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Objective: The present study aimed to compare the effects of a deep learning-based digital application with digital application physical therapy (DPT) and those of conventional physical therapy (CPT) on back pain intensity, limited functional ability, lower extremity weakness, radicular symptoms, limited range of motion (ROM), functional movement, quality of life, cost-effectiveness, and postintervention questionnaires for perceived transmission risk of COVID-19 and satisfaction results in 100 participants with low back pain (LBP). Methods: One hundred participants with LBP were randomized into either DPT or CPT groups, three times per week over four weeks. Outcome measures included the (1) Oswestry Disability Index, (2) Quebec Back Pain Disability Scale, (3) Roland-Morris Disability Questionnaire (RMDQ), (4) Numeric Pain Rating Scale, (5) functional movement screen (FMS), (6) short form-12, (7) lower extremity strength, (8) ROM of trunk flexion, extension, and bilateral side bending, (9) questionnaires for perceived transmission risk of COVID-19, (10) preliminary cost-effectiveness, and (11) postintervention satisfaction questionnaire results. The analysis of variance was conducted at p < 0.05. Results: Analysis of variance showed that DPT showed superior effects, compared to CPT on RMDQ, hip extensor strength, transmission risk of COVID-19, as well as satisfaction. Both groups showed significant improvement pre- and postintervention, suggesting that DPT is as effective as CPT, and was superior in preliminary cost-effectiveness and transmission risk of COVID-19. Conclusions: Our results provide novel, promising clinical evidence that DPT was as effective as CPT in improving structural and functional impairment, activity limitation, and participation restriction. Our results highlight the successful incorporation of DPT intervention for clinical outcome measures, lower extremity strength, trunk mobility, ADL improvement, QOL improvement, and FMS in LBP.
RESUMO
Background: As life expectancy increases, understanding the mechanism for late-life depression and finding a crucial moderator becomes more important for mental health in older adults. Childhood adversity increases the risk of clinical depression even in old age. Based on the stress sensitivity theory and stress-buffering effects, stress would be a significant mediator, while social support can be a key moderator in the mediation pathways. However, few studies have tested this moderated mediation model with a sample of older adults. This study aims to reveal the association between childhood adversity and late-life depression in older adults, taking into consideration the effects of stress and social support. Methods: This study used several path models to analyze the data from 622 elderly participants who were never diagnosed with clinical depression. Results: We found that childhood adversity increases the odds ratio of depression by approximately 20% in older adults. Path model with mediation demonstrates that stress fully mediates the pathway from childhood adversity to late-life depression. Path model with moderated mediation also illustrates that social support significantly weakens the association between childhood adversity and perceived stress. Conclusion: This study provides empirical evidence to reveal a more detailed mechanism for late-life depression. Specifically, this study identifies one crucial risk factor and one protective factor, stress and social support, respectively. This brings insight into prevention of late-life depression among those who have experienced childhood adversity.
