Patients' Measurement Priorities for Remote Measurement Technologies to Aid Chronic Health Conditions: Qualitative Analysis
Jazyk angličtina Země Kanada Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
32519975
PubMed Central
PMC7315360
DOI
10.2196/15086
PII: v8i6e15086
Knihovny.cz E-zdroje
- Klíčová slova
- mHealth, patient involvement, qualitative analysis, remote measurement technology,
- MeSH
- chronická nemoc MeSH
- cvičení MeSH
- depresivní porucha unipolární * diagnóza terapie MeSH
- lidé MeSH
- mobilní aplikace * MeSH
- technologie MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
BACKGROUND: Remote measurement technology (RMT), including the use of mobile phone apps and wearable devices, may provide the opportunity for real-world assessment and intervention that will streamline clinical input for years to come. In order to establish the benefits of this approach, we need to operationalize what is expected in terms of a successful measurement. We focused on three clinical long-term conditions where a novel case has been made for the benefits of RMT: major depressive disorder (MDD), multiple sclerosis (MS), and epilepsy. OBJECTIVE: The aim of this study was to conduct a consultation exercise on the clinical end point or outcome measurement priorities for RMT studies, drawing on the experiences of people with chronic health conditions. METHODS: A total of 24 participants (16/24 women, 67%), ranging from 28 to 65 years of age, with a diagnosis of one of three chronic health conditions-MDD, MS, or epilepsy-took part in six focus groups. A systematic thematic analysis was used to extract themes and subthemes of clinical end point or measurement priorities. RESULTS: The views of people with MDD, epilepsy, and MS differed. Each group highlighted unique measurements of importance, relevant to their specific needs. Although there was agreement that remote measurement could be useful for tracking symptoms of illness, some symptoms were specific to the individual groups. Measuring signs of wellness was discussed more by people with MDD than by people with MS and epilepsy. However, overlap did emerge when considering contextual factors, such as life events and availability of support (MDD and epilepsy) as well as ways of coping (epilepsy and MS). CONCLUSIONS: This is a unique study that puts patients' views at the forefront of the design of a clinical study employing novel digital resources. In all cases, measuring symptom severity is key; people want to know when their health is getting worse. Second, symptom severity needs to be placed into context. A holistic approach that, in some cases, considers signs of wellness as well as illness, should be the aim of studies employing RMT to understand the health of people with chronic conditions.
Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom
Janssen Pharmaceuticals LLC Titusville NJ United States
Merck Research Labs IT Merck Sharpe and Dohme Prague Czech Republic
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Rodgers A, Vaughan P, Prentice T, Tan-Torres Edejer T, Evans D, Lowe J. The World Health Report: Reducing Risks, Promoting Healthy Life. Geneva, Switzerland: World Health Organization; 2002. [2020-01-27]. https://www.who.int/whr/2002/en/whr02_en.pdf?ua=1.
Piwek L, Ellis DA, Andrews S, Joinson A. The rise of consumer health wearables: Promises and barriers. PLoS Med. 2016 Feb;13(2):e1001953. doi: 10.1371/journal.pmed.1001953. PubMed DOI PMC
Wicks P, Hotopf M, Narayan VA, Basch E, Weatherall J, Gray M. It's a long shot, but it just might work! Perspectives on the future of medicine. BMC Med. 2016 Nov 07;14(1):176. doi: 10.1186/s12916-016-0727-y. PubMed DOI PMC
Simblett S, Greer B, Matcham F, Curtis H, Polhemus A, Ferrão J, Gamble P, Wykes T. Barriers to and facilitators of engagement with remote measurement technology for managing health: Systematic review and content analysis of findings. J Med Internet Res. 2018 Jul 12;20(7):e10480. doi: 10.2196/10480. PubMed DOI PMC
Callard F, Rose D, Wykes T. Close to the bench as well as at the bedside: Involving service users in all phases of translational research. Health Expect. 2012 Dec;15(4):389–400. doi: 10.1111/j.1369-7625.2011.00681.x. PubMed DOI PMC
Our Data-Driven Future in Healthcare: People and Partnerships at the Heart of Health Related Technologies. London, UK: The Academy of Medical Sciences; 2018. Nov, [2020-01-27]. https://acmedsci.ac.uk/file-download/74634438.
Mueller T, Leon AC, Keller MB, Solomon DA, Endicott J, Coryell W, Warshaw M, Maser JD. Recurrence after recovery from major depressive disorder during 15 years of observational follow-up. Am J Psychiatry. 1999 Jul;156(7):1000–1006. doi: 10.1176/ajp.156.7.1000. PubMed DOI
Hollis C, Sampson S, Simons L, Davies EB, Churchill R, Betton V, Butler D, Chapman K, Easton K, Gronlund TA, Kabir T, Rawsthorne M, Rye E, Tomlin A. Identifying research priorities for digital technology in mental health care: Results of the James Lind Alliance Priority Setting Partnership. Lancet Psychiatry. 2018 Oct;5(10):845–854. doi: 10.1016/s2215-0366(18)30296-7. PubMed DOI
MQ. [2020-01-27]. Depression https://www.mqmentalhealth.org/mental-health/conditions/depression.
Crowe S. Research Matters. London, UK: MS Society; 2014. [2020-01-27]. Finding the top 10 research priorities http://www.jla.nihr.ac.uk/news-and-publications/downloads/Research-Matters-Newsletter-Jan_Feb-2014.pdf.
