Falls are the leading cause of fatal and non-fatal unintentional injuries in older people. The use of Exergames (active, gamified video-based exercises) is a possible innovative, community-based ...approach. This study aimed to determine the effectiveness of a tailored OTAGO/FaME-based strength and balance Exergame programme for improving balance, maintaining function and reducing falls risk in older people.
A two-arm cluster randomised controlled trial recruiting adults aged 55 years and older living in 18 assisted living (sheltered housing) facilities (clusters) in the UK. Standard care (physiotherapy advice and leaflet) was compared to a tailored 12-week strength and balance Exergame programme, supported by physiotherapists or trained assistants. Complete case analysis (intention-to-treat) was used to compare the Berg Balance Scale (BBS) at baseline and at 12 weeks. Secondary outcomes included fear of falling, mobility, fall risk, pain, mood, fatigue, cognition, healthcare utilisation and health-related quality of life, and self-reported physical activity and falls.
Eighteen clusters were randomised (9 to each arm) with 56 participants allocated to the intervention and 50 to the control (78% female, mean age 78 years). Fourteen participants withdrew over the 12 weeks (both arms), mainly for ill health. There was an adjusted mean improvement in balance (BBS) of 6.2 (95% CI 2.4 to 10.0) and reduced fear of falling (p = 0.007) and pain (p = 0.02) in the Exergame group. Mean attendance at sessions was 69% (mean exercising time of 33 min/week). Twenty-four percent of the control group and 20% of the Exergame group fell over the trial period. The change in fall rates significantly favoured the intervention (incident rate ratio 0.31 (95% CI 0.16 to 0.62, p = 0.001)). The point estimate of the incremental cost-effectiveness ratio (ICER) was £15,209.80 per quality-adjusted life year (QALY). Using 10,000 bootstrap replications, at the lower bound of the NICE threshold of £20,000 per QALY, there was a 61% probability of Exergames being cost-effective, rising to 73% at the upper bound of £30,000 per QALY.
Exergames, as delivered in this trial, improve balance, pain and fear of falling and are a cost-effective fall prevention strategy in assisted living facilities for people aged 55 years or older.
The trial was registered at ClinicalTrials.gov on 18 Dec 2015 with reference number NCT02634736 .
Traditionally, proteomics is the high‐throughput characterization of the global complement of proteins in a biological system using cutting‐edge technologies (robotics and mass spectrometry) and ...bioinformatics tools (Internet‐based search engines and databases). As the field of proteomics has matured, a diverse range of strategies have evolved to answer specific problems. Chemical proteomics is one such direction that provides the means to enrich and detect less abundant proteins (the ‘hidden’ proteome) from complex mixtures of wide dynamic range (the ‘deep’ proteome). In pharmacology, chemical proteomics has been utilized to determine the specificity of drugs and their analogues, for anticipated known targets, only to discover other proteins that bind and could account for side effects observed in preclinical and clinical trials. As a consequence, chemical proteomics provides a valuable accessory in refinement of second‐ and third‐generation drug design for treatment of many diseases. However, determining definitive affinity capture of proteins by a drug immobilized on soft gel chromatography matrices has highlighted some of the challenges that remain to be addressed. Examples of the different strategies that have emerged using well‐established drugs against pharmaceutically important enzymes, such as protein kinases, metalloproteases, PDEs, cytochrome P450s, etc., indicate the potential opportunity to employ chemical proteomics as an early‐stage screening approach in the identification of new targets.
There are high levels of work disability, absenteeism (sick leave) and presenteeism (reduced productivity) amongst people with inflammatory arthritis. WORKWELL is a multi-centre, randomised ...controlled trial of job retention vocational rehabilitation for employed people with inflammatory arthritis. The trial tested the effectiveness and cost-effectiveness of the WORKWELL programme compared to the receipt of written self-help information only. Both arms continued to receive usual care. In March 2020, due to the COVID-19 pandemic, the WORKWELL trial paused to recruitment and intervention delivery. To successfully re-start, protocol amendments were rapidly submitted and changes to existing trial procedures were made. The WORKWELL protocol was adapted in response to both the practical issues likely faced by many clinical research studies active across NHS sites during the pandemic and additional trial-specific challenges. A key eligibility criterion for the trial required participants to be in paid work for at least 15 h per week. However, UK national lockdowns led to a substantial proportion of the workforce suddenly being furloughed or unable to work, and many people with arthritis taking immunosuppressive medications were asked to shield themselves. Thus, the number of eligible participants was reduced. Those continuing to work were harder to identify, as hospital clinics moved to remote delivery, and also to then screen, consent and treat, as the hospital research staff and clinical therapists were re-deployed. New recruitment and consent strategies were applied, and where sites had reduced capacity, responsibilities were absorbed by the trial management team. Remote intervention delivery and electronic data capture were also implemented. By rapidly adapting the WORKWELL protocol and procedures, the trial successfully reopened to recruitment in July 2020, only 4 months after the trial pause. We were able to achieve recruitment figures above the pre-COVID target and maintain a high retention rate. In addition, we found many of the protocol changes beneficial, as these streamlined trial procedures, thus improving efficiency. It is likely that many strategies implemented in response to the pandemic may become standard practice in future research within trials of a similar design and methodology.Trial registration: ClinicalTrials.gov NCT03942783 . Retrospectively registered on 08 May 2019. ISRCTN Registry ISRCTN61762297 . Retrospectively registered on 13 May 2019.
