Development, evaluation and implementation of a digital behavioural health treatment for chronic pain: study protocol of the multiphase DAHLIA project

Sara Laureen Bartels, Sophie I Johnsson, Katja Boersma, Ida Flink, Lance M McCracken, Suzanne Petersson, Hannah L Christie, Inna Feldman, Laura E Simons, Patrick Onghena, Johan W S Vlaeyen, Rikard K Wicksell, Sara Laureen Bartels, Sophie I Johnsson, Katja Boersma, Ida Flink, Lance M McCracken, Suzanne Petersson, Hannah L Christie, Inna Feldman, Laura E Simons, Patrick Onghena, Johan W S Vlaeyen, Rikard K Wicksell

Abstract

Introduction: Chronic pain affects about 20%-40% of the population and is linked to mental health outcomes and impaired daily functioning. Pharmacological interventions are commonly insufficient for producing relief and recovery of functioning. Behavioural health treatment is key to generate lasting benefits across outcome domains. However, most people with chronic pain cannot easily access evidence-based behavioural interventions. The overall aim of the DAHLIA project is to develop, evaluate and implement a widely accessible digital behavioural health treatment to improve well-being in individuals with chronic pain.

Methods and analysis: The project follows the four phases of the mHealth Agile Development and Evaluation Lifecycle: (1) development and pre-implementation surveillance using focus groups, stakeholder interviews and a business model; (2) iterative optimisation studies applying single case experimental design (SCED) method in 4-6 iterations with n=10 patients and their healthcare professionals per iteration; (3) a two-armed clinical randomised controlled trial enhanced with SCED (n=180 patients per arm) and (4) interview-based post-market surveillance. Data analyses include multilevel modelling, cost-utility and indicative analyses.In October 2021, inter-sectorial partners are engaged and funding is secured for four years. The treatment content is compiled and the first treatment prototype is in preparation. Clinical sites in three Swedish regions are informed and recruitment for phase 1 will start in autumn 2021. To facilitate long-term impact and accessibility, the treatment will be integrated into a Swedish health platform (www.1177.se), which is used on a national level as a hub for advice, information, guidance and e-services for health and healthcare.

Ethics and dissemination: The study plan has been reviewed and approved by Swedish ethical review authorities. Findings will be actively disseminated through peer-reviewed journals, conference presentations, social media and outreach activities for the wider public.

Trial registration number: NCT05066087.

Keywords: MENTAL HEALTH; PAIN MANAGEMENT; PUBLIC HEALTH.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
mHealth agile development and evaluation lifecycle (Wilson et al, 2018).
Figure 2
Figure 2
DAHLIA project overview including highlights of each study and time plan. FU, follow-up; HCP, healthcare professional; RCT, randomised controlled trial; SCED, single case experimental design; TAU, treatment as usual.
Figure 3
Figure 3
Example of a DAHLIA Persona with chronic pain.
Figure 4
Figure 4
DAHLIA treatment micro-session elements. Note: the name ‘DAHLIA treatment’ is mainly for academic settings; in the www.1177.se web-platform, a more intuitive treatment name will be chosen. HCP, healthcare professional.
Figure 5
Figure 5
The DAHLIA treatment components.
Figure 6
Figure 6
Template of business model canvas (based on Osterwald and Pigneur, 2010). Grey boxes: example aspects of the DAHLIA business model; the final model will be a result of the stakeholder interviews.
Figure 7
Figure 7
General overview of the optimisation studies and specific procedure in each iteration. FU, follow-up; HCP, healthcare professional; SCED, single case experimental design.

