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Movement Disorders, Parkinson's Disease
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Can Objective Measurements Improve Treatment Outcomes in Parkinson’s Disease?

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Published Online: May 22nd 2014 European Neurological Review, 2014;9(1):27–30 DOI: http://doi.org/10.17925/ENR.2014.09.01.27
Authors: Filip Bergquist, Malcolm Horne
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Abstract:
Overview

Many examples in medicine show that therapies are most effective when measurement is used to guide their implementation, dose and effects. There are effective symptomatic therapies for the motor symptoms of Parkinson’s disease, which improve quality of life and have a health economic justification for their subsidisation. As measurement should lead to more effective deployment of these therapies, even in a percentage of cases, then costs of therapy would be reduced and by that percentage. We conclude that there is a clear need for continuous objective measures of dyskinesia and bradykinesia while patients go about their normal daily activities. The benefit of measurement would be greatest if these measures were directed at treating fluctuations.

Keywords

Parkinson’s disease, bradykinesia, dyskinesia, fluctuations, measurement, treatment, scales, accelerometry, outcomes, compliance

Article:

‘You can’t manage what you can’t measure’ is a well-acknowledged maxim attributed variously to Peter Drucker, W Edward Demming and Bill Hewlett (of Hewlett Packard), but was probably first expressed by Lord Kelvin, the British scientist associated with, among other discoveries, the first and second law of thermodynamics. It is a concept well entrenched in medicine, as attested by the measuring of blood glucose, blood pressure, renal function, etc. to manage their related disorders. It seems likely therefore that measurement would aid in the management of Parkinson’s disease (PD). However, it is worth recalling that in clinical management, measuring is only useful if it leads to a decision (usually a therapeutic decision) and to do that the measure should correlate closely with the desired consequence of the decision.

With regard to the second point, measuring blood glucose in diabetes is successful because it directly reflects treatment with insulin, and elevated blood glucose causes long-term pathology. However, the terms diabetes mellitus come from a time when the condition was recognised by ‘too much urine’ and ‘honey in the urine’. Many disorders have passed through a stage where they can only be recognised by symptoms, before measures reflecting the underlying pathology are devised. James Parkinson identified some of the classic motor features of PD, but it was objective measurements with hand dynamometers that helped Charcot to dismiss the idea that PD, at the time known as paralysis agitans, was characterised by weakness and to stress that a core symptom was slowness.1 In the 1980s, David Marsden2 pointed out that bradykinesia is the major underlying instigator of many of the motor manifestations of the disorder. Although it is now recognised that PD is much more than a motor condition and has many other features of frontal lobe dysfunction3 and involves peripheral, 4–6 as well as central widespread neuropathology, 7 recognition of motor symptoms are essential for both diagnosis and disease management. Non-motor symptoms may require treatment to be revised, but the decisions to start treatment, to add a different class of drugs, to alter the number of daily administrations or to consider advanced treatment with deep brain stimulation (DBS) or continuous pump treatments are invariably triggered by motor symptoms. The available therapies for the non-motor symptoms of PD are few and not well studied. By contrast, effective symptomatic treatment of motor symptoms has been available for over 50 years and improved motor function is generally associated also with improved non-motor symptoms, so if ‘measuring is only useful if it leads to a therapeutic decision’ then measuring the motor symptoms of PD has a higher likelihood of improving treatment outcomes in PD than measuring non-motor symptoms. The rest of this review will therefore concentrate on the question of whether objective measurements of motor symptoms can improve treatment outcomes in PD.

Does Treating Motor Symptoms Affect Outcomes?
The four cardinal motor symptoms of PD, bradykinesia, rigidity, tremor and postural instability,8,9 are all considered to reflect dopaminergic failure and are therefore targets of dopamine replacement therapy.

