Effects of Occupational Stress Management Intervention Programs- A Meta-Analysis.pdf oke bana.pdf

Effects of Occupational Stress Management Intervention Programs:A Meta-Analysis

Katherine M. Richardson and Hannah R. RothsteinBaruch College, City University of New York

A meta-analysis was conducted to determine the effectiveness of stress management interventionsin occupational settings. Thirty-six experimental studies were included, representing 55 interven-tions. Total sample size was 2,847. Of the participants, 59% were female, mean age was 35.4, andaverage length of intervention was 7.4 weeks. The overall weighted effect size (Cohens d) for allstudies was 0.526 (95% confidence interval ! 0.364, 0.687), a significant medium to large effect.Interventions were coded as cognitivebehavioral, relaxation, organizational, multimodal, oralternative. Analyses based on these subgroups suggested that intervention type played a mod-erating role. Cognitivebehavioral programs consistently produced larger effects than other typesof interventions, but if additional treatment components were added the effect was reduced.Within the sample of studies, relaxation interventions were most frequently used, and organiza-tional interventions continued to be scarce. Effects were based mainly on psychological outcomevariables, as opposed to physiological or organizational measures. The examination of additionalmoderators such as treatment length, outcome variable, and occupation did not reveal significantvariations in effect size by intervention type.

Keywords: stress management, meta-analysis, employee intervention

Employee stress has increasingly become a con-cern for many organizations. To paraphrase the fa-ther of stress, Hans Selye, stress is an unavoidableconsequence of life, and therefore an unavoidableconsequence of organizations. Americans are work-ing longer and harder, and job stress continues toincrease. The average work year for prime-age work-ing couples in the United States increased by nearly700 hours in the past two decades (Murphy & Sauter,2003; U.S. Department of Labor, 1999). From 1997to 2001, the number of workers calling in sick be-cause of stress tripled. The American Institute ofStress reported that stress is a major factor in up to80% of all work-related injuries and 40% of work-place turnovers (Atkinson, 2004). This is not solelyan American phenomenon. The Confederation ofBritish Industry reported stress as the second highestcause of absenteeism among nonmanual workers inthe United Kingdom, and the European Foundationfor the Improvement of Living and Working Condi-tions reported that stress affects a third of the Euro-

pean working population (Giga, Cooper, & Faragher,2003). In Australia, most states report an increasingnumber of annual workers compensation claimsresulting from workplace stress (Caulfield, Chang,Dollard, & Elshaug, 2004). Organizations provide amajor portion of the total stress experienced by aperson as a result of the amount of time spent on thejob, the demands for performance, and the interactionwith others in the workplace (DeFrank & Cooper,1987).

Although it is not possible to eliminate stress en-tirely, people can learn to manage it. Many organi-zations have adopted stress management training pro-grams to try and reduce the stress levels of theirworkforce. A stress management intervention (SMI)is any activity or program initiated by an organizationthat focuses on reducing the presence of work-relatedstressors or on assisting individuals to minimize thenegative outcomes of exposure to these stressors(Ivancevich, Matteson, Freedman, & Phillips, 1990).Interest in strategies to reduce stress at work hasincreased steadily since the 1970s. According to theU.S. Department of Health and Human Services,national surveys conducted in 1985, 1992, and 1999found the prevalence of stress management and coun-seling programs among private-sector worksites inthose years was 27%, 37%, and 48%, respectively(Nigam, Murphy, & Swanson, 2003). The popularity

Katherine M. Richardson and Hannah R. Rothstein, De-partment of Management, Zicklin School of Business, Ba-ruch College, City University of New York.

Correspondence concerning this article should be addressedto Katherine M. Richardson, Baruch College, City Universityof New York, One Bernard Baruch Way, New York, NY10010. E-mail: [email protected]

Journal of Occupational Health Psychology2008, Vol. 13, No. 1, 6993

Copyright 2008 by the American Psychological Association1076-8998/08/$12.00 DOI: 10.1037/1076-8998.13.1.69

69

of worksite stress management programs has grownsignificantly abroad as well as in the United States.

Job Stress and Interventions

Newman and Beehr (1979, p.1) defined job stressas a situation wherein job-related factors interactwith the worker to change his or her psychologicaland/or physiological condition such that the person isforced to deviate from normal functioning. Implicitin this definition is the belief that work-related factorsare a cause of stress and that the individual outcomesmay be psychological, physiological, or some com-bination of these. A SMI may attempt to change thesework-related factors, assist employees in minimizingthe negative effects of these stressors, or both.Ivancevich et al. (1990) developed a conceptualframework for the design, implementation, and eval-uation of SMIs. According to the model, interven-tions can target three different points in the stresscycle: (a) the intensity of stressors in the workplace,(b) the employees appraisal of stressful situations, or(c) the employees ability to cope with the outcomes.The components of actual SMIs vary widely, encom-passing a broad array of treatments that may focus onthe individual, the organization, or some combination(DeFrank & Cooper, 1987; Giga, Noblet, Faragher,& Cooper, 2003).

Interventions may be classified as primary, sec-ondary, or tertiary. Primary interventions attempt toalter the sources of stress at work (Murphy & Sauter,2003). Examples of primary prevention programsinclude redesigning jobs to modify workplace stres-sors (cf. Bond & Bunce, 2000), increasing workersdecision-making authority (cf. Jackson, 1983), orproviding coworker support groups (cf. Carson et al.,1999; Cecil & Forman, 1990; Kolbell, 1995). Incontrast, secondary interventions attempt to reducethe severity of stress symptoms before they lead toserious health problems (Murphy & Sauter, 2003).Tertiary interventionssuch as employee assistanceprogramsare designed to treat the employeeshealth condition via free and confidential access toqualified mental health professionals (Arthur, 2000).The most common SMIs are secondary preventionprograms aimed at the individual and involve instruc-tion in techniques to manage and cope with stress(Giga, Cooper, & Faragher et al., 2003). Examplesare cognitivebehavioral skills training, meditation,relaxation, deep breathing, exercise, journaling, timemanagement, and goal setting.

Cognitivebehavioral interventions are designedto educate employees about the role of their thoughts

and emotions in managing stressful events and toprovide them with the skills to modify their thoughtsto facilitate adaptive coping (cf. Bond & Bunce,2000). These interventions are intended to changeindividuals appraisal of stressful situations and theirresponses to them. For example, employees aretaught to become aware of negative thoughts or irra-tional beliefs and to substitute positive or rationalideas (Bellarosa & Chen, 1997).

Meditation, relaxation, and deep-breathing inter-ventions are designed to enable employees to reduceadverse reactions to stresses by bringing about aphysical and/or mental state that is the physiologicalopposite of stress (cf. Benson, 1975). Typically, inmeditation interventions, the employee is taught tofocus on a single object or an idea and to keep allother thoughts from his or her mind, although someprograms teach employees to observe everything thatgoes through their mind without getting involvedwith or attached to them. Meditation interventionsoften also include relaxation therapy and deep breath-ing exercises. Relaxation therapy focuses on the con-scious and controlled release of muscle tension. Deepbreathing exercises focus on increasing the intake ofoxygen and the release of carbon dioxide, althoughmuscle and mental relaxation is often an additionalgoal of slowing and deepening the breath.

Exercise programs generally focus on providing aphysical release from the tension that builds up instressful situations, increasing endorphin production,or both, although some have the goal of focusing theemployees attention on physical activity (rather thanon the stressors) or providing an outlet for anger orhostility (cf. Bruning & Frew, 1987).

Journaling interventions require the employee tokeep a journal, log, or diary of the stressful events inhis or her life (cf. Alford, Malouff, & Osland, 2005).The journal is used as a means of assisting the em-ployee to monitor stress levels, to identify the recur-ring causes of stress, and to note his or her reactions.Journals are also used to formulate action plans formanaging stress.

Time management and goal-setting interventionsare designed to help people manage their time better,both on and off the job. Employees often operateunder time pressure and are required to work onmultiple tasks simultaneously. Working under suchconditions can be particularly stressful. Time man-agement interventions provide skills training in theareas of goal setting, scheduling and prioritizingtasks, self-monitoring, problem solving, delegating,negotiating, and conflict resolution (cf. Bruning &Frew, 1987; N. C. Higgins, 1986).

70 RICHARDSON AND ROTHSTEIN

Electromyogram (EMG) biofeedback training pro-vides participants with continuous feedback of mus-cle tension levels (cf. Murphy, 1984b). The objectiveis to consciously trigger the relaxation response andcontrol involuntary stress responses from the auto-nomic nervous system. Through the use of electronicfeedback measuring forehead muscle tension or handtemperature, patients receive feedback via audibletones or visual graphs on a computer screen. Duringthe process they learn to monitor their physiologicalarousal levels and physically relax their bodies, ac-tually changing their heart rate, brain waves, musclecontractions, and more.

