Type 2 Diabetes Affects Joint Pain Severity in People with Localized Osteoarthritis: A Retrospective Study.

OBJECTIVE: To examine the association between type 2 diabetes (T2D) and pain severity in people with localized osteoarthritis (OA) and to explore the association between glycemic control, measured by hemoglobin A1c (HbA1c) level, and pain severity in people with localized OA and T2D. DESIGN: Retrospective study. SETTING: A tertiary medical center. SUBJECTS: Data from 819 patients (mean age = 65.08±9.77 years, 54.3% women) were used. METHODS: Patients were grouped to localized OA only (N = 671) and localized OA+T2D (N = 148) based on diagnosis codes. An index date was set as the first diagnosis date of localized OA and linked to pain severity, measured by numeric rating scale from 0 to 10. HbA1c values were obtained for patients with T2D within six months of the index date. Multiple linear regression was used. RESULTS: After controlling for age, gender, body mass index (BMI); diagnoses of depression, hypertension, dyslipidemia; OA locations; and medication list (+/- 90 days of the index date), T2D was significantly associated with increased pain severity (B = 1.07, 95% confidence interval [CI] = 0.25 to 1.88, P = 0.014). For patients with T2D and localized OA with available data for HbA1c (N = 87), the results showed that an increased HbA1c value was significantly associated with higher pain severity (B = 0.36, 95% CI = 0.036 to 0.67, P = 0.029) after controlling for age, gender, BMI, medications, and OA locations. CONCLUSION: T2D was associated with higher pain severity in people with localized OA, and poor glycemic control was associated with higher pain severity in people with localized OA+T2D. Clinicians should emphasize that better HbA1c control might help with pain management in people with T2D and OA.

Effects of the combination of pulsed electromagnetic field with progressive resistance exercise on knee osteoarthritis: A randomized controlled trial.

BACKGROUND: Knee osteoarthritis (OA) is a common and disabling disease among the elderly population. The optimal conservative treatment for knee OA is not well established. OBJECTIVE: This study aimed to assess the effectiveness of pulsed electromagnetic field (PEMF) combined with progressive resistance exercise (PRE) in improving physical function and pain in patients with knee OA. METHODS: Thirty-four patients with knee OA (17 in each group) participated in a single-blind randomized control study. Patients were randomly assigned to receive 24 sessions of either combined PEMF and PRE (treatment group) or PRE only (control group). Patients were evaluated at pre-treatment, post-treatment (2 months), and at 3-month and 6-month follow-ups using the Knee Injury and Osteoarthritis Outcome Score (KOOS), Numeric Pain Rating Scale (NPRS); walking speed and 5-times chair stand test. Mixed ANOVA was used for statistical analysis with Bonferroni adjustments. RESULTS: There was no significant group-by-time interaction for any outcome (P> 0.05). However, both groups scored significantly higher on the NPRS and KOOS at post-treatment, 3-, and 6-month follow-up compared to their baseline. Further, both groups completed the 5-times chair stand test and walking speed test with significantly less time at all post-treatment time points than the pre-treatment. None of the study outcomes (NPRS, KOOS, walking speed, and 5 times chair stand) were significantly different between groups at any of the time points. CONCLUSION: Both treatment options, PRE only versus PRE with PEMF, were equally effective in decreasing pain and improving physical function in patients with knee OA. This would suggest that the optimal parameters for PEMF that may show beneficial effects for knee OA when added to PRE training need to be determined.

Multisite Pain and Intensity were Associated with History Fall among Older Adults: A Cross-Sectional Study.

Purpose: This study examined the independent associations among multisite pain, pain intensity, and the risk of falls, including a history of falls in the previous 12 months and frequent falls (≥ two falls vs one or two falls) among community-dwelling older adults. Methods: A cross-sectional design from Wave 2 of the National Social Life, Health, and Aging Project was used. Data on pain intensity and location (45 sites) over the past 4 weeks were collected. Multisite pain was categorized into four groups: none, one, two, and three or more sites. The main outcomes of falls were a history of falls and frequent falls. The covariates included age, sex, race, body mass index, education, medications, and comorbidities. Results: Among 3,196 participants in Wave 2, 2,697 were included because of missing key variables related to pain and fall history. The prevalence of falls and frequent falls were 30.3% (n = 817) and 12.6% (n = 339), respectively. Multisite pain at ≥ three sites (odds ratio (OR) 2.04, confidence interval (CI) [1.62, 2.57]; p < 0.001) and two sites (OR 1.72, 95% CI [1.30, 2.27]; p < 0.001) was significantly associated with an increased risk of falls. An increase in pain intensity was significantly associated with an increased risk of fall (OR 1.28, 95% CI [1.15, 1.44], p < 0.001), independent of multisite pain. Multisite pain at ≥3 sites (OR 2.19, 95% CI [1.56, 3.07], p < 0.001) and 2 sites (OR 1.54, 95% CI [1.01, 2.34], p = 0.045) was associated with an increased risk of frequent falls. An increase in pain intensity was associated with risk of frequent falls (OR 1.64, 95% CI [1.40, 1.91], p < 0.001), independent of multisite pain. Conclusion: Multisite pain and pain intensity were associated with a history of falls and frequent falls among older adults, emphasizing the need for routine pain evaluation to develop fall prevention strategies in this population.

Predicting mechanical neck pain intensity in computer professionals using machine learning: identification and correlation of key features.

