Measuring Suboptimal Health Status in clinical practice: Which tool can I use?

Suboptimal health status (SHS) can be systematically assessed in your clinical practice. Find out how this tool may help you in identifying and managing SHS.

Around one in every two individuals may have suboptimal health status (SHS), a subclinical state between health and disease characterised by fatigue and a constellation of other physical symptoms, but with no diagnosable disease condition.1-4 As the rate of this condition is expected to increase with rapid urbanisation, a validated tool to assess SHS is be required to guide diagnosis and management.

How can I measure SHS in individuals?

The suboptimal health status questionnaire (SHSQ-25) is a 25-item questionnaire encompassing five subscales: fatigue, cardiovascular system, digestive tract, immune system, and mental status.5 It may be used to assess individuals from the general population and primary care service.3,5 This self-rated questionnaire asks an individual to rate a specific statement on a five-point Likert-type scale, based on how often they suffered specific health complaints in the preceding three months: never or almost never (no points), occasionally (1 point), often (2 points), very often (3 points), and always (4 points).5 The points are then added up to yield a score ranging from 0–100 points. A score of greater than 35 indicates SHS.5,6

How can using the SHSQ-25 help in my practice?

The SHSQ-25 has a number of potential uses in clinical practice. Firstly, it may be used to assess health risks. Individuals with higher SHS scores have a higher risk of cardiovascular disease than those with lower scores.3 They have more severe elevations in established cardiovascular risk factors, such as blood pressure, plasma glucose, total cholesterol, triglyceride levels and body mass index (BMI). High SHS score has also been associated with higher levels of serum cortisol, indicating that stress may be a possible contributor.7

The SHSQ-25 can also be used as a practical tool to screen at-risk individuals, such as those with type 2 diabetes. In a cross-sectional study that included 241 patients with type 2 diabetes mellitus and 264 healthy individuals as controls, high SHS was shown to be significantly correlated with a sedentary lifestyle (p=0.034) and elevated systolic and diastolic blood pressure (p=0.001 for both).8 These findings suggest that SHSQ-25 may be used in conjunction with conventional screening to identify type 2 diabetes mellitus with other cardiovascular risk factors.

SHSQ-25 scores as a guide for holistic intervention

The SHSQ-25 may be used as a guide in improving the health status of individuals with SHS. A multicentre, randomised, double-blind, placebo-controlled trial used the SHSQ-25 to identify individuals with SHS. Among those with suboptimal cardiovascular health and SHS with abnormal blood pressure, fasting plasma glucose, blood lipids or BMI were then randomised to receive 4 g/day of n-3 long-chain polyunsaturated fatty acids or placebo for 3 months. After intervention, those on active therapy had a significantly greater decrease in BMI compared to those on placebo (−0.29 ± 0.06 kg/m2 vs −0.02 ± 0.06 kg/m2, p=0.003).9 These findings suggest that the SHSQ-25 may be used to characterise those with suboptimal health with the intention of providing active intervention strategies.

Additional studies are recommended to assess the potential role of SHSQ-25 in predictive, preventive and personalised medicine, such as the prediction of risk and treatment response and the individualised planning of preventive interventions. The SHSQ-25 may also be a useful tool in the monitoring of response to health-promoting lifestyle interventions.


  1. Wu S, et al. Int J Environ Res Public Health 2016;13:339.
  2. Chen J, et al. Int J Environ Res Public Health 2017;14:240.
  3. Wang W, Yan Y. Clin Transl Med 2012;1:28.
  4. Wang W, et al. EPMA J 2014;5:4.
  5. Yan YX, et al. J Epidemiol 2009;19:333-341.
  6. Kupaev V, et al. EPMA J 2016;7:19.
  7. Yan YX, et al. Stress 2015;18:29–34.
  8. Adua E, et al. EPMA J 2017;8:345–355.
  9. Zeng Q, et al. Nutr Metab Cardiovasc Dis 2017;27:964-970.