Heritable Connective Tissue Disorders (HCTD) show an overlap in the physical features that can evolve in childhood. It is unclear to what extent children with HCTD experience burden of disease. This study aims to quantify fatigue, pain, disability and general health with standardized validated questionnaires.METHODS: This observational, multicenter study included 107 children, aged 4-18 years, with Marfan syndrome (MFS), 58%; Loeys-Dietz syndrome (LDS), 7%; Ehlers-Danlos syndromes (EDS), 8%; and hypermobile Ehlers-Danlos syndrome (hEDS), 27%. The assessments included PROMIS Fatigue Parent-Proxy and Pediatric self-report, pain and general health Visual-Analogue-Scales (VAS) and a Childhood Health Assessment Questionnaire (CHAQ).RESULTS: Compared to normative data, the total HCTD-group showed significantly higher parent-rated fatigue T-scores (M = 53 (SD = 12), p = 0.004, d = 0.3), pain VAS scores (M = 2.8 (SD = 3.1), p < 0.001, d = 1.27), general health VAS scores (M = 2.5 (SD = 1.8), p < 0.001, d = 2.04) and CHAQ disability index scores (M = 0.9 (SD = 0.7), p < 0.001, d = 1.23). HCTD-subgroups showed similar results. The most adverse sequels were reported in children with hEDS, whereas the least were reported in those with MFS. Disability showed significant relationships with fatigue (p < 0.001, rs = 0.68), pain (p < 0.001, rs = 0.64) and general health (p < 0.001, rs = 0.59).CONCLUSIONS: Compared to normative data, children and adolescents with HCTD reported increased fatigue, pain, disability and decreased general health, with most differences translating into very large-sized effects. This new knowledge calls for systematic monitoring with standardized validated questionnaires, physical assessments and tailored interventions in clinical care.
Background: Effective and sustainable interventions are needed to counteract the decline in physical function and sarcopenia in the growing aging population. The aim of this study was to determine the 6 and 12 month effectiveness of blended (e-health + coaching) home-based exercise and a dietary protein intervention on physical performance in community-dwelling older adults. Methods: This cluster randomized controlled trial allocated 45 clusters of older adults already engaged in a weekly community-based exercise programme. The clusters were randomized to three groups with ratio of 16:15:14; (i) no intervention, control (CON); (ii) blended home-based exercise intervention (HBex); and (iii) HBex with dietary protein counselling (HBex-Pro). Both interventions used a tablet PC with app and personalized coaching and were targeting on behaviour change. The study comprised coached 6 month interventions with a 6 month follow-up. The primary outcome physical performance was assessed by modified Physical Performance Test (m-PPT). Secondary outcomes were gait speed, physical activity level (PAL), handgrip muscle strength, protein intake, skeletal muscle mass, health status, and executive functioning. Linear mixed models of repeated measured were used to assess intervention effects at 6 and 12 months. Results: The population included 245 older adults (mean age 72 ± 6.5 (SD) years), 71% female, and 54% co-morbidities observed. Dropout of the intervention was 18% at 6 months and 26% at 12 months. Participants were well functioning, based on an m-PPT score of 33.9 (2.8) out of 36. For the primary outcome m-PPT, no significant intervention effects (HBex, +0.03, P = 0.933; HBex-Pro, −0.13, P = 0.730) were found. Gait speed (+0.20 m/s, P = 0.001), PAL (+0.06, P = 0.008), muscle strength (+2.32 kg, P = 0.001), protein intake (+0.32 g/kg/day, P < 0.001), and muscle mass (+0.33 kg, P = 0.017) improved significantly in the HBex-Pro group compared with control group after 6 month intervention. The protein intake, muscle mass, and strength remained significantly improved after 12 months as compared with those of control. Health change and executive functioning improved significantly in both intervention groups after 6 months. Conclusions: This HBex and dietary protein interventions did not change the physical performance (m-PPT) in community-dwelling older adults. Changes were observed in gait speed, PAL, muscle mass, strength, and dietary protein intake, in response to this combined intervention.
MULTIFILE
Which factors are important for effectiveness of sport- and health-related apps? Results of focus groups with experts.Dallinga, J, van der Werf, J , Janssen, M, Vos, S, Deutekom-Baart de la Faille, M.A huge amount of sport- and health-related smartphone applications (apps) is available in the app stores [1]. These apps are often used by individual recreational athletes participating in running, walking or cycling [2]. Exercise apps ideally should support athletes and encourage them to be physical active in a frequent and healthy way. In order to reach these goals, more insight into the value of different app features is necessary. With this knowledge the health enhancing effects of apps can be improved. Therefore the aim of this study was to identify which features in sport- and health-related apps are important for stimulating and maintaining physical activity. Two focus groups (n=4 & n=3) were organized to identify and rank app features relevant for increasing and maintaining physical activity. These groups were facilitated by two of the authors (JD and JvdW). A nominal group technique was used. Seven behavioral and sport scientists participated in the focus groups consisting of three consultation rounds. In the first round these experts were asked to individually list all factors that they found necessary for increasing and maintaining physical activity. After that, all factors were collected, explained and listed on a white board. In the second round the experts were asked to individually rank the ten most important features. Subsequently, these rankings were discussed groupwise. In the last round, the experts individually made a final ranking of the ten most important features. In addition, they were also asked to appoint a score to each feature (0-100), to indicate the importance.The participants in the focus groups generated 28 and 24 features respectively in round one. After combining these features and checking for duplicates, we reduced the number of features to 25. Factors with highest frequency in the top 10 most important factors were ‘usability’ (n=7), ‘monitoring’ (n=5), ‘fun’ (n=5), ‘anticipating/context awareness’ (n=5) and ‘motivational feedback’ (n=4). Factors with highest importance scores were ‘instructional feedback’ (95.0), ‘motivating/challenging’ (95.0), ‘monitoring’ (92.5), ‘peer rating and peer use’ (92.0) and ‘motivational feedback’ (91.3). In conclusion, based on opinions of behavioral and sport scientists several app features were extracted related to physical activity, with instructional feedback and features that motivate or challenge the athlete as most important. A smart and tailored app may need to be developed that can provide feedback and anticipate on the environment. A feature for monitoring and a fun element may need to be included as well. Interestingly, usability was mentioned by all experts, this seems to be a premise for effectiveness of the app. Based on the results of this study, currently available exercise app rating scales could be revised [3, 4].This research is cofinanced by ‘Regieorgaan SIA’, part of the Netherlands Organisation for Scientific Research (NWO) and by the Dutch national program COMMIT.References[1] Yuan S, Ma W, Kanthawala S, Peng W. Keep Using My Health Apps: Discover Users' Perception of Health and Fitness Apps with the UTAUT2 Model. Telemed J E Health. 2015 Sep;21(9):735-41. doi: 10.1089/tmj.2014.0148.[2] Dallinga JM., Janssen M, van der Bie J, Nibbeling N, Krose B, Goudsmit J, Megens C, Baart de la Faille-Deutekom M en Vos S. De rol van innovatieve technologie in het stimuleren van sport en bewegen in de steden Amsterdam en Eindhoven. Vrijtijdstudies. 2016, 34 (2): 43-57.[3] Abraham C, Michie S. A taxonomy of behavior change techniques used in interventions. Health Psychol. 2008 May;27(3):379-87. doi: 10.1037/0278-6133.27.3.379.[4] Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth. 2015 Mar 11;3(1):e27. doi: 10.2196/mhealth.3422