There is consensus that dopaminergic stimulation is effective in controlling bradykinesia, the key symptom of PD, 10 and to a lesser extent tremor and rigidity. 8,11 A good response to dopamine stimulation supports the diagnosis of idiopathic PD and a lack of response implies a less favourable prognosis. Opinions are divided between early intervention and intervening later when symptoms are sufficient to interfere with daily activities and/or lifestyle. The interventionists have concluded from reviews of the evidence12–18 that bradykinesia causes morbidity and that medications, levodopa (L-DOPA) in particular, have a low incidence of side effects in this early stage. Pragmatists on the other hand, argue that there is no Class 1 Cochrane evidence19,20 for early intervention, so the best time to start therapy is when patients advise that symptoms are troublesome. Regardless of when therapy is initiated, achieving good control of motor symptoms is important for quality of life (QoL), as there is a direct contribution of the motor component of disability (mostly as a consequence of bradykinesia) to poor QoL. 21–23 Despite the emphasis on non-motor symptoms in recent years, a longitudinal study of QoL in PD24 showed that the greatest contributor was loss of mobility, especially in the period before the onset of falls and dementia. Thus, there are reasons to adequately treat bradykinesia and to establish whether the condition is indeed a form of PD that is responsive to treatment.

At the onset of disease, the management of PD is the management of bradykinesia, and all therapies have a relatively long half-life. Consequently, the motor state is relatively constant and does not vary greatly from day to day, or over the day. At this stage and prior to onset of symptom fluctuations, a single assessment adequately measures improvement in symptoms.

With time, patients notice the re-emergence of tremor or bradykinesia prior to the next dose – ‘wearing-off’ – and dyskinesia at the time of maximum dopaminergic stimulation. 25–32 The time from commencement of therapy to the onset of fluctuations and dyskinesia is similar33 and occurs after 4–6 years in 40 % of patients and by 9 years in ~70 %.33–37 The incidence is higher in younger patients, with 92 % of patients experiencing fluctuations after 5 years of treatment. 38 Once fluctuations have developed, the result of assessment will depend on when the test is performed in relation to consumption of medication, and thus more continuous measures are desirable. Often fluctuations become evident to other observers before they are noticed by the patient, who may fail to report them. Failing to detect early fluctuations may constitute a lost opportunity to stabilise the treatment and to improve outcome. Indeed, randomised trials aimed at evaluating the risk of fluctuations with different treatments (for example, comparing agonists with L-DOPA) may be confounded by the presence of patients whose risk of developing fluctuations is low, or by failure to pinpoint their time of onset. If the development of motor fluctuations could be detected by objective continuous measurement then it is possible to establish whether patients benefit from early deployment of continuous dopamine stimulation.

When fluctuations are firmly established, the aims of therapy are to minimise bradykinesia, including that resulting from fluctuations, and to curtail the complications of excess dopaminergic transmission, which include dyskinesia and the non-motor features associated with increased dopaminergic transmission. Thus the goal of therapeutic management is to maximise ‘on’ time by reducing wearing-off, while at the same time avoiding excess therapy. When wearing-off first presents, treatment effect is predictable. In time, however, this response becomes increasingly unpredictable so that patients cannot plan their days, and the restriction in what daily activities can be managed is much larger than when activities can be planned to ‘good’ periods of the day.

Treatment of fluctuations entails adjusting the frequency or size of L-DOPA doses, adding agents that prolong the effect of L-DOPA or addition of longer-acting D2 agonists. Effective treatment of wearing off and fluctuations requires a close adherence to medication schedules, yet poor medication compliance is relatively common in PD39–42 and worst in young patients with many doses/day or when patients were depressed. 39 Non-adherence has been linked to higher total healthcare costs, despite lower drug costs, 43 and presumably this contributes to the poorer QoL of non-adherent patients.39 Importantly, managing physicians may be unaware of both the lack of compliance and that the patients are undertreated because of this compliance failure rather than loss of response to medication. Being able to objectively measure motor symptoms in relation to compliant drug intake could remove the problem of knowing if lack of response is lack of drug effect or due to poor compliance.

Dyskinesia can be managed by compromising between reduction in bradykinesia and severity of dyskinesia, addition of amantadine or the addition of advanced therapies (DBS, DuoDopa, apomorphine – see Fox et al. 201119 for evidence of benefit). The guidelines for the timing of the introduction of advanced therapies use wording such as ‘when best medical therapies fail to adequately control motor symptoms.’ This wording reflects the current difficulties in objectively measuring motor control. The decision in terms of ‘if or when’ to introduce these therapies is important because they are expensive, may pose health risks for the patient and are intrusive. There may also be a window of optimum benefit, especially for DBS, 44 so delaying their introduction may introduce the risk of their implementation while forgoing the time when they would have been more beneficial. Effective measurement of fluctuation would enable clinicians to identify, educate and guide patients towards advanced treatment at the time they most benefit from it. A further consideration is that while dyskinesia itself may not have a major impact on QoL, the frequent attendants of impulsivity, dysphoria and anxiety do. Thus dyskinesia can be a surrogate measure of impaired impulse control and affective non-motor symptoms. 45 Nonmotor complications of PD, including those that directly stem from the use of dopaminergic therapy directed at alleviating bradykinesia, 3,46–51 are associated with excess disability, worse QoL, poorer outcomes and caregiver burden. 24 Many studies do not explicitly untangle each component’s contribution to reduction in QoL, but instead look at the effects of ‘non-motor’ features on QoL. However, ‘treatment policies capable of reducing or delaying motor fluctuations would be expected to increase QoL and reduce some of the economic burden of PD’52 by reducing both motor fluctuations and those non-motor symptoms caused by fluctuations.