References

    1. Todd A, McNamara CL, Balaj M, et al. . The European epidemic: pain prevalence and socioeconomic inequalities in pain across 19 European countries. Eur J Pain 2019;23:1425–36. 10.1002/ejp.1409
    1. Clauw DJ, Häuser W, Cohen SP, et al. . Considering the potential for an increase in chronic pain after the COVID-19 pandemic. Pain 2020;161:1694. 10.1097/j.pain.0000000000001950
    1. Kemp HI, Corner E, Colvin LA. Chronic pain after COVID-19: implications for rehabilitation. Br J Anaesth 2020;125:436–40. 10.1016/j.bja.2020.05.021
    1. Breivik H, Collett B, Ventafridda V, et al. . Survey of chronic pain in Europe: prevalence, impact on daily life, and treatment. Eur J Pain 2006;10:287–333. 10.1016/j.ejpain.2005.06.009
    1. Langley P, Müller-Schwefe G, Nicolaou A, et al. . The societal impact of pain in the European Union: health-related quality of life and healthcare resource utilization. J Med Econ 2010;13:571–81. 10.3111/13696998.2010.516709
    1. Miaskowski C, Blyth F, Nicosia F, et al. . A biopsychosocial model of chronic pain for older adults. Pain Med 2020;21:1793–805. 10.1093/pm/pnz329
    1. McCracken LM, Turk DC. Behavioral and cognitive-behavioral treatment for chronic pain: outcome, predictors of outcome, and treatment process. Spine 2002;27:2564–73. 10.1097/00007632-200211150-00033
    1. Clauw DJ, Essex MN, Pitman V, et al. . Reframing chronic pain as a disease, not a symptom: rationale and implications for pain management. Postgrad Med 2019;131:185–98. 10.1080/00325481.2019.1574403
    1. Vlaeyen JWS, Morley S. Cognitive-Behavioral treatments for chronic pain: what works for whom? Clin J Pain 2005;21:1–8. 10.1097/00002508-200501000-00001
    1. Hayes SC, Villatte M, Levin M, et al. . Open, aware, and active: contextual approaches as an emerging trend in the behavioral and cognitive therapies. Annu Rev Clin Psychol 2011;7:141–68. 10.1146/annurev-clinpsy-032210-104449
    1. Vlaeyen JWS, Crombez G, Linton SJ. The fear-avoidance model of pain. Pain 2016;157:1588–9. 10.1097/j.pain.0000000000000574
    1. Hughes LS, Clark J, Colclough JA, et al. . Acceptance and commitment therapy (act) for chronic pain. Clin J Pain 2017;33:552–68. 10.1097/AJP.0000000000000425
    1. Feliu-Soler A, Montesinos F, Gutiérrez-Martínez O, et al. . Current status of acceptance and commitment therapy for chronic pain: a narrative review. J Pain Res 2018;11:2145. 10.2147/JPR.S144631
    1. Hayes SC, Hofmann SG, Stanton CE, et al. . The role of the individual in the coming era of process-based therapy. Behav Res Ther 2019;117:40–53. 10.1016/j.brat.2018.10.005
    1. McCracken L. Psychological approaches to chronic pain management: where are we coming from and where might we go? Revista de la Sociedad Española del Dolor 2018;25:57–63.
    1. Breivik H, Eisenberg E, O’Brien T. The individual and societal burden of chronic pain in Europe: the case for strategic prioritisation and action to improve knowledge and availability of appropriate care. BMC Public Health 2013;13:1–14. 10.1186/1471-2458-13-1229
    1. Fashler SR, Cooper LK, Oosenbrug ED, et al. . Systematic review of multidisciplinary chronic pain treatment facilities. Pain Res Manag 2016;2016:5960987. 10.1155/2016/5960987
    1. Moman RN, Dvorkin J, Pollard EM, et al. . A systematic review and meta-analysis of Unguided electronic and mobile health technologies for chronic Pain-Is it time to start prescribing electronic health applications? Pain Med 2019;20:2238–55. 10.1093/pm/pnz164
    1. Puntillo F, Giglio M, Brienza N, et al. . Impact of COVID-19 pandemic on chronic pain management: looking for the best way to deliver care. Best Pract Res Clin Anaesthesiol 2020;34:529-537. 10.1016/j.bpa.2020.07.001
    1. Slattery BW, Haugh S, O'Connor L, et al. . An evaluation of the effectiveness of the modalities used to deliver electronic health interventions for chronic pain: systematic review with network meta-analysis. J Med Internet Res 2019;21:e11086. 10.2196/11086
    1. Lee J-A, Choi M, Lee SA, et al. . Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak 2018;18:1–18. 10.1186/s12911-018-0591-0
    1. Eccleston C, Blyth FM, Dear BF, et al. . Managing patients with chronic pain during the COVID-19 outbreak: considerations for the rapid introduction of remotely supported (eHealth) pain management services. Pain 2020;161:889. 10.1097/j.pain.0000000000001885