SMIs may focus on any one particular strategyoutlined above, such as relaxation training (cf. Fiedler,Vivona-Vaughan, & Gochfeld, 1989; R. K. Peters,Benson, & Porter, 1977), or combine multiple com-ponents to create a comprehensive training regimenthat may include any of the following: cognitivebehavioral skills, meditation, assertiveness, socialsupport, and so forth (cf. Bruning & Frew, 1986,1987; de Jong & Emmelkamp, 2000; Zolnierczyk-Zreda, 2002). A trained instructor or counselor ineither small group or one-on-one sessions generallyteaches the methods, but techniques are sometimesself-taught with the aid of books and tapes (cf.Aderman & Tecklenberg, 1983; Murphy, 1984b;Vaughn, Cheatwood, Sirles, & Brown, 1989) andincreasingly via computers (cf. Hoke, 2003;Shimazu, Kawakami, Irimajiri, Sakamoto, & Amano,2005). Treatment programs are usually administeredover several weeks and may take place at the work-site or at an outside location. Depending on thetreatment method used, session length may vary from15-min breaks to all-day seminars.

Which Interventions Are Most Effective?

The effectiveness of SMIs is measured in a varietyof ways. Researchers may assess outcomes at theorganizational level (e.g., absenteeism or productiv-ity) or at the individual level, using psychological(e.g., stress, anxiety, or depression) or physiological(e.g., blood pressure or weight) measures. Given thewide array of stress management programs and out-come variables, there has been much debate in theliterature as to which interventions, if any, are mosteffective (Briner & Reynolds, 1999; Bunce &Stephenson, 2000; Caulfield et al., 2004; DeFrank &Cooper, 1987; Giga, Noblet, Faragher, & Cooper, 2003;Ivancevich et al., 1990; Mimura & Griffiths, 2002;Murphy, 1984a; Newman & Beehr, 1979; Nicholson,Duncan, Hawkins, Belcastro, & Gold, 1988; van der

Hek & Plomp, 1997). Some critics have claimed thatstudies in this area are inconclusive and based largelyon anecdotes, testimonials, and methodologically weakresearch (Briner & Reynolds, 1999; Ivancevich et al.,1990).

Newman and Beehr (1979) were among the firstresearchers to perform a comprehensive narrativereview of personal and organizational strategies forhandling job stress, examining both medical and psy-chological literature. They reported a significant lackof empirical research in the domain and challengedindustrial/organizational psychologists to bring theirexperience to the field of job stressemployee health(Newman & Beehr, 1979). During the 1980s, severalresearchers reviewed the SMI literature (DeFrank &Cooper, 1987; Murphy, 1984a; Nicholson et al.,1988). Murphy (1984a) performed a narrative reviewof 13 studies and concluded that they generally dem-onstrated acceptable positive effects, but noted thatthe reports varied significantly in terms of the ade-quacy of the methodology used (p. 2). DeFrank andCooper (1987) criticized the methodological qualityof the studies reviewed by Murphy. Three of the 13studies from that review did not use control groups,and the majority had small sample sizes with lowpower and short follow-up time frames, making itdifficult to assess the maintenance of gains.

Nicholson et al. (1988) expanded on Murphys(1984a) work and identified 62 published evaluationsof stress management programs from the medicine,public health, psychology, and education fields.These included case studies, preexperiments, quasi-experiments, and experimental studies (Nicholson etal., 1988). Of the 62 studies examined, only 18 pro-vided adequate data to quantitatively summarize theresults. Within these 18 studies, the average improve-ment in the treatment groups was equal to threequarters of one standard deviation in the controlgroup scores, yielding mildly encouraging results(Nicholson et al., 1988). This translates into a Cohensd of 0.75, which approaches a large effect (J. Cohen,1988). However, only a third of the studies in Nicholsonet al.s review included participants from occupationalsettings. The remainder consisted of participants from avariety of groups, including schoolchildren, juveniledelinquents, college students, epilepsy patients, hyper-tension patients, and substance abusers.

Over the past two decades, intervention studies inoccupational settings have proliferated, and research-ers have conducted more focused reviews to examinetheir effectiveness (Briner & Reynolds, 1999; Bunce& Stephenson, 2000; Caulfield et al., 2004; Giga, No-blet, Faragher, & Cooper, 2003; Mimura & Griffiths,

71EFFECTS OF STRESS MANAGEMENT INTERVENTIONS

2002; van der Hek & Plomp, 1997). Results havecontinued to be mixed. Briner and Reynolds con-ducted a narrative review of organization-level inter-ventions (e.g., job redesign) and concluded that theyoften have little or no effect. Bunce andStephenson examined interventions that focused onindividual-level outcomes and reviewed 27 studies,assessing their levels of descriptive information andstatistical power. They reported that at present thequality of reporting and research design is such that itis difficult to form an impression of what type of SMIis appropriate to whom, and in what circumstances(p. 198).

More recent narrative reviews have focused onparticular job occupations, such as the nursing pro-fession (Mimura & Griffiths, 2002) or mental healthprofessionals (Edwards, Hannigan, Fothergill, &Burnard, 2002), or specific geographic regions,such as the United Kingdom (Giga, Noblet, Faragher,& Cooper, 2003) and Australia (Caulfield et al.,2004). A problem with such focused reviews, how-ever, is that they result in a small number of studies,and each study may compare multiple interventionmethods or outcomes. This makes it difficult to ob-tain precise estimates of the relative effectiveness ofdifferent interventions, or to assess generalizability ofresults.

The accumulation of stress management studiesacross a wide variety of occupational and geographicsettings, assessing a multitude of intervention tactics,calls for a systematic review. In behavioral and med-ical research, as well as in other fields, meta-analysishas become the widely accepted technique for assess-ing the effectiveness of interventions. It has replacedthe traditional narrative assessment of a body ofresearch as a better way to accumulate data andsynthesize them into generalizable knowledge (Eden,2002). The primary goals of meta-analysis are toderive the best estimate of the population effect sizeand to determine whether there are any sources ofvariance around this effect (Rothstein, McDaniel, &Borenstein, 2002).

A study by van der Klink, Blonk, Schene, and vanDijk (2001) used meta-analytic techniques to exam-ine the effectiveness of worksite stress interventions.The researchers meta-analyzed data from 48 inter-ventions published in 45 articles between 1977 and1996. A small but significant overall effect size wasfound (d! 0.34). When effects were broken down byintervention type, cognitivebehavioral interventionsachieved the largest effect size (d ! 0.68), followedby multimodal (d ! 0.51), relaxation (d ! 0.35), andorganization-focused programs (d ! 0.08). The

present study seeks to update the van der Klink et al.meta-analysis. We believe that a new meta-analysis isappropriate for three reasons. First, although thevan der Klink et al. review was published in 2001, itincludes studies published only through 1996, and aconsiderable number of new, methodologically rig-orous studies have been conducted since that time.There is a growing consensus that meta-analyses andsystematic reviews, particularly those with healthpolicy or practice implications, should be updatedwhenever a sizable number of new studies appear (cf.Chalmers & Haynes, 1994; Clark, Donovan, &Schoettker, 2006; J. P. T. Higgins & Green, 2005).This review expands the eligibility timeframethrough early 2006.

Second, van der Klink et al. (2001) included stud-ies of varying study designs and methodologicalquality. Nine of their included studies used quasi-experimental designs, and others did not include ano-treatment control or comparison group. In addi-tion, the van der Klink et al. meta-analysis includedseven randomized experiments that did not reportsufficient statistics to calculate an effect size or thatreported results of significant outcomes and not oth-ers. They did this by making assumptions about theprobability values in these studies. As one of theconsistent criticisms of studies of SMIs is that theyare methodologically weak, our goal was to focusonly on studies with methodologically strong de-signs. We therefore included only those interventionsthat were evaluated using a true experimental design,with random assignment of participants to treatmentand control groups. This reduces the threat of selec-tion bias and increases the internal validity of theincluded studies (Cook & Campbell, 1976) and,therefore, the validity of the meta-analytic results.

Finally, the van der Klink et al. (2001) review wasbased entirely on published journal articles and ex-cluded dissertations, conference proceedings, bookchapters, and the like. This exclusion of so-calledgray literature has been shown to increase thethreat that publication bias (a tendency toward prep-aration, submission, and publication of research find-ings that is based on the nature and direction of theresearch results rather than on the quality of theresearch; Dickersin, 2005) will affect the meta-analytic results. Publication bias affects the degree towhich published literature is representative of all theavailable scientific evidence; when the research thatis readily available differs in its results from theresults of all the research that has been done in anarea, we are in danger of drawing the wrong conclu-sion about that body of research (Rothstein, Sutton,


& Borenstein, 2005). In order to minimize the threatof publication bias, the current meta-analysis in-cluded experimental studies from both published andunpublished sources.

