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Dive into the research topics where Nadia Frowd is active.

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Featured researches published by Nadia Frowd.


Clinical Rehabilitation | 2016

The Falls In Care Home study: a feasibility randomized controlled trial of the use of a risk assessment and decision support tool to prevent falls in care homes

Gemma M. Walker; Sarah Armstrong; Adam Gordon; John Gladman; Kate Robertson; Marie Ward; Simon Conroy; Gail Arnold; Janet Darby; Nadia Frowd; Wynne Williams; Sue Knowles; Pip Logan

Objective: To explore the feasibility of implementing and evaluating the Guide to Action Care Home fall prevention intervention. Design: Two-centre, cluster feasibility randomized controlled trial and process evaluation. Setting: Purposive sample of six diverse old age/learning disability, long stay care homes in Nottinghamshire, UK. Subjects: Residents aged over 50 years, who had fallen at least once in the past year, not bed-bound, hoist-dependent or terminally ill. Interventions: Intervention homes (n = 3) received Guide to Action Care Home fall prevention intervention training and support. Control homes (n = 3) received usual care. Outcomes: Recruitment, attrition, baseline and six-month outcome completion, contamination and intervention fidelity, compliance, tolerability, acceptance and impact. Results: A total of 81 of 145 (56%) care homes expressed participatory interest. Six of 22 letter respondent homes (27%) participated. The expected resident recruitment target was achieved by 76% (52/68). Ten (19%) residents did not complete follow-up (seven died, three moved). In intervention homes 36/114 (32%) staff attended training. Two of three (75%) care homes received protocol compliant training. Staff valued the training, but advised greater management involvement to improve intervention implementation. Fall risks were assessed, actioned and recorded in care records. Of 115 recorded falls, 533/570 (93%) of details were complete. Six-month resident fall rates were 1.9 and 4.0 per year for intervention and control homes, respectively. Conclusions: The Guide to Action Care Home is implementable under trial conditions. Recruitment and follow-up rates indicate that a definitive trial can be completed. Falls (primary outcome) can be ascertained reliably from care records.


Pain | 2018

Traits associated with central pain augmentation in the Knee Pain in the Community (KPIC) cohort

Kehinde Akin-Akinyosoye; Nadia Frowd; Laura Marshall; Joanne Stocks; Gwen Sascha Fernandes; Ana M. Valdes; Daniel F. McWilliams; Weiya Zhang; Michael Doherty; Eamonn Ferguson; David A. Walsh

Abstract This study aimed to identify self-report correlates of central pain augmentation in individuals with knee pain. A subset of participants (n = 420) in the Knee Pain and related health In the Community (KPIC) baseline survey undertook pressure pain detection threshold (PPT) assessments. Items measuring specific traits related to central pain mechanisms were selected from the survey based on expert consensus, face validity, item association with underlying constructs measured by originating host questionnaires, adequate targeting, and PPT correlations. Pain distribution was reported on a body manikin. A “central pain mechanisms” factor was sought by factor analysis. Associations of items, the derived factor, and originating questionnaires with PPTs were compared. Eight self-report items measuring traits of anxiety, depression, catastrophizing, neuropathic-like pain, fatigue, sleep disturbance, pain distribution, and cognitive impact were identified as likely indices of central pain mechanisms. Pressure pain detection thresholds were associated with items representing each trait and with their originating scales. Pain distribution classified as “pain below the waist additional to knee pain” was more strongly associated with low PPT than were alternative classifications of pain distribution. A single factor, interpreted as “central pain mechanisms,” was identified across the 8 selected items and explained variation in PPT (R2 = 0.17) better than did any originating scale (R2 = 0.10-0.13). In conclusion, including representative items within a composite self-report tool might help identify people with centrally augmented knee pain.