Obesity and sedentary behaviour, risk factors for knee osteoarthritis in middle-age, are increasing in younger adults. The objectives of this study were to estimate the prevalence of knee problems in ...young adults, to characterise these problems and explore the relationship with physical activity, physical inactivity and obesity.
Presence of knee problems was collected through self-report questionnaire from staff and students of one university aged 18-39; direct measurement of weight and height was taken and activity measured using the International Physical Activity Questionnaire. Twelve-month prevalence of knee problems was estimated. Logistic regression was used to investigate the relationship between knee problems and physical activity levels, sitting time and body mass index.
The prevalence of knee problems was high (31.8% 95% CI 26.9 to 37.2%) among the 314 participants; knee pain was the most common dominant symptom (65%). Only high physical activity levels (OR 2.6 95% CI 1.4-4.9) and mental distress (OR 2.3 95% CI 1.2-4.6) were independent risk factors for knee problems.
Knee problems were common among young adults, who were staff and students of a university. With increasing obesity prevalence, populations are being encouraged to become more active. More attention may need to be paid towards prevention of knee problems in such programmes, and further research is warranted.
Alternatives to hospital follow-up (HFU) following treatment for cancer have been advocated. Telephone follow-up (TFU) and patient-initiated follow-up are being implemented but it is unclear if these ...approaches will meet the preferences and needs of patients. This study aimed to explore the preferences of endometrial cancer patients and their levels of satisfaction with HFU and nurse-led TFU.
A cross-sectional survey design was utilised and a questionnaire was administered to 236 patients who had participated in a randomised controlled trial comparing HFU with TFU for women diagnosed with Stage I endometrial cancer (ENDCAT trial).
211 (89.4%) patients returned the questionnaire; 105 in the TFU group and 106 in the HFU group. The TFU group were more likely to indicate that appointments were on time (p < 0.001) and were more likely to report that their appointments were thorough (p = 0.011). Participants tended to prefer what was familiar to them. Those in the HFU group tended to prefer hospital-based appointments while the TFU group tended to prefer appointments with a clinical nurse specialist, regardless of locality.
To provide patient centred follow-up services we need to ensure that patient preferences are taken into account and understand that patients may come to prefer what they have experienced. Patient initiated approaches may become standard and preferred practice but TFU remains a high-quality alternative to HFU and may provide an effective transition between HFU and patient-initiated approaches.
•Participants tended to prefer what they had experienced in terms of follow-up.•Overall, high levels of satisfaction with information and service were reported.•The telephone group were more likely to indicate that appointments were on time.•The telephone group were more likely to report that their appointments were thorough.
The identification of proteins involved in tumour progression or which permit enhanced or novel therapeutic targeting is essential for cancer research. Direct MALDI analysis of tissue sections is ...rapidly demonstrating its potential for protein imaging and profiling in the investigation of a range of disease states including cancer. MALDI‐mass spectrometry imaging (MALDI‐MSI) has been used here for direct visualisation and in situ characterisation of proteins in breast tumour tissue section samples. Frozen MCF7 breast tumour xenograft and human formalin‐fixed paraffin‐embedded breast cancer tissue sections were used. An improved protocol for on‐tissue trypsin digestion is described incorporating the use of a detergent, which increases the yield of tryptic peptides for both fresh frozen and formalin‐fixed paraffin‐embedded tumour tissue sections. A novel approach combining MALDI‐MSI and ion mobility separation MALDI‐tandem mass spectrometry imaging for improving the detection of low‐abundance proteins that are difficult to detect by direct MALDI‐MSI analysis is described. In situ protein identification was carried out directly from the tissue section by MALDI‐MSI. Numerous protein signals were detected and some proteins including histone H3, H4 and Grp75 that were abundant in the tumour region were identified.