Our meta-analysis had two main goals. First, it wasintended to bring together and synthesize experimen-tal studies of SMIs that have been conducted (andreported) in a variety of disciplines (e.g., education,health care, organizational studies, and psychology)to identify what works, how well it works, and whereor for whom it works. Second, we hoped to use ourmeta-analytic results to identify areas in the stressmanagement literature where additional primarystudies are needed.

To summarize, we intended our systematic reviewto improve on the van der Klink et al. (2001) meta-analysis by drawing from an additional decade ofstudies, by including only randomized experimentswith sufficient statistical data to compute effect sizeswithout making additional assumptions about proba-bility values, and by incorporating a more compre-hensive literature search strategy. Rather than ex-clude the seven randomized experiments from thevan der Klink et al. study for which they inferred pvalues, we performed a sensitivity analysis using theeffect sizes for these studies that were imputed byvan der Klink et al. to determine whether their inclu-sion changes our results. Our goals are to synthesizeresearch from a variety of disciplines to assess whathas been learned and also to identify areas in whichadditional primary studies are needed.

Method

Literature Search

Studies that assessed the effectiveness of a work-site SMI were collected from a variety of sources.Because our intent was to build on the van der Klinket al. (2001) meta-analysis, we began by obtaining all45 studies used in that review. Second, we conductedan electronic search of six databases: AcademicSearch Premier, British Library Direct, DissertationsAbstracts, ERIC, ProQuest ABI Inform Global, andPsycARTICLES. These databases were chosen toobtain studies from different countries in a broadrange of research fields, including social sciences,health care, and education. In addition, several ofthese databases include both published and unpub-lished studies. For the Academic Search Premier,British Library Direct, ERIC, and PsycARTICLESdatabases, we entered the following search terms onthree lines: employee or work or management, AND

stress or wellness, AND program* or intervention orprevention. This same procedure was followed forthe Dissertations Abstract database, but it yielded noresults. We therefore changed the search criteria totwo lines: worksite and stress and management, ANDprogram or intervention or prevention. For the Pro-Quest ABI Inform Global database, the same searchterms were used but were entered on one line withclassification codes, as follows: SU(employee orwork or management) AND ((stress or wellness) w/2(program* or intervention or prevention)) ANDCC(9130 or 5400). The classification codes 9130 and5400 indicate experimental and theoretical work,respectively. These electronic searches yielded942 documents (Academic Search Premier ! 377,British Library Direct ! 255, ERIC ! 122,PsycARTICLES ! 99, Dissertation Abstracts !31, and Proquest ABI Inform Global ! 58).

Third, we performed a network search and e-mailed colleagues knowledgeable in the field to see ifthey recommended any studies. We also attended theAmerican Psychological Association/National Insti-tute for Occupational Safety and Health Work,Stress, and Health 2006 conference and reviewed theconference proceedings. Fourth, we performed asnowball search and reviewed the reference list ofeach article obtained to identify additional citationsbeyond the electronic search. Finally, we searchedprivate and government-sponsored Web sites devotedto stress research to locate additional unpublishedliterature, including those for the American Institutefor Stress (www.stress.org), the Canadian Instituteof Stress (www.stresscanada.org), and the NationalInstitute for Occupational Health and Safety(www.cdc.gov/niosh).

Criteria for InclusionA study had to meet the following criteria to be

included in the meta-analysis: (a) be an experimentalevaluation of a primary or secondary SMI (i.e., em-ployee assistance programs were excluded); (b) in-clude participants from the working population (i.e.,studies involving students were excluded) who arenot already diagnosed as having a major psychiatricdisorder (e.g., depression or posttraumatic stress) orstress-related somatic disorder (e.g., hypertension);(c) use random assignment to treatment and controlconditions; (d) report sample sizes, means, and stan-dard deviations for both treatment and a no-treatmentor waiting-list control group; if means and standarddeviations were not reported, some other type ofstatistic that could be converted into a standardized


mean effect size (Cohens d) was necessary; and (e)be written in English after 1976. This date was cho-sen because it is the year the APA Task Force onHealth Research published a report that exhortedpsychologists, including industrial/organizationalpsychologists, to take a role in examining the healthproblems of Americans (Beehr & Newman, 1978).

Among the 45 articles used in the van der Klink etal. (2001) meta-analysis, 19 met the inclusion criteriaoutlined above and were included in our study. Theremaining 26 articles were excluded for the followingreasons: Nine used a quasi-experimental design with-out random assignment, 8 did not include a no-treatment comparison group, 7 did not report suffi-cient statistics to calculate an effect size, 1 used astudent sample, and 1 did not include outcome vari-ables related to stress or strain. We reviewed theabstracts of the 942 articles identified in the elec-tronic search online to determine whether to obtainthe studies for a full text review. Most studies wereexcluded on initial abstract review because theylacked a control group, did not include appropriateparticipants (e.g., used students or patients with clin-ically diagnosed disorders), assessed employee atti-tudes about interventions rather than the behavioraleffects of the interventions, or had already been iden-tified via the van der Klink et al. study. In total, 50experimental studies were obtained for a full textreview. Of these, 37 were excluded because theywere quasi-experimental (17), did not use a controlgroup (9), used a student or patient sample (7), or didnot include the appropriate statistics (4). Thirteenstudies met all the inclusion criteria and were in-cluded in the meta-analysis (9 peer-reviewed journalarticles and 4 unpublished dissertations). In addition,19 from the van der Klink et al. meta-analysis wereincluded. During this process, we identified 19 othernonempirical review articles and also obtained themto examine the reference lists for additional studies.

The network search resulted in four usable studies(one journal article, two book chapters, and one un-published dissertation). The snowball search resultedin 24 additional documents for review, and 2 of thesewere used in the study. The searches on the stresswebsites yielded no usable studies. In total, 38 arti-cles were included in the current meta-analysis, rep-resenting 36 separate studies and 55 interventions.

Coding Procedure

We coded the reports at five levels: study, treat-mentcontrol contrast, sample, outcome, and effectsize. Study-level coding recorded the articles full

citation, publication type, number of treatmentcontrol contrasts, and source of article (e.g., data-base). The treatmentcontrol contrast level was cre-ated because several of the studies evaluated morethan one type of treatment. Although some of thesewere totally independent evaluations, some had mul-tiple treatment groups (each with nonoverlappingparticipants) that were compared with a single con-trol group. We considered each of these treatmentcontrol contrasts as a separate comparison for codingpurposes. At this level of coding, we recorded infor-mation about the treatment and comparison groups,including program components (e.g., cognitivebehavioral skills training, meditation, exercise, etc.),where the treatment and comparison groups worked,where the program was delivered, who delivered thetreatment, and the duration of the program. We usedthis information to code the intervention type asprimary, secondary, or a combination. At the samplelevel, we coded the number of people in the sample(before attrition), the types of workers, and demo-graphic information including country of partici-pants. At the outcome level, we recorded each de-pendent variable and categorized them according topsychological measures (e.g., general mental health,anxiety, or depression), physiological measures (e.g.,diastolic and systolic blood pressure or pulse), andorganizational measures (e.g., productivity or absen-teeism). We also coded the type of measurementscale (e.g., continuous) and the source of the data(e.g., self-report). For each outcome variable, we thencoded the necessary information to calculate its effectsize, including treatment and comparison group sam-ple sizes, the statistical data, the direction of theeffect size, and whether this difference was reportedas statistically significant by the original investigator.We both coded the initial 10% of studies to assessintercoder reliability. Because there was close to100% agreement, Katherine M. Richardson coded theremaining studies, reviewing unclear data when nec-essary with Hannah R. Rothstein until a consensusdecision was reached.

Two of the studies that met our inclusion criteriaused groups as the unit of random assignment totreatment or control conditions, rather than assigningindividuals. This clustering of individuals withingroups reduces the effective sample size of thesestudies. As we have no basis for estimating thiseffect, or for adjusting the weights of these twostudies, we used the original sample sizes in ouranalyses. This should not have much of an impact onthe overall effect size estimates or on the moderatoranalyses, but it will inflate the statistical significance


levels of these two studies and underestimate theconfidence intervals (CIs) for them.