The Need for Measurement of Motor States
Today, movement disorder clinicians take careful histories designed to identify wearing off and its timing in relationship to medications and to understand the timing and extent of dyskinesia. There are many reasons why, despite a careful and time-consuming history, their best intentions are thwarted. The retrospective approach depends upon the patient’s ability to recognise each of the three states (normal, dyskinesia and bradykinesia). Unfortunately, patients are notoriously poor in understanding the difference between dyskinesia and tremor. There are also problems with recall, which are not just ‘forgetfulness’, but also that immediacy of the current state dominates a patient’s idea of his or herself. So if bradykinesia is the current problem, they have little recollection that last visit dyskinesia was their major concern, and when asked how the last hour was they are more likely to describe the last 10 minutes. People also assess themselves subjectively – a patient comes to regard a certain level of bradykinesia or dyskinesia as ‘normal’ and will only comment when they are worse than the perceived normal. This is important to remember, because objective measurements, by contrast, use a population referenced baseline that may or may not be relevant to the individual patient. The subjective assessment is also coloured by the affective state and dysphoria of the falling stage of dyskinesia may lead to overrating of the ‘OFF’ motor state. 53 If someone asks you how often you were hungry or sleepy in the past month or two you may struggle to recall and report that. It may be a challenge even for healthy subjects to reason objectively when asked about past everyday events, so recalling patterns of motor fluctuations can be far beyond the capacity of PD patients with failing executive function.

If the argument that ‘management is difficult without measurement’ is accepted, then it will be difficult to manage bradykinesia and dyskinesia without knowing its extent, whether interventions were successful and whether therapies were used as directed. Many European countries subsidise the cost of therapies for treating bradykinesia and managing dyskinesia because they enhance QoL and have a health economic justification. If measurement leads to more effective deployment of these therapies, even in a percentage of cases, then costs of therapy would be reduced and QoL improved by that percentage.

How to Measure Motor Symptoms
The clinical examination of a patient involves the assessment of abnormal motor function and findings are referenced to the examiners previous experiences, coloured by recent impressions and expressed in words. Rating scales, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), standardise the examination and present the findings as a pre-defined Likert scale. While these scales reduce the variability of assessments and produce measures that can be compared statistically over time, intra- and inter-rater variability remains high54 even with the use of video recordings and training. Other frequently used measures of motor impairment and treatment responses include tests that measure the time to perform a defined movement. For example, the Core Assessment Program for Intracerebral Transplantation (CAPIT) protocol55 includes the stand-walk-sit test, the timed pronationsupination test, the finger dexterity or finger tap test and the hand/ arm movement test, which all register movement time. 55 The typical bradykinetic movement pattern with gradually reduced amplitudes and frequencies in repeated movements is however not assessed in timed tests, and the repertoire of symptoms measured in timed tests is restricted. 56–58 This affects the sensitivity of tests as, for example, the chance of detecting abnormal movement is higher with a compound than with a singular movement. 59 Even so, measuring relatively simple movements can add diagnostic information. 58,60

All tests that involve the assessment of a predefined movement (passive or active), timed or rated, are in a general sense intermittent and cross-sectional. As discussed, PD is a disorder in which the symptoms frequently fluctuate throughout the day and from day to day. Thus a test at a single point in time may fail to capture the full range of variation. In their review, Maetzler et al. 61 described and listed many examples of motor features of PD that an office or laboratory examination fails to capture. They emphasise the need for measures that are continuous, do not require direct elicitation of data from the patient or user and that approximate the real-world activities of the patient. 61

