Digital Patient Engagement in Technology-Supported Neurorehabilitation

NCT07794072 — Not yet recruiting, 129 participants, Catholic University, Italy

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Digital Patient Engagement and Psychosocial Dimensions in Technology-Supported Neurorehabilitation

RegistryNCT07794072, acronym PSY-NeT-Engage
StatusNot yet recruiting
Study typeObservational
Enrollment129 (Estimated)
SponsorCatholic University, Italy (Other); collaborators: Istituti Clinici Scientifici Maugeri SpA
ConditionsStroke, Parkinson Disease (PD), Motor Neuron Disease, Spinal Cord Injury, Traumatic Brain Injury, Neurological Disorders, Ataxia
Datesstart 2026-10-01 (estimated), primary completion 2027-07-01 (estimated), completion 2027-07-01 (estimated), first posted 2026-08-31, last updated 2026-09-15
Locations3 sites in Italy

Related Papers Related Patents Related Grants Related Experts
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-Summary

The goal of this observational study is to investigate digital patient engagement and its psychosocial correlates and potential predictors among adults aged 18-85 years with neurological disorders who are undergoing technology-supported neurorehabilitation using robotic systems for upper-limb training and/or virtual reality-based technologies.

Specifically, the study will examine the roles of hope and the quality of the patient-healthcare professional relationship as the potential predictors of engagement with rehabilitation technologies. It will also explore the contribution of disease-management self-efficacy, technology anxiety, and sociodemographic, clinical, functional, and rehabilitation-related characteristics. Finally, the study will examine whether digital patient engagement predicts the perceived psychosocial impact of rehabilitation technologies, behavioural intention to continue using them, and overall satisfaction with the rehabilitation pathway.

Participants will:

* continue their standard neurorehabilitation pathway without any modification to routine clinical procedures;
* complete a computerized self-report questionnaire via the Qualtrics digital platform at a single time point after completing at least three rehabilitation sessions using the technologies under study.

No experimental intervention, comparison group, or follow-up assessment is planned.

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+Detailed description

PSY-NeT-Engage is an academic, non-profit, multicentre, cross-sectional observational study designed to characterize digital patient engagement during technology-supported neurorehabilitation and to examine factors associated with differences in engagement. The study will be conducted among patients already receiving standard neurorehabilitation involving robotic systems for upper-limb training and/or virtual reality-based rehabilitation technologies at participating clinical centres. It will not assess the clinical or therapeutic efficacy of these technologies, functional recovery, or the achievement of rehabilitation goals, nor will it introduce any experimental intervention or modification to the standard rehabilitation pathway. Because the data will be collected cross-sectionally, the findings will be interpreted as exploratory associations and predictive relationships within the specified statistical models.

Data will be collected once for each participant during the standard neurorehabilitation pathway, after the participant has completed at least three rehabilitation sessions using the technologies under study. Consequently, participants may be assessed at different stages of rehabilitation and after completing different numbers of technology-supported sessions.

Patient-reported data will be collected through a single computerized self-report questionnaire administered via the Qualtrics digital platform using a password-protected tablet. Questionnaire completion is expected to require approximately 15 minutes. Research staff will be available to provide procedural clarification and technical assistance without influencing participants' responses. Relevant sociodemographic, clinical, functional, and rehabilitation-related variables will be extracted from clinical documentation. Variables not available in the clinical records will be collected directly from participants. No follow-up assessment is planned.

In addition to the primary outcome measure of digital patient engagement, the study includes secondary outcome measures of overall satisfaction with the rehabilitation pathway, behavioural intention to continue using rehabilitation technologies, and the perceived psychosocial impact of rehabilitation technologies. Four other pre-specified exploratory variables will also be assessed: hope, perceived quality of the patient-healthcare professional relationship, technology anxiety, and disease-management self-efficacy. These variables will be examined as potential psychosocial, relational, and individual predictors of digital patient engagement. Disease-management self-efficacy will be evaluated as a potential moderator.

Hope will be assessed using the 2-item Brief Hopelessness Scale with Positive Valence (Brief-H-Pos), which measures positive expectations regarding the future and the perceived attainability of personal goals. Higher scores indicate greater hope.

The perceived quality of the patient-healthcare professional relationship will be assessed using the 8-item Nordic Patient Experiences Questionnaire (NORPEQ). The questionnaire evaluates relevant dimensions of the patient's care experience, including communication with healthcare professionals, perceived care and attention, and trust in the healthcare team. Higher scores indicate a more positive care experience and better perceived relationship quality.

