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Virtual Intervention to Support Self-Management of Antiretroviral Therapy

among Persons Living with HIV

José Côté, Inf., Ph. D.

Titulaire de la Chaire de recherche sur les nouvelles pratiques en soins infirmiers

Godin, G., Ramirez-Garcia, P., Rouleau, G., Bourbonnais, A., Guéhéneuc, YG., Tremblay, C., Otis, J.

Team

Gaston Godin, Faculty of Nursing, Laval University, Canada

Pilar Ramirez-Garcìa, Faculty of Nursing, Université de Montréal

Geneviève Rouleau, CRCHUM, Chair for Research into New Practices in Nursing

Anne Bourbonnais, Faculty of Nursing, Université de Montréal

Yann-Gaël Guéhéneuc, École Polytechnique, Montreal, Canada

Cécile Tremblay, Research Center of the Centre Hospitalier de l’Université de Montréal

Joanne Otis, Canada Research Chair in Health Education, Université du Québec à Montréal

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Background

• Living with HIV necessitates long-term healthcare follow-up particularly with respect to management of antiretroviral therapy (ART).

• With the enormous possibilities afforded by information and communication technologies (ICT), we developed a virtual nursing intervention (*VIH-TAVIE™) to empower persons living with HIV (PLHIV) to manage their ART and their symptoms optimally.

*HIV Treatment, Virtual Nursing Assistance and Education

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VIH-TAVIE

• 4 Web-based computer sessions, 20-30 minutes long

• Hosted by a “virtual nurse” who engages the user in a self-management skills-learning process.

• Targets the development and consolidation of skills to enhance the individual’s ability to act.

• Interaction between nurse and patient in an asynchronous way (based on a decision tree).

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Côté J, Ramìrez-Garcia P, Rouleau G, Saulnier D, Guéhéneuc YG, Hernandez A,Godin G. A nursing virtual intervention : Real-time support for managing antiretroviral therapy. Computers, Informatics, Nursing 2011. 29(1): p. 43-51. PMID: 21099544

Objective

Compare the effectiveness of two kinds of health care follow-up–traditional and virtual–in terms of promoting ART adherence among PLHIV.

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METHODS

Study design

•Quasi-experimental study : to evaluate the capacity of both kinds of follow-up to optimize medication adherence (primary

•Adherence is a behavioural indicator that can be predicted in part by cognitive and affective variables (secondary outcomes), particularly sense of self-efficacy and attitude towards drug intake, which in turn can be explained by perceived social support and absence of symptoms.

•Three measurement times: (T0), and three months (T3) and six months (T6) later.

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Measures

• Adherence (Godin et al., 2003)

– intake of at least 90% of prescribed tablets

• Self-efficacy regarding medication intake (Godin et al., 2003 )

• Attitude toward medication intake (Godin et al., 2005)

• Symptom-related discomfort: Self-Completed HIV Symptom Index (Justice et al., 2001)

• Perceived social support: Medical Outcome Survey (Badia et al., 1999, 2000)

• Stress : stressfulness subscale (4 items) of the Stress Appraisal Measure (Peacock and Wong (1990)

• Immunologic and viral indicators

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Sample

• PLHIV recruited had to be at least 18 years old and on ART for at least six months.

• Pregnant women, people with uncontrolled psychiatric conditions, and active intravenous drug users were not eligible for the study.

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Non-random assignment

• The two groups (n=179) were formed on the basis of the recruitment sites

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Virtual follow-up (n=99) Traditionnal follow-up (n=80)

Participants who benefitted from VIH-TAVIE (4 interactive web-based

computer sessions) could also consult their regular healthcare

teams.

Meeting (lasted 20 minutes) with healthcare professionals over a

period of three to four months. The meetings covered medication,

symptoms, and problems encountered. Personalized health

advice was given on these occasions.