Statistical Procedures

Comprehensive Meta Analysis Version 2 software(Borenstein, Hedges, Higgins, & Rothstein, 2005)was used to conduct the statistical analyses. Thestandardized mean difference (J. Cohen, 1992;Lipsey & Wilson, 2001) was calculated to representthe intervention effects reported in the eligible stud-ies. This effect size statistic is defined as the differ-ence between the treatment and control group meanson an outcome variable divided by their pooled stan-dard deviations. For this meta-analysis, the randomeffects model was most appropriate as heterogeneitywas expected owing to the variety of interventiontypes and occupational settings. Our procedures wereanalogous to a HunterSchmidt (1990) bare-bonesmeta-analysis in that we made no corrections forstatistical artifacts other than sampling error. Ourdecision was based on the lack of sufficient infor-mation in the retrieved studies to appropriatelymake such corrections (e.g., scale reliabilities). Bynot adjusting for artifacts, it is expected that thecalculated mean effect sizes will underestimatetheir actual values (Hunter & Schmidt, 1990). Wecalculated effect sizes at the treatment controlcontrast level. Our meta-analysis represents thecombined effect of 55 independent interventionswithin 36 studies. For studies that reported multi-ple outcome variables, all applicable effect sizesthat could be extracted were calculated and coded.However, we followed Lipsey and Wilsons (2001)advice that including multiple effect sizes from thesame intervention violates the assumption of inde-pendent data points that is fundamental to mostcommon forms of statistical analysis and inflatesthe sample size (N of effect sizes rather than N ofinterventions). They recommended either using theaverage of the outcomes at the treatment controlcontrast level to get a combined effect or selectingthe most representative outcome measure withineach module. Owing to the wide range of outcomevariables within our population of studies, we usedthe average of the outcomes at the interventionlevel for our overall analysis rather than introducesubjectivity into the analysis process by selectingwhat we felt would be most representative. Wealso looked at individual outcomes in a series ofsubgroup analyses. Each effect size was weightedby its precision, so that interventions with larger

samples contributed more to the estimate of thepopulation effect size.

Results

Demographics

Table 1 summarizes the studies selected for themeta-analysis. Thirty-eight articles were identifiedthat met the inclusion criteria, representing 36 sepa-rate studies and 55 interventions. Total sample sizewas 2,847 before attrition and 2,376 after attrition.Individual sample sizes after attrition ranged from 14to 219 participants, with a mean of 49 per interven-tion. The participants represented a wide range ofoccupations, including office workers, teachers,nurses and hospital staff, factory workers, mainte-nance personnel, and social services staff. Two thirdsof the studies were conducted in the United States,and the remainder represented a diverse range ofcountries, including Australia, Canada, China (Tai-wan and Hong Kong), Israel, Japan, the Netherlands,Poland, and the United Kingdom. Fifty-nine percentof the participants were female (based on 28 studies)and mean age was 35.4 (based on 18 studies). Aver-age intervention length was 7.4 weeks. The meannumber of treatment sessions was 7.5, each lasting anaverage of 12 hr.

Types of InterventionsThe studies primarily assessed secondary interven-

tion strategies to reduce the severity of an employeesstress symptoms. Only 8 studies included compo-nents that were considered primary intervention strat-egies, such as increasing workers decision-makingauthority (e.g., participatory action research) or socialsupport within the organization. All the interventionstudies compared at least one treatment group to ano-treatment or waiting list control. Fourteen studiesevaluated two treatment groups versus the same com-parison group, and 2 studies evaluated three treat-ment groups versus the same control. Each treat-ment control contrast was treated as a separateintervention for analysis purposes. The majority ofstudies (k! 24) evaluated interventions conducted ina group-training environment. Other modes of treat-ment included individual counseling sessions (k !3); self-taught techniques using the Internet, tapes, orbooks (k ! 5); or a combination of varying methods(k ! 4). Twenty-five studies (69%) included relax-ation and meditation techniques. Twenty studies(56%) included cognitivebehavioral skills training.


Tabl

e1

Prim

ary

Stud

ies

Incl

uded

inth

eM

eta-

Anal

ysis

Aut

hor(s

)and

year

Trea

tmen

t-con

trol

contr

ast

Sam

ple

sizea

and

desc

riptio

nbTr

eatm

entc

om

pone

nts

Out

com

esm

easu

redc

Leng

thIn

terv

entio

nty

peA

derm

an&

Teck

lenbe

rg(19

83)

AV

ario

us

org

aniza

tions

(T!

21,C!

15)

Gene

rals

tress

educ

ation

sem

inar

,plu

smed

itatio

nan

dre

laxati

ontra

inin

gvi

aau

diot

apes

Anx

iety

(Ben

dig,1

956)

12w

eeks

Relax

ation

BV

ario

us

org

aniz

atio

ns

(T!

19,C!

15)

Gen

eral

stre

ssed

ucati

onse

min

arA

ltern

ative

Alfo

rd,M

alouf

f,&

Osla

nd(20

05)

ACh

ildpr

ote

ctiv

ese

rvice

soffi

cers,

Aus

tralia

(T!

31,C!

30)

Jour

nalw

ritin

gab

outr

ecen

tst

ress

reac

tions

and

emotio

ns

Gen

eral

men

talh

ealth

(GHQ

-12),

posit

ive

affe

ct,neg

ativ

eaf

fect,

jobsa

tisfa

ction

(JIG)

3da

ysA

ltern

ativ

e

Berto

ch,N

ielso

n,Cu

rley,

&Bo

rg(19

89)

ATe

ache

rs(T!

15,C!

15)

Hol

istic

prog

ram

:dee

pbr

eath

ing,

exer

cise,

relax

ation

,socia

lsupp

ort,

asse

rtive

ness

,and

nutri

tion

Stre

ss(D

SP),

stre

ss(O

SI),s

tress

(teach

erst

ress

mea

sure

),st

ress

(struc

tured

clini

cali

nter

view

)

12w

eeks

Mul

timod

al

Bond

&Bu

nce

(2000

)A

Offi

cew

ork

ers(

T!

24,

C!

20)

A

ccep

tance

com

mitm

ent

ther

apy

:cog

nitiv

e-be

havi

oral

skill

sto

enha

nce

emot

iona

lcop

ing

Gene

ralm

enta

lhea

lth(G

HQ-12

),dep

ress

ion

(Beck

),mot

ivati

on,

jobsa

tisfa

ction

,pr

open

sity

toin

nova

te

14w

eeks

Cogn

itive

-be

havi

oral

BO

ffice

work

ers

(T!

21,

C!

20)

In

nova

tion

prom

otio

npr

ogra

m:g

oals

ettin

g,pa

rticip

atory

actio

n,an

dpl

anni

ngto

enha

nce

prob

lem-fo

cuse

dco

ping

Org

aniza

tiona

l

Brun

ing

&Fr

ew(19

86,

1987

)A

Offi

cer

work

ers

(T!

16,

C!

16)

Man

agem

ents

kills

base

don

cogn

itive

-beh

avio

ral

tech

niqu

es,g

oals

ettin

g,tim

em

anag

emen

t,co

mm

unica

tion,

plan

ning

Pulse

,sys

tolic

&di

asto

licbl

ood

pres

sure

,galv

anics

kin

resp

onse

8w

eeks

Multi

moda

l

BO

ffice

work

ers

(T!

15,

C!

16)

Relax

ation

and

med

itatio

nte

chni

ques

Relax

ation

CO

ffice

work

ers

(T!

15,

C!

16)

Exer

cise

prog

ram

Alte

rnati

ve

(tabl

eco

ntin

ues)


Tabl

e1

(conti

nued

)

Aut

hor(s

)and

year

Trea

tmen

t-con

trol

contr

ast

Sam

ple

sizea

and

desc

riptio

nbTr

eatm

entc

om

pone

nts

Out

com

esm

easu

redc

Leng

thIn

terv

entio

nty

pe

Carso

net

al.(19

99)

AN

urse

s,U

nited

Kin

gdom

(T!

27,C!

26)

Socia

lsupp

ortg

roup

toen

hanc

eco

ping

abili

ties

Stre

ss(D

CL),

socia

lsu

ppor

t(SO

S),se

lf-es

teem

(RSE

S),em

otio

nale

xha

ustio

n(M

BI),

gene

ral

men

talh

ealth

(GHQ

)

5w

eeks

Org

aniz

atio

nal

Cecil

&Fo

rman

(1990

)A

Teac

hers

(T!

16,C!

19)

Stre

ssin

ocul

ation

train

ing

(Meic

henb

aum,

1977

)Sc

hool

stre

ss,t

ask-

base

dst

ress

,job

satis

facti

on,r

ole

over

load

,soci

alsu

ppor

t(all

SISS

),co

ping

skill

s

6w

eeks

Cogn

itive-

beha

vior

al

BTe

ache

rs(T!

17,C!

19)

Cow

orke

rsupp

ortg

roup

Org

aniza

tiona

l

Chen

(2006

)A

Offi

cew

ork

ers,

Isra

el(T

!24

units

,C!

