Virtual Intervention to Support Self-management of Antiretroviral Therapy among Persons Living with HIV

1. Tous droits réservés ®
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.

2. 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
Tous droits réservés ©

3. 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
Tous droits réservés ©

4. 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).
Tous droits réservés ©

5. 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

6. Objective
Compare the effectiveness of two kinds of
health care follow-up–traditional and virtual–in
terms of promoting ART adherence among
PLHIV.
Tous droits réservés ©

7. METHODS

8. 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.
Tous droits réservés ©

9. 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
Tous droits réservés ©

10. 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.
Tous droits réservés ©

11. Non-random assignment
• The two groups (n=179) were formed on the basis of the recruitment sites
Tous droits réservés ©
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.

12. RESULTS

13. ©
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

14. 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

15. 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

16. 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

17. 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)

18. 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).

19. 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.

21. Funding

22. References (1)
• Badia X, Podzamczer D, Garcìa M, López-Lavid C, Consiglio E. A randomized study comparing instruments for
measuring health-related quality of life in HIV-infected patients. Spanish MOS-HIV and MQOL-HIV Validation
Group. Medical Outcomes Study HIV Health Survey. AIDS, 1999. 13(13): p. 1727-1735. PMID: 10509575
• Badia X, Podzamcer D, Casado A, López-Lavid C,García M. Evaluating changes in health status in HIV-infected
patients: Medical Outcomes Study-HIV and Multidimensional Quality of Life-HIV quality of life questionnaires.
Spanish MOS-HIV and MQOL-HIV Validation Group. AIDS, 2000. 14(10): p. 1439-1447. PMID: 10930160
• Bärnighausen T, Chaiyachati K, Chimbindi N, Peoples A, Haberer J,Newell M-L. Interventions to increase
antiretroviral adherence in sub-Saharan Africa: a systematic review of evaluation studies. The Lancet Infectious
Diseases, 2011. 11(12): p. 942-951. PMID: 22030332
• Côté J, Godin G, Ramirez P, Gagnon ML,Rouleau G. Program development for enhancing adherence to antiretroviral
therapy among persons living with HIV. AIDS Patient Care and STDs, 2008. 22(12): p. 965-975. PMID: 19072103
• 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

23. References (2)
• Côté J, Rouleau G, Godin G, Ramirez-Garcia P, Guéhéneuc Y-G, Nahas G, Tremblay C, Otis J, Hernandez A.
Acceptability and feasibility study of a virtual intervention to help persons living with HIV manage their daily
therapies. Journal of Telemedicine and Telecare 2012. 18: p. 409-412. PMID: 23034932
• de Bruin M. Standard care impact on effects of highly active antiretroviral therapy adherence interventions: A
meta-analysis of randomized controlled trials. Arch Intern Med, 2010. 170: p. 240 - 250. PMID: 20142568
• Fisher J, Amico KR, Fisher W, Cornman D, Shuper P, Trayling C, Redding C, Barta W, Lemieux A, Altice F, Dieckhaus
K, Friedland G. Computer-Based Intervention in HIV Clinical Care Setting Improves Antiretroviral Adherence: The
LifeWindows Project. AIDS and Behavior, 2011. 15(8): p. 1635-1646. PMID: 21452051
• Godin G, Côté J, Naccache H, Lambert LD,Trottier S. Prediction of adherence to antiretroviral therapy: A one year
longitudinal study. AIDS Care, 2005. 17: p. 493-504. PMID: 16036235
• Godin G, Gagné C, Naccache H. Validation of a self-reported questionnaire assessing adherence to antiretroviral
medication. AIDS Patient Care and STDs, 2003. 17: p. 325-332. PMID: 12952734
• Hersch RK, Cook RF, Billings DW, Kaplan S, Murray D, Safren S, Goforth J,Spencer J. Test of a web-based program to
improve adherence to HIV medications. AIDS and Behavior, 2013. 17(9): p. 2963-76. PMID: 23760634
• Horvath T, Azman H, Kennedy G,Rutherford G. Mobile phone text messaging for promoting adherence to
antiretroviral therapy in patients with HIV infection. Cochrane Database of Systematic Reviews, 2012. CD009756(3)
PMID: 22419345

24. References (3)
• Justice AC, Holmes W, Gifford AL, Rabeneck L, Zackin R, Sinclair G, Weissman S, Neidig J, Marcus C, Chesney M, Cohn SE, Wu
AW. Development and validation of a self-completed HIV symptom index. Journal of Clinical Epidemiology, 2001. 54(12,
supplement 1): p. S77-S90. PMID: 11750213
• León A, Cáceres C, Fernández E, Chausa P, Martin M, Codina C, Rousaud A, Blanch J, Mallolas J, Martinez E, Blanco JL,
Laguno M, Larrousse M, Milinkovic A, Zamora L, Canal N, Miró JM, Gatell JM, Gómez EJ, García F. A New Multidisciplinary
Home Care Telemedicine System to Monitor Stable Chronic Human Immunodeficiency Virus-Infected Patients: A Randomized
Study. PLoS ONE, 2011. 6(1): p. e14515. PMID: 21283736
• Linn AJ, Vervloet M, van Dijk L, Smit EG,Van Weert J. Effects of eHealth interventions on medication adherence: a systematic
review of the literature. Journal of medical Internet research, 2011. 13(4)
• Mathes T, Pieper D, Antoine SL,Eikermann M. Adherence-enhancing interventions for highly active antiretroviral therapy HIV
infected patients - a systematic review. HIV Med, 2013. 14(10): p. 583-95. PMID: 23773654
• in Ortego C, Huedo-Medina TB, Llorca J, Sevilla L, Santos P, Rodriguez E, Warren MR,Vejo J. Adherence to highly active
antiretroviral therapy (HAART): a meta-analysis. AIDS and Behavior, 2011. 15(7): p. 1381-96. PMID: 21468660
• Peacock EJ,Wong PTP. The stress appraisal measures: A multidimensional approach to cognitive appraisal. Stress Medicine,
1990. 6: p. 227-236.
• Pellowski JA, Kalichman SC. Recent Advances (2011-2012) in Technology-Delivered Interventions for People Living with HIV.
Current HIV/AIDS reports, 2012. 9: p. 326-334. PMID: 22922945
• Saberi P, Johnson MO. Technology-Based Self-Care Methods of Improving Antiretroviral Adherence: A Systematic Review.
PLoS ONE, 2011. 6(11): p. e27533. PMID: 22140446
Linn AJ, Vervloet M, van Dijk L, Smit EG,Van Weert J. Effects of eHealth interventions on medication adherence: a systematic review
of the literature. Journal of medical Internet research, 2011. 13(4)