Quality of Life Research

, Volume 24, Issue 7, pp 1575–1583 | Cite as

Health perceptions and symptom burden in primary care: measuring health using audio computer-assisted self-interviews

  • Keiki Hinami
  • Jennifer Smith
  • Catherine D. Deamant
  • Romina Kee
  • Diana Garcia
  • William E. Trick



To assess the relationships among somatic symptoms and health perception measures in data collected from the implementation of audio computer-assisted self-interview (ACASI) technology in a primary care clinic of a safety-net healthcare system.


We approached 2,848 English- or Spanish-speaking patients to complete an ACASI-administered survey before their clinic appointment between April 2011 and July 2012. We administered the National Institutes of Health Patient-Reported Outcomes Measurement Information System (PROMIS) Global Health-10 assessing General Self-Rated Health (GSRH), Global Physical and Mental Health; Memorial Symptom Assessment Scale (MSAS) assessing symptom burden; and the Patient Health Questionnaire-2 (PHQ-2). We calculated population attributable fractions (PAF) of symptoms on poorly perceived health.


Participation rate was 90 %, but 51 % of observations were analyzable. Mean age was 57 years; 53 % were non-Hispanic black; and 20 % completed the survey in Spanish. All but 2 % reported at least one symptom most commonly lack of energy (87 %) and pain (83 %). The MSAS was well correlated with PHQ-2 (r = 0.65) and Global Physical Health (r = −0.65), but less with GSRH (r = −0.49). All negative health perception measures were largely attributable to lack of energy and pain, while depression-range PHQ-2 was attributable also to less prevalent symptoms including decreased appetite and sexual disinterest.


Symptom burden was less correlated with GSRH than with other measures of poor health perception. Fatigue and pain contributed the highest PAF to all measures of perceived poor health. Success with collecting PROMs in a resource-limited clinical setting demonstrates that the implementation of ACASI technology is feasible.


Technology assessment Underserved populations Comorbidity Primary care redesign 



Funded by the Agency for Healthcare Research and Quality (AHRQ): R24 HS19481-01 to support technology implementation. Drs. Hinami and Trick had full access to all the data and take responsibility for their integrity and the accuracy of the data analysis.

Conflict of interest

The authors report no conflicts of interest, relevant financial interests, activities, relationships, and affiliations that influenced this work.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Keiki Hinami
    • 1
  • Jennifer Smith
    • 2
  • Catherine D. Deamant
    • 2
  • Romina Kee
    • 1
  • Diana Garcia
    • 1
  • William E. Trick
    • 1
  1. 1.Collaborative Research Unit, Department of MedicineCook County Health and Hospitals SystemChicagoUSA
  2. 2.Division of General Internal Medicine, Department of MedicineCook County Health and Hospitals SystemChicagoUSA

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