Introduction: Mechanical neck pain has become prevalent among computer professionals possibly because of prolonged computer use. This study aimed to investigate the relationship between neck pain intensity, anthropometric metrics, cervical range of motion, and related disabilities using advanced machine learning techniques. Method: This study involved 75 computer professionals, comprising 27 men and 48 women, aged between 25 and 44 years, all of whom reported neck pain following extended computer sessions. The study utilized various tools, including the visual analog scale (VAS) for pain measurement, anthropometric tools for body metrics, a Universal Goniometer for cervical ROM, and the Neck Disability Index (NDI). For data analysis, the study employed SPSS (v16.0) for basic statistics and a suite of machine-learning algorithms to discern feature importance. The capability of the kNN algorithm is evaluated using its confusion matrix. Results: The "NDI Score (%)" consistently emerged as the most significant feature across various algorithms, while metrics like age and computer usage hours varied in their rankings. Anthropometric results, such as BMI and body circumference, did not maintain consistent ranks across algorithms. The confusion matrix notably demonstrated its classification process for different VAS scores (mild, moderate, and severe). The findings indicated that 56% of the pain intensity, as measured by the VAS, could be accurately predicted by the dataset. Discussion: Machine learning clarifies the system dynamics of neck pain among computer professionals and highlights the need for different algorithms to gain a comprehensive understanding. Such insights pave the way for creating tailored ergonomic solutions and health campaigns for this population.

Patient-Reported Measures Associated with the Return to Pre-Injury Levels of Sport 2 Years after Anterior Cruciate Ligament Reconstruction.

The International Knee Documentation Committee Subjective Knee Form (IKDC2000) and the Knee Injury and Osteoarthritis Outcome Score (KOOS) are knee-specific measures. However, their association with a return to sports after anterior cruciate ligament reconstruction (ACLR) is unknown. This study aimed to investigate the association between the IKDC2000 and the KOOS subscales and the return to the same pre-injury level of sport two years after ACLR. Forty athletes that were two years post-ACLR participated in this study. Athletes provided demographic information, filled out the IKDC2000 and KOOS subscales, and indicated whether they returned to any sport and whether they returned to the same pre-injury level (same duration, intensity, and frequency). In this study, 29 (72.5%) athletes returned to play any sport and eight (20%) returned to the same pre-injury level. The IKDC2000 (r: 0.306, p = 0.041) and KOOS quality of life (KOOS-QOL) (r: 0.294, p = 0.046) significantly correlated with the return to any sport, but it was age (r: -0.364, p = 0.021), BMI (r: -0.342, p = 0.031), IKDC2000 (r: 0.447, p = 0.002), KOOS-pain (r: 0.317, p = 0.046), KOOS sport and recreation function (KOOS-sport/rec)(r: 0.371, p = 0.018), and KOOS QOL (r: 0.580, p > 0.001) that significantly correlated with a return to the same pre-injury level. High KOOS-QOL and IKDC2000 scores were associated with returning to any sport, and high KOOS-pain, KOOS-sport/rec, KOOS-QOL, and IKDC2000 scores were all associated with returning to the same pre-injury level of sport.

The Relationship Between Using Smartphones andText Neck Syndrome in Online Learning Among University Students in Jordan: A Survey Study.

BACKGROUND: The use of online learning methods has expanded considerably in many countries since the declaration of COVID-19 as a global pandemic by the World Health Organization. Smartphones are widely used for voice and text messaging, checking emails, and distance learning. The aim of this study was to investigate the relationship between using smartphones and text neck syndrome. MATERIAL AND METHODS: A cross-sectional study was conducted among university students in Jordan between February and March 2023. All undergraduate students were invited to participate. A self-administered online (Google forms) questionnaire was distributed by posting the link to the questionnaire on students' groups through social media websites such as Facebook, Twitter, and WhatsApp. RESULTS: A total of 171 students responded to the survey. The participants included 103 (60%) females and 68 (40%) males. Approximately 79% of the participants were less than 22 years old. Almost half of the participants reported pain at neck (54%) and shoulder (51%), while about 61% of participants suffered from upper back pain. CONCLUSIONS: 1. Smartphones are widely becoming essential in educational technology, and more concern should be expended to increase the awareness about optimal and healthy usage of smartphones by restricting usage duration in order to reduce neck and shoulder pain and associated poor functioning in daily living activities. 2. Poor patterns of smartphone use increase the likelihood of neck pain. 3. Neck movement limitations are not associated with age or gender. 4. Students who did not report limitation in neck movement were less likely to have difficulty with reading.

Low Back Pain Prevalence among Distance Learning Students.

Background: Low back pain as a symptom affects many individuals around the globe regardless of their economic status or sociodemographic characteristics. During the 2019 COVID-19 pandemic, students found themselves obligated to sit down for long periods of time. The aim of this current study is to investigate the impact of these prolonged periods of sitting down in front of computers on developing a new episode of low back pain. Methods and Materials: This research adopted an observational cross-section study design. Students who are currently enrolled or had experienced distance learning classes in the last 6 months were eligible to participate. An online-based questionnaire was developed by the investigators through reviewing the literature with relevant objectives. McNemar's test was used to compare certain variables between two periods before and during online distance learning. We used paired t-tests to compare pain intensity before, during, and after online learning, while a chi-square test was used to investigate correlations between factors influencing low back pain. Results: A total of 84 students participated in the study­46 (54.8%) females and 38 (45.2%) males. Before online distance learning, only 42.9% of participants reported low back pain, while only 20% had a back injury. The mean pain scores before, during, and after online distance learning were (2.85 ± 2.16, 4.79 ± 2.6, and 4.76 ± 2.7), respectively. The pain scores before online learning were significantly lower than pain scores during and after online distance learning (p < 0.05), respectively. Conclusion: The study findings suggested that low back pain prevalence increased among students during the COVID-19 pandemic. Future research should study participants' behavior during the online learning and assess the long-run impact of distance learning among high-school and undergraduate students.