Abstract: Background: Chronic obstructive pulmonary disease (COPD) and asthma have a high prevalence and disease burden. Blended self-management interventions, which combine eHealth with face-to-face interventions, can help reduce the disease burden. Objective: This systematic review and meta-analysis aims to examine the effectiveness of blended self-management interventions on health-related effectiveness and process outcomes for people with COPD or asthma. Methods: PubMed, Web of Science, COCHRANE Library, Emcare, and Embase were searched in December 2018 and updated in November 2020. Study quality was assessed using the Cochrane risk of bias (ROB) 2 tool and the Grading of Recommendations, Assessment, Development, and Evaluation. Results: A total of 15 COPD and 7 asthma randomized controlled trials were included in this study. The meta-analysis of COPD studies found that the blended intervention showed a small improvement in exercise capacity (standardized mean difference [SMD] 0.48; 95% CI 0.10-0.85) and a significant improvement in the quality of life (QoL; SMD 0.81; 95% CI 0.11-1.51). Blended intervention also reduced the admission rate (relative ratio [RR] 0.61; 95% CI 0.38-0.97). In the COPD systematic review, regarding the exacerbation frequency, both studies found that the intervention reduced exacerbation frequency (RR 0.38; 95% CI 0.26-0.56). A large effect was found on BMI (d=0.81; 95% CI 0.25-1.34); however, the effect was inconclusive because only 1 study was included. Regarding medication adherence, 2 of 3 studies found a moderate effect (d=0.73; 95% CI 0.50-0.96), and 1 study reported a mixed effect. Regarding self-management ability, 1 study reported a large effect (d=1.15; 95% CI 0.66-1.62), and no effect was reported in that study. No effect was found on other process outcomes. The meta-analysis of asthma studies found that blended intervention had a small improvement in lung function (SMD 0.40; 95% CI 0.18-0.62) and QoL (SMD 0.36; 95% CI 0.21-0.50) and a moderate improvement in asthma control (SMD 0.67; 95% CI 0.40-0.93). A large effect was found on BMI (d=1.42; 95% CI 0.28-2.42) and exercise capacity (d=1.50; 95% CI 0.35-2.50); however, 1 study was included per outcome. There was no effect on other outcomes. Furthermore, the majority of the 22 studies showed some concerns about the ROB, and the quality of evidence varied. Conclusions: In patients with COPD, the blended self-management interventions had mixed effects on health-related outcomes, with the strongest evidence found for exercise capacity, QoL, and admission rate. Furthermore, the review suggested that the interventions resulted in small effects on lung function and QoL and a moderate effect on asthma control in patients with asthma. There is some evidence for the effectiveness of blended self-management interventions for patients with COPD and asthma; however, more research is needed. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019119894; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=119894
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PURPOSE: To investigate factors that influence participation in and needs for work and other daytime activities among individuals with severe mental illnesses (SMI). METHODS: A latent class analysis using routine outcome monitoring data from 1069 patients was conducted to investigate whether subgroups of individuals with SMI can be distinguished based on participation in work or other daytime activities, needs for care in these areas, and the differences between these subgroups. RESULTS: Four subgroups could be distinguished: (1) an inactive group without daytime activities or paid employment and many needs for care in these areas; (2) a moderately active group with some daytime activities, no paid employment, and few needs for care; (3) an active group with more daytime activities, no paid employment, and mainly met needs for care; and (4) a group engaged in paid employment without needs for care in this area. Groups differed significantly from each other in age, duration in MHC, living situation, educational level, having a life partner or not, needs for care regarding social contacts, quality of life, psychosocial functioning, and psychiatric symptoms. Differences were not found for clinical diagnosis or gender. CONCLUSIONS: Among individuals with SMI, different subgroups can be distinguished based on employment situation, daytime activities, and needs for care in these areas. Subgroups differ from each other on patient characteristics and each subgroup poses specific challenges, underlining the need for tailored rehabilitation interventions. Special attention is needed for individuals who are involuntarily inactive, with severe psychiatric symptoms and problems in psychosocial functioning.
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Rationale: The dietary protein counselling of the VITAMIN trial showed to be effective in increasing the protein intake in community-dwelling older adults up to 1.41 g/kg/day after 6-months intervention and sustaining this intake up to 1.24 g/kg/day at 12-months. In this sub-analysis we determine how the increased protein intake was established. Methods: The VITAMIN (VITal AMsterdam older adults IN the city) RCT randomised older adults into: control, exercise, or exercise plus dietary counselling (protein) group. The dietary counselling intervention was blended, by use of face-to-face contacts and videoconferencing during a 6-month intervention, followed by a 6-month follow-up. Dietary intake was measured by a 3d dietary record at 0, 6 and 12 months (m). Sub-group analysis included characterisation of protein sources, product groups, resulting amino acid intake, and intake per meal moment. Linear Mixed Models were performed with SPSSv25; whereas time and time*group interaction were defined as fixed factors, and the protein group as reference. Results: In total 212 subjects were eligible for analysis (72.2 ± 6.3y), with an average protein intake at baseline of 77.8 (20) g/day and 1.08 (0.3) g/kg/day. Animal protein (g) accounted as major source (6m +25.6 (2.7) p<0.001 | 12m +15.6 (2.8) p<0.001), with the main increase in dairy products (g) (6m +14.2 (1.5) p<0.001 | 12m +10.0 (1.5) p<0.001), followed by fish and meat. This resulted in significant changes in amino acid intake: e.g. leucine (g) 6m +2.3 (0.3) p<0.001 | 12m +1.1 (0.3) p<0.001. Significant increased intake for the protein group was seen at all 6 meal moments, and particularly at breakfast (g) 6m +6.2 (1.0) p<0.001 | 12m +6.5 (1.1) p<0.001) and lunch (g) 6m +7.2 (1.3) p<0.001 | 12m +4.0 (1.3) p=0.003. Conclusion: Blended dietary counselling was effective in increasing protein intake in a lifestyle intervention in community-dwelling older adults. This was predominantly achieved by consuming more animal protein sources, particularly dairy products, and especially during breakfast and lunch. Grant / Research Support from: FrieslandCampina
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Low back pain is the leading cause of disability worldwide and a significant contributor to work incapacity. Although effective therapeutic options are scarce, exercises supervised by a physiotherapist have shown to be effective. However, the effects found in research studies tend to be small, likely due to the heterogeneous nature of patients' complaints and movement limitations. Personalized treatment is necessary as a 'one-size-fits-all' approach is not sufficient. High-tech solutions consisting of motions sensors supported by artificial intelligence will facilitate physiotherapists to achieve this goal. To date, physiotherapists use questionnaires and physical examinations, which provide subjective results and therefore limited support for treatment decisions. Objective measurement data obtained by motion sensors can help to determine abnormal movement patterns. This information may be crucial in evaluating the prognosis and designing the physiotherapy treatment plan. The proposed study is a small cohort study (n=30) that involves low back pain patients visiting a physiotherapist and performing simple movement tasks such as walking and repeated forward bending. The movements will be recorded using sensors that estimate orientation from accelerations, angular velocities and magnetometer data. Participants complete questionnaires about their pain and functioning before and after treatment. Artificial analysis techniques will be used to link the sensor and questionnaire data to identify clinically relevant subgroups based on movement patterns, and to determine if there are differences in prognosis between these subgroups that serve as a starting point of personalized treatments. This pilot study aims to investigate the potential benefits of using motion sensors to personalize the treatment of low back pain. It serves as a foundation for future research into the use of motion sensors in the treatment of low back pain and other musculoskeletal or neurological movement disorders.