Diaries have been used as an attempt to approach continuous measurement but their limitations are well recorded. 62 They are labour intensive and have a low compliance rate, particularly if extended for more than 3 days63,64 and patients may delay recording until the end of each day (or even the waiting room). 65 Paper diaries have a particularly poor compliance rate with compliance often faked, 65,66 but the use of electronic diaries is mainly confined to clinical trials or where patients are carefully selected on their ability to fill in diaries. 62–64

Recent advances in motion sensors and information storage and handling make continuous, unobtrusive assessment potentially feasible. Although accelerometers have been available for many years, recent computing and data storage capacity has made it possible to use them outside research. Cheap miniaturised accelerometers have become integrated into many electronic devices, including laptops, tablets, smartphones and watches. Actigraphs have been used in PD to measure gait and stability, 67 to trace the development and progression of motor disability over time68 and diurnal fluctuations.69 Importantly, it is possible to use mathematical algorithms to recognise movement patterns that clinically experienced raters recognise as bradykinesia68,70–72 and dyskinesia. 70,73–75 As it is now possible to handle an amount of data that would only 10 years ago have been completely daunting, accelerometers and gyrometers have the potential to objectively monitor motor symptom variations in PD.

Conclusion
As reviewed elsewhere,61 systems for continuous measurement are beginning to emerge. In this discussion, focus has been on measuring motor manifestations, with the justification that measurement can lead to a (therapeutic) decision and thus implies the availability of effective therapies. This is by no means to negate the importance however of measurement as tool for better understanding and describing disease and for discovering therapies. Arguably, useful diseasemodifying therapies may have been overlooked simply because the measurement tools were insufficiently sensitive to identify their diseasemodifying benefit. 14 Similarly, better measurement of many of the nonmotor symptoms leads to better understanding of their relationship to motor symptoms and other features of PD and this is an essential precursor to the development of therapies.

On the question of whether objective measurement would improve treatment outcomes, our conclusion is that there is a clear need for continuous objective measures of dyskinesia and bradykinesia that can detect the appearance and patterns of motor fluctuations. The time resolution of validated disease rating scales is insufficient for this purpose and patient diaries are only reliable in selected trained patients. Affordable devices for motion monitoring using accelerometers are available and are good candidate techniques to achieve this. The influence of objective measurements on therapeutical decision-making, patient and carer QoL and the development of PD complications should be further studied. Objective measurements could have a considerable positive effect on PD related health economy by improving outcomes via increasing the detection of untreated bradykinesia (including when manifest as wearing off), early fluctuations and by improving the selection of patients that benefit most from advanced treatments. Also in the evaluation of individual response to altered treatment objective measuring can confirm that the therapeutic decisions have the intended effect.

Article Information:
Disclosure

Filip Bergquist has no conflicts of interest to declare. Malcolm Horne owns intellectual property and shares in a company that manufactures and sells a device that provides objective measures of Parkinson’s disease.

Correspondence

Malcolm Horne, Florey Institute for Neuroscience and Mental Health, The Kenneth Myer Building, Melbourne University, Parkville, Victoria 3010, Australia. E: malcolm.horne@florey.edu.au
An erratum to this article can be found below.