Background:
The intracluster correlation coefficient is a key input parameter for sample size determination in cluster-randomised trials. Sample size is very sensitive to small differences in the ...intracluster correlation coefficient, so it is vital to have a robust intracluster correlation coefficient estimate. This is often problematic because either a relevant intracluster correlation coefficient estimate is not available or the available estimate is imprecise due to being based on small-scale studies with low numbers of clusters. Misspecification may lead to an underpowered or inefficiently large and potentially unethical trial.
Methods:
We apply a Bayesian approach to produce an intracluster correlation coefficient estimate and hence propose sample size for a planned cluster-randomised trial of the effectiveness of a systematic voiding programme for post-stroke incontinence. A Bayesian hierarchical model is used to combine intracluster correlation coefficient estimates from other relevant trials making use of the wealth of intracluster correlation coefficient information available in published research. We employ knowledge elicitation process to assess the relevance of each intracluster correlation coefficient estimate to the planned trial setting. The team of expert reviewers assigned relevance weights to each study, and each outcome within the study, hence informing parameters of Bayesian modelling. To measure the performance of experts, agreement and reliability methods were applied.
Results:
The 34 intracluster correlation coefficient estimates extracted from 16 previously published trials were combined in the Bayesian hierarchical model using aggregated relevance weights elicited from the experts. The intracluster correlation coefficients available from external sources were used to construct a posterior distribution of the targeted intracluster correlation coefficient which was summarised as a posterior median with a 95% credible interval informing researchers about the range of plausible sample size values. The estimated intracluster correlation coefficient determined a sample size of between 450 (25 clusters) and 480 (20 clusters), compared to 500–600 from a classical approach. The use of quantiles, and other parameters, from the estimated posterior distribution is illustrated and the impact on sample size described.
Conclusion:
Accounting for uncertainty in an unknown intracluster correlation coefficient, trials can be designed with a more robust sample size. The approach presented provides the possibility of incorporating intracluster correlation coefficients from various cluster-randomised trial settings which can differ from the planned study, with the difference being accounted for in the modelling. By using expert knowledge to elicit relevance weights and synthesising the externally available intracluster correlation coefficient estimates, information is used more efficiently than in a classical approach, where the intracluster correlation coefficient estimates tend to be less robust and overly conservative. The intracluster correlation coefficient estimate constructed is likely to produce a smaller sample size on average than the conventional strategy of choosing a conservative intracluster correlation coefficient estimate. This may therefore result in substantial time and resources savings.
Background and Aims: IsCGM is increasingly used by people with type 1 diabetes. The impact on psychosocial outcomes and experiences reported by intervention participants relative to controls were ...assessed in a sub-study of the UK multi-centre RCT (n=156, Females 44%, Baseline HbA1c 71±9 mmol/mol, age 44±15) to determine impact on participants’ lived experiences.
Methods: mixed-methods sub-study including validated questionnaires (all) and free-text questions (intervention only) exploring diabetes-related distress, device and treatment satisfaction and user experience. Questionnaires were administered at baseline and again at 24 weeks. HCPs also completed questionnaires post-study.
Results: isCGM participants reported significantly lower feelings of powerlessness (p=0.045) and management distress (p<0.001) in the Type 1 diabetes distress score than the control participants. There were no significant differences in depression (PHQ-9) or eating disorders measures, but diabetes treatment satisfaction (p<0.001) and glucose monitoring satisfaction (p<0.001) were greater for isCGM participants. Free-text data shows improved quality of life reported by n=33 isCGM participants including better diabetes control, improved control overnight, reduced anxiety and greater understanding of glucose trends and data. Reported downsides were cited by n=39 isCGM participants and included lack of accuracy, poor connectivity and sensor irritation. All participants would continue to use the device if given the choice. HCPs (n=24) reported improved communication and information sharing with participants, with easy access to the data. Their most commonly cited benefit was improved HbA1c.
Conclusion: moderate improvements in psychosocial outcomes on validated measures were reported by intervention participants using isCGM. Challenges with technology persist, although these did not dampen enthusiasm by participants for continued use.
