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@ARTICLE{Ophey:155824,
      author       = {Ophey, Anja and Rehberg, Sarah and Giehl, Kathrin and
                      Eggers, Carsten and Reker, Paul and Eimeren, Thilo and
                      Kalbe, Elke},
      title        = {{P}redicting {W}orking {M}emory {T}raining {R}esponsiveness
                      in {P}arkinson's {D}isease: {B}oth '{S}ystem {H}ardware' and
                      {R}oom for {I}mprovement {A}re {N}eeded.},
      journal      = {Neurorehabilitation and neural repair},
      volume       = {35},
      number       = {2},
      issn         = {1552-6844},
      address      = {Thousand Oaks, Calif.},
      publisher    = {Sage},
      reportid     = {DZNE-2021-00984},
      pages        = {117 - 130},
      year         = {2021},
      abstract     = {Background. Patients with Parkinson's disease (PD) are
                      highly vulnerable to develop cognitive dysfunctions, and the
                      mitigating potential of early cognitive training (CT) is
                      increasingly recognized. Predictors of CT responsiveness,
                      which could help to tailor interventions individually, have
                      rarely been studied in PD. This study aimed to examine
                      individual characteristics of patients with PD associated
                      with responsiveness to targeted working memory training
                      (WMT). Methods. Data of 75 patients with PD (age: 63.99 ±
                      9.74 years, $93\%$ Hoehn $\&$ Yahr stage 2) without
                      cognitive dysfunctions from a randomized controlled trial
                      were analyzed using structural equation modeling. Latent
                      change score models with and without covariates were
                      estimated and compared between the WMT group (n = 37), who
                      participated in a 5-week adaptive WMT, and a waiting list
                      control group (n = 38). Results. Latent change score models
                      yielded adequate model fit (χ2-test p > .05, SRMR ≤ .08,
                      CFI ≥ .95). For the near-transfer working memory
                      composite, lower baseline performance, younger age, higher
                      education, and higher fluid intelligence were found to
                      significantly predict higher latent change scores in the WMT
                      group, but not in the control group. For the far-transfer
                      executive function composite, higher self-efficacy
                      expectancy tended to significantly predict larger latent
                      change scores. Conclusions. The identified associations
                      between individual characteristics and WMT responsiveness
                      indicate that there has to be room for improvement (e.g.,
                      lower baseline performance) and also sufficient 'hardware'
                      (e.g., younger age, higher intelligence) to benefit in
                      training-related cognitive plasticity. Our findings are
                      discussed within the compensation versus magnification
                      account. They need to be replicated by methodological
                      high-quality research applying advanced statistical methods
                      with larger samples.},
      keywords     = {Age Factors / Aged / Cognitive Dysfunction: etiology /
                      Cognitive Dysfunction: physiopathology / Cognitive
                      Dysfunction: rehabilitation / Cognitive Remediation / Female
                      / Humans / Intelligence: physiology / Male / Memory,
                      Short-Term: physiology / Middle Aged / Neuronal Plasticity:
                      physiology / Outcome Assessment, Health Care / Parkinson
                      Disease: complications / Parkinson Disease: physiopathology
                      / Parkinson Disease: rehabilitation / Precision Medicine /
                      Prognosis / Psychomotor Performance: physiology /
                      Single-Blind Method / Therapy, Computer-Assisted /
                      Parkinson’s disease (Other) / cognitive training (Other) /
                      precision medicine (Other) / predictors of training
                      responsiveness (Other) / structural equation modeling
                      (Other) / working memory (Other)},
      cin          = {Patient Studies (Bonn)},
      ddc          = {610},
      cid          = {I:(DE-2719)1011101},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33410387},
      doi          = {10.1177/1545968320981956},
      url          = {https://pub.dzne.de/record/155824},
}