    1. Onghena P, Edgington ES. Customization of pain treatments: single-case design and analysis. Clin J Pain 2005;21:56–68. 10.1097/00002508-200501000-00007
    1. Schreiweis B, Pobiruchin M, Strotbaum V, et al. . Barriers and facilitators to the implementation of eHealth services: systematic literature analysis. J Med Internet Res 2019;21:e14197. 10.2196/14197
    1. Higgins KS, Tutelman PR, Chambers CT, et al. . Availability of researcher-led eHealth tools for pain assessment and management: barriers, facilitators, costs, and design. Pain Rep 2018;3:e686. 10.1097/PR9.0000000000000686
    1. Skivington K, Matthews L, Simpson SA, et al. . A new framework for developing and evaluating complex interventions: update of medical Research Council guidance. BMJ 2021;374:n2061. 10.1136/bmj.n2061
    1. Wilson K, Bell C, Wilson L, et al. . Agile research to complement agile development: a proposal for an mHealth research lifecycle. NPJ Digit Med 2018;1:1–6. 10.1038/s41746-018-0053-1
    1. Craig P, Dieppe P, Macintyre S, et al. . Developing and evaluating complex interventions: the new medical Research Council guidance. BMJ 2008;337:a1655. 10.1136/bmj.a1655
    1. Gentili C, Zetterqvist V, Rickardsson J, et al. . ACTsmart – development and feasibility of digital acceptance and commitment therapy for adults with chronic pain. NPJ Digit Med 2020;3:1–12. 10.1038/s41746-020-0228-4
    1. Silva S, Teixeira A. Design and development for individuals with ASD: fostering multidisciplinary approaches through personas. J Autism Dev Disord 2019;49:2156–72. 10.1007/s10803-019-03898-1
    1. Miaskiewicz T, Kozar KA. Personas and user-centered design: how can personas benefit product design processes? Des Stud 2011;32:417–30. 10.1016/j.destud.2011.03.003
    1. Gerdle B, Åkerblom S, Brodda Jansen G, Jansen GB, et al. . Who benefits from multimodal rehabilitation - an exploration of pain, psychological distress, and life impacts in over 35,000 chronic pain patients identified in the Swedish Quality Registry for Pain Rehabilitation. J Pain Res 2019;12:891. 10.2147/JPR.S190003
    1. Gerdle B, Åkerblom S, Stålnacke B-M, et al. . The importance of emotional distress, cognitive behavioural factors and pain for life impact at baseline and for outcomes after rehabilitation - a SQRP study of more than 20,000 chronic pain patients. Scand J Pain 2019;19:693–711. 10.1515/sjpain-2019-0016
    1. Huijnen IPJ, Rusu AC, Scholich S, et al. . Subgrouping of low back pain patients for targeting treatments: evidence from genetic, psychological, and activity-related behavioral approaches. Clin J Pain 2015;31:123–32. 10.1097/AJP.0000000000000100
    1. Børøsund E, Mirkovic J, Clark MM, et al. . A stress management APP intervention for cancer survivors: design, development, and usability testing. JMIR Form Res 2018;2:e19. 10.2196/formative.9954
    1. Cordier L, Diers M. Learning and Unlearning of pain. Biomedicines 2018;6:67. 10.3390/biomedicines6020067
    1. Sharp TJ. Chronic pain: a reformulation of the cognitive-behavioural model. Behav Res Ther 2001;39:787–800. 10.1016/s0005-7967(00)00061-9
    1. Rickardsson J, Zetterqvist V, Gentili C, et al. . Internet-Delivered acceptance and commitment therapy (iACT) for chronic pain-feasibility and preliminary effects in clinical and self-referred patients. Mhealth 2020;6:27. 10.21037/mhealth.2020.02.02
    1. Bandura A. Social cognitive theory of self-regulation. Organ Behav Hum Decis Process 1991;50:248–87. 10.1016/0749-5978(91)90022-L
    1. Bartels SL, van Knippenberg RJM, Köhler S, et al. . The necessity for sustainable intervention effects: lessons-learned from an experience sampling intervention for spousal carers of people with dementia. Aging Ment Health 2020;24:2082–93. 10.1080/13607863.2019.1647130
    1. Guest G, Namey E, McKenna K. How many focus groups are enough? building an evidence base for nonprobability sample sizes. Field methods 2017;29:3–22. 10.1177/1525822X16639015
    1. Gruters AAA, Christie HL, Ramakers IHGB, et al. . Neuropsychological assessment and diagnostic disclosure at a memory clinic: a qualitative study of the experiences of patients and their family members. Clin Neuropsychol 2021;35:1398–414. 10.1080/13854046.2020.1749936