13units

,N!

219

parti

cipan

ts)

Act

ive

learn

ing

expe

rienc

ede

signe

dto

incr

ease

parti

cipan

tspe

rson

alre

sourc

es

Socia

lsupp

ort(

House

,19

81),

perc

eived

contro

l(Ka

rasek

,19

79)

1w

eek

Alte

rnat

ive

Colli

ns(20

04)

AO

ffice

work

ers(

T!

9,C

!9)

Cogn

itive

-beh

avio

rals

kills

,co

mm

unica

tion,

relax

ation

tech

niqu

es,t

ime

man

agem

ent

Stre

ss(JS

I),bu

rnou

t(M

BI),

gene

ral

phys

icalh

ealth

,an

xiet

y(ST

AI)

5w

eeks

Multi

moda

l

BO

ffice

work

ers

(T!

8,C

!9)

Cogn

itive

-beh

avio

rals

kills

,re

laxati

onte

chni

ques

Mul

timod

al

deJo

ng&

Emm

elkam

p(20

00)

AV

ario

us

org

aniza

tions,

the

Net

herla

nds(

T!

45,C!

41)

Mus

clere

laxati

on,c

ogn

itive

-be

havi

oral

skill

s,pr

oblem

solv

ing,

asse

rtive

ness

train

ing

(taug

htby

clini

cal

psyc

holo

gist)

Anx

iety

(Dutc

hSTA

I),ge

nera

lmen

talh

ealth

(GHQ

),ge

nera

lph

ysica

lhea

lth,

socia

lsupp

ort(

SSI),

role

over

load

(OSQ

),job

diss

atisfa

ction

(OSQ

)

8w

eeks

Multi

moda

l

BV

ario

us

org

aniz

atio

ns,

the

Net

herla

nds(

T!

44,C!

41)

Mus

clere

laxati

on,c

ogn

itive

-be

havi

oral

skill

s,pr

oblem

solv

ing,

asse

rtive

ness

train

ing

(taug

htby

train

edpa

rapr

ofes

siona

ls)

Mul

timod

al

(tabl

eco

ntin

ues)


Tabl

e1

(contin

ued)

Aut

hor(s

)and

year

Trea

tmen

t-con

trol

contr

ast

Sam

ple

sizea

and

desc

riptio

nbTr

eatm

entc

om

pone

nts

Out

com

esm

easu

redc

Leng

thIn

terv

entio

nty

pe

Fava

etal

.(19

91)

AA

rmy

offi

cers

(T!

20,C!

17)

Stre

ssTy

pe-A

beha

vior

redu

ction

prog

ram

,co

gniti

ve-b

ehav

iora

lsk

ills,

relax

ation

,self

-es

teem

enha

ncem

ent

Stre

ss(gl

obal

asse

ssm

ento

frec

ent

stre

ss),

stre

ss(PS

S),ge

nera

lmen

talh

ealth

(Kell

ners

Sym

ptom

Q),e

xer

cise

attit

ude

28w

eeks

Mul

timod

al

Fied

ler,V

ivon

a-V

augh

an,&

Goc

hfeld

(1989

)

AH

azar

dous

was

tew

ork

ers(

T!

31,C

!30

)

Prog

ress

ive

musc

lere

laxati

on,d

eep

brea

thin

g

Systo

licbl

ood

pres

sure

,di

asto

licbl

ood

pres

sure

,gen

eral

sever

ityin

dex

(SCL-

90)

9w

eeks

Rela

xat

ion

Gan

ster,

May

es,S

ime,

&Th

arp

(1982

)A

Offi

cew

ork

ers

(T!

36,C!

34)

Cogn

itive

-beh

avio

ral

skill

s(M

eiche

nbau

m,19

75),

prog

ress

ive

musc

lere

laxati

on

Anx

iety,

depr

essio

n,irr

itatio

n,ge

nera

lph

ysica

lhea

lth,

epin

ephr

ine,

nore

pine

phrin

e

8w

eeks

Multi

moda

l

Gild

ea(19

88)

AFo

sterc

are

agen

cyw

ork

ers(

T!

9,C

!8)

Stre

ss/an

ger

man

agem

entt

rain

ing:

cogn

itive

-beh

avio

ral

skill

s,jou

rnalin

g,re

laxati

on,e

duca

tion,

ange

rcontro

l

Systo

licbl

ood

pres

sure

,di

asto

licbl

ood

pres

sure

,dep

ress

ion,

gene

ralp

hysic

alhe

alth,

hosti

lity,

and

anxiet

y(al

lSCL

-90)

n/a

Mul

timod

al

BFo

ster

care

agen

cyw

ork

ers(

T!

8,C

!8)

Relax

ation

,journ

aling

Relax

ation

N.C

.Hig

gins

(1986

)A

Offi

cew

ork

ers(

T!

17,C!

18)

Relax

ation

,sys

tem

atic

dese

nsiti

zatio

nEm

otio

nale

xha

ustio

n(M

BI),

stra

in(PS

Q),

abse

ntee

ism

6w

eeks

Rela

xat

ion

BO

ffice

work

ers,

mix

ed(T!

18,C!

18)

Ratio

nale

mot

ive

ther

apy,

time

man

agem

ent,

goal

setti

ng,a

sser

tiven

ess

train

ing

Mul

timod

al

Hok

e(20

03)

AV

ario

usorg

aniza

tions

(T!

46,C!

53)

Twice

wee

kly

e-m

ails

that

teac

hde

epbr

eath

ing,

exer

cise,

hypn

osis,

journa

ling,

med

itatio

nan

dre

laxati

on,i

mag

ery

Stre

ss(PS

S),de

pres

sion,

anxiet

y,an

ger,

daily

hass

les

12w

eeks

Mul

timod

al

(tabl

eco

ntin

ues)


Tabl

e1

(contin

ued)

Aut

hor(s

)and

year

Trea

tmen

t-con

trol

contr

ast

Sam

ple

sizea

and

desc

riptio

nbTr

eatm

ent

com

pone

nts

Out

com

esm

easu

redc

Leng

thIn

terv

entio

nty

pe

Jack

son

(1983

)A

Hos

pital

work

ers(

N!

66)

Intro

ducti

onofs

taff

mee

tings

toin

crea

sest

affp

artic

ipati

on

Role

confli

ct,ro

leam

bigu

ity,s

ocia

lsu

ppor

t,ge

nera

lm

enta

lhea

lth(G

HQ),

jobsa

tisfa

ction

,ab

sent

eeism

24w

eeks

Org

aniza

tiona

l

Kol

bell

(1995

)A

Child

prot

ectiv

ese

rvice

swork

ers(

T!

13,C!

12)

Med

itatio

nan

dre

laxati

ontra

inin

gvia

audi

otap

es

Emot

iona

lexha

ustio

n(M

BI),

gene

ral

phys

icalh

ealth

(BSI)

,abs

entee

ism

4w

eeks

Rela

xat

ion

BCh

ildpr

ote

ctiv

ese

rvice

swork

ers(

T!

13,C!

12)

Socia

lsupp

ortg

roup

Org

aniza

tiona

l

Lee

&Cr

ocke

tt(19

94)

AH

ospi

talnurs

es,

Taiw

an,C

hina

(T!

29,C!

28)

Ass

ertiv

enes

stra

inin

gba

sed

on

ratio

nal-

emotiv

eth

erap

y(E

llis,1

962)

Ass

ertiv

enes

s(RA

S),st

ress

(PSS)

2w

eeks

Cogn

itive-

beha

vior

al

Mad

di,K

ahn,

&M

addi

(1998

)A

Offi

cew

ork

ers,

mid

-

level

man

ager

s(T

!18

,C!

16)

H

ardi

ness

train

ing,

ba

sed

on

cogn

itive

-be

havi

oral

tech

niqu

es

Har

dine

ss,jo

bsa

tisfa

ction

,stra

in,

gene

ralp

hysic

alhe

alth,

socia

lsupp

ort

10w

eeks

Cogn

itive

-be

havi

oral

BO

ffice

work

ers,

mid

-

level

man

ager

s(T

!12

,C!

16)

Relax

ation

and

med

itatio

nRe

laxati

on

Mur

phy

(1984

b)A

Hig

hway

main

tenan

cew

ork

ers(

T!

15,C

!8)

Elec

trom

yogr

aphi

cbi

ofee

dbac

kG

ener

alph

ysica

lhea

lthan

dan

xiet

y(B

SI);

trait

anxiet

y(ST

AI);

jobdi

ssati

sfacti

on.

10da

ysA

ltern

ative

BH

ighw

aym

ainte

nan

cew

ork

ers(

T!

