The extent to which family physicians record their patients’ exercise in medical records: a scoping review

Abstract

ObjectiveExercise is an effective modality for the prevention and treatment of chronic conditions and family physicians are the healthcare providers tasked to manage patients’ chronic disease status. However, little is known about the exercise documentation in family-physician records. Therefore, a scoping review was conducted to describe family-physician-recorded exercise-related advice to patients in electronic medical records.DesignScoping review.SettingPrimary care clinics.Search strategyPubMed, Medline, SPORTDiscus, Google, Dissertations & Theses Global, OCLC PapersFirst (via First Search) and included references were searched between 1 January 1990 and 10 June 2018. Extracted information included year, geographic origin, data input structure, input frequency and content of exercise inputs in family physicians’ electronic medical records. The primary outcomes are the structure, purpose and frequency of inputs.ResultsOf a possible 1758 documents, 83 remained after a title and abstract scan and 22 after a full-text review. These documents included 32 findings of physical activity/exercise medical record documentation: counselling/advising patients (50.0%), status (12.5%), embedded questionnaires (12.5%), status as a risk factor (12.5%), health promotion documentation (6.3%), inactivity status (3.1%) and grading (3.1%). The frequency of exercise inputs in primary care records vary from as low as 0.4% of patients with documentation of physical activity health promotion inputs to as high as 87.8% of patients with exercise or physical activity status recorded. The majority of included documents (63.6%) were focused on patients with identified chronic conditions.ConclusionThe findings suggest that the structure and purpose of exercise documentation is often unclear or unspecified. Studies that present exercise information from family-physician medical records tend to focus on patients with specific chronic conditions and present little detail about the field from which information was extracted. The review found that the proportion of patients with physical activity or exercise information is often less than half.

‘Function First’: how to promote physical activity and physical function in people with long-term conditions managed in primary care? A study combining realist and co-design methods

“…Despite a rise in initiatives and research, 61 physical activity promotion in primary care remains inconsistent. [62][63][64][65][66][67] Exercise referral schemes have shown small positive effects on physical activity, 33 but with low attendance and completion rates. 68 69 There are many barriers to exercise referral at an individual, social and system level.…”

Estimating Cardiorespiratory Fitness Without Exercise Testing or Physical Activity Status in Healthy Adults: Regression Model Development and Validation

“…Because physical activity is a key contributor to CRF, including a PAS variable in an NEECRF model improves accuracy [ 3 , 8 , 16 ]. However, a recent review of distinct EHRs across 20 countries found that only 18.8% of family practice clinics had structured PAS questionaries embedded within the EHR, with documented PAS in the EHR ranging from 10% to 86% [ 15 ]. Notably, no validated questionaries designed for PAS-based NEECRF calculations were used.…”

Accuracy of Non-Exercise Estimated Cardiorespiratory Fitness in Japanese Adults

“…As expected, the PAS model correlations are higher than our eCRF model without PAS; beyond genetics, PAS is an essential variable for modifying CRF [ 2 , 4 , 11 ]. Though these eCRF PAS-based correlations are higher, access to specific PAS data from existing EMR datasets is not likely to have been recorded in EMRs [ 7 , 25 ].…”

“…Given the type of variables used to calculate eCRF, a potentially rich data source may be found in electronic medical records (EMRs). However, EMRs do not typically have PAS information; therefore, it is impossible to conduct epidemiological studies using existing EMR data using PAS-based eCRF models [ 7 ]. However, EMRs do contain independent variables that have been used in previous eCRF PAS models (age, gender, BMI, resting heart rate, blood pressure, smoking) [ 8 ].…”