    1. Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today 2004;24:105–12. 10.1016/j.nedt.2003.10.001
    1. Kirk MA, Kelley C, Yankey N, et al. . A systematic review of the use of the consolidated framework for implementation research. Implementation Science 2015;11:1–13. 10.1186/s13012-016-0437-z
    1. Skivington K, Matthews L, Craig P, et al. . Developing and evaluating complex interventions: updating medical Research Council guidance to take account of new methodological and theoretical approaches. The Lancet 2018;392:S2. 10.1016/S0140-6736(18)32865-4
    1. van Limburg M, van Gemert-Pijnen JEWC, Nijland N, et al. . Why business modeling is crucial in the development of eHealth technologies. J Med Internet Res 2011;13:e124. 10.2196/jmir.1674
    1. Damschroder LJ, Aron DC, Keith RE, et al. . Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implementation Science 2009;4:1–15. 10.1186/1748-5908-4-50
    1. Osterwalder A, Pigneur Y. Business model generation: a Handbook for visionaries, game changers, and challengers. John Wiley & Sons, 2010.
    1. Christie HL, Schichel MCP, Tange HJ, et al. . Perspectives from Municipality Officials on the adoption, Dissemination, and implementation of electronic health interventions to support caregivers of people with dementia: inductive thematic analysis. JMIR Aging 2020;3:e17255. 10.2196/17255
    1. Braun V, Clarke V. What can “thematic analysis” offer health and wellbeing researchers? Taylor & Francis, 2014.
    1. Christie HL, Boots LMM, Peetoom K, et al. . Developing a plan for the sustainable implementation of an electronic health intervention (partner in balance) to support caregivers of people with dementia: case study. JMIR Aging 2020;3:e18624. 10.2196/18624
    1. Dam AEH, van Boxtel MPJ, Rozendaal N, et al. . Development and feasibility of Inlife: a pilot study of an online social support intervention for informal caregivers of people with dementia. PLoS One 2017;12:e0183386. 10.1371/journal.pone.0183386
    1. Bartels SL, van Knippenberg RJM, Malinowsky C, et al. . Smartphone-Based experience sampling in people with mild cognitive impairment: feasibility and usability study. JMIR Aging 2020;3:e19852. 10.2196/19852
    1. Davis FD. A technology acceptance model for empirically testing new end-user information systems: theory and results. Massachusetts Institute of Technology, 1985.
    1. Lavefjord A, Sundström FTA, Buhrman M, et al. . Assessment methods in single case design studies of psychological treatments for chronic pain: a scoping review. J Contextual Behav Sci 2021;21:121–35. 10.1016/j.jcbs.2021.05.005
    1. Kratochwill TR, Hitchcock J, Horner R. Single-case designs technical documentation. What works clearinghouse, 2010.
    1. Tate RL. The risk-of-bias in N-of-1 trials (RoBiNT) scale : an expanded manual for the critical appraisal of single-case reports / Robyn L Tate, Ulrike Rosenkoetter, Donna Wakim, Linda Sigmundsdottir, Janet Doubleday, Leanne Togher, Skye McDonald, Michael Perdices. John Walsh Centre for Rehabilitation Research: St Leonards NSW, 2015.
    1. Tate RL, Perdices M, Rosenkoetter U, et al. . Revision of a method quality rating scale for single-case experimental designs and N-of-1 trials: the 15-item risk of bias in N-of-1 trials (RoBiNT) scale. Neuropsychol Rehabil 2013;23:619–38. 10.1080/09602011.2013.824383
    1. Perdices M, Tate RL, Rosenkoetter U. An algorithm to evaluate methodological rigor and risk of bias in single-case studies. Behav Modif 2019;0145445519863035:145445519863035. 10.1177/0145445519863035
    1. Verhagen SJW, Hasmi L, Drukker M, et al. . Use of the experience sampling method in the context of clinical trials. Evid Based Ment Health 2016;19:86–9. 10.1136/ebmental-2016-102418
    1. Dragioti E, Wiklund T, Alföldi P, et al. . The Swedish version of the insomnia severity index: factor structure analysis and psychometric properties in chronic pain patients. Scand J Pain 2015;9:22–7. 10.1016/j.sjpain.2015.06.001
    1. Rolffs JL, Rogge RD, Wilson KG. Disentangling components of flexibility via the Hexaflex model: development and validation of the multidimensional psychological flexibility inventory (MPFI). Assessment 2018;25:458–82. 10.1177/1073191116645905
