Rehabilitation Psychology 2014, Vol. 59, No. 2, 125-135

© 2014 American Psychological Association 0090-5550/14/$ 12.00 DOl: 10.1037/a0036188

Evaluation of an Online Health Promotion Program for Vocational Rehabilitation Consumers Catherine Ipsen, Casey Ruggiero, Bethany Rigles, Duncan Campbell, and Nancy Arnold University of Montana

Purpose/Objective: The purpose of this study was to test the comparative effectiveness of three variations of an online-based health promotion program for improving health and employment outcomes in a sample of Vocational Rehabilitation consumers. Research Method/Design: A total of 222 VR consumers participated in a randomized trial of three health promotion variations and provided baseline, 2-, 4-, and 6-month data. Data were analyzed using repeated measures ANOVA. The three health promotion variations included (a) FACTSHEETS—a series of four electronic factsheets; (b) HPE—an online interactive health promotion website that included health behavior content and tailored action planning, and (c) HPE + MI—the online health promotion website plus two 30-minute calls with a trained motivational interviewer. Results: Contrary to expectations, evidence did not support between-group differences based on intervention intensity. In fact, the Factsheet, HPE, and HPE + MI participants all experienced significant reductions in secondary conditions, F(2.85, 489) = 7.808, p < .001, HRQoL symptom days, F(2.7, 495) = 4.795, p = .004; and significant improvements in healthy lifestyle behaviors, F(2.6, 495) = 3.66, p = .017 over the 6-month study period. Although this study did not include a control group, a control group from another study with a similar population did not experience similar outcomes. Conclusion/Implications: People with disabilities experience significantly higher rates of secondary health conditions and lower employment rates than people without disabilities. The combination of these factors signifies the need for health promotion programming outside the work setting. Keywords: health promotion, vocational rehabilitation, online

Impact and Implications

Introduction

• Health promotion programs targeting people with disabilities have typically been delivered via in-person group workshop formats (e.g., Ipsen, Ravesloot, Arnold, & Seekins, 2012; Lorig, Ritter, & Plant, 2005; Lorig, Sobel, Ritter, Laurent, & Hobbs, 2001; Ravesloot, Seekins, & White, 2005;

Health data from the Centers for Disease Control indicate that people with disabilities engage in less physical activity and expe­ rience higher rates of obesity, cardiovascular disease, and symp­ toms of psychological distress than people without disability (CDC, 2010). Likewise, data from the 2001 Washington State Behavioral Risk Factor Surveillance Survey (BRFSS) show that people with disabilities experience significantly higher rates of chronic pain, sleep problems, fatigue, weight problems, muscle spasms, falls or injuries, bowel/bladder problems, depression, and anxiety than the general population (Kinne, Patrick, & Doyle, 2004). Many of these conditions are described as secondary health conditions for people with disabilities or “medical, social, emo­ tional, mental, family, or community problems that a person with a primary disabling condition likely experiences” (U.S. DHHS, 2000, chap. 6., p. 25).

Stuifbergen, Becker, Blozis, Timmerman, & Kullberg, 2003). This study extends the field by exploring the effectiveness of health promotion content delivery via electronic formats. • The study confirms that health promotion programming offered re­ motely helps people with disabilities reduce limitation from secondary health conditions and improve health promoting lifestyle behaviors. • The HPE applications are unique because they are low-cost interven­ tions that can be delivered broadly, and they may be particularly important for rural people who experience barriers to attend in-person groups (Ipsen et al., 2012).

Secondary Conditions and Employment

This article was published Online First April 14, 2014. Catherine Ipsen, Casey Ruggiero, Bethany Rigles, Rural Institute, Uni­ versity of Montana; Duncan Campbell, Department of Psychology, Uni­ versity of Montana; Nancy Arnold, Rural Institute, University of Montana. This research was supported by grant H133B080023 from the National Institute on Disability and Rehabilitation Research, Department of Educa­ tion. The opinions expressed reflect those of the authors and are not necessarily those of the funding agency. Correspondence concerning this article should be addressed to Catherine Ipsen, PhD, University of Montana Rural Institute, 52 Corbin Hall, Mis­ soula, MT 59812. E-mail: [email protected]

Unfortunately, secondary health conditions are associated with worse employment outcomes. In a study of vocational rehabilita­ tion (VR) consumers, Ipsen, Seekins, and Arnold (2011) found that higher reported rates of secondary conditions at baseline predicted lower probability of employment 18 months later, after controlling for demographic characteristics, disability severity, baseline em­ ployment status, receipt of social insurance payments, and voca­ tional services received. Similarly, several studies report negative relationships between employment and specific secondary condi125

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IPSEN, RUGGIERO, RIGLES, CAMPBELL, AND ARNOLD

tions, such as depression (Crisp, 2005; Samkange-Zeeb, Altenhoner, Berg, & Schott, 2006; Walid & Zaytseva, 2011 ; Zivin et al,, 2012), sleep problems (Crisp, 2005; Lee et ah, 2009), pain (Crisp, 2005; Gauthier, Sullivan, Adams, Stanish, & Thibault, 2006), and fatigue (Leone et al., 2006). Evidence also suggests that the rela­ tionship between employment and secondary conditions is bidi­ rectional and employment is a protective factor against many of these same health conditions (Dooley, Fielding, & Levi, 1996; Jin, Shah, & Svododa, 1997; Ross & Mirowsky, 1995; Zabkiewicz, 2010 ).