Technology anxiety will be assessed using the 11-item Abbreviated Technology Anxiety Scale (ATAS), which evaluates negative affective responses to technology, including apprehension, confidence, and perceived competence in using technological tools. Higher scores indicate greater technology anxiety.

Disease-management self-efficacy will be assessed using the 10-item Self-Efficacy Self-Care Scale (SCSES), which evaluates participants' confidence in their ability to maintain their health, monitor their condition, and manage symptoms while living with a chronic condition. Higher scores indicate greater disease-management self-efficacy. This variable will be examined as a secondary individual exposure factor and as a potential moderator of the associations between hope, the patient-healthcare professional relationship, and digital patient engagement.

For measures without an available validated Italian version, a research-specific Italian translation will be developed using forward-backward translation followed by review by an expert panel. The translated measures and data-collection procedures will initially be piloted in a subgroup of 15 participants to assess item comprehensibility and the adequacy of questionnaire administration. These translations are intended exclusively for research use and will not constitute validated instruments for routine clinical practice.

The minimum target sample of 129 participants was established through an a priori power analysis for structural equation modelling, assuming a moderate effect size of 0.30, statistical power of 0.80, and a two-sided significance level of α = 0.05. Consecutive non-probability sampling will be used, and data collection may continue beyond the minimum target throughout the planned recruitment period to improve the robustness and generalizability of the findings.

The statistical analysis will include descriptive statistics, assessment of variable distributions and outliers, and evaluation of internal consistency using Cronbach's alpha. Exploratory factor analysis will be used to examine the factor structure of measures without a validated Italian version. Initial associations will be evaluated using Pearson or Spearman correlation coefficients, as appropriate. Structural equation modelling will be applied within a confirmatory-exploratory framework to examine the plausibility, direction, and magnitude of the hypothesized direct, indirect, and total associations. Exploratory moderation analyses will be conducted using regression-based interaction models, and multiple regression models will be used to examine additional associations specified in the protocol. Robust estimators will be considered when non-normality or influential outliers could materially affect the results.

+Arms and interventions

ArmTypeInterventionsDescription
Patients Undergoing Technology-Supported NeurorehabilitationAdult patients with neurological disorders undergoing standard neurorehabilitation involving upper-limb robotic rehabilitation systems and/or virtual reality-based rehabilitation technologies at participating ICS Maugeri centres.

+Outcome measures

Primary outcomes

  • Digital patient engagement score assessed using the TWente Engagement with Ehealth Technologies Scale after completion of at least 3 rehabilitation sessions with the technologies [At baseline, single assessment (Day 1)]: Digital patient engagement in the use of neurorehabilitation technologies will be measured using the TWente Engagement with Ehealth Technologies Scale (TWEETS), a 9-item self-report scale assessing cognitive, emotional, and behavioral engagement with eHealth technologies. Items are rated on a 4-point Likert scale from 1 = strongly disagree to 4 = strongly agree. A total score is calculated by summing the 9 items, with possible scores ranging from 9 to 36. Higher scores indicate higher digital patient engagement. "Not applicable" responses, if selected, are treated as missing and handled according to the pre-specified scoring plan.

Secondary outcomes

  • Overall satisfaction with rehabilitation score assessed using the Overall Satisfaction with Rehabilitation Scale after completion of at least 3 rehabilitation sessions with the technologies [At baseline, single assessment (Day 1)]: Overall satisfaction with the rehabilitation pathway will be assessed using the Overall Satisfaction with Rehabilitation Scale (OSWRS), a 5-item self-report measure assessing global patient satisfaction with rehabilitation, including perceived progress, satisfaction with recovery timing, affective response to rehabilitation, and overall satisfaction. Items are rated on a 7-point Likert scale from 1 = strongly disagree to 7 = strongly agree. A total score is calculated by summing the 5 items, with possible scores ranging from 5 to 35. Higher scores indicate higher overall satisfaction with the rehabilitation pathway.
  • Behavioral intention to continue using rehabilitation technologies score assessed using adapted UTAUT items after completion of at least 3 rehabilitation sessions with the technologies [At baseline, single assessment (Day 1)]: Behavioral intention to continue using rehabilitation technologies will be assessed using 3 items adapted from the Unified Theory of Acceptance and Use of Technology (UTAUT). The items assess the participant's intention and planning to continue using the rehabilitation technologies in the near future. Items are rated on a 7-point Likert scale from 1 = strongly disagree to 7 = strongly agree. A total score is calculated by summing the 3 items, with possible scores ranging from 3 to 21. Higher scores indicate stronger behavioral intention to continue using rehabilitation technologies.
  • Perceived psychosocial impact of rehabilitation technologies score assessed using the Psychosocial Impact of Assistive Devices Scale short form after completion of at least 3 rehabilitation sessions with the technologies [At baseline, single assessment (Day 1)]: The perceived psychosocial impact of rehabilitation technologies will be measured using the short version of the Psychosocial Impact of Assistive Devices Scale (PIADS-10). The scale includes 10 items assessing the perceived impact of the device on competence, adaptability, and self-esteem. Items are rated on a 7-point scale from -3 = has decreased to +3 = has increased. A total score is calculated by summing the 10 items, with possible scores ranging from -30 to +30. Higher positive scores indicate greater perceived positive psychosocial impact of the rehabilitation technologies, while negative scores indicate perceived negative psychosocial impact.

