Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study

Haomiao Jin, Shinyi Wu, Haomiao Jin, Shinyi Wu

Abstract

Background: SMS text messaging is an inexpensive, private, and scalable technology-mediated assessment mode that can alleviate many barriers faced by the safety net population to receive depression screening. Some existing studies suggest that technology-mediated assessment encourages self-disclosure of sensitive health information such as depressive symptoms while other studies show the opposite effect.

Objective: This study aimed to evaluate the validity of using SMS text messaging to screen depression and related conditions, including anxiety and functional disability, in a low-income, culturally diverse safety net primary care population.

Methods: This study used a randomized design with 4 study groups that permuted the order of SMS text messaging and the gold standard interview (INTW) assessment. The participants for this study were recruited from the participants of the prior Diabetes-Depression Care-management Adoption Trial (DCAT). Depression was screened by using the 2-item and 8-item Patient Health Questionnaire (PHQ-2 and PHQ-8, respectively). Anxiety was screened by using the 2-item Generalized Anxiety Disorder scale (GAD-2), and functional disability was assessed by using the Sheehan Disability Scale (SDS). Participants chose to take up the assessment in English or Spanish. Internal consistency and test-retest reliability were evaluated by using Cronbach alpha and intraclass correlation coefficient (ICC), respectively. Concordance was evaluated by using an ICC, a kappa statistic, an area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. A regression analysis was conducted to examine the association between the participant characteristics and the differences in the scores between the SMS text messaging and INTW assessment modes.

Results: Overall, 206 participants (average age 57.1 [SD 9.18] years; females: 119/206, 57.8%) were enrolled. All measurements except the SMS text messaging-assessed PHQ-2 showed Cronbach alpha values ≥.70, indicating acceptable to good internal consistency. All measurements except the INTW-assessed SDS had ICC values ≥0.75, indicating good to excellent test-retest reliability. For concordance, the PHQ-8 had an ICC of 0.73 and AUROC of 0.93, indicating good concordance. The kappa statistic, sensitivity, and specificity for major depression (PHQ-8 ≥8) were 0.43, 0.60, and 0.86, respectively. The concordance of the shorter PHQ-2, GAD-2, and SDS scales was poor to fair. The regression analysis revealed that a higher level of personal depression stigma was associated with reporting higher SMS text messaging-assessed PHQ-8 and GAD-2 scores than the INTW-assessed scores. The analysis also determined that the differences in the scores were associated with marital status and personality traits.

Conclusions: Depression screening conducted using the longer PHQ-8 scale via SMS text messaging demonstrated good internal consistency, test-retest reliability, and concordance with the gold standard INTW assessment mode. However, care must be taken when deploying shorter scales via SMS text messaging. Further regression analysis supported that a technology-mediated assessment, such as SMS text messaging, may create a private space with less pressure from the personal depression stigma and therefore encourage self-disclosure of depressive symptoms.

Trial registration: ClinicalTrials.gov NCT01781013; https://ichgcp.net/clinical-trials-registry/NCT01781013.

International registered report identifier (irrid): RR2-10.2196/12392.

Keywords: comorbidity; depression; diabetes mellitus; health information technology; mobile health; patient reported outcome measures; primary care; screening; text messaging.

Conflict of interest statement

Conflicts of Interest: None declared.

©Haomiao Jin, Shinyi Wu. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 26.03.2020.

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