Access to Health Promotion Although participation in health promotion programming can help people with disabilities manage many secondary health con­ ditions (Lorig et ah, 2005; Pelletier, 2001; Ravesloot et ah, 2005; Stuifbergen et al., 2003), access to programming is limited for people who are not employed because most health promotion initiatives are attached to work-sponsored health insurance bene­ fits (Healthy Weight Commitment Foundation, 2011; Pelletier, 1996, 2001; Ross, Bernheim, Bradley, Teng, & Gallo, 2007). This is a particular problem for rural people with disabilities who are underemployed, not employed, or lack employer-sponsored health insurance coverage more commonly available from large employ­ ers (Ipsen, Seekins, & Ravesloot, 2010; StatsRRTC, 2008; Tu, 2004). Community-based health promotion programs targeting people with disabilities have helped participants manage secondary health conditions, but the majority include location-based delivery such as on-site group meetings with lay facilitators (Ipsen et al., 2012; Lorig et al., 2005, 2001; Ravesloot et al., 2005; Stuifbergen et al., 2003). While costs to implement such programs are reasonable, some participants have trouble attending in-person meetings due to transportation barriers, health complications, or ongoing commit­ ments (Ipsen et al., 2012). Additionally, programs are not broadly available across the United States. It appears that many people with disabilities experience a com­ pounded disadvantage. They have lower employment rates due to secondary health conditions and limited access to health promotion activities because they are not employed. Thus, one possible way to increase the probability of employment for this group, is to increase access to health promotion activities outside the work setting in an online format that is not location specific (Ipsen, 2006).

Vocational Rehabilitation One potential access point for health programing delivery is the state and federal Vocational Rehabilitation (VR) program, which assists people with disabilities in preparing for gainful employ­ ment. VR agencies are located throughout the United States and close approximately 580,000 client cases each year (RSA, 2009). VR offers a systemic delivery access point for people who are not employed or lack viable alternatives for obtaining or paying for health promotion services. Further, VR consumers appear to need such services. Deficits in employment, educational attainment, and private health insurance coverage (Ipsen et a l, 2010; RSA, 2009; Stats RRTC, 2008) are reasons for exploring inclusion of health promotion programming within VR’s array of services. Past re­

search shows that higher ratings of secondary health conditions lower the probability of employment (Ipsen et ah, 2011). There­ fore, VR consumers who could manage their secondary conditions more effectively might improve their employment outcomes. Additionally, Internet and telephone methods have been shown effective for delivering health promotion information (Glueckauf & Lustria, 2009; Irvine, Ary, Grove, & Gilfillan-Morton, 2004; Murray, Bums, See Tai, Lai, & Nazareth, 2009; Prochaska, Butterworth et al., 2008), and they provide a means for cost-effective delivery for individuals at a distance from established services or who experience transportation barriers to reach services. This includes rural people (Abbott, Klein, & Ceichomski, 2008; Gore & Leu werke, 2008; Zelvin & Speyer, 2004) and individuals who are place bound due to transportation, accessibility, or disability fac­ tors (Shaw & Shaw, 2006; Zelvin & Speyer, 2004).

Health Promotion Programming The purpose of this study was to test the comparative effective­ ness of three variations of an online-based health promotion pro­ gram for improving health in a sample of VR consumers. We hypothesized that participation in the Health Plans to Employment (HPE) programs would result in less prevalence and severity of secondary health conditions, fewer reported symptom days, and increased health-promoting lifestyle behaviors, but at different levels based on intervention intensity, ranging from passive infor­ mation to motivational interviewing. The three variations included (a) FACTSHEET group that received a series of four electronic health behavior factsheets that addressed the link between many common secondary health conditions and four self-management strategies, including stress management, physical activity, nutri­ tion, and sleep/relaxation; (b) HPE group who were directed to an interactive health promotion website, which included health be­ havior content and tailored action planning; and (c) HPE + MI group that included the health promotion website plus two 30minute calls with a trained motivational interviewer.

Method We beta tested the HPE website content with 11 adults with disabilities recruited from Summit Independent Living Center, a local advocacy and resource center for persons with disabilities, and the National Center on Physical Activity and Disability. Beta testers received a link to the website and navigated through the site as though they were participating in the program. We conducted a telephone interview with each beta tester after they went through the website and asked about the helpfulness and understandability of website content, problems or concerns encountered while on the site, including technical or navigational difficulties, how long it took to complete the goal-setting process, and if they had sugges­ tions on how to improve the site. We also tested the HPE + MI component with five additional adults with disabilities from Sum­ mit ILC and Partnership Health Center, a local federally qualified health center. These participants completed the HPE online pro­ cess and two MI calls.

