##"###I####'########'#'#####""""##"#"##""'#""1#"#'########"#"##"##""'#"##'#"'#'##"'##I1#"####"###############'################'##"""#'##.#•••#••#####1######################'##"'###'##'###""""'#'#'"###••••••##•••I'##'#'###'#'###"'#"#'##"#"##"""""""""'" ••••#••#.#.###IIIIIIIIII****1****1****1****I****I****I****1****1****I****I****I****I

0.000 100.000 200.000 300.000 400.000 500.000 600.000

Gram Mean =source

485.4906 Number of observations =55 df II\S F

53

BetweenWithin

898961.4422015676.303

4 224740.36148 41993.256

5.352

Figure 1. Average calorie intake by meal.

Table 3Percent of Calories from Carbohydrate, Protein, and Fat by Meal

Source

MealBreakfastLunchAfternoon SnackSupperEvening Snack

Carbohydrate

4839674437

Protein

1619122017

Fat

3642223646

DIABETES SELF-MONITORING 221

evening so as to reduce the frequency and size of even­ing snacks.

Figure 2 presents the results of running CROSSTABon the variables depressed by the amount of food eaten(overeating). This patient did not make full use of theresponse scale onthedepression variable, inthatsheneverusedthe double-negative or double-positive ratings. Ex­amination of the adjusted residuals in this table (z-scoresmeasuring the association between pairs of categories)

•••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••**.***.***SMA S - Self-!blitorin] Analysis SystemAuthor - tevid G. SChlurxit, lb.D. (version 3.0)

tete= 2/13/198 Time= 13:15:18Analysis of dietary intake for a 17-year old IIXM patient.5elf-Dalitored situations, food, blood glucose, exercise, am insulin in IIXMFile Family .. a:eat EXtension = lac****************.**.***.******.**.***••*.*.**********.*************************

** C R 0 SST A B S ** ** C R 0 SST A B S ** ** C R 0 SST A B S ** ** C

crosstabulation of (rcw) depress by (001) overeat

Total JU.IIlIber of obseIvations .. 53

umr jsrt CNer missI I I I II OI OI OI 01 0

prcb I 0.00000 I 0.00000 I 0.00000 I 0.00000 I 0.00000P c/r I 0.00000 I 0.00000 I 0.00000 I 0.00000 IP ric I 0.00000 I 0.00000 I 0.00000 I 0.00000 IZ-res I 0.00 I 0.00 I 0.00 I 0.00 I

I -I I I I-I II 4I 81 OI 13

prcb I 0.01887 I 0.07547 I 0.15094 I 0.00000 I 0.24528P clr I 0.07692 I 0.30769 I 0.61538 I 0.00000 IP ric I 0.16667 I 0.14286 I 0.57143 I 0.00000 IZ-res I -0.39 I -1.17 I 2.55 I -1.12 I

I I I I IN I 2 I 9 I 4 I 3 I 18

prcb I 0.03774 I 0.16981 I 0.07547 I 0.05660 I 0.33962P clr I 0.11111 I 0.50000 I 0.22222 I 0.16667 IP ric I 0.33333 I 0.32143 I 0.28571 I 0.60000 IZ-res I -0.03 I -0.18 I -0.36 I 1.02 I

I I---I----1---I+ I 3 I 14 I 2 I 2 I 21

prcb I 0.05660 I 0.26415 I 0.03774 I 0.03774 I 0.39623P clr I 0.14286 I 0.66667 I 0.09524 I 0.09524 IP ric I 0.50000 I 0.50000 I 0.14286 I 0.40000 IZ-res I 0.41 I 0.98 I -1.59 I 0.01 I

I I 1-----1----It+ I a I 1 I 0 I 0 I 1

prcb I 0.00000 I 0.01887 I 0.00000 I 0.00000 I 0.01887P clr I 0.00000 I 1.00000 I 0.00000 I 0.00000 IP ric I 0.00000 I 0.03571 I 0.00000 I 0.00000 IZ-res I -0.34 I 0.65 I -0.52 I -0.31 I

I 1- 1---1-----1miss I OI 01 OI 01 0prcb I 0.00000 I 0.00000 I 0.00000 I 0.00000 I 0.00000P clr I 0.00000 I 0.00000 I 0.00000 I 0.00000 IP ric I 0.00000 I 0.00000 I 0.00000 I 0.00000 IZ-res I 0.00 I '0.00 I 0.00 I 0.00 I

I I-----1----I----I6 28 14 5

0.11321 0.52830 0.26415 0.09434au-square = 15. 041

Figure 2. Overeating x depression.

222 SCHLUNDT AND BELL

shows that there is a significant association between feel­ing somewhat depressed and overeating. The conditionalprobability of overeating given depression is 0.62 as com­pared with 0.22 for neutral and 0.10 for positive. Exami­nation of the CROSSTAB of meal x depression showedthat negative moods were not associated with any partic­ular meal. There was no significant association betweenovereating and anxiety, stress, or hunger. To further ana­lyze the impact of overeating, the average calories xcategory of overeating was generated. This analysisshowed that the mean caloric intake at overeating episodeswas 653, compared with 456 for just right and 434 forundereating. An additional therapeutic goal of teachingthis patient strategies other than overeating for dealingwith depression was identifiedon the basis of this analysis.

Although hunger was not associated with overeating,it was strongly related to caloric intake. When this pa­tient rated herself as very hungry, she consumed an aver­age of over 900 calories at each meal, compared with 350calories when she was somewhat full. These results sug­gest that any intervention strategy employed must enablethis patient to avoid becoming very hungry.

Compliance with blood glucose testing was examined.She tested and recorded blood glucose levels for nearlyevery breakfast and two thirds of her suppers. Her com­pliance with testing before lunch, however, was poor inthat she only tested 25% of the time. The relationship be­tween blood glucose values and hunger ratings was ex­amined using INTCAT. For this patient, lower blood glu­cose levels were associated with stronger hunger ratings.Her average blood glucose values at breakfast, lunch, andsupper were 302, 220, and 305, respectively, all indicat­ing poor control of her diabetes. The high morning valueis probably a consequence of her uncontrolled snackingin the evening.

Four goals were initially identified as intervention tar­gets for this patient: reducing the calorie size and fat con­tent of meals, reducing or eliminating overeating in theevening, learning alternative strategies for coping withdepression, and lowering premeal blood glucose values.In addition, the patient was warned that as she gains bet­ter control over her blood glucose, she may experienceincreased levels of hunger that may function as a stimu­lus for overeating. Consequently, this patient was coun­seled on using lower calorie foods and smaller meals asa way to eliminate hunger without overfeeding herself.

DISCUSSION

The Self-Monitoring Analysis System (SMAS) waspresented as a microcomputer application with manypotential uses in behavioral medicine. As with many as­sessment tools, it has seen its first application in a clini­cal research setting. The use of the system is somewhatexpensive in that it requires a microcomputer, the pro­gram, and time for coding, entering, and analyzing thedata. It does, however, result in a fine-grained analysis

of behavior that would take more than 10 times as longto do by hand. SMAS opens many new possibilities forprecision in the field of behavioral assessment, as was il­lustrated by the case study presented.

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