Background: An increasing number of research designs are using text messaging (SMS) as a means of self-reported symptom and outcome monitoring in a variety of long-term health conditions, including severity ratings of depressed mood. The validity of such a single item SMS score to measure latent depression is not currently known and is vital if SMS data are to inform clinical evaluation in the future.
Methods: A sub-set of depressed participants in the UK ACUDep trial submitted a single SMS text score (R-SMS-DS) between 1 and 9 on how depressed they felt around the same time as completing the PHQ-9 depression questionnaire on paper at 3 months follow-up of the trial. Exploratory categorical data factor analysis (EFA) was used to ascertain the alignment of R-SMS-DS scores with the factor structure of the PHQ-9. Any response bias with regard to age or gender was assessed by differential item functioning (DIF) analysis.
Results: Depression scores based on the PHQ-9 and R-SMS-DS at 3 months were available for 337 participants (74 % female; mean age: 42 years, SD = 11.1), 213 of which completed the two outcomes within 6 days of each other. R-SMS-DS scores aligned with the underlying latent depression of the PHQ-9 (factor loading of 0.656) and in particular its affective rather than somatic dimension. The R-SMS-DS score was most strongly correlated with depressed mood (r = 0.607), feeling bad about oneself (r = 0.588) and anhedonia (r = 0.573). R-SMS-DS responses were invariant with respect to gender (p = 0.302). However, there was some evidence for age related response bias (p = 0.031), with older participants being more likely to endorse lower R-SMS-DS scores than younger ones.
Conclusions: The R-SMS-DS used in the ACUDep trial was found to be a valid measure of latent affective depression with no gender related response bias. This text message item may therefore represent a useful assessment and monitoring tool meriting evaluation in further research. For future study designs we recommend the collection of outcome data by new health technologies in combination with gold standard instruments to ensure concurrent validity.
- Factor analysis
- Response bias
- Text messaging