A counselor in your pocket: feasibility of mobile health tailored messages to support HIV medication adherence

Figures & data

Table 1 Message tailoring dimensions

Construct	What is assessed to determine tailoring	Difference between tailored message versions	Specific matching prediction and relevant theory of health behavior	Average construct validity rating of ten tailored messages
Control beliefs	High vs low sense of control over problems	Emphasis on reasons for change (“think about”) vs actions to take (“do”)	Problem-solving focus → ↑ adherence for high control beliefs; reasons for change better for low control beliefs (Prochaska and DiClemente’s transtheoretical model^Citation33)	M =3.58 (SD =0.19)
Mood	Level of positive emotional arousal (high vs low)	Focus on feared outcomes (permanent vs short-term), and use of affect-inducing words	Feared consequences → ↑ adherence for high (positive) mood; lower-fear presentation better for low (negative) mood (Leventhal’s illness perception model^Citation34)	M =3.86 (SD =0.31)
Situational stress	Level of current stress (high vs low) from multiple sources	Deep focus (content) vs surface focus (vivid images, expert quotes, personal relevance)	For high stress, surface focus → ↑ adherence; content focus better for low stress when people process information more deeply (Lazarus and Folkman’s coping theory^Citation35)	M =3.69 (SD =0.29)
Coping	Approach (domain of gains) vs avoidance (domain of losses)	Emphasis on benefits of behavior change vs costs of not changing behavior	Gain frame → ↑ adherence for high (active) coping; loss frame better for low (passive) coping (Kahneman and Tversky’s prospect theory^Citation36)	M =3.91 (SD =0.12)
Social support	Perceived current social support (+)and stigma (−)	Focus on self vs others in reasons for behavior change	Other-focus → ↑ adherence if support is seen as high; self-focus better if support is low (Bronfenbrenner’s socio-ecological model^Citation37)	M =3.72 (SD =0.26)

Notes: Construct validity ratings were made by 15 independent experts in health behavior change research, using a scale from 1= “not relevant” to 4= “very relevant and succinct”. Ratings were averaged across experts, and across ten different base messages corresponding to different barriers to adherence. Experts also gave a rating of 3.84 points for the messages’ overall integration and consistency, and 3.89 points for their relevance to ART adherence and clinical utility, on the same 4-point scale. The ten barriers identified through consultation with HIV primary care experts in a previous stage of development were: 1) low perceived importance of treatment, 2) lack of confidence in treatment, 3) adverse effects of treatment, 4) feeling healthy, 5) feeling sick, 6) lack of financial resources, 7) treatment complexity, 8) stigma, 9) alcohol or other drug use, and 10) forgetting.

Abbreviations: vs, versus; M, mean; SD, standard deviation; HIV, human immunodeficiency virus.

Figure 1 CONSORT diagram showing recruitment, enrollment, and retention.

Abbreviations: PLWH, persons living with HIV; HIV, human immunodeficiency virus; MEMS, medication event monitoring system; CONSORT, Consolidated Standards of Reporting Trials.

Table 2 Participant demographics by order of study conditions

Variable	AB condition order (matched first)	BA condition order (mismatched first)	Significant difference? Yes/no
Age, years	M =43.7 (SD =5.87)	M =41.8 (SD =9.23)	No: t(30) =0.71, P=0.48, d =0.25
Sex	35% women (6/17)	10% women (2/20)	Yes: χ^2=4.59, P=0.03, ϕ =0.37
Race/ethnicity	47% non-White (8/17)  3 Latino/Latina  2 African–American  1 Native American  2 other ethnicity	55% non-White (11/20)  3 Latino/Latina  6 African–American  2 Native American	No: χ^2=0.003, P=0.06, ϕ =0.01
Sexual orientation	29% GLBT (5/17)	85% GLBT (17/20)	Yes: χ^2=10.5, P=0.001, ϕ =0.56
Years of education	M =12.9 (SD =1.45)	M =13.0 (SD =2.54)	No: t(27) =0.11, P=0.92, d =0.04
Baseline MEMS adherence (%)	M =72% (SD =22%)	M =87% (SD =20%)	No: t(25) =1.85, P=0.08, d =0.64

Abbreviations: MEMS, medication event monitoring system; SD, standard deviation; GLBT, gay, lesbian, bisexual, or transgender; M, mean.

Figure 2 Example of tailored messaging intervention.

Abbreviation: meds, medications.

Table 3 Summary statistics for self-report feasibility measures

Variable	N	Possible range	M (SD)	Minimum	Maximum
Acceptance	14	0–100	85.3 (17.9)	49	100
Ease of use	25	1–7	5.96 (1.06)	3.24	7.00
Usefulness	5	0–4	2.79 (0.23)	2.50	3.13
Would do it again	5	0–4	3.00 (0.00)	3.00	3.00
Would recommend to a friend	5	0–4	3.40 (0.55)	3.00	4.00

Notes: N varies by analysis because the technology acceptance questionnaire was completed by participants at the end of the baseline phase for the first two measures (with some missing data on the acceptance item), and the tailored messaging survey was completed at the end of the intervention phase, with sample size affected by attrition. Results on the perceived usefulness measures are therefore less reliable.

Abbreviations: SD, standard deviation; N, number of responses; M, mean.

Figure 3 Change in adherence over time.

Notes: AB order = matched, then mismatched messages; BA order = mismatched, then matched messages. Covariates in the model are evaluated at the following average values: sex =0.72 (1= male, 0= female); baseline MEMS =0.8354; number of days with observations =7.48 days.
Abbreviation: MEMS, medication event monitoring system.

ProchaskaJONorcrossJDiClementeCChanging for GoodNew YorkAvon Books1995

 Google Scholar

LeventhalHDiefenbachMLeventhalEAIllness cognition: using common sense to understand treatment adherence and affect cognition interactionsCognitive Therapy and Research199216143163

 Web of Science ®Google Scholar

LazarusRSFolkmanSStress, Appraisal, and CopingNew YorkSpringer1984

 Google Scholar

KahnemanDTverskyAChoices, values, and framesNew YorkCambridge University Press2000

 Google Scholar

ReifsniderEGallagherMForgioneBUsing ecological models in research on health disparitiesJournal of Professional Nursing200521421622216061168

 PubMed Web of Science ®Google Scholar