Intervention Variations Factsheets. The HPE factsheet intervention included a series of four factsheets emailed every 2 weeks to participants. Each

HEALTH PROMOTION FOR VR CONSUMERS

factsheet included specific health behavior management strategies in the areas of stress management, physical activity, nutrition, and sleep/relaxation. The factsheet content replicated the HPE website self-management pages. HPE. The HPE website intervention was a tailored selfdirected online health promotion program designed for widespread adoption at little cost. Once participants logged into the website, they completed a brief assessment of limitation from nine preva­ lent secondary health conditions including anxiety, fatigue, chronic pain, depressed mood, physical fitness, weight, sleep, gastrointestinal, and cardiovascular problems. Based on assess­ ment results, individuals self-directed through lifestyle behavior pages related to improved self-management of their most limiting conditions. Specifically, the website self-management pages focused on four health behaviors, including stress management, physical ac­ tivity, nutrition, and sleep/relaxation. Each page included specific health behavior management strategies, worksheets or tools, and links to well-established external resources, such as USDA’s MyPlate, helpguide.org, or the National Center on Physical Activity and Disability. Users could navigate backward to explore different self-management areas or forward toward a series of pages for developing health behavior action plans. The goal-setting page asked the participant to assess current behavior patterns, explore motivation for change, and write a long-term goal in one health behavior area of interest. Using this as the basis for change, participants considered potential barriers to progress and developed problem-solving steps. Together the goal­ setting and action-planning pages led to a printable action plan where participants could make modifications and report on prog­ ress. Once a plan was submitted, users received automated e-mail reminders for reporting new progress and updating activities. HPE + MI. The HPE plus motivational interviewing (MI) intervention included two telephone-delivered MI sessions during the first month of the intervention to increase participant engage­ ment in the HPE website. Small doses of MI in combination with other health promotion programming, have been shown to increase positive behavior changes relative to health promotion program­ ming alone (Ang, Kesavalu. Lydon, Lane, & Bigatti, 2007; Camp­ bell et al., 2009; Linden, Butterworth, & Prochaska, 2010; Miller & Rose, 2009). The first MI call was scheduled immediately prior to receiving the link to the HPE website and the second was provided 3 weeks after. The first call explored how participants’

127

health behaviors may differ from their health values and beliefs and how their current health behaviors impacted their lives. The second call focused on participant health behavior change goals from the HPE website and explored and reinforced participant motivation to take steps toward achieving stated goals. We ex­ pected that the MI component would increase engagement in the HPE online program and magnify long term benefits of the inter­ vention. Fifteen percent of the MI calls were selected randomly for independent expert review by a certified Motivational Interview­ ing Treatment Integrity (MITI) coder. Using the MITI scale, calls were evaluated on five global dimensions using a 1 to 5 rating scale. Average scores indicated that Motivational Interviewers were MI adherent in all dimensions, including evocation (4.6), collaboration (4.5), autonomy (4.7), empathy (4.4), and direction (4.7), (Moyers, Martin, Manuel, Miller, & Ernst, 2010). We used Abraham and Michie’s (2008) taxonomy of behavior change techniques to compare the three intervention variations in Table 1.

Participant Recruitment After securing approvals to conduct this research from our Institutional Review Board and the Council of State Administra­ tors of Vocational Rehabilitation’s (CSAVR) research committee, we identified and recruited two state general VR agencies in Washington and North Carolina. In each state, agency personnel mailed recruitment/screening postcards to 600 randomly selected VR consumers with primary physical disability between the ages of 21 and 65. Recruitment postcards asked about interest in an Internet-based health behavior change program and outlined tech­ nology inclusion criteria including access to a personal computer with Internet and an established e-mail address. Researchers had no identifying information about individual VR consumers until they returned a prepaid postcard expressing interest in participat­ ing in the study. Washington participants (n = 142) were randomly assigned to one of the three conditions (factsheet, HPE, and HPE + MI) and North Carolina participants ( n = 80) were randomly assigned to one of two conditions (factsheet and HPE) based on order of baseline materials received. We began recruitment in Washington and delivered the HPE + MI condition only in Washington to accommodate dissertation deadlines for a graduate student deliv-

Table 1 Behavior Change Techniques Across Intervention Variations Factsheets 1. Provide general information

HPE 1. 2. 3. 4. 5. 6. 7. 8. 9.