Hughes EG, Thomas RH. Epilepsy treatment priorities: Answering the questions that matter. J Neurol Neurosurg Psychiatry. 2017 Nov;88(11):999–1001. doi: 10.1136/jnnp-2016-315135. PubMed DOI
Kessler RC, Andrews G, Mroczek D, Ustun B, Wittchen H. The World Health Organization Composite International Diagnostic Interview short-form (CIDI-SF) Int J Methods Psychiatr Res. 2006 Nov;7(4):171–185. doi: 10.1002/mpr.47. DOI
Simblett S, Matcham F, Siddi S, Bulgari V, Barattieri di San Pietro C, Hortas López J, Ferrão J, Polhemus A, Haro JM, de Girolamo G, Gamble P, Eriksson H, Hotopf M, Wykes T, RADAR-CNS Consortium Barriers to and facilitators of engagement with mHealth technology for remote measurement and management of depression: Qualitative analysis. JMIR Mhealth Uhealth. 2019 Jan 30;7(1):e11325. doi: 10.2196/11325. PubMed DOI PMC
Simblett SK, Bruno E, Siddi S, Matcham F, Giuliano L, López JH, Biondi A, Curtis H, Ferrão J, Polhemus A, Zappia M, Callen A, Gamble P, Wykes T, RADAR-CNS Consortium Patient perspectives on the acceptability of mHealth technology for remote measurement and management of epilepsy: A qualitative analysis. Epilepsy Behav. 2019 Aug;97:123–129. doi: 10.1016/j.yebeh.2019.05.035. PubMed DOI
Simblett SK, Evans J, Greer B, Curtis H, Matcham F, Radaelli M, Mulero P, Arévalo MJ, Polhemus A, Ferrao J, Gamble P, Comi G, Wykes T, RADAR-CNS Consortium Engaging across dimensions of diversity: A cross-national perspective on mHealth tools for managing relapsing remitting and progressive multiple sclerosis. Mult Scler Relat Disord. 2019 Jul;32:123–132. doi: 10.1016/j.msard.2019.04.020. PubMed DOI
QSR International. [2020-01-27]. NVivo https://www.qsrinternational.com/nvivo/home.
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001 Sep;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x. PubMed DOI PMC
Turner-Stokes L, Siegert RJ. A comprehensive psychometric evaluation of the UK FIM + FAM. Disabil Rehabil. 2013;35(22):1885–1895. doi: 10.3109/09638288.2013.766271. PubMed DOI PMC
Baker GA, Smith DF, Jacoby A, Hayes JA, Chadwick DW. Liverpool Seizure Severity Scale revisited. Seizure. 1998 Jun;7(3):201–205. doi: 10.1016/s1059-1311(98)80036-8. PubMed DOI
Ames ME, Leadbeater BJ. Depressive symptom trajectories and physical health: Persistence of problems from adolescence to young adulthood. J Affect Disord. 2018 Nov;240:121–129. doi: 10.1016/j.jad.2018.07.001. PubMed DOI
Beekman A, Penninx B, Deeg D, Ormel J, Braam A, van Tilburg W. Depression and physical health in later life: Results from the Longitudinal Aging Study Amsterdam (LASA) J Affect Disord. 1997 Dec;46(3):219–231. doi: 10.1016/s0165-0327(97)00145-6. PubMed DOI
Bruce M, Hoff RA. Social and physical health risk factors for first-onset major depressive disorder in a community sample. Soc Psychiatry Psychiatr Epidemiol. 1994 Jul;29(4):165–171. doi: 10.1007/bf00802013. PubMed DOI
Boeschoten RE, Braamse AM, Beekman AT, Cuijpers P, van Oppen P, Dekker J, Uitdehaag BM. Prevalence of depression and anxiety in multiple sclerosis: A systematic review and meta-analysis. J Neurol Sci. 2017 Jan 15;372:331–341. doi: 10.1016/j.jns.2016.11.067. PubMed DOI
Fiest KM, Dykeman J, Patten SB, Wiebe S, Kaplan GG, Maxwell CJ, Bulloch AG, Jette N. Depression in epilepsy: A systematic review and meta-analysis. Neurology. 2012 Nov 21;80(6):590–599. doi: 10.1212/wnl.0b013e31827b1ae0. PubMed DOI PMC
Wiglusz MS, Landowski J, Cubała WJ. Prevalence of anxiety disorders in epilepsy. Epilepsy Behav. 2018 Feb;79:1–3. doi: 10.1016/j.yebeh.2017.11.025. PubMed DOI
Tennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, Parkinson J, Secker J, Stewart-Brown S. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS): Development and UK validation. Health Qual Life Outcomes. 2007 Nov 27;5:63. doi: 10.1186/1477-7525-5-63. PubMed DOI PMC
Kind P, Hardman G, Macran S. UK Population Norms for EQ-5D. York, UK: Centre for Health Economics, University of York; 1999. Nov, [2020-01-27]. https://www.york.ac.uk/media/che/documents/papers/discussionpapers/CHE%20Discussion%20Paper%20172.pdf.
Schulze‐Bonhage A, Kühn A. Unpredictability of seizures and the burden of epilepsy. In: Schelter B, Timmer J, Schulze-Bonhage A, editors. Seizure Prediction in Epilepsy: From Basic Mechanisms to Clinical Applications. Weinheim, Germany: WILEY-VCH; 2008. pp. 1–10.
Innovative Medicines Initiative (IMI) [2020-01-27]. https://www.imi.europa.eu/
RADAR-CNS. [2020-01-27]. https://www.radar-cns.org/