All study participants underwent for bone mineral density (BMD) measurement by dual-energy X-ray absorptiometry (DEXA), and OP diagnoses were consistent with the Chinese guidelines for the diagnosis and treatment of senile OP (2018 edition) (23). OP is a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DEXA). DEXA scans are the most commonly used diagnostic method for OP, and the findings are usually expressed by a T Score: T ≥ −1.0 is normal; −2.5 < T < −1.0 is low bone; T ≤ −2.5 is OP; and severe OP is T ≤ −2.5 with fragility fracture (23). The inclusion criteria were as follows: age ≥ 60 years, diagnosis of OP, and informed and voluntary willingness to participate in the survey. The exclusion criteria were as follows: cognitive impairment; inability to understand questionnaire content; comorbidities that affect activity, such as malignant disease, thyroid, parathyroid or adrenal gland disease; and a history of depression.

Convenience sampling was used in this study. A cross-sectional study using questionnaires was performed among older persons with OP in the outpatient department of West China Hospital from Between June 1, to December 28, 2021. For data collection, the number of participants should be 10 times the number of independent variables in studies that investigate a correlation between 2 variables or multivariate variables (24). In this study, a multivariate analysis was chosen to assess the relationship of 17 independent variables. Therefore, the minimum required sample size was at least 170. Each participant completed three questionnaires: a questionnaire assessing demographic data, the Self-rating Depression Scale (SDS), and the Physical Activity Scale for the Elderly (PASE). Members of the research team interviewed each participant in the outpatient department of West China Hospital of Sichuan University. The trained investigators used uniform guidelines to explain the purpose of the study and its significance as well as the questionnaire completion methods to the participants; then, participants gave their consent and signed the informed consent form before the survey. Participants completed the questionnaires independently, and the questionnaires were collected by the researchers immediately after completion.

The data were verified and entered by two researchers, and a database was built using EpiData 3.1 software (EpiData–Comprehensive Data Management and Basic Statistical Analysis System, EpiData Association, Odense, Denmark), Then, the data were double-checked. Incomplete questionnaires with missing items were deleted.

Depression was assessed using Zung’s SDS (25). The Chinese version of Cronbach’s α was 0.784 (26). The SDS is composed of 20 items and assesses the frequency of symptoms in the past week using a 4-point Likert scale: none of the time or rarely, some of the time, a good part of the time, most or all of the time. In the 20 items, there are 10 positive and 10 negative items, which are assigned a score of 1, 2, 3, or 4 for the positive items and 4, 3, 2, or 1 for the negative items. The 20 item scores are added to obtain the original scores, which are multiplied by 1.25 to calculate the standard score. Higher scores indicate worse depression. In this study, depression scores were classified as follows: without depression, < 50 points, and with depression, ≥ 50 points (27).

The PASE was used to assess the PA of the participants. The PASE was developed by Washburn et al. (28). The test–retest reliability, which was assessed over a 3–7 week interval, was 0.75 (28). The questionnaire includes 12 items that are used to assess PA over the past 7 days based on 3 domains of activity: leisure, household, and work-related activity. Leisure PA includes the following activities: walking outside: light, moderate, and strenuous sports; and activities aimed at muscle strength or endurance exercise. The possible responses for each question are “never,” “seldom,” “sometimes,” or “often.” Respondents were asked to indicate the number of days per week they engaged in each activity and then the number of hours per day. The type of activity performed and the average time spent on that activity each day were recorded. Household PA includes light housework, heavy housework, home repairs, lawn work or yard care, outdoor gardening, and taking care of others, and those items use a “yes” or “no” response format. Work-related PA includes work for pay or as a volunteer and the hours of work involved. PASE scores are from weights and frequency values for each of 12 types of activity. The calculation is performed by multiplying the amount of time spent (hours/day over a seven-day period) in each participation category by the PASE weighted value and then summing each contribution for a total score. The total score ranges from 0 to 400 or more. The higher the score is, the higher the level of PA (29).

According to the results of the survey, the demographic data were classified into the following categories: age group (60–74, ≥ 75); gender (male, female); marital status (married, divorced, or widowed); education (primary school and below, junior high school, high school, college and above); past occupation (manual labor, intellectual labor); cohabitation status (alone or nursing home, cohabitation with spouse, cohabitation with child); and income (pension or social security, child support, others).