+Eligibility

Sex: All, ages: 18 Years to 85 Years

Inclusion Criteria:

* Aged between 18 and 85 years.
* Diagnosed with one of the following neurological conditions: stroke, Parkinson's disease, ataxia, motor neuron disease, spinal cord injury, traumatic brain injury, or post-neurosurgical neurological outcomes.
* Have completed at least three rehabilitation sessions using the technologies under study, representing the minimum exposure required for participants to become sufficiently familiar with the technology and to provide informed feedback on their experience of use.
* Have adequate cognitive functioning, defined as the ability to understand study-related information, provide informed consent, and reliably complete the planned assessment measures, as determined by the clinical team during routine clinical evaluation.
* Have sufficient proficiency in Italian to understand the study information and complete the assessment measures reliably.
* Can use the digital tools required for data collection, e.g., a tablet, either independently or with compensatory aids and/or assistance from the data collection staff.

Exclusion Criteria:

* Diagnosis of an acute or non-stabilized psychiatric disorder, such as an active psychotic episode or a depressive episode with severe symptoms, that may interfere with informed participation in the study.
* Severe uncompensated sensory impairment, such as severe visual or hearing impairment, that prevents the use of the digital tools required for data collection.
* Motor impairment that prevents interaction with a tablet, even with compensatory aids and/or assistance from the data collection staff.

+Locations (3)

FacilityCityCountryStatus
ICS Maugeri IRCCS - Istituto Scientifico di MilanoMilanItaly
ICS Maugeri IRCCS - Istituto Scientifico di MontescanoMontescanoItaly
ICS Maugeri IRCCS - Istituto Scientifico di PaviaPaviaItaly