Disclosure
K.Barnard: Advisory Panel; Abbott Diabetes, Roche Diabetes Care, Sanofi, Consultant; LifeScan Diabetes Institute, Tandem Diabetes Care, Inc., Research Support; Novo Nordisk. M.Burns: None. H.Thabit: Advisory Panel; Dexcom, Inc., Roche Diabetes Care, Research Support; Dexcom, Inc., Speaker's Bureau; Eli Lilly and Company. E.G.Wilmot: Advisory Panel; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi, Roche Diabetes Care, Embecta, Consultant; Springer Healthcare, Research Support; Abbott Diabetes, Diabetes UK, Insulet Corporation, Novo Nordisk, NIH - National Institutes of Health, Speaker's Bureau; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi, Ypsomed AG, Glooko, Inc. L.Leelarathna: Advisory Panel; Abbott Diabetes, Medtronic, Insulet Corporation, Sanofi, Research Support; Abbott Diabetes, Novo Nordisk, Speaker's Bureau; Sanofi, Abbott Diabetes. V.P.Taxiarchi: None. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. S.Neupane: Advisory Panel; Quin, Abbott Diabetes, Roche Diabetes Care, Insulet Corporation, Other Relationship; Abbott Diabetes. G.Rayman: Other Relationship; Diabetes UK, Novo Nordisk, Research Support; Abbott Diabetes, Speaker's Bureau; Abbott Diabetes, Lilly Diabetes, Sanofi. S.Lumley: None. I.C.Cranston: Research Support; Eli Lilly and Company, Speaker's Bureau; Abbott Diabetes, AstraZeneca, Bristol-Myers Squibb Company, Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Sanofi, Viatris Inc. P.Narendran: Advisory Panel; Omnipod, Speaker's Bureau; Abbott Diabetes, Lilly Diabetes. C.J.Sutton: None.
Funding
Diabetes UK (18/0005836)
The recently published1, multi-site, FLASH-UK, randomized controlled trial (n=156, males 56%, Baseline HbA1c 71±9 mmol/mol, age 44±15) showed improved HbA1c and sensor-based metrics in adults with ...type 1 DM and an HbA1c ≥58 mmol/mol. Here we present results from pre-specified subgroup analysis for HbA1c (Table 1) and “Time in range (TIR)” (Table 2).
The effect of isCGM on both HbA1c and TIR did not differ significantly across subgroups for baseline HbA1c category, treatment modality, prior structured education, sex, deprivation status or depression category. The effect of isCGM on HbA1c was larger for younger people. Those with at least a bachelor’s degree had greater TIR improvement.
Conclusion: isCGM is effective across a range of clinical and demographic factors.
1N Engl J Med. 2022 Oct 20;387(16):1477-1487
Disclosure
L.Leelarathna: Advisory Panel; Abbott Diabetes, Medtronic, Insulet Corporation, Sanofi, Research Support; Abbott Diabetes, Novo Nordisk, Speaker's Bureau; Sanofi, Abbott Diabetes. V.P.Taxiarchi: None. M.Burns: None. M.E.Camm: None. H.Thabit: Advisory Panel; Dexcom, Inc., Roche Diabetes Care, Research Support; Dexcom, Inc., Speaker's Bureau; Eli Lilly and Company. E.G.Wilmot: Advisory Panel; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi, Roche Diabetes Care, Embecta, Consultant; Springer Healthcare, Research Support; Abbott Diabetes, Diabetes UK, Insulet Corporation, Novo Nordisk, NIH - National Institutes of Health, Speaker's Bureau; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk, Sanofi, Ypsomed AG, Glooko, Inc. On behalf of the flash-uk trial study group: n/a. C.J.Sutton: None. M.Evans: Advisory Panel; Zucara Therapeutics, Pila Pharma, Dexcom, Inc., Other Relationship; Novo Nordisk, AstraZeneca, Abbott Diabetes, Speaker's Bureau; Eli Lilly and Company. S.Neupane: Advisory Panel; Quin, Abbott Diabetes, Roche Diabetes Care, Insulet Corporation, Other Relationship; Abbott Diabetes. G.Rayman: Other Relationship; Diabetes UK, Novo Nordisk, Research Support; Abbott Diabetes, Speaker's Bureau; Abbott Diabetes, Lilly Diabetes, Sanofi. S.Lumley: None. I.C.Cranston: Research Support; Eli Lilly and Company, Speaker's Bureau; Abbott Diabetes, AstraZeneca, Bristol-Myers Squibb Company, Eli Lilly and Company, Insulet Corporation, Novo Nordisk, Sanofi, Viatris Inc. P.Narendran: Advisory Panel; Omnipod, Speaker's Bureau; Abbott Diabetes, Lilly Diabetes. K.Barnard: Advisory Panel; Abbott Diabetes, Roche Diabetes Care, Sanofi, Consultant; LifeScan Diabetes Institute, Tandem Diabetes Care, Inc., Research Support; Novo Nordisk.
Funding
Diabetes UK (18/0005836)
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