Provide general information Prompt intention formation Prompt barrier identification Prompt specific goal setting Prompt review of behavioral goals Prompt self-monitoring behavior Use follow-up prompts Stress management Time management

HPE + MI 1. Provide general information 2. Prompt intention formation 3. Prompt barrier identification 4. Prompt specific goal setting 5. Prompt review of behavioral goals 6. Prompt self-monitoring behavior 7. Use follow-up prompts 8. Stress management 9. Time management 10. Prompt self-talk 11. Motivational interviewing

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IPSEN, RUGGIERO, RIGLES, CAMPBELL, AND ARNOLD

ering the MI component. Recruitment may have been higher in Washington because access to technology was a selection criteria and there were more technology users in the state overall (File, 2013). Figure 1 outlines participant recruitment and study group procedures.

Participants The average participant age was 45 (11.9), 60% were female, and most were Caucasian (79%) and African American (15%). Three percent of participants had less than a high school education, 11% had completed high school, 61% had some college, and 25% had a college education or higher. At baseline, 7% of participants were employed full time, and 18% were employed part time. North Carolina had significantly higher representation from African Americans (30% vs. 6% in Washington); but overall, respondents from Washington and North Carolina were similar in terms of education, employment, and disability type. There were not any statistical differences between groups based on assignment to the intervention variations. Table 2 describes baseline disability de­ mographics. Individuals could endorse more than one condition, so percentages add to more than 100%.

Measures To test our research hypotheses, we collected data on health and health behavior outcomes. Respondents provided data on each of

the described variables and measures at baseline and 2-, 4- and 6-months follow-up. Sum of Secondary Conditions Surveillance Instrument (SCSI). We measured secondary conditions with an abbreviated version of the validated SCSI (Seekins, Clay, & Ravesloot, 1994; Seekins & Ravesloot, 2000; Seekins, Smith, McCleary, Clay, & Walsh, 1990). The abbreviated SCSI assesses the prevalence and severity of 32 health-related conditions (e.g., pain, fatigue, weight problems, depression, urinary tract infection) amenable to health promotion efforts (Ravesloot et ah, 2005). Respondents used a scale to indicate how limiting each condition was, where 0 = rarely or never limits, 1 = mild or infrequent limitation ( 1-5 hours per week), 2 = moderate limitation (6-10 hours per week), and 3 = significant limitation (more than 11 hours per week). A sum score across all 32 secondary conditions provided an overall mea­ sure of limitation from secondary health-related conditions. Scores could range from 0 to 96. Seekins and colleagues (1994) reported internal consistency for the SCSI of .88. Factor analytic studies support construct validity of the SCSI and demonstrate its useful­ ness with individuals representing diverse impairment types (Ravesloot, Seekins, & Walsh, 1997). Behavioral Risk Factor Surveillance System Health Related Quality of Life module (BRFSS HRQoL-14). Seven questions from the BRFSS HRQoL-14 measured prevalence (in days per month) of health problems (Centers for Disease Control, 2011a). Respondents estimated how many days in the past 30 days (a) their

r HPE Factsheets

HPE O n lin e + M l

HPE O n lin e

In te r v e n tio n (n = 9 2 )

In te rv e n tio n (n = 3 9 )

In te rv e n tio n (n = 9 1 )

B aseline + 1 w e e k

B aseline + 1 w e e k

B aseline + 1 w e e k

Fact sheets sent

In itial M l contact

Em ailed link t o HPE (8

b iw eek ly fo r 8 w eeks

w ee kly e m a il u p d a te P ost M l Session

rem inders)

Em ailed link to HPE

2nd M l Session 3 w eeks a fte r HPE link received

2 , 4 , a n d 6 M o n th

2 , 4 , a n d 6 M o n th

F o llo w U p

F o llo w U p

F o llo w U p

Survey packet sent w ith

Survey packet sent w ith

Survey packet sent w ith

$ 1 5 stipen d

$ 1 5 stipend

$ 1 5 stipend

Figure 1.

Participant flow chart.

2 , 4 , a n d 6 M o n th

HEALTH PROMOTION FOR VR CONSUMERS

129

Table 2 Participant Disability Type and Severity Indicators Disability type

%

Disability severity indicators

%

Cognitive impairment Mental health impairment Physical impairment Sensory impairment Substance abuse or dependence

20% 33% 89% 15% 4%

Receives SSI Receives SSDI Requires help from other to perform activities of daily living (ADLs) Requires help from others for instrumental activities of daily living (AIDLs) Uses special equipment

