Procedure
We were initially supported in the development of search terms by five non-health professional parents. We asked these parents to describe the terminology they would use when searching for health care advice if their child had heel pain. Parents preferred online search terms were “child heel pain” or “my child has heel pain”.
Next, we used scraping software (Thruuu by app.samuelschmitt.com) in September 2021. We used this software to extract the top 300 location specific websites (top 100 in each region of interest) through their organic ranking by Google search engine results page analysis. This software program uses specific keywords entered by the user to identify websites with the search terms and extracts both the webpage URL from Google and key information from that website. This method of data acquisition and software program was chosen to eliminate influence of website choice through any saved settings, location blockers or “cookies” on authors computers. This method of data acquisition was also chosen to minimise the impact the influence of paid advertising where possible, on the ranking of the website. We used the terms “child” “heel” “pain” as three separate words in the scraping program in addition to limit country of origin to USA, UK and Australia. It was pre-determined to exclude websites only describing children’s heel pain presentations inconsistent with calcaneal apophysitis (e.g., inflammatory disease), and exclusion occured during manual data extraction by research team. Where a site was excluded based on its content, we moved to the next ranked site on the extraction list until we had 50 sites extracted for each country.
Next, the data were extracted by the scraping program in the form of an excel spreadsheet. This extracted data included the website link and organic ranking. The scraping program provided additional information such as number of images and word count. All other website information was manually extracted by the author team in a purpose-built spreadsheet to minimise the influence of any online advertising that would potentially change the webpage content in the scraping program’s extraction.
The first 50 websites provided from the Thruuu extraction of each geographical search were manually checked by all authors for location matching. Where one was outside the location, an additional was included until we had a final 150 websites (50 from each geographic location) to audit. All additional data were extracted by one author and checked by a second author (USA = JJW and CMW, UK = SL and CMW, Australia = SL and JJW). As two authors (JJW, CMW) are practicing health professionals in the UK and Australia, JJW and CMW did not audit or secondary check websites arising from the country they worked in to minimise bias.
We extracted information into three domains: credibility, readability, and accuracy of online advertising information. Our created purpose-built spreadsheet used elements from the DISCERN tool [
11] and JAMA instrument [
12] designed to describe or measure quality of patient information into the categories of credibility, readability and accuracy. Both tools are designed predominantly for written information and when there are published treatment guidelines to assess against. As there is no published diagnostic or treatment guidelines for calcaneal apophysitis, the questions were not applicable, therefore we applied the principles from both into the purpose-built spreadsheet. Therefore, the quality of patient information aligning with these tools included the credibility domains relating to publisher information and date of publication. Readability information first calculated using the Statistical Measure of Gobbledygook (SMOG) score, number of words and percentage of complex words. This was performed by cutting and pasting the entire information from the website into an open access SMOG online readability calculator (
https://www.webfx.com/tools/read-able/). The SMOG score is calculated by the number of words containing three or more syllables within three passages of ten sentences or more [
13]. The output is an estimate of the number of years of education a person requires to understand the text. This online calculator also determined the number of words and the percentage of complex words (polysyllabics, with three or more syllables). Lastly, collation of accuracy relating to the condition-related data were developed using an inductive coding framework [
14]. In this section, our team built the initial framework through adding elements where new ones arose. As the framework evolved, we returned to previously extracted data and recoded as appropriate. We then used published systematic reviews and clinical trials to determine if there was evidence supporting each of the statements. The final purpose-built extraction elements are outlined in Additional file
1.
Data extracted was described in frequencies, means and standard deviations (SD). There are no consensus-based methods for the diagnosis of calcaneal apophysitis. Research commonly advocates and supports clinicians to diagnose calcaneal apophysitis based on its signs and symptoms through history taking, pain on palpation consistent with location of apophysis at the heel, and in the absence of any other localised inflammation or joint pain as a potential indicator of systemic inflammation [
15,
16]. Webpages describing these signs and symptoms were considered aligned with evidence. It is also commonly accepted that medical imaging is not sensitive or specific for apophysitis diagnosis [
17,
18]. Based on this we considered recommendations for additional tests and images to diagnosed calcaneal apophysitis to not be aligned with evidence.
Once we extracted the treatment elements, we aligned them with treatment groupings as described below. The treatment elements were extracted from the most recently published systematic review [
19] and randomised clinical trials (cross over effect, treatment A versus placebo, treatment A versus treatment B, or a wait and see methodology) [
20‐
24] found through a forward chaining search on Google Scholar using the most recent systematic review as the content source as at 17
th of September, 2021 [
19]. We then grouped these treatments through the proposed mechanism for action. These groupings were developed with an experienced group of seven clinician researchers with experience in apophyseal injury management external to this present research. These clinical researchers were physiotherapists (
n = 4) and podiatrists (
n = 3) all having greater than 10 years extensive clinical and research experience relating to lower limb apophyseal conditions. We considered evidence supporting use, to be treatments studied in randomised control trials or quasi-randomised control trials [
20,
21]. If we were unable to find any clinical trials examining the intervention, or they were studied without evidence supporting effectiveness, we grouped the treatments as having no known evidence supporting use for the condition.
Therefore, the extracted treatments groups were:
1.
Those treatment groupings with evidence support through randomised control trials supporting use for this condition: load reduction strategies [
20] exercise focused on building strength (eccentric) [
20], rest or reassurance about the benign nature of this condition [
20], orthoses/bracing/taping for force distribution [
21‐
23], footwear or heel cushioning [
21], or heel lifts [
21,
24].
2.
Those treatment grouping without evidence supporting use for this condition identified by authors through the systematic review and subsequent clinical trials: pharmaceutical interventions, stretching, manual therapy (e.g. massage, foam roller), other mechanical or electrotherapy treatment modalities such as extracorporeal shock wave therapy or laser, surgery, weight management
The accuracy of diagnostic methods and treatments were coded as i) All advertised recommendations supported by evidence, ii) More advertised elements supported by evidence than not, iii) Equal number of advertised elements supported and not supported by evidence iv) More advertised elements without support of evidence than those with v) All advertised elements inconsistent with evidence. This grouping approach has been broadly employed in another study investigating advertising online treatment recommendations [
25].