Received

2014-05-06T00:00:00

References

  1. Goetz CG, Charcot and Parkinson’s disease. In: Factor S, Weiner WJ (eds), Parkinson’s Disease: Diagnosis and Clinical Management, New York: Demos Medical Publishing, 2007.
  2. Marsden CD, Which motor disorder in Parkinson’s disease indicates the true motor function of the basal ganglia, Ciba Found Symp, 1984;107:225–41.
  3. Weintraub D, Burn DJ, Parkinson’s disease: the quintessential neuropsychiatric disorder, Mov Disord, 2011;26:1022–31.
  4. Langston JW, The Parkinson’s complex: parkinsonism is just the tip of the iceberg, Ann Neurol, 2006;59:591–96.
  5. Iwanaga K, Wakabayashi K, Yoshimoto M, et al., Lewy body-type degeneration in cardiac plexus in Parkinson’s and incidental Lewy body diseases, Neurology, 1999;52:1269–71.
  6. Braak H, de Vos RA, Bohl J, Del Tredici K, Gastric alphasynuclein immunoreactive inclusions in Meissner’s and Auerbach’s plexuses in cases staged for Parkinson’s diseaserelated brain pathology, Neurosci Lett, 2006;396:67–72.
  7. Braak H, Ghebremedhin E, Rub U, et al., Stages in the development of Parkinson’s disease-related pathology, Cell Tissue Res, 2004;318:121–34.
  8. National Institute for Health and Clinical Excellence, ‘Nice Clincal Guideline 35: Parkinson’s disease – diagnosis and management in primary and secondary care (full guideline), 2006. Available at: http://www.nice.org.uk/cg35 (accessed 10 May 2014).
  9. Lees AJ, Hardy J, Revesz T, Parkinson’s disease, Lancet, 2009;373:2055–66.
  10. Marsden CD, Slowness of movement in Parkinson’s disease, Mov Disord, 1989;4(Suppl. 1):S26–37.
  11. Oertel WH, Berardelli A, Bloem BR, et al., Early (uncomplicated) Parkinson’s disease. In: European Handbook of Neurological Management: Volume 1, 2nd Edition, London: Blackwell Publishing Ltd, 2011.
  12. Fahn S, Oakes D, Shoulson I, et al., Levodopa and the progression of Parkinson’s disease, N Engl J Med, 2004;351:2498–508.
  13. Henchcliffe C, Severt WL, Disease modification in Parkinson’s disease, Drugs Aging, 2011;28:605–15.
  14. Olanow CW, Rascol O, Hauser R, et al., A double-blind, delayed-start trial of rasagiline in Parkinson’s disease, N Engl J Med, 2009;361:1268–78.
  15. Holford NH, Chan PL, Nutt JG, et al., Disease progression and pharmacodynamics in Parkinson disease – evidence for functional protection with levodopa and other treatments, J Pharmacokinet Pharmacodyn, 2006;33:281–311.
  16. Holford NH, Nutt JG, Interpreting the results of Parkinson’s disease clinical trials: time for a change, Mov Disord, 2011;26:569–77.
  17. Vlaar A, Hovestadt A, van Laar T, Bloem BR, The treatment of early Parkinson’s disease: levodopa rehabilitated, Pract Neurol, 2011;11:145–52.
  18. Connolly BS, Lang AE, Pharmacological treatment of Parkinson disease: a review, JAMA, 2014;311:1670–83.
  19. Fox SH, Katzenschlager R, Lim SY, et al., The Movement Disorder Society Evidence-Based Medicine Review Update: treatments for the motor symptoms of Parkinson’s disease, Mov Disord, 2011;26(Suppl. 3):S2–41.
  20. Clarke CE, Patel S, Ives N, et al., Should treatment for Parkinson’s disease start immediately on diagnosis or delayed until functional disability develops?, Mov Disord, 2011;26:1187–93.
  21. Kuopio AM, Marttila RJ, Helenius H, et al., The quality of life in Parkinson’s disease, Mov Disord, 2000;15:216–23.
  22. Shearer J, Green C, Counsell CE, Zajicek JP, The impact of motor and non motor symptoms on health state values in newly diagnosed idiopathic Parkinson’s disease, J Neurol, 2012;259:462–8.
  23. Schrag A, Selai C, Jahanshahi M, Quinn NP, The EQ-5D – a generic quality of life measure – is a useful instrument to measure quality of life in patients with Parkinson’s disease, J Neurol Neurosurg Psychiatry, 2000;69:67–73.
  24. Forsaa EB, Larsen JP, Wentzel-Larsen T, et al., Predictors and course of health-related quality of life in Parkinson’s disease, Mov Disord, 2008;23:1420–7.
  25. Hely MA, Morris JG, Reid WG, et al., The Sydney Multicentre Study of Parkinson’s disease: a randomised, prospective five year study comparing low dose bromocriptine with low dose levodopa-carbidopa, J Neurol Neurosurg Psychiatry, 1994;57:903–10.