18% 35% 14% 39% 36%

physical health was not good; (b) their mental health was not good; (c) their poor physical or mental health kept them from doing usual activities; (d) their pain made it hard to do usual activities; (e) they felt sad, blue, or depressed; (f) they felt worried, tense, or anxious; and (g) they believed that they did not get enough rest or sleep. Other studies have used these seven questions as a scale of symp­ tom limitation to examine secondary health conditions for people with disabilities (Ipsen, 2006; Kinne et al., 2004; Ravesloot et al., 1997, 2005). When using these items as a scale, total symptom days are averaged to determine symptom days per month. Lower scores are associated with good perceived quality of life and scores of 14 or more days have been used as a threshold for “substantial level of impairment” (Centers for Disease Control, 2011b). Over­ all, individual HRQoL items show moderate to excellent testretest reliability among adults with disability and community sam­ ples (Andresen, Catlin, Wyrwich, & Jackson-Thompson, 2003; Nanda & Andresen, 1998), and good construct validity (Hennessy, Moriarty, Zack, Scherr, & Brackbill, 1994; Mielenz, Jackson, Currey, DeVellis, & Callahan, 2006; Nanda & Andresen, 1998). Health Promoting Lifestyle Profile II (HPLP II). The HPLP-II measures six dimensions of lifestyle behavior, including health responsibility, physical activity, nutrition, spiritual growth, interpersonal relations, and stress management (Walker, Sechrist, & Pender, 1987). Fifty-two items rate engagement in specific health behaviors on a 4-point scale ranging from never to rou­ tinely. Items are aggregated into six subscales, representing differ­ ent dimensions of lifestyle behavior, and a total score. Within the subscales, internal consistency ranges from .79 to .94 and the total score has a Cronbach’s alpha value of .94 (Walker et ah, 1987). Principal components analyses support a 6-factor model, corre­ sponding to the six health behavior domains (Walker et al., 1987). The HPLP-n is frequently used to evaluate health promotion practices of individuals with disabilities (Ennis, Thain, Boggild, Baker, & Young, 2006; Stuifbergen et al., 2003; Zemper et al., 2003). Proxy control group. In the absence of a true control group, we used control group data from a similar study exploring an in-person health promotion workshop called Working Well with a Disability (i.e., Ipsen et al., 2012). The proxy control group was also recruited from Vocational Rehabilitation and had similar demographic characteristics including average participant age (45); gender (54% female), race (74% Caucasian and 17% African American), and employment status at baseline (9% employed full-time and 20% employed part-time). The proxy control group was slightly less educated (x2 = 14.99, p = .010). Seven percent had less than a high school education, 23% had completed high school, 53% had some college, and 18% had a college education or higher.

Data Analyses We used mixed design repeated measures ANOVA to compare health changes over time. A priori power analysis suggested we needed to recruit approximately 190 participants to detect mean group differences with small to medium effect sizes ( f = .18), and 102 participants to detect mean group differences with medium effect sizes ( f = .25), with .80 power, and .05 level of significance (Cohen, 1992; Faul, Erdfelder, Lang, & Buchner, 2007), assuming equal study groups. We did not have resources to meet higher recruitment levels for the HPE-MI group due to staff requirements related to MI phone calls. Our intention was to pilot the process and we acknowledge that the HPE + MI variation was underpow­ ered for medium effects when study attrition rates were factored in. In some cases grouped data did not meet assumptions of nor­ mality. We conducted a square root transformation of both the SCSI and HRQoL measures to address this issue, which resulted in normally distributed data. For each outcome variable, statistics are reported for participants who provided data at all data-collection points, which reduced sample sizes. The fact sheet, HPE, and HPE + MI groups had full data sets from 83%, 71%, and 63% of respondents, respectively. At baseline, completers (M = 46, SD = 11.7) were significantly older than noncompleters (M = 41, SD = 11.7) , 1(220) = 3.039, p = .003 and advanced age was related to better HRQoL, F(9, 492) = 2.453, p = .012 outcomes. There did not appear to be any other systematic differences between indi­ viduals who completed all follow-up study measures with those who did not. We did not perform any imputation procedures to replace miss­ ing data. Although attrition was a problem, data were not missing at random and in this situation imputation procedures are not recommended (Rencher & Christensen, 2012).

Results Secondary Conditions Table 3 provides descriptive statistics of the raw SCSI data for intervention groups at each data collection point. Recall, however, that prior to conducting repeated measures ANOVA, we per­ formed a square root transformation of the SCSI scores to address normality violations in the data. Mauchley’s Test of Sphericity indicated that the assumption of sphericity was violated, x2(5) = 13.88, p = .016, and a Greenhouse-Geisser correction was used. There was a significant within-subjects effect of time on the SCSI F(2.85, 489) = 7.808, p < .001, r|p = .046 but no group-by-time interactions F(5.70, 489) = 1.119, p = .350 or between-subjects effects F(2, 163) =

IPSEN, RUGGIERO, RIGLES, CAMPBELL, AND ARNOLD

130

Table 3 Sum o f Secondary Conditions Surveillance Instrument (SCSI) Descriptive Statistics Group

Baseline

2 Months

4 Months

6 Months

Fact sheets (n = 76)

M = 23.9 Median = 24 SD = 13.1 M = 25.1 Median = 28 SD = 13.6 M = 27.0 Median = 24 SD = 14.0

M = 22.9 Median = 22 SD = 13.3 M = 24.0 Median = 23 SD = 13.7 M = 27.1 Median = 27 SD = 18.6

M = 21.8 Median = 20 SD = 13.5 M = 23.7 Median = 24 SD = 14.1 M = 24.4 Median = 17 SD = 18.1

M = 22.5 Median = 20 SD = 14.1 M = 21.9 Median = 22 SD = 14.3 M = 24.1 Median = 21 SD = 16.0

HPE (n = 65)

HPE + MI (n = 25)

.237, p = .789 based on intervention assignment. Within-subjects contrasts indicated a linear trend to the data F (l, 163), = 19.77, p < .001, T)p = .108. Figure 2 shows square root mean values over time for each group.

