Study design and participants
This was an online, cross-sectional survey study conducted between January and May 2022. Trained research assistants distributed the survey through social media platforms such as X and WhatsApp among mothers in Saudi Arabia. The participants were selected using a convenience sampling and included women who met the following criteria: 1) aged 18–49 years because the prevalence of fall risk factors tends to increase in women after menopause, typically after the age of 50 [3, 23, 25,26,27]; 2) able to read and understand the Arabic language independently; and 3) had delivered a baby. This study was reviewed and approved by the Institutional Review Board of the Ministry of Health, Buraydah, Saudi Arabia (Approval No.:1443-225277). The study was conducted in accordance with approved guidelines and all participants were informed about the study’s purpose. Participants who agreed to participate signed an informed consent form before completing the survey.
The authors developed the questionnaire that underwent expert review. This self-administered online survey consisted of three main parts: personal sociodemographic data, presence comorbidities, and questions concerning the prevalence and the number of falls per person, and the consequences of falls among mothers over the past 12 months.
The sociodemographic characteristics part covered several factors, such as age, height in centimetres (cm), weight in kilograms (kg), and body mass index (BMI), calculated by dividing weight in kg by height in m2. It also included information on nationality, employment status, educational level, current city of residence, marital status, smoking status, pregnancy status, yearly income, and medical comorbidities. The medical comorbidities included the following conditions: asthma, diabetes mellitus, high blood pressure, high cholesterol level, heart diseases, arthritis, osteoporosis, cancer and tumors, hypothyroidism, liver disease, psychological disorders, anemia, and colon disorders.
The part on the prevalence of falls captured data on the frequency of falls within the past 12 months and the resulting consequences. The study adapted the World Health Organization’s definition of falls, which is ‘an event which results in a person coming to rest inadvertently on the ground, floor, or other lower level.’ 
To ensure the questionnaire’s validation, it was first tested with a sample of eligible mothers (n = 15). This allowed the authors to assess the survey’s design, gather feedback on the survey’s language and usability, and make any necessary adjustments. Minor modifications were made to the questions based on the feedback received to ensure a clear understanding and obtain the required information. The questionnaire included a consent section at the beginning.
Regarding falls, participants were asked, ‘Have you experienced any falls within the last 12 months?’ If the response given was affirmative, the participants were further asked, ‘How many times have you fallen in the past 12 months?’ For those who reported one or more falls, additional questions were posed regarding the resulting injuries. The consequences of falls were categorized into four groups: fracture, muscle and ligament injury, pain, and no injury. Participants were categorized into (faller) has at least one fall, and (non-faller) has no history of fall. These questions aimed to estimate the prevalence of women who reported falls over the past 12 months, evaluate the rate of falls for each participant who experienced a fall, and assess the resulting injuries from those falls .
The main outcomes of this study focused on the history of falls and the number of falls experienced by participants. The data collected from the questionnaires were analysed statistically using the IBM Statistical Package for Social Science software (version 28, SPSS Inc., Chicago, Illinois, USA). Demographic characteristics and medical comorbidities were reported as mean (m) or median, standard deviation (SD) or interquartile range (IQR), count (n), frequency (f), and percentage (%). To compare fallers and non-fallers, statistical tests such as chi-square or Fisher’s exact tests were used for categorical variables, while the independent t-test was used to analyse continuous variables.
To determine the association between medical comorbidities and fall history (differentiating between fallers and non-fallers), a binary logistic regression analysis was conducted. The enter method was used to include all variables simultaneously in the model. The selection of variables for inclusion was based on their clinical relevance to falls and their significance level in the unadjusted model. Medical comorbidities were entered as predictors, while fall status (yes for fallers and no for non-fallers) was the dependent variable (outcome). Odds ratios (OR) with 95% confidence intervals (95% CIs) were calculated for each medical comorbidity. In the primary analysis, potential confounders were controlled for, including age, education, and BMI.
Few answers on income range, height, and residential region entered were not applicable (N/A) because few participants preferred not to declare or incomplete data were provided. Therefore, missing variables were handled via case-wise deletion.