    1. McCaffery M, Beebe A. The numeric pain rating scale instructions. Pain 1989.
    1. Darnall BD, Sturgeon JA, Cook KF, et al. . Development and validation of a daily pain catastrophizing scale. J Pain 2017;18:1139–49. 10.1016/j.jpain.2017.05.003
    1. Wicksell RK, Lekander M, Sorjonen K, et al. . The Psychological Inflexibility in Pain Scale (PIPS)--statistical properties and model fit of an instrument to assess change processes in pain related disability. Eur J Pain 2010;14:771.e1-14. 10.1016/j.ejpain.2009.11.015
    1. Cleeland CS, Ryan KM. Pain assessment: global use of the brief pain inventory. Ann Acad Med Singap 1994;23:129-38.
    1. Nicholas MK, McGuire BE, Asghari A. A 2-item short form of the pain self-efficacy questionnaire: development and psychometric evaluation of PSEQ-2. J Pain 2015;16:153–63. 10.1016/j.jpain.2014.11.002
    1. Valentine JC, Tanner‐Smith EE, Pustejovsky JE, et al. . Between‐case standardized mean difference effect sizes for single‐case designs: a primer and tutorial using the scdhlm web application. Campbell Syst Rev 2016;12:1–31. 10.4073/cmdp.2016.1
    1. Michiels B, Tanious R, De TK, et al. . A randomization test wrapper for synthesizing single-case experiments using multilevel models: a Monte Carlo simulation study. Behav Res Methods 2020;52:654–66. 10.3758/s13428-019-01266-6
    1. Tate RL, Perdices M, Rosenkoetter U, et al. . The single-case reporting guideline in behavioural interventions (SCRIBE) 2016 statement. Phys Ther 2016;96:e1–10. 10.2522/ptj.2016.96.7.e1
    1. Van den Noortgate W, Onghena P. The aggregation of single-case results using hierarchical linear models. Behav Anal Today 2007;8:196–209. 10.1037/h0100613
    1. Declercq L, Cools W, Beretvas SN, et al. . MultiSCED: A tool for (meta-)analyzing single-case experimental data with multilevel modeling. Behav Res Methods 2020;52:177–92. 10.3758/s13428-019-01216-2
    1. Rozental A, Andersson G, Boettcher J, et al. . Consensus statement on defining and measuring negative effects of internet interventions. Internet Interv 2014;1:12–19. 10.1016/j.invent.2014.02.001
    1. Rozental A, Kottorp A, Forsström D, et al. . The negative effects questionnaire: psychometric properties of an instrument for assessing negative effects in psychological treatments. Behav Cogn Psychother 2019;47:559–72. 10.1017/S1352465819000018
    1. Dworkin RH, Turk DC, Wyrwich KW, et al. . Interpreting the clinical importance of treatment outcomes in chronic pain clinical trials: IMMPACT recommendations. J Pain 2008;9:105–21. 10.1016/j.jpain.2007.09.005
    1. Shek DTL, Ma CMS, Ma C. Longitudinal data analyses using linear mixed models in SPSS: concepts, procedures and illustrations. ScientificWorldJournal 2011;11:42–76. 10.1100/tsw.2011.2
    1. Simons LE, Harrison LE, O'Brien SF, et al. . Graded exposure treatment for adolescents with chronic pain (get living): protocol for a randomized controlled trial enhanced with single case experimental design. Contemp Clin Trials Commun 2019;16:100448. 10.1016/j.conctc.2019.100448