HPLP-II

HRQoL Average Symptom Days Table 4 provides descriptive statistics of HRQoL Average Symptom Days for intervention groups at each data collection point. Like the SCSI, prior to conducting repeated measures ANO VA, we performed a square root transformation of the HRQoL Average Symptom Days scores to address normality violations in the data. Mauchley’s Test of Sphericity indicated that the assumption of sphericity was violated, x2(5) = 28.73, p < .001, and a Greenhouse-Geisser correction was used. There was a significant within-subjects effect of time on the HRQoL Symptom Days F(2.7, 495) = 4.795, p = .004, p j = .028 but no group-by-time interaction F(5.4, 495) = .866, p = .510 or between-subjects effects F(2, 165) = .054, p = .948 based on intervention assign­ ment. Within-subjects contracts indicated both linear F (l, 165), =

— ♦ — F a c t s h e e ts (n = 7 6 ) - * - H P E (n = 6 5 ) —* ~ H P E + M I (n = 2 5 )

B a s e lin e

2 M o n th s

7.510,p = .007, Tjp = .044 and quadratic F (l, 165), = 4.506, p = .035, rip = .027 trends to the data. Figure 3 shows square root means over time for each group.

4 M o n th s

6 M o n th s

Figure 2. Sum of Secondary Conditions Surveillance Instrument (SCSI) means over time.

Mauchley’s Test of Sphericity indicated that the assumption of sphericity was violated, x2(5) = 38.36, p < .001, and a Greenhouse-Geisser correction was used. There was a significant within-subjects effect of time on the HPLP II F(2.6, 495) = 3.66, p = .017, Pp = .022 but no group-by-time interaction F(5.2, 495) = .617, p = .693 or between-subjects effects F(2, 165) = .646, p = .525 based on intervention assignment. Within-subjects contracts indicated a linear F (l, 165), = 7.169, p = .008, pp = .042 trend to the data. Figure 4 shows means over time for each group.

Proxy Control Group Comparisons The study did not include a control group to compare with intervention variations. Originally, we anticipated that participants would utilize fact sheet information less because it represented a more passive intervention. We anticipated that this lower rate of engagement would result in a pseudocontrol group. The data showed, however, that the fact sheet group experienced similar improvements in health promoting lifestyle behaviors and similar reductions in reported secondary conditions and HRQoL symptom days when compared with the HPE and HPE + MI groups. This resulted in uncertainty about whether intervention participation was improving health or there was some natural progression to­ ward an improved health status related to entering the VR pro­ gram. We did collect control group data for another study that exam­ ined an 8-week on-site health promotion intervention called Work­ ing Well with a Disability. Although the data collection periods were not exactly the same, the study samples were both comprised of VR consumers with a primary physical impairment, and both provided baseline and 6-month data for the HRQoL Symptom Days measure. Combining these two data sets, we ran a repeated measures ANOVA on the square root transformation of the HRQoL Symp­ tom Days between groups over time. There was a significant within-subjects effect of time on the HRQoL Symptom Days F (l, 276) = 7.327, p = .007, -rip = .026 and a significant group-by-time interaction F(3, 276) = 3.832, p = .010, T|p = .040. Between-

HEALTH PROMOTION FOR VR CONSUMERS

131

Table 4 HRQoL Symptom Days Descriptive Statistics Group

Baseline

Fact sheets (n = 77)

M = 11.8 Median = 10.6 SD = 7.5 M = 12.2 Median = 10.7 SD = 8.0 M = 13.9 Median = 14.3 SD = 9.4

HPE (n = 66) HPE + MI (n = 25)

2 Months

M = 11.0

Median = 10.0 SD = 7.6 M = 11.7 Median = 10.3 SD = 8.5 M = 11.3 Median = 9.4 SD = 8.7

Median = 9.4 SD = 7.4 M = 11.5 Median = 10.7 SD = 7.6 M = 10.8 Median = 9.7 SD = 8.8

Adherence to Intervention Protocols We received follow-up program evaluation data from 88% of the study sample (« = 196). Fact sheets (n = 86). Respondents indicated they read factsheets on stress management (66%), physical activity (62%), nutrition (64%) and sleep and relaxation (64%). Respondents were asked to rate the usefulness of each fact sheet they read. Using a Likert-type scale where 1 = not useful and 5 = very useful, the average ratings were 3.93 for stress management, 3.88 for physical activity, 3.94 for nutrition, and 3.79 for sleep and relaxation fact sheets. HPE (n = 77). The majority of respondents (81%) said they logged onto the FIPE website and toured through different second­ ary health condition and health management pages. Sixty-nine percent of HPE respondents developed at least one health behavior goal and action plan. Respondents indicated that they read the self-management pages on stress management (44%), physical activity (56%), nutrition (39%), and sleep and relaxation (47%).