11,C

!8)

Mus

clere

laxati

onvia

cass

ette

tape

sRe

laxati

on

Pete

rs,B

enso

n,&

Porte

r(19

77);

Pete

rs,

Bens

on,&

Pete

rs(19

77)

AO

ffice

work

ers,

Unite

dK

ingd

om(T!

54,

C!

36)

Dail

y15

-min

brea

ks,

taug

htsp

ecifi

cre

laxati

onte

chni

que

with

deep

brea

thin

g

Gen

eral

phys

icalh

ealth

(Symp

tomsI

ndex

),pr

oduc

tivity

,socia

lsu

ppor

t,ha

ppin

ess,

systo

lic&

dias

tolic

bloo

dpr

essu

re

8w

eeks

Rela

xat

ion

BO

ffice

work

ers,

Unite

dK

ingd

om(T!

36,

C!

36)

Dail

y15

-min

brea

ks,n

ore

laxati

onte

chni

ques

taug

ht

Relax

ation

(tabl

eco

ntin

ues)


Tabl

e1

(contin

ued)

Aut

hor(s

)and

year

Trea

tmen

t-con

trol

contr

ast

Sam

ple

sizea

and

desc

riptio

nbTr

eatm

entc

om

pone

nts

Out

com

esm

easu

redc

Leng

thIn

terv

entio

nty

pePe

ters

&Ca

rlson

(1999

)A

Uni

versi

tym

ainten

ance

wor

kers

(T!

21,C

!19

)

Hea

lthed

ucati

on,

cogn

itive

-beh

avio

ral

skill

s,go

alse

tting

,an

dre

laxati

on

Anx

iety,

ange

r,an

dde

pres

sion

(STPI)

,he

alth

self-

effic

acy,

jobsa

tisfa

ction

,sy

stolic

/dias

tolic

bloo

dpr

essu

re,

chol

ester

ol

10w

eeks

Mul

timod

al

Prui

tt(19

92)

AA

rmy

pers

onne

l(T!

31,C!

33)

Stre

ssaw

aren

ess

educ

ation

,as

serti

vene

ss,t

ime

man

agem

ent,

relax

ation

via

audi

otap

es

Anx

iety

(STAI

),an

xiet

y(SC

L-90

),sy

stolic

&di

asto

licbl

ood

pres

sure

n/a

Mul

timod

al

Shap

iro,A

stin,

Bish

op,

&Co

rdova

(2005

)A

Hea

lthca

repr

ofes

siona

ls(T!

18,C!

10)

Min

dful

ness

-bas

edst

ress

redu

ction

(med

itatio

n,de

epbr

eath

ing,

yoga

)

Burn

out,

gene

ral

men

talh

ealth

(GSI)

,pe

rceiv

edst

ress

8w

eeks

Rela

xat

ion

Shar

p&

Form

an(19

85)

ATe

ache

rs(T!

30,C

!30

)St

ress

inoc

ulati

ontra

inin

g(M

eiche

nbau

m,19

77)

Anx

iety

(TQ4

),an

xiet

y(ST

AI,s

tate

),an

xiet

y(ST

AI,tr

ait)

4w

eeks

Cogn

itive-

beha

vior

al

BTe

ache

rs(T!

30,C

!30

)Cl

assro

omm

anag

emen

tsk

illst

rain

ing

Alte

rnati

ve

Shim

azu,

Kaw

akam

i,Iri

maji

ri,Sa

kam

oto,

&A

man

o(20

05)

AO

ffice

work

ers

ina

const

ruct

ion

mac

hine

ryco

mpa

ny,J

apan

(T!

100,

C!

104)

Self-

pace

donlin

ein

terve

ntio

nte

achi

ngco

gniti

ve-b

ehav

iora

lan

dco

ping

tech

niqu

es

Self-

effic

acy,

stre

ss(B

JSQ)

,gen

eral

phys

icalh

ealth

,job

satis

facti

on

11w

eeks

Cogn

itive

-be

havi

oral

Stan

ton

(1991

)A

Adm

inist

rativ

eoffi

cew

ork

ers,

Aus

tralia

(T!

15,C!

15)

Eg

o-en

hanc

emen

tre

laxati

onte

chni

ques

Stre

ss(s

tress

ther

mom

eter

)n/a

Relax

ation

Thom

ason

&Po

nd(19

95)

ACu

stodi

alst

aff(

T!

14,C!

13)

Cogn

itive

-beh

avio

ral

skill

s,re

laxati

on,

imag

ery,

and

self-

man

agem

ents

kills

Gen

eral

men

talh

ealth

(SCL-

90),

anxiet

y(ST

AI),

jobsa

tisfa

ction

(JIG)

,bl

ood

pres

sure

6w

eeks

Multi

moda

l

BCu

stodi

alst

aff(

T!

13,C!

13)

Cogn

itive

-beh

avio

ral

skill

s,re

laxati

on,a

nd

imag

ery

Mul

timod

al

CCu

stodi

alst

aff(

T!

14,C!

13)

Pers

onal

deve

lopm

ent

skill

sA

ltern

ative

(tabl

eco

ntin

ues)


Tabl

e1

(conti

nued

)

Aut

hor(s

)and

year

Trea

tmen

t-con

trol

contr

ast

Sam

ple

sizea

and

desc

riptio

nbTr

eatm

entc

om

pone

nts

Out

com

esm

easu

redc

Leng

thIn

terv

entio

nty

pe

Tsai

&Cr

ocke

tt(19

93)

AH

ospi

talnurs

es,

Taiw

an,C

hina

(T!

68,C!

69)

Relax

ation

train

ing

base

don

cogn

itive

-beh

avio

ral

mode

lofr

elaxa

tion

(Smith

,199

0)

Gen

eral

men

talh

ealth

(Chin

eseG

HQ)

,stre

ss(C

hinese

NSC

)

5w

eeks

Rela

xat

ion

Tunn

eclif

fe,L

each

,&Tu

nnec

liffe

(1986

)A

Teac

hers

,A

ust

ralia

(T!

7,C!

7)Co

llabo

rativ

ebe

havi

oral

consu

ltatio

nfo

cuse

don

prob

lem-so

lvin

gap

proa

ch

Teac

hers

tress

(TOS

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Many of the interventions had multiple components(such as cognitive behavioral skills training andmeditation). Fourteen of the studies evaluated inter-ventions with four or more treatment components.Table 1 provides a summary of the included studies.

Outcome Variables

Each study contained multiple outcome mea-sures. We selected and coded all dependent vari-ables that related to stress, including psychologi-cal, physiological, and organizational outcomes.This resulted in more than 60 different outcomevariables, or an average of 3 4 outcomes perstudy. We averaged these outcomes at the treat-ment control contrast level to calculate a com-bined effect size for each intervention. Psycholog-ical measures were used in 35 out of 36 studies.The most common among these were stress (k !14), anxiety (k ! 13), general mental health (k !11), and job/work satisfaction (k ! 10). Unfortu-nately, there was no uniform scale used for anyconstruct. For example, stress was measured via 11different scales, including the Job Stress Index(Sandman, 1992), Occupational Stress Inventory(Osipow & Spokane, 1983), Perceived Stress Scale(Cohen, Kamarck, & Mermelstein, 1983), Personaland Organizational Quality Assessment (Barrios-Choplin & Atkinson, 2000), and Teacher StressMeasure (Pettegrew & Wolf, 1982).

Physiological measures were used in a quarter ofthe studies, and the most common of these was sys-tolic and diastolic blood pressure. Other physiologi-cal measures were epinephrine and norepinephrinelevels, galvanic skin response, and cholesterol. Onlysix studies measured organizational-specific out-comes. Four studies assessed absenteeism, and twoexamined productivity.

Effect SizesA combined analysis, using the inverse-variance

weighted average effect size from each individualintervention, yielded a significant effect size acrossall studies (d ! 0.526, 95% CI ! 0.364, 0.687). Thisis considered a medium to large effect size (J. Cohen,1988). By comparison, van der Klink et al.s (2001)meta-analysis yielded a small combined effect size(d ! 0.34, 95% CI ! 0.27, 0.41). We checked forheterogeneity of effects in two ways. First, we usedthe traditional chi-square statistic to test the hypoth-esis that all of the observed heterogeneity was due tosampling error variance. The Q value was highlyTa

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significant (Q ! 202.6, p " .001), indicating thatthere was more heterogeneity of effects than could beaccounted for by sampling error. Second, we used theI2 statistic: I2! [(Q df)/Q]# 100%, where Q is thechi-square statistic and df is its degree of freedom(Higgins, Thompson, Deeks, & Altman, 2003). I2represents the amount of variability across studiesthat is attributable to between-study differencesrather than to sampling error variability. In this case,the I2 statistic suggests that 73% of the total varianceis due to between-study variance, or heterogeneity,rather than to sampling error. Both statistics sug-gested the likely presence of moderators, so we pro-ceeded to conduct subgroup analyses.