RESULTS

©

Variables Traditional

follow-up (n=80)

Virtual follow-

up (n=99)

p value

Gender, n (%)a 0.151 (b)

Male 71 (88.8) 82 (82.8)

Age (years), mean (SD) 49 (9.2) 47 (7.61) 0.062 (c)

Ethnicity, n (%) <0.001 (b)

Canadian 41 (51.2) 89 (89.9)

Marital status, n (%) <0.001 (b)

Single 41 (51.2) 80 (80.8)

Married or living as couple 31 (38.8) 7 (7.1)

Employment status, n (%) <0.001 (b)

Working/student 37 (46.2) 14 (14.1)

Insurance/retired 16 (20) 9 (9.1)

Welfare 19 (23.8) 64 (64.6)

Education levels, n (%) 0.001 (b)

Primary 5 (6.2) 7 (7.1)

Secondary 27 (33.8) 54 (54.5)

College 23 (28.7) 29 (29.3)

University 25 (31.2) 9 (9.1)

Annual income, n (%)* 0.003 (b)

< CAD $14 999 33 (41.2) 67 (67.7)

$15 000 - $34 999 20 (25) 21 (21.3)

$35 000 – $54 999 10 (12.6) 5 (5)

> CAD $55 000 11 (13.8) 3 (3)

Demographic characteristics of the participants in both groups

Variables Traditional follow-

up

(n=80)

Virtual follow-

up (n=99)

p value

Years of HIV infection, mean (SD)

14.4 (7.3)

13.4 (7.7)

0.365 (c)

Years on antiretroviral therapy, mean

(SD)

11.65 (6.6)

9.86 (6.5)

0.077 (c)

Treatment interruption 3 months before

T0, n (%)

4 (5.1)

15 (15.2) 0.03 (b)

Viral load less than 50 copies,

n (%)

7/67 (89.6)

15/82

(81.7)

0.179 (b)

CD4 count (cells/μl), mean (SD) 540 (293)

441 (237) 0.021 (c)

Clinical characteristics of the participants in both groups

Effect of the kind of follow-up on pills adherence (% of adherence≥90%) using generalized estimating equations (GEE)

Kind of follow-up T0

% adherence ≥ 90

T3

% adherence ≥ 90

T6

% adherence ≥ 90

Traditional follow-

up (n=80)

79.7 85.7 92.7

Virtual follow-up

(n=99)

83.5 90.4 89.6

Group x Time interaction, Z= -1.36, p=0.1743

Time effect, Z= -1.96, p=0.0496

Cognitive and affective variables of the participants in both groups

Cognitive and

affective variables

Traditional follow-up

(n=80)

Mean (SD)

Virtual follow-up

(n=99)

Mean (SD)

p valuea

Symptoms countb 9.85 (7.17) 12.57 (6.91) 0.011

Symptoms botherc 21.16 (17.19) 29.07 (18.23) 0.004

Attituded 23.9 (5.32) 23.46 (4.69) 0.554

Stresse 6.23 (3.67) 7.45 (4.04) 0.037

Self-efficacyf 1246.25 (206.22) 1192.89 (196.89) 0.079

Social supportg 70.23 (21.61) 60.77 (20.03) 0.003

(a) Student’s t-test Possible range : (b) 0-24; (c) 0-96; (d) 6-30; (e) 4-20; (f) 0-1400; (g) 19-95

Effect of kind of follow-up on cognitive and affective variables using ANOVA

Group x Time interaction

F, p value

Time effect

F, p value

Variables/kind of follow-up

Symptoms count

Virtual (n=67) F=0.322, p=0.572 F=4.166, p=.044

Traditional (n=31)

Symptoms bother

Virtual (n=67) F=0.562, p=0.455 F=4.127, p=.045

Traditional (n=31)

Attitude

Virtual (n=67) F=3.759, p=0.056 F=1.069, p=0.304

Traditional (n=29)

Stress

Virtual (n=68) F=0.871, p=0.353 F=1.915, p=0.170

Traditional (n=32)

Self-efficacy

Virtual (n=68) F=0.268, p=0.606 F=1.416, p=0.237

Traditional (n=32)

Social support (total score)

Virtual (n=68) F=0.184, p=0.669 F=5.647, p=0.019

Traditional (n=32)

Discussion

• Two groups improved in adherence at six months but did not differ in this regard.

• Results of web-based HIV medication adherence similar to VIH-TAVIE: Life Windows Project (Fisher et al., 2011); Hersch et al. (2013) study.

• Interventions using mobile telephones and SMS/text messaging (Horvath et al., 2012).

• Difficulty of observing improvement in adherence among PLHIV: Ceiling effect (high baseline adherence) and comparison groups benefit from adherence-enhancing components in their usual follow-up (Mathes et al., 2013; de Bruin et al., 2010).

Limitation/conclusion

• Absence of randomization, deep selection bias

• Conservative statistical strategies were used to address the problem of attrition.

• ICT-assisted intervention have shown promise as effective means of maintaining and improving medication adherence: more research is needed to determine their efficacy with larger trials.

Funding

References (1)

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