+Publications

  • Klaic M, Galea MP. Using the Technology Acceptance Model to Identify Factors That Predict Likelihood to Adopt Tele-Neurorehabilitation. Front Neurol. 2020 Dec 2;11:580832. doi: 10.3389/fneur.2020.580832. eCollection 2020. PMID 33343488
  • Warraich Z, Kleim JA. Neural plasticity: the biological substrate for neurorehabilitation. PM R. 2010 Dec;2(12 Suppl 2):S208-19. doi: 10.1016/j.pmrj.2010.10.016. PMID 21172683
  • Danzl MM, Etter NM, Andreatta RD, Kitzman PH. Facilitating neurorehabilitation through principles of engagement. J Allied Health. 2012 Spring;41(1):35-41. PMID 22544406
  • Kelders SM, Kip H, Greeff J. Psychometric Evaluation of the TWente Engagement with Ehealth Technologies Scale (TWEETS): Evaluation Study. J Med Internet Res. 2020 Oct 9;22(10):e17757. doi: 10.2196/17757. PMID 33021487
  • Di Nitto M, Durante A, Torino F, Bolgeo T, Damico V, Ghizzardi G, Zerulo SR, Alvaro R, Vellone E, Biagioli V. Validity and Reliability of the Self-Care of Chronic Illness Inventory and Self-Care Self-Efficacy Scale in Patients Living With Cancer. J Adv Nurs. 2025 Dec;81(12):8620-8632. doi: 10.1111/jan.16823. Epub 2025 Feb 19. PMID 39968728
  • Ritter PL, Lorig K. The English and Spanish Self-Efficacy to Manage Chronic Disease Scale measures were validated using multiple studies. J Clin Epidemiol. 2014 Nov;67(11):1265-73. doi: 10.1016/j.jclinepi.2014.06.009. Epub 2014 Aug 3. PMID 25091546
  • Wild D, Grove A, Martin M, Eremenco S, McElroy S, Verjee-Lorenz A, Erikson P; ISPOR Task Force for Translation and Cultural Adaptation. Principles of Good Practice for the Translation and Cultural Adaptation Process for Patient-Reported Outcomes (PRO) Measures: report of the ISPOR Task Force for Translation and Cultural Adaptation. Value Health. 2005 Mar-Apr;8(2):94-104. doi: 10.1111/j.1524-4733.2005.04054.x. PMID 15804318
  • Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). 2000 Dec 15;25(24):3186-91. doi: 10.1097/00007632-200012150-00014. No abstract available. PMID 11124735
  • Fraser L, Burnell M, Salter LC, Fourkala EO, Kalsi J, Ryan A, Gessler S, Gidron Y, Steptoe A, Menon U. Identifying hopelessness in population research: a validation study of two brief measures of hopelessness. BMJ Open. 2014 May 30;4(5):e005093. doi: 10.1136/bmjopen-2014-005093. PMID 24879829
  • Andrich, R., Pedroni, F., & Vanni, G. (2003). Psychosocial impact of assistive devices: Italian localization of the PIADS instrument. Assistive Technology-Shaping the Future, GM Craddock, LP McCormack, RB Reilly, and HTP Knops, Eds, 917-921.
  • Yang, C., Wu, C. F., Wang, J., Chen, W. C., Chang, H., & Xu, D. D. (2024). A study on the intention of upper limb hemiplegic patients to use interactive gaming devices for hand rehabilitation. The Design Journal, 27(3), 470-492. https://doi.org/10.1080/14606925.2024.2334139
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  • Cressman, J. M., & Dawson, K. A. (2011). Evaluation of the Use of Healing Imagery in Athletic Injury Rehabilitation. Journal of Imagery Research in Sport and Physical Activity, 6(1). https://doi.org/10.2202/1932-0191.1060
  • Matamala-Gomez M, Maisto M, Montana JI, Mavrodiev PA, Baglio F, Rossetto F, Mantovani F, Riva G, Realdon O. The Role of Engagement in Teleneurorehabilitation: A Systematic Review. Front Neurol. 2020 May 6;11:354. doi: 10.3389/fneur.2020.00354. eCollection 2020. PMID 32435227
  • Zanatta F, Giardini A, Pierobon A, D'Addario M, Steca P. A systematic review on the usability of robotic and virtual reality devices in neuromotor rehabilitation: patients' and healthcare professionals' perspective. BMC Health Serv Res. 2022 Apr 20;22(1):523. doi: 10.1186/s12913-022-07821-w. PMID 35443710
  • Yu DS, De Maria M, Barbaranelli C, Vellone E, Matarese M, Ausili D, Rejane RE, Osokpo OH, Riegel B. Cross-cultural applicability of the Self-Care Self-Efficacy Scale in a multi-national study. J Adv Nurs. 2021 Feb;77(2):681-692. doi: 10.1111/jan.14617. Epub 2020 Dec 9. PMID 33295675
  • Wilson ML, Huggins-Manley AC, Ritzhaupt AD, Ruggles K. Development of the Abbreviated Technology Anxiety Scale (ATAS). Behav Res Methods. 2023 Jan;55(1):185-199. doi: 10.3758/s13428-022-01820-9. Epub 2022 Mar 25. PMID 35338456
  • Oltedal S, Garratt A, Bjertnaes O, Bjornsdottir M, Freil M, Sachs M. The NORPEQ patient experiences questionnaire: data quality, internal consistency and validity following a Norwegian inpatient survey. Scand J Public Health. 2007;35(5):540-7. doi: 10.1080/14034940701291724. PMID 17852989
  • Day H, Jutai J, Campbell KA. Development of a scale to measure the psychosocial impact of assistive devices: lessons learned and the road ahead. Disabil Rehabil. 2002 Jan 10-Feb 15;24(1-3):31-7. doi: 10.1080/09638280110066343. PMID 11827152
  • Rosa D, Villa G, Marcomini I, Nardin E, Gianfranceschi E, Faini A, Pengo MF, Bilo G, Croce A, Manara DF, Parati G. Psychometric Properties of the TWente Engagement with Ehealth Technologies Scale (TWEETS) Among Patients with Hypertension in Italy. High Blood Press Cardiovasc Prev. 2025 Jan;32(1):61-68. doi: 10.1007/s40292-024-00688-4. Epub 2024 Oct 29. PMID 39472407

+Investigators