Daily step count and walking speed as general measures of patient wellbeing

Simple measures of a patient’s walking capacity such as walking speed (WS) and daily step count (DSC) are often measured by activity trackers in fitness watches or smartphones, giving insight into a patient’s daily walking patterns without the influence of the Hawthorne effect. Both metrics have literature backing their importance as measures of general health. Reduction in WS is a key characteristic of ageing and frailty (1), as well as a predictor of falls (2), a finding in many neurological diseases (3,4) and a predictor of mortality regardless of age (5). While increasing daily activity, measured by DSC, is linked to lower all-cause mortality by reducing incidence of metabolic syndrome and related diseases (6), it is itself impacted in many disease states. Patient WS and DSC can be collected from activity trackers (7) built into fitness watches and smartphones, with some early reports of devices collecting these metrics in day-to-day living (8,9).

Simple and objective assessment of walking would be of significant benefit for physicians to monitor a patient’s overall health, in conjunction with other routine health metrics and vital signs. Objective outcome assessments overcome limitations of subjective, patient-reported outcome measures, which suffer from poor reliability, recall and reporting bias (10) and a lack of capacity for continuous assessment (11). Simple scores which incorporate DSC and WS may assist the rapid identification of individuals, or indeed populations, with declining health, facilitating early intervention, which may delay the typical increased healthcare costs and diminished quality of life associated with ageing and frailty.

Unfortunately commercial smart devices tend to have low levels of inaccuracy (3–10%) in step detection (12), but this increases to 40% in distance-based calculations using GPS software (13). Fortunately, distance calculations can also be made by a device’s built in accelerometer without sacrificing much battery longevity. Finally, for assessment of patients using screening tools from these metrics, it would be necessary to know the normal range of these metrics. Fortunately, a number of citizen science projects collected by phone apps like Argus have generated large databases of population gait factors, stratified by gender, age and location (14).

Acknowledgments

Funding: None.

Footnote

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/jss-2020-03). RJM serves as an unpaid Editor-in-Chief of Journal of Spine Surgery from Sep 2015 to Sep 2025.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bohannon RW, Williams Andrews A. Normal walking speed: a descriptive meta-analysis. Physiotherapy 2011;97:182-9. [Crossref] [PubMed]
2. Pirker W, Katzenschlager R. Gait disorders in adults and the elderly: A clinical guide. Wien Klin Wochenschr 2017;129:81-95. [Crossref] [PubMed]
3. Mobbs RJ. Gait velocity (walking speed) is an indicator of spine health, and objective measure of pre and post intervention recovery for spine care providers. J Spine Surg 2020;6:353-5. [Crossref] [PubMed]
4. Paker N, Bugdayci D, Goksenoglu G, et al. Gait speed and related factors in Parkinson's disease. J Phys Ther Sci 2015;27:3675-9. [Crossref] [PubMed]
5. Studenski S, Perera S, Patel K, et al. Gait speed and survival in older adults. JAMA 2011;305:50-8. [Crossref] [PubMed]
6. Hall KS, Hyde ET, Bassett DR, et al. Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. Int J Behav Nutr Phys Act 2020;17:78. [Crossref] [PubMed]
7. Ghent F, Mobbs RJ, Mobbs RR, et al. Assessment and Post-Intervention Recovery After Surgery for Lumbar Disk Herniation Based on Objective Gait Metrics from Wearable Devices Using the Gait Posture Index. World Neurosurg 2020. [Crossref] [PubMed]
8. Mobbs RJ, Phan K, Maharaj M, et al. Physical Activity Measured with Accelerometer and Self-Rated Disability in Lumbar Spine Surgery: A Prospective Study. Global Spine J 2016;6:459-64. [Crossref] [PubMed]
9. Chakravorty A, Mobbs RJ, Anderson DB, et al. The role of wearable devices and objective gait analysis for the assessment and monitoring of patients with lumbar spinal stenosis: systematic review. BMC Musculoskelet Disord 2019;20:288. [Crossref] [PubMed]
10. Silfee VJ, Haughton CF, Jake-Schoffman DE, et al. Objective measurement of physical activity outcomes in lifestyle interventions among adults: A systematic review. Prev Med Rep 2018;11:74-80. [Crossref] [PubMed]
11. Mobbs RJ, Mobbs RR, Choy WJ. Proposed objective scoring algorithm for assessment and intervention recovery following surgery for lumbar spinal stenosis based on relevant gait metrics from wearable devices: the Gait Posture index (GPi). J Spine Surg 2019;5:300-9. [Crossref] [PubMed]
12. Johnson M, Turek J, Dornfeld C, et al. Validity of the Samsung Phone S Health application for assessing steps and energy expenditure during walking and running: Does phone placement matter? Digit Health 2016;2:2055207616652747. [Crossref] [PubMed]
13. Ata R, Gandhi N, Rasmussen H, et al. Clinical validation of smartphone-based activity tracking in peripheral artery disease patients. NPJ Digit Med 2018;1:66. [Crossref] [PubMed]
14. Althoff T, Sosič R, Hicks JL, et al. Large-scale physical activity data reveal worldwide activity inequality. Nature 2017;547:336-9. [Crossref] [PubMed]