—•

6 Months

M = 11.0

group effects were not significant F(3, 276) = .679, p — .565 based on intervention assignment. Figure 5 shows estimated mar­ ginal means over time for each group.

—♦ “

4 Months

M = 11.3 Median = 9.4 SD = 7.7 M = 11.2 Median = 9.4 SD = 7.9 M = 9.9 Median = 8.8 SD = 7.0

Respondents rated the usefulness of the pages visited using a Likert-type scale where 1 = not useful and 5 = very useful. The average ratings were 3.55 for stress management, 3.50 for physical activity, 3.69 for nutrition, and 3.86 for sleep and relaxation. HPE + MI (n = 33). Almost all HPE + MI participants (97%) participated in at least one MI call and 70% participated in both calls. HPE + MI participants also logged on to the HPE website (76%) and set a health behavior goal and action plan (76%). Respondents indicated they read the self-management pages on stress management (43%), physical activity (36%), nu­ trition (30%), and sleep and relaxation (39%). Respondent ratings of the usefulness of the pages were 4.31 for stress management, 3.83 for physical activity, 3.90 for nutrition, and 3.69 for sleep and relaxation. Unlike the fact sheet condition where participants received all self-management content, HPE and HPE + MI participants selfdirected through the content using a more targeted approach based on their unique secondary conditions and desired health behavior change areas. The HPE and HPE + MI groups interfaced with fewer self-management content areas than the Fact sheet group as a result of the self-directed model.

♦

F a c t s h e e ts (n = 7 7 )

F a c t s h e e ts (n = 7 6 )

—• — H P E (n = 6 4 )

— H P E (n = 6 6 )

H P E + M I (n = 2 8 )

B a s e lin e

Figure 3.

2 M o n th s

4 M o n th s

6 M o n th s

HRQoL symptom days means over time.

B a s e lin e

Figure 4.

2 M o n th s

4 M o n th s

HPLP II means over time.

6 M o n th s

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IPSEN, RUGGIERO, RIGLES, CAMPBELL, AND ARNOLD

♦

F a c t s h e e ts (n = 8 0 )

—• — HPE (n = 7 2 ) T f e — H P E + M I (n = 3 0 )

B a s e lin e

Figure 5. group.

6 M o n th s

HRQoL symptom days means over time with proxy control

Discussion Contrary to expectation, there was no evidence to support between-group differences based on intervention variations. In fact, the Fact sheet, HPE, and HPE + MI participants all experi­ enced significant health, behavior, and quality of life improve­ ments over the 6-month study period. These improvements were not evident in a control group from a similar study (Ipsen et ah, 2012). One interesting aspect of the data is that the intervention groups experienced different comparative outcomes. For instance, while the entire sample reported 1 day less per month of HRQoL symptom days, the HPE + MI group actually reported a 4-day reduction per month (13.9 days to 9.9 days). Likewise, while the entire sample had a 10% decline in limitation from secondary conditions, the HPE group experienced a 14% decline. While these significant changes may seem small, even one fewer symptom days has the potential to tip the scales in terms of ability to work or function independently (Ipsen, 2006). Additionally, trends in the data indicate that results may become magnified over a longer time horizon (Ipsen et ah, 2012). Given the present data, it appears likely that the variety of different online and telephone-based delivery modalities utilized in the present study effectively pro­ mote positive health behavior changes in people with disabilities, and further research exploring the comparative effectiveness of these groups in a variety of populations is needed. Lack of differential outcomes across variations may relate to self-selection bias. Individuals who agreed to participate in the study may have been ready to engage in the health promotion content, and more likely to benefit from passive approaches than the general population (De Leon, 1998). The study time horizon may also have been too short to fully capture differential effects. Previous research suggests that small doses of information only health promotion interventions can effectively promote health be­ havior change for certain groups, but that changes tend to be more