  26. Hughes AJ, Frankel JP, Kempster PA, et al., Motor response to levodopa in patients with parkinsonian motor fluctuations: a follow-up study over three years, J Neurol Neurosurg Psychiatry, 1994;57:430–34.
  27. Nutt JG, Pharmacodynamics of levodopa in Parkinson’s disease, Clin Exp Pharmacol Physiol,1995;22:837–40.
  28. Nutt JG, Carter JH, Van Houten L, Woodward WR, Short- and long-duration responses to levodopa during the first year of levodopa therapy, Ann Neurol, 1997;42:349–55.
  29. McColl CD, Reardon KA, Shiff M, Kempster PA, Motor response to levodopa and the evolution of motor fluctuations in the first decade of treatment of Parkinson’s disease, Mov Disord, 2002;17:1227–34.
  30. Reardon KA, Shiff M, Kempster PA, Evolution of motor fluctuations in Parkinson’s disease: a longitudinal study over 6 years, Mov Disord, 1999;14:605–11.
  31. Nutt JG, Pharmacokinetics and pharmacodynamics of levodopa, Mov Disord, 2008;23(Suppl. 3):S580–4.
  32. Nutt JG, Carter JH, Lea ES, Sexton GJ, Evolution of the response to levodopa during the first 4 years of therapy, Ann Neurol, 2002;51:686–93.
  33. Nutt JG, Chung KA, Holford NH, Dyskinesia and the antiparkinsonian response always temporally coincide: a retrospective study, Neurology, 2010;74:1191–7.
  34. Nutt JG, Woodward WR, Carter JH, Gancher ST, Effect of longterm therapy on the pharmacodynamics of levodopa. Relation to on-off phenomenon, Arch Neurol, 1992;49:1123–30.
  35. Metman LV, van den Munckhof P, Klaassen AA, et al., Effects of supra-threshold levodopa doses on dyskinesias in advanced Parkinson’s disease, Neurology, 1997;49:711–3.
  36. Mouradian MM, Juncos JL, Fabbrini G, et al., Motor fluctuations in Parkinson’s disease: central pathophysiological mechanisms, Part II, Ann Neurol, 1988;24:372–8.
  37. Ahlskog JE, Muenter MD, Frequency of levodopa-related dyskinesias and motor fluctuations as estimated from the cumulative literature, Mov Disord, 2001;16:448–58.
  38. Schrag A, Ben-Shlomo Y, Brown R, et al., Young-onset Parkinson’s disease revisited – clinical features, natural history, and mortality, Mov Disord, 1998;13:885–94.
  39. Grosset KA, Bone I, Grosset DG, Suboptimal medication adherence in Parkinson’s disease, Mov Disord, 2005;20:1502–7.
  40. Grosset KA, Reid JL, Grosset DG, Medicine-taking behavior: implications of suboptimal compliance in Parkinson’s disease, Mov Disord, 2005;20:1397–404.
  41. Kulkarni AS, Balkrishnan R, Anderson RT, et al., Medication adherence and associated outcomes in medicare health maintenance organization-enrolled older adults with Parkinson’s disease, Mov Disord, 2008;23:359–65.
  42. Tarrants ML, Denarie MF, Castelli-Haley J, et al., Drug therapies for Parkinson’s disease: A database analysis of patient compliance and persistence, Am J Geriatr Pharmacother, 2010;8:374–83.
  43. Davis KL, Edin HM, Allen JK, Prevalence and cost of medication nonadherence in Parkinson’s disease: evidence from administrative claims data, Mov Disord, 2010;25:474–80.
  44. Schuepbach WM, Rau J, Knudsen K, et al., Neurostimulation for Parkinson’s disease with early motor complications, N Engl J Med, 2013;368:610–22.
  45. Evans AH, Kettlewell J, McGregor S, et al., A conditioned response as a measure of impulsive-compulsive behaviours in Parkinson’s disease, PLoS One, 2014;9:e89319.
  46. Schrag A, Hovris A, Morley D, et al., Caregiver-burden in Parkinson’s disease is closely associated with psychiatric symptoms, falls, and disability, Parkinsonism Relat Disord, 2006;12:35–41.
  47. Schrag A, Jahanshahi M, Quinn N, How does Parkinson’s disease affect quality of life? A comparison with quality of life in the general population, Mov Disord, 2000;15:1112–8.
  48. Martinez-Martin P, Rodriguez-Blazquez C, Kurtis MM, Chaudhuri KR; NMSS Validation Group, The impact of nonmotor symptoms on health-related quality of life of patients with Parkinson’s disease, Mov Disord, 2011;26:399–406.
  49. Martinez-Martin P, The importance of non-motor disturbances to quality of life in Parkinson’s disease, J Neurol Sci, 2011;310:12–6.