Intervention-Level Moderators

To search for moderators, we classified the inter-ventions into more homogeneous subgroups and per-formed analyses on these groupings, again using theaverage outcome effect size for each intervention,when more than one outcome was assessed. First, wecoded our interventions into the same categories usedin the van der Klink et al. (2001) meta-analysis:cognitivebehavioral, relaxation, organizational, ormultimodal (multiple component). Seven interven-tions could not be classified into these groupings.These studies evaluated interventions composed ofexercise or EMG feedback, journaling, personalskills development, or classroom management train-ing (for teachers). We therefore created an alterna-tive intervention category, while recognizing thatthis grouping did not represent a specific type ofintervention. Table 2 shows the average effect sizefor interventions in each of these categories.

The results in Table 2 show that the average effectsizes of both the cognitivebehavioral and the relax-ation intervention categories were larger than theanalogous average effects from the van der Klink et

al. (2001) meta-analysis. Somewhat surprisingly,there was a great deal of heterogeneity of effects inthe cognitivebehavioral intervention category (I2 !89.5, Q ! 57.0, p " .001). The alternative interven-tion category had the second largest average effectsize (d ! 0.909, 95% CI ! 0.318, 1.499), although italso had a wide confidence interval and, as would beexpected, substantial heterogeneity (I2 ! 85.3, Q !40.8, p " .001). Organizational interventions yieldedvirtually no effect, also consistent with the priormeta-analysis. Multimodal interventions, however,yielded a significant but small effect size in thepresent study (d ! .239, 95% CI ! 0.092, 0.386),which is lower than the van der Klink et al. study.The present meta-analysis included 15 studies (19interventions) with multimodal programs, whereasthe van der Klink et al. study included only 8 studiesin this category. Within such multimodal programs,cognitivebehavioral, relaxation, and even organiza-tional techniques can all be included as treatmentcomponents. Among the 19 multimodal interventionsin our meta-analysis, 5 included cognitivebehav-ioral components, 3 included relaxation components,and 11 included both cognitivebehavioral and re-laxation components. This makes it difficult to assesswhether the specific components, the mixture of com-ponents, the number of components, or a combina-tion of these factors is causing the intervention effect.Another way to categorize our studies is to look at thenumber of components per intervention. Table 3shows the effect sizes based on these subgroups.

The results in Table 3 suggest that interventionsthat focus on a single component are more effectivethan those that focus on multiple components. Thegeneral trend is that as each component is added, theeffect is reduced. However, there was significantheterogeneity among the one-component studies(I2 ! 81.6, Q ! 103.0, p " .001), and the results inTable 3 are confounded by intervention type. For

Table 2Cohens d and Confidence Intervals on the Basis of Intervention Type

Intervention type k N d 95% CI yCognitive-behavioral 7 448 1.164** 0.456, 1.871 .68*Relaxation 17 705 0.497*** 0.309, 0.685 .35*Organizational 5 221 0.144 $0.123, 0.411 .08Multimodal 19 862 0.239** 0.092, 0.386 .51*Alternative 7 455 0.909** 0.318, 1.499 N/ANote. k! number of interventions; d! combined effect size; CI! confidence interval; y!Cohens d from van der Klink et al. (2001).* p " .05. ** p " .01. *** p " .001.


example, the one-component interventions with thelargest effects were cognitivebehavioral interven-tions (k ! 2, d ! 1.230, 95% CI ! $0.968, 3.428).Four of the single-component interventions taughtsome form of personal development skills that spe-cifically related to increasing personal resources ormanagement skills that would assist employees intheir jobs (e.g., classroom management training).These yielded a significant large effect (d ! 1.154,95% CI! 0.332, 1.975). Seven used relaxation as thetreatment and obtained a significant medium effect(d ! 0.501, 95% CI ! 0.161, 0.841).

Among the studies with two treatment compo-nents, relaxation techniques were most likely to bethe primary treatment component. This was the casewith 10 interventions, yielding a significant mediumeffect size (d ! 0.502, 95% CI ! 0.265, 0.739).Seven interventions used cognitivebehavioral tech-niques as the primary treatment component, and theseyielded a significant large effect size (d ! 0.913,95% CI ! 0.320, 1.505). Among the studies withfour or more treatment components, cognitivebehavioral skills training and relaxation were likelyto be incorporated. Nine interventions included bothcognitive behavioral and relaxation components(d ! 0.296, 95% CI ! 0.086, 0.507), three includedcognitivebehavioral components (d ! 0.201, 95%CI ! $0.119, 0.522), and three included relaxationcomponents (d ! 0.215, 95% CI ! $0.069, 0.499).

We also examined whether treatment durationmade a difference among the interventions. The av-erage length was 7.4 weeks, but the range was 3 daysto 7 months. We therefore grouped studies accordingto the length of the intervention period (three studiesdid not provide this information). Because treatmentlength may be confounded with type of intervention,we further classified the studies based on interventiontype. Table 4 shows the effect sizes based on these

subgroups. The All studies column suggests thatshorter interventions are more effective than longerones. However, when one examines the data by in-tervention type, the pattern does not hold as firmly.The majority of interventions fall under the relax-ation and multimodal categories, but there are a lim-ited number of studies in each cell. Relaxation inter-ventions consistently produce medium-sized effects,regardless of length. Multimodal programs, in con-trast, appear to lose effect as treatment length in-creases.

Outcome-Level Moderators

Another way to classify the data into subgroups isto examine the outcome measures used in the studies.Outcome measures could be psychological, physio-logical, or organizational in nature, and we codedthem into 40 general categories. Some measures wereused more frequently than others. To assess whichmeasures produced larger effects and to examinewhether outcome variables differed between inter-vention types, we performed a subgroup analysis ofintervention type crossed with the outcomes we notedwere used most frequently. Table 5 shows the effectsizes based on these subgroups.

Disaggregating the data by outcome variable andtreatment type results in a small number of interven-tions in certain cells, but the analysis does illustrateseveral interesting findings. First, no studies thatevaluated single-mode cognitivebehavioral or orga-nizational interventions measured physiological out-comes. Thus, the large effect of the single-modecognitivebehavioral programs is based solely onpsychological and (less frequently) organizationalmeasures. Likewise, the large effects of alternativeinterventions are also based primarily on psycholog-ical variables. A pattern thus emerges in which out-

Table 3Cohens d and Confidence Intervals on the Basis of the Number ofTreatment Components

No. oftreatment

components k N d 95% CIOne 20 946 0.643*** 0.309, 0.977Two 18 970 0.607*** 0.346, 0.868Three 2 59 $0.104 $0.627, 0.418Four or more 15 716 0.271** 0.102, 0.440Note. k ! number of interventions; d ! combined effect size; CI ! confidence interval.** p " .01. *** p " .001.


come variables are likely to be chosen on the basis ofthe type of intervention. For example, interventionsthat focus on the individual (e.g., cognitivebehavioral, journaling, and stress education) use psy-chological measures, and those that focus on organi-zational changes generally include organizationalmeasures. The result is that we are left with gaps inthe body of research. We are unable to assess whetheralternative treatment programs (e.g., journaling, ex-ercise, and personal coping skills)which have alarge effect on psychological and physiological out-comesproduce similar results using organizationalmeasures. Regarding specific organizational out-comes, measures of productivity appear to producelarger effects than absenteeism, but there is a generallack of studies that use such measures. In general,

there are no uniform outcome measures used to as-sess the effectiveness of stress management pro-grams. Only one third of the interventions reportedusing an actual measure of stress, and among thesethere was significant variation in the scales chosen.

To obtain a more pure assessment of whether typeof outcome measure played a moderating role, weselected only those studies for which psychologicaloutcomes were provided in combination with eitherphysiological or organizational measures. In otherwords, the same samples of participants were beingmeasured using both types of outcomes. All of thepsychological measures were based on self-report,continuous scales. This may affect the reliability ofthe outcomes. The physiological measures, in con-trast, were more likely to be administered by an

Table 4Cohens d on the Basis of Length of Treatment and Intervention Type

Length oftreatment All studies

Treatment type

CB Relax Org Multi Alternative

14 weeks 0.804*** (15) 1.477** (2) 0.560* (5) $0.097 (1) 0.274 (3) 1.217* (4)58 weeks 0.396*** (22) 1.576 (2) 0.500*** (7) $0.003 (2) 0.291* (9) 0.585* (2)912 weeks 0.346* (10) 1.230 (2) 0.315 (3) N/A 0.089 (4) 0.250 (1)%12 weeks 0.401** (4) 0.323 (1) N/A 0.328 (2) 0.718* (1) N/ANote. Numbers in parentheses represent total number of interventions. CB ! cognitive-behavioral; Relax ! relaxation;Org ! organizational; Multi ! multimodal.* p " .05. ** p " .01. *** p " .001.

