Designing a mHealth clinical decision support system for Parkinson's disease: a theoretically grounded user needs approach.

BACKGROUND: Despite the established evidence and theoretical advances explaining human judgments under uncertainty, developments of mobile health (mHealth) Clinical Decision Support Systems (CDSS) have not explicitly applied the psychology of decision making to the study of user needs. We report on a user needs approach to develop a prototype of a mHealth CDSS for Parkinson's disease (PD), which is theoretically grounded in the psychological literature about expert decision making and judgement under uncertainty. METHODS: A suite of user needs studies was conducted in 4 European countries (Greece, Italy, Slovenia, the UK) prior to the development of PD_Manager, a mHealth-based CDSS designed for Parkinson's disease, using wireless technology. Study 1 undertook Hierarchical Task Analysis (HTA) including elicitation of user needs, cognitive demands and perceived risks/benefits (ethical considerations) associated with the proposed CDSS, through structured interviews of prescribing clinicians (N = 47). Study 2 carried out computational modelling of prescribing clinicians' (N = 12) decision strategies based on social judgment theory. Study 3 was a vignette study of prescribing clinicians' (N = 18) willingness to change treatment based on either self-reported symptoms data, devices-generated symptoms data or combinations of both. RESULTS: Study 1 indicated that system development should move away from the traditional silos of 'motor' and 'non-motor' symptom evaluations and suggest that presenting data on symptoms according to goal-based domains would be the most beneficial approach, the most important being patients' overall Quality of Life (QoL). The computational modelling in Study 2 extrapolated different factor combinations when making judgements about different questions. Study 3 indicated that the clinicians were equally likely to change the care plan based on information about the change in the patient's condition from the patient's self-report and the wearable devices. CONCLUSIONS: Based on our approach, we could formulate the following principles of mHealth design: 1) enabling shared decision making between the clinician, patient and the carer; 2) flexibility that accounts for diagnostic and treatment variation among clinicians; 3) monitoring of information integration from multiple sources. Our approach highlighted the central importance of the patient-clinician relationship in clinical decision making and the relevance of theoretical as opposed to algorithm (technology)-based modelling of human judgment.

Relationship between pain and motor and non-motor symptoms in Parkinson's disease.

BACKGROUND AND PURPOSE: Although female gender, depressive symptoms and medical conditions predisposing to pain are more common in patients with Parkinson's disease (PD) with pain, no study has yet explored the relationship between pain and other non-motor symptoms (NMS). METHODS: A total of 321 consecutive patients with PD [190 men/131 women aged 68.3 (SD 9.2) years] attending four Italian movement disorder clinics were studied. Demographic/clinical data were obtained by a standardized interview and the NMS scale. The association of pain with motor and NMS was assessed by multivariable logistic regression models. RESULTS: At the time of the study, 180 patients with PD (56%) reported chronic pain that, in most cases, was described as being muscular or arthralgic pain. Pain preceded the onset of motor signs in 36/180 patients. In the main-effect model, factors independently associated with pain were female sex [odds ratio (OR), 2.1; P = 0.01], medical conditions predisposing to pain (OR, 2.9; P < 0.001), Hoehn-Yahr staging (OR, 1.9; P = 0.04), motor complications (OR, 4.7; P = 0.04) and NMS belonging to the sleep/fatigue (OR, 1.6; P = 0.04) and mood/cognition (OR, 1.6; P = 0.03) domains. Most explanatory variables in the multivariable analysis were similarly distributed in patients in whom pain may have been related to PD or to a cause other than PD. CONCLUSIONS: We confirm that pain in PD is more frequent in women and in subjects with medical conditions predisposing to painful symptoms. Moreover, this strengthens the association between pain and motor severity measures and NMS domains, particularly sleep and mood disturbances.