  50. Martinez-Martin P, Jeukens-Visser M, Lyons KE, et al., Healthrelated quality-of-life scales in Parkinson’s disease: critique and recommendations, Mov Disord, 2011;26:2371–80.
  51. Chaudhuri KR, Healy DG, Schapira AH, Non-motor symptoms of Parkinson’s disease: diagnosis and management, Lancet Neurol, 2006;5:235–45.
  52. Keranen T, Kaakkola S, Sotaniemi K, et al., Economic burden and quality of life impairment increase with severity of PD, Parkinsonism Relat Disord, 2003;9:163–8.
  53. Evans AH, Farrell MJ, Gibson SJ, et al., Dyskinetic patients show rebound worsening of affect after an acute L-dopa challenge, Parkinsonism Relat Disord, 2012;18:514–9.
  54. Post B, Merkus MP, de Bie RM, et al., Unified Parkinson’s disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable? Mov Disord, 2005;20:1577–84.
  55. Langston JW, Widner H, Goetz CG, et al., Core assessment program for intracerebral transplantations (Capit), Mov Disord, 1992;7:2–13.
  56. Steg G, Johnels B, Physiological mechanisms and movement analysis in Parkinson’s disease, Mol Neurobiol, 1994;9:143–7.
  57. Johnels B, Ingvarsson PE, Thorselius M, et al., Disability profiles and objective quantitative assessment in Parkinson’s disease, Acta Neurol Scand, 1989;79:227–38.
  58. Zackrisson T, Bergquist F, Holmberg B, et al., Evaluation of the objective posturo-locomotor-manual method in patients with parkinsonian syndromes, Front Neurol, 2013;4:95.
  59. Ingvarsson PE, Gordon AM, Forssberg H, Coordination of manipulative forces in Parkinson’s disease, Exp Neurol, 1997;145:489–501.
  60. Neely KA, Planetta PJ, Prodoehl J, et al., Force control deficits in individuals with Parkinson’s disease, multiple systems atrophy, and progressive supranuclear palsy, PLoS One, 2013;8:e58403.
  61. Maetzler W, Domingos J, Srulijes K, et al., Quantitative wearable sensors for objective assessment of Parkinson’s disease, Mov Disord, 2013;28:1628–37.
  62. Papapetropoulos SS, Patient diaries as a clinical endpoint in Parkinson’s disease clinical trials, CNS Neurosci Ther, 2012;18:380–7.
  63. Antonini A, Martinez-Martin P, Chaudhuri RK, et al., Wearing-off scales in Parkinson’s disease: critique and recommendations, Mov Disord, 2011;26:2169–75.
  64. Hauser RA, Deckers F, Lehert P, Parkinson’s disease home diary: further validation and implications for clinical trials, Mov Disord, 2004;19:1409–13.
  65. Stone AA, Shiffman S, Schwartz JE, et al., Patient noncompliance with paper diaries, BMJ, 2002;324:1193–4.
  66. Stone AA, Shiffman S, Capturing momentary, self-report data: a proposal for reporting guidelines, Ann Behav Med, 2002;24:236–43.
  67. Horak FB, Mancini M, Objective biomarkers of balance and gait for Parkinson’s disease using body-worn sensors, Mov Disord, 2013;28:1544–51.
  68. Pan W, Ohashi K, Yamamoto Y, Kwak S, Power-law temporal autocorrelation of activity reflects severity of parkinsonism, Mov Disord, 2007;22:1308–13.
  69. Katayama S, Actigraph analysis of diurnal motor fluctuations during dopamine agonist therapy, Eur Neurol, 2001;46 (Suppl. 1):11–7.
  70. Griffiths RI, Kotschet K, Arfon S, et al., Automated assessment of bradykinesia and dyskinesia in Parkinson’s disease, J Parkinsons Dis, 2012;2:47–55.
  71. Mera TO, Heldman DA, Espay AJ, et al., Feasibility of homebased automated Parkinson’s disease motor assessment, J Neurosci Methods, 2012;203:152–6.
  72. Salarian A, Russmann H, Wider C, et al., Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system’, IEEE Trans Biomed Eng, 2007;54:313–22.
  73. Keijsers NL, Horstink MW, Gielen SC, Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks, Mov Disord, 2003;18:70–80.
  74. Tsipouras MG, Tzallas AT, Rigas G, et al., Automated levodopainduced dyskinesia assessment, Conf Proc IEEE Eng Med Biol Soc, 2010;2010:2411–4.
  75. Tsipouras MG, Tzallas AT, Fotiadis DI, Konitsiotis S, On automated assessment of levodopa-induced dyskinesia in Parkinson’s disease, Conf Proc IEEE Eng Med Biol Soc, 2011;2011:2679–82.

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