Table 5Cohens d on the Basis of Outcome Variables and Intervention Type

Outcome variable All studies

Treatment type


PsychologicalAll combined 0.535*** (52) 1.154** (7) 0.507*** (16) 0.134 (5) 0.258** (18) 0.905** (6)Stress 0.727*** (18) 1.007** (5) 0.834** (5) $0.314 (2) 0.595** (5) 1.367*** (1)Anxiety 0.678*** (22) 2.390*** (1) 0.611*** (5) N/A 0.418** (12) 0.841 (4)Mental health 0.441*** (16) 0.708* (1) 0.405* (5) 0.167 (3) 0.518 (5) 0.616* (2)Work-related outcomesa 0.183 (23) 0.682 (4) $0.381 (4) 0.243 (4) $0.115 (7) 0.794 (4)

PhysiologicalAll combined 0.292* (14) N/A 0.312 (5) N/A 0.166 (7) 0.714** (2)

OrganizationalAll combined 0.267 (11) 0.606 (1) 0.534*** (4) 0.247 (3) $0.122 (3) N/AProductivity 0.703*** (4) 0.606 (1) 0.661*** (2) 0.989** (1) N/A N/AAbsenteeism $0.059 (7) N/A 0.213 (2) $0.159 (2) $0.122 (3) N/A

Note. Numbers in parentheses represent total number of interventions. CB ! cognitive-behavioral; Relax ! relaxation;Org ! organizational; Multi ! multimodal.a Includes job/work satisfaction, motivation, social support, daily hassles, role ambiguity, role overload, and perceivedcontrol.* p " .05. ** p " .01. *** p " .001.


independent, trained examiner or via a medical de-vice (e.g., blood pressure monitor). The organiza-tional measures were based on company records(e.g., absenteeism) and self-report data (e.g., produc-tivity or propensity to innovate). Table 6 shows theeffect sizes based on these subgroups.

The data in Table 6 are based on a small number ofstudies and should therefore be interpreted cau-tiously. Even so, it appears that intervention typecontinues to confound outcome effects. The resultsfor all studies combined suggest that psychologicaland physiological outcome variables produce compa-rable effect sizes. However, this depends on type ofintervention. For example, for the alternative sub-group, the physiological measures produced largereffect sizes. When studies measured both psycholog-ical and organizational variables, psychological out-comes produced larger effects than organizationaloutcomes. But this may also depend on treatmenttype, as the relationship is reversed for the relaxationand organizational subgroups. Table 6 does alert us tothe overreliance on self-report measures in interven-tion studies. Among the 55 interventions, only 11measured both psychological and physiological out-comes, and 11 measured both psychological and or-ganizational outcomes.

Sample-Level Moderators

The final moderator analysis we performed wasbased on industry sector. Many early interventionstudies were performed in the health care or educa-tion fields. We classified studies into subgroups onthe basis of three industry sectors: office, health care,and education. We further grouped the results by

intervention type. Table 7 shows the effect sizesbased on these subgroups. Results depict effect sizesin the general direction of prior analyses, with cog-nitive behavioral producing the largest effects.However, what may be more interesting is to exam-ine the distribution of intervention type by industry.For example, multimodal interventions appear morelikely to be used in office settings, perhaps becausethere has been no solid empirical evidence as to themost effective treatment in this particular setting, andtherefore a potpourri approach is used. In contrast,relaxation interventions appear more often withinhealth care settings, and cognitivebehavioral inter-ventions in education settings. However, disaggregat-ing the data produces small numbers of studies ineach cell, so these interpretations may be unstable.

Outlier Analysis

We performed outlier analyses by examining for-est plots of the effect sizes and confidence intervals,for all studies combined and at the subgroup levels.One alternative intervention was identified as a pos-sible outlier because of its very large effect size andthe fact that its confidence interval did not fall intothe range of similar interventions for that subgroup.We therefore excluded this study (which representedtwo alternative interventions: exercise only and lis-tening to music; Taylor, 1991) from the analysis.However, based on a sensitivity analysis, we notethat if we included this study in our calculations, thecombined overall effect size would increase slightly(d ! 0.618, 95% CI ! 0.432, 0.903).

Table 6Cohens d on the Basis of Outcome Variable and Intervention Type for Selected Studies

Outcome variable All studies

Treatment type


Psychological vs.physiological

Psychological 0.227* (11) N/A 0.303* (4) N/A 0.151 (6) 0.250 (1)Physiological 0.219 (11) N/A 0.282 (4) N/A 0.115 (6) 0.601 (1)Psychological vs.

organizationalPsychological 0.285** (11) 0.253 (1) 0.376* (4) 0.187 (3) 0.197 (3) N/AOrganizational 0.267 (11) 0.606 (1) 0.534*** (4) 0.247 (3) $0.122 (3) N/ANote. Numbers in parentheses represent total number of interventions. CB ! cognitive-behavioral; Relax ! relaxation;Org ! organizational; Multi ! multimodal.* p " .05. ** p " .01. *** p " .001.


Sensitivity Analysis

We performed a sensitivity analysis to examinewhether including seven additional studies from thevan der Klink et al. (2001) meta-analysis wouldchange our overall results. These particular studiesdid not meet our inclusion criteria because, accordingto our review, they did not report sufficient statisticsto calculate an effect size.1 They were included in thevan der Klink et al. study on the basis of assumptionsmade by those authors. However, rather than excludethese interventions entirely, we used the reportedeffect sizes from the van der Klink et al. study andadded them to our meta-analysis. This slightly re-duced our combined overall effect size (d ! 0.469,95% CI ! 0.328, 0.609) but still yielded a mediumeffect.

Publication Bias

We performed an analysis to examine whether thecombined effect size from published studies differedfrom that of unpublished studies. The current meta-analysis included five unpublished dissertations, rep-resenting eight interventions. The combined effectsize from these eight interventions, using the averageeffect size across outcomes, yielded a significantmedium to large effect (d ! 0.553, 95% CI !$0.126, 1.232, p ! .110). In comparison, the com-bined effect from published studies was .509 (95%CI ! 0.356, 0.661, p " .001). In this case, it appearsthat including unpublished studies slightly increasedthe size of our overall average effect, whereas thegeneral concern is that omission of unpublished workupwardly biases the effect size. We have no unam-biguous explanation for the direction of difference inour particular case. There were no apparent differ-ences in methodological quality between the twogroups of studies.

Another way to detect publication bias in a meta-analysis is the trim-and-fill technique, developedby Duval and Tweedie (2000). Trim and fill is anonparametric method designed to estimate and ad-just a funnel plot for the number and outcomes ofmissing studies (Duval, 2005). We used this methodon the overall effect size distributions and estimatedthe number of missing studies at six, all to the rightof the mean. This suggests that the combined effectof 0.526 is understated and that the potential impactof including the proposed missing studies wouldincrease the effect to 0.595. However, one limitationof the trim-and-fill method is that if the data areheterogeneous, the technique may impute studies thatare not really missing. Study-related factors maydistort the appearance of the funnel plot (Duval,2005). To adjust for heterogeneity, we performedadditional trim-and-fill analyses at the subgrouplevel, examining intervention type crossed with out-come. We were able to use the method only if therewas a minimum of three interventions in the sub-group. On the basis of the analyses, multimodal in-terventions was the only category that producedgreater than one missing study. On the basis of thepsychological outcome measures, it appeared thatthree studies were missing on the left side of themean, which would suggest an overstated effect andwould decrease the overall effect for multimodalinterventions (d ! 0.190). We stress that the maingoal of the trim-and-fill method is as a sensitivity

1 For example, one study contained three distinct treat-ment modules but combined the participants of each intoone group and compared it with the control. Two studiesreported results of only the statistically significant findings,and several others failed to report all required statistics (e.g.,means with no standard deviations). In such cases, van derKlink et al. (2001) used p values, making assumptions whenno such value was reported (e.g., nonsignificant findings).

Table 7Cohens d Based on Industry Sector and Intervention Type

Industry All studies

Treatment type


Office 0.680*** (19) 0.872 (3) 0.619*** (7) 0.162 (1) 0.395* (6) 1.337** (2)Health care 0.492*** (8) 0.988*** (1) 0.462*** (4) 0.208 (2) 1.307* (1) N/AEducation 1.255** (7) 1.662* (3) 1.523* (1) 0.056 (1) 0.524 (1) 2.037*** (1)Note. Numbers in parentheses represent total number of interventions. CB ! cognitive-behavioral; Relax ! relaxation;Org ! organizational; Multi !

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