Archive | 2015

NHS Outcomes Framework 2012–13

John Gladman; Rowan Harwood; Simon Conroy; Pip Logan; Rachel Elliott; Rob Jones; Sarah Lewis; Jane Dyas; Justine Schneider; Davina Porock; Kristian Pollock; Sarah Goldberg; Judi Edmans; Adam Gordon; Lucy Bradshaw; Matthew Franklin; Katherine Whittamore; Isabella Robbins; Aidan Dunphy; Karen Spencer; Janet Darby; Lukasz Tanajewski; Vladislav Berdunov; Georgios Gkountouras; Pippa Foster; Nadia Frowd


BMC Musculoskeletal Disorders | 2017

Knee pain and related health in the community study (KPIC): a cohort study protocol

Gwen Sascha Fernandes; Aliya Sarmanova; Sophie C. Warner; Hollie L. Harvey; K. Akin-Akinyosoye; Helen Richardson; Nadia Frowd; Laura Marshall; Joanne Stocks; Michelle C. Hall; Ana M. Valdes; David A. Walsh; Weiya Zhang; Michael Doherty


Age and Ageing | 2015

45ARE ACCELEROMETERS A USEFUL WAY TO MEASURE ACTIVITY IN CARE HOME RESIDENTS

Gemma M. Walker; Pip Logan; Adam Gordon; Simon Conroy; Sarah Armstrong; Kate Robertson; Marie Ward; Nadia Frowd; Janet Darby; G. Arnold; John Gladman


Archive | 2018

Prediction of persistent knee pain by pressure pain detection thresholds: results from the Knee Pain In the Community cohort (KPIC)

Daniel F. McWilliams; Nadia Frowd; Laura Marshall; Joanne Stocks; Aliya Sarmanova; Gwen Sascha Fernandes; Michelle C. Hall; Weiya Zhang; Michael Doherty; David A. Walsh


Rheumatology | 2016

181 Pain Sensitivity in Healthy Volunteers and People with Knee Osteoarthritis

Hafiz Hassan; Daniel F. McWilliams; Nadia Frowd; D. Wilson; David A. Walsh


Archive | 2015

The Identification of Seniors at Risk score to predict clinical outcomes and health service costs in older people discharged from UK acute medical units: the Acute Medical Unit Outcome Study – baseline patient data collection form

John Gladman; Rowan Harwood; Simon Conroy; Pip Logan; Rachel Elliott; Rob Jones; Sarah Lewis; Jane Dyas; Justine Schneider; Davina Porock; Kristian Pollock; Sarah Goldberg; Judi Edmans; Adam Gordon; Lucy Bradshaw; Matthew Franklin; Katherine Whittamore; Isabella Robbins; Aidan Dunphy; Karen Spencer; Janet Darby; Lukasz Tanajewski; Vladislav Berdunov; Georgios Gkountouras; Pippa Foster; Nadia Frowd


Archive | 2015

Umbrella review of tools to assess the risk of poor outcome in older people attending acute medical units: data extraction (results) table

John Gladman; Rowan Harwood; Simon Conroy; Pip Logan; Rachel Elliott; Rob Jones; Sarah Lewis; Jane Dyas; Justine Schneider; Davina Porock; Kristian Pollock; Sarah Goldberg; Judi Edmans; Adam Gordon; Lucy Bradshaw; Matthew Franklin; Katherine Whittamore; Isabella Robbins; Aidan Dunphy; Karen Spencer; Janet Darby; Lukasz Tanajewski; Vladislav Berdunov; Georgios Gkountouras; Pippa Foster; Nadia Frowd


Archive | 2015

Acute Medical Unit Comprehensive Geriatric Assessment Intervention Study: baseline patient-identifiable data form

John Gladman; Rowan Harwood; Simon Conroy; Pip Logan; Rachel Elliott; Rob Jones; Sarah Lewis; Jane Dyas; Justine Schneider; Davina Porock; Kristian Pollock; Sarah Goldberg; Judi Edmans; Adam Gordon; Lucy Bradshaw; Matthew Franklin; Katherine Whittamore; Isabella Robbins; Aidan Dunphy; Karen Spencer; Janet Darby; Lukasz Tanajewski; Vladislav Berdunov; Georgios Gkountouras; Pippa Foster; Nadia Frowd

Collaboration


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Adam Gordon

University of Nottingham

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Janet Darby

University of Nottingham

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John Gladman

University of Nottingham

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Pip Logan

University of Nottingham

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Simon Conroy

University of Leicester

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Jane Dyas

University of Nottingham

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Judi Edmans

University of Nottingham

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