    1. Kent DM, Rothwell PM, Ioannidis JP. Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal. Trials 2010;11:1–11.
    1. Lillie EO, Patay B, Diamant J, et al. . The N-of-1 clinical trial: the ultimate strategy for individualizing medicine? Per Med 2011;8:161–73. 10.2217/pme.11.7
    1. Nikles J, Mitchell GK, Schluter P, et al. . Aggregating single patient (N-of-1) trials in populations where recruitment and retention was difficult: the case of palliative care. J Clin Epidemiol 2011;64:471–80. 10.1016/j.jclinepi.2010.05.009
    1. Björk S, Norinder A. The weighting exercise for the Swedish version of the EuroQol. Health Econ 1999;8:117–26. 10.1002/(sici)1099-1050(199903)8:2<117::aid-hec402>;2-a
    1. Matthews JN, Altman DG, Campbell MJ, et al. . Analysis of serial measurements in medical research. BMJ 1990;300:230–5. 10.1136/bmj.300.6719.230
    1. Barber J, Thompson S. Multiple regression of cost data: use of generalised linear models. J Health Serv Res Policy 2004;9:197–204. 10.1258/1355819042250249
    1. Drummond MF, Sculpher MJ, Claxton K. Methods for the economic evaluation of health care programmes. Oxford university press, 2015.
    1. Fenwick E, Claxton K, Sculpher M. Representing uncertainty: the role of cost-effectiveness acceptability curves. Health Econ 2001;10:779–87. 10.1002/hec.635
    1. Boots LM, de Vugt ME, Smeets CM, et al. . Implementation of the blended care self-management program for caregivers of people with early-stage dementia (partner in balance): process evaluation of a randomized controlled trial. J Med Internet Res 2017;19:e423. 10.2196/jmir.7666
    1. Gell NM, Rosenberg DE, Demiris G, et al. . Patterns of technology use among older adults with and without disabilities. Gerontologist 2015;55:412–21. 10.1093/geront/gnt166
    1. Kampmeijer R, Pavlova M, Tambor M, et al. . The use of e-health and m-health tools in health promotion and primary prevention among older adults: a systematic literature review. BMC Health Serv Res 2016;16:467–79. 10.1186/s12913-016-1522-3
    1. Wicksell RK, Olsson GL, Melin L. The Chronic Pain Acceptance Questionnaire (CPAQ)-further validation including a confirmatory factor analysis and a comparison with the Tampa Scale of Kinesiophobia. Eur J Pain 2009;13:760–8. 10.1016/j.ejpain.2008.09.003
    1. Lilja JL, Frodi-Lundgren A, Hanse JJ, et al. . Five Facets Mindfulness Questionnaire--reliability and factor structure: a Swedish version. Cogn Behav Ther 2011;40:291–303. 10.1080/16506073.2011.580367
    1. Rickardsson J, Zetterqvist V, Kemani MK, et al. . Assessing values – psychometric properties of the Swedish version of the Valuing questionnaire in adults with chronic pain. J Contextual Behav Sci 2019;14:40–9. 10.1016/j.jcbs.2019.08.009
    1. Åkerblom S, Perrin S, Fischer MR, et al. . A validation and generality study of the committed action questionnaire in a Swedish sample with chronic pain. Int J Behav Med 2016;23:260–70. 10.1007/s12529-016-9539-x
    1. Löve J, Moore CD, Hensing G. Validation of the Swedish translation of the general self-efficacy scale. Qual Life Res 2012;21:1249–53. 10.1007/s11136-011-0030-5
    1. Larsson M, Nordeman L, Holmgren K, et al. . Prevention of sickness absence through early identification and rehabilitation of at-risk patients with musculoskeletal pain (PREVSAM): a randomised controlled trial protocol. BMC Musculoskelet Disord 2020;21:1–13. 10.1186/s12891-020-03790-5
    1. Linton SJ, Boersma K. Early identification of patients at risk of developing a persistent back problem: the predictive validity of the Orebro musculoskeletal pain questionnaire. Clin J Pain 2003;19:80–6. 10.1097/00002508-200303000-00002
    1. Ilmarinen J. Work ability--a comprehensive concept for occupational health research and prevention. Scand J Work Environ Health 2009;35:1–5. 10.5271/sjweh.1304
    1. Mattsson M, Sandqvist G, Hesselstrand R, et al. . Validity and reliability of the patient health Questionnaire-8 in Swedish for individuals with systemic sclerosis. Rheumatol Int 2020;40:1675–87. 10.1007/s00296-020-04641-1
    1. Nordin M, Nordin S. Psychometric evaluation and normative data of the Swedish version of the 10-item perceived stress scale. Scand J Psychol 2013;54:502–7. 10.1111/sjop.12071

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