temporary (Stadler, Oettingen, & Gollwitzer, 2010). Prochaska, Reading, and Evers (2008) describe five stages of change readiness in their Transtheoretical Model ranging from precontempla­ tion—no intent to create a health behavior change; to contempla­ tion—thinking about change, but not in the immediate future; preparation—intend to make change in the near future; action— actively engaged in behavior change, and maintenance— success­ fully engaged in new behavior for 6 months (Prochaska, Butterworth et ah, 2008). MI is particularly suited to individuals in the precontemplation and contemplation stages because it elicits or stimulates change talk to overcome ambivalence and increase engagement (Britt, Hudson, & Blampied, 2004). In our study sample, only 17% of participants were in precontemplation or contemplation stages with regard to stress management, 34% for physical activity, 19% for nutrition, and 20% for sleep and relax­ ation. The small sample size for the HPE + MI group, in combi­ nation with few participants in the early stages of change, may have undervalued the comparative effectiveness of the MI inter­ vention variation. Finally, comparisons may have been impacted by different levels of exposure. While all intervention groups had the ability to review the four different self-management areas (i.e., stress man­ agement, physical activity, nutrition, and sleep/relaxation), it was apparent from the program evaluation data that the self-directed nature of the HPE website resulted in fewer participants reviewing all content. This effect was magnified for the HPE + MI group, which may have been a direct result of MI sessions, in which many participants identified a target focus area prior to entering the website and may have bypassed exploration into other self­ management areas. One positive outcome of the MI participation, however, related to goal setting. All HPE + MI participants who logged into the HPE website set a health behavior change goal versus only 75% of the HPE-only group.

Limitations The study had a number of limitations that affected our ability to fully describe program impacts. Lack of a true control group was the most significant oversight. When we conceived of the study, we anticipated that the fact sheet group would not engage with the material and could serve as a proxy for comparison. Lesson learned. We were able to compare the HPE variations with a control group from a similar study, however, and when these data were included in an analysis of health-related quality of life, there was a significant group-by-time interaction, indicating positive intervention effects. Attrition over the study span also impacted our ability to detect change. The fact sheet, HPE, and HPE + MI groups had full data sets from only 83%, 71%, and 63% of respondents, respectively. Lower response rates among HPE + MI participants were particularly prob­ lematic since that group was smaller from the beginning. Since there were not baseline differences between intervention groups, one might assume that drop-out was related to the intervention condition. Al­ though this question cannot be answered, it is possible that more intense interventions led to respondent reactivity or burden and less willingness to complete follow-up surveys. We examined the data to see if there were differences between those who provided full data with those who did not. Completers were slightly older (46 vs. 41). When we explored this further, we deter-

HEALTH PROMOTION FOR VR CONSUMERS

mined that advanced age was related to better HRQoL outcomes, which may have inflated our findings. At the same time, the fact that older people benefitted more from the intervention provides informa­ tion about future recruitment efforts and the likely health benefits for an older cohort. Self-selection into the study may also have influenced outcomes. Differential outcomes may have been truncated if the beneficial impacts of more active engagement components were lost on an already action-oriented participant pool. Future efforts may require more active recruitment of participants in the precontempla­ tion and contemplation stages of change. Self-report data collection also posed potential problems related to response bias and could have played a role in inflating program outcomes. Unfortunately, it is difficult to secure objective data such as health insurance claims, for triangulation, due to the variety of social and private insurance con­ figurations across participants. Another significant limitation of the study was the short time horizon for data collection and inability to detect longer-term out­ comes such as employment. Due to the fact that the average VR case is open 20 months (RSA, 2009), we could not evaluate the impacts of the HPE on long-term VR employment outcomes (Ipsen et al., 2012). Future studies should address this limitation to fully understand the role that health promotion may play in facilitating VR outcomes over time.

Conclusions People with disabilities experience significantly higher rates of secondary health conditions and lower employment rates than people without disabilities (Kinne et al., 2004; StatsRRTC, 2006). The com­ bination of these factors signifies the need for health promotion programming outside the work setting. The PIPE program (including variations) addresses this gap for a significant portion of the popula­ tion who may benefit from such services but don’t currently have access. The HPE applications are unique because they are low-cost interventions that can be delivered broadly, and they may be partic­ ularly important for rural people who experience barriers to attend in-person groups (Ipsen et al., 2012). Although online-based program­ ming may not be appropriate for the whole population, evidence suggests a growing number of people with disabilities have access to the Internet and e-mail (Ipsen, Rigles, Arnold, & Seekins, 2013), and technology infrastructure is now reaching even the most rural com­ munities (Federal Communications Commission, 2011). Application of the HPE within the VR system is a natural fit because VR serves a significant portion of unemployed people with disabilities. Additionally, there is evidence to suggest that reducing the rate of secondary health conditions can help people with disabil­ ities gain or maintain employment (Ipsen et al., 2011). Section 103(a) of the Rehabilitation Act defines allowable VR services as “any services described in an individualized plan for employment neces­ sary to assist any individual with a disability in preparing for, secur­ ing, retaining, or regaining an employment outcome . . Given the growing evidence about the relationship between health and employ­ ment, promoting access to health promotion activities appears to fit within VR’s array of services. Finally, due to the low cost and easy scalability of HPE applications, there are many additional venues for promoting health promotion access for people with disabilities such as Centers of Independent Living, rural health clinics, and employers.

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Received February 22, 2013 Revision received January 17, 2014 Accepted January 28, 2014 ■

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