Health situation factors were measured as follows. BMI was obtained as the weight (kg) divided by the square of the height (m). We categorized BMI into four groups: underweight, BMI < 18.5 kg/m²; normal weight, BMI 18.5–23.9 kg/m²; overweight, BMI 24–27.9 kg/m²; and obesity, BMI ≥ 28 kg/m² (30). Other factors included smoking history (no or yes); alcohol consumption history (no or yes); pain (no or yes); and history of fracture (no or yes). Comorbidity was defined as the presence of ≥2 conditions (no or yes). Polypharmacy was defined as simultaneously taking ≥5 medications (no or yes). Participation in social activities was categorized as none, 1–2 times/year, 1–2 times/month, or 1–2 times/week. The modified Barthel index (MBI) was used to assess activities of daily living (ADLs). ADLs includes feeding, dressing, grooming, bathing, bowel and bladder control, toilet use, transfer, mobility, and climbing stairs. The total score ranges from 0 to 100, with lower scores indicating worse ADL impairment. We divided ADL into four statuses: no impairment = 100; mild impairment = 61–99; moderate impairment = 41–60; and severe impairment = 0–40 (31, 32). The frailty phenotype scale by Fried et al. (33), was used to assess frailty, and it includes five domains: weight loss, self-reported exhaustion, slow walking speed, weakness, and low PA. Each domain is assigned 0–1 points, with a range of 0–5 points: non-frail = 1, prefrail = 2, and frail = 3 (33).

SPSS software (Version 20.0. IBM Inc., Armonk, NY) was used for statistical analyses. Categorical data are presented as frequencies and percentages. Continuous data conforming to a normal distribution are presented as the means and standard deviations, and T-tests or ANOVA were used for comparisons between groups. Continuous data that did not conform to a normal distribution are represented by median and interquartile range, and nonparametric tests were used for comparisons between groups. Multivariate logistic regression was used to analyze the influencing factors of depression. Depression status (i.e., with or without depression) was the dependent variable, and the independent variables included those that were significant (p < 0.2) in the univariate analysis, including demographic data (age, gender, marital status, education, past occupation, cohabitation status, income, BMI, smoking history, alcohol consumption history, pain, history of fracture, comorbidity, polypharmacy, participation in social activities, ADLs, and frailty status) and PA scores. Multivariate linear regression was used to analyze the influencing factors of PA. PA scores were the dependent variable, and considering the interference factors, the independent variables included the demographic variables that were significant (p < 0.2) in the univariate analysis (age, sex, marital status, education, past occupation, cohabitation status, income, BMI, smoking history, alcohol consumption history, pain, history of fracture, comorbidity, polypharmacy, participation in social activities, ADLs, frailty status and depression). The correlation between depression and PA was analyzed. Pearson correlation analysis was used if the data followed a normal distribution, and Spearman correlation analysis was used if the data did not follow a normal distribution. A p < 0.05 was considered to indicate statistical significance.

207 subjects completed the questionnaire, and the number of effective questionnaires (questionnaires included in the analysis) was 173. Effective questionnaire recovery rate was 83.57%. The participants ranged in age from 60 to 94 years, with an average age of 70.63 ± 9.02 years. The sample also had the following characteristics: 60–74 years old, 65.9%; female, 83.2%; married, 79.8%; in pain, 91.3%; comorbidity, 70.5%; no participation in social activities, 45.1%; frail, 71.7%; and depressed, 70.5% (Table 1).

The median depression score in 173 older persons with OP was 62.5 (24.38). A total of 122 participants (70.5%) were depressed. There were significant differences in depression scores by age, cohabitation status, income, pain, comorbidity, polypharmacy, participation in social activities, ADL, and frailty status (p < 0.05) (Table 1).

The median PA score in 173 older persons with OP was 69.29 (116.64). There were statistically significant differences in PA scores according to age, marital status, education, cohabitation status, income, pain, polypharmacy, participation in social activities, ADLs, frailty status, and depression (p < 0.05) (Table 1).

A multivariate logistic regression stepwise method was used to analyze the influencing factors of depression and showed that pain and PA were the main risk factors for depression in older persons with OP (p < 0.05). Participants with pain were 4.47 times more likely to be at risk of depression than those without pain. For each 1-point increase in PA score, the odds ratio for depression risk was 0.99 (Table 2).

Multiple linear regression was used to analyze the effects of age, marital status, education, cohabitation status, income, smoking history, pain, history of fracture, polypharmacy, participation in social activities, ADLs, frailty status and depression on PA, and the final constructed multiple linear regression model had statistical significance (F = 2.397, p = 0.001). The model could explain 55.9% of the variance in PA (corrected R² = 0.517). Female, participation in social activities, ADLs, and depression were the main risk factors for PA in older persons with OP (p < 0.05) (Table 3).

Depression and PA scores showed a nonnormal distribution; therefore, Spearman correlation coefficients were calculated to analyzed the correlation between depression and PA. The results of the correlation analysis showed that depression was negatively correlated with PA (R = − 0.510, p = 0.000). This means that the higher the PA scores were, the lower the depression scores (Figure 1).