Plan for today

  • Dementia
    • Overview
    • Determinants
    • Linking procrastination
  • Markov models
    • Using Markov models for dementia
    • Data analysis
  • Conclusions

Dementia & Procrastination

Dementia & Procrastination

What is dementia

  • Syndrome of progressive, usually irreversible cognitive decline
  • Includes Alzheimer’s, vascular, Lewy body types
  • Affects millions worldwide1
  • Focus on modifiable risks for prevention

Dementia & Procrastination

The Role of Mild Cognitive Impairment

  • Cognitive changes beyond normal aging - memory lapses, poor decisions
  • Often precedes dementia2–4
  • Early detection crucial

Dementia & Procrastination

Apathy vs. Procrastination

  • Apathy
    • Loss of motivation
    • Nearly doubles dementia risk5
  • Procrastination
    • Intentional delay despite intent
    • Signals executive function breakdown

Research question

Could procrastination signal broader cognitive and motivational decline?

Markov Models

Markov Models

Core Concept

To analyze longitudinal patterns of cognitive decline, we used a discrete-time, first-order Markov model.

  • The model hinges on the Markov property:
    • Let \(X_t\) denote an individual’s cognitive state at time \(t\), where \(X_t \in S = \{1, 2, \dots, K\}\).
    • Transition to state \(j\) at \(t + 1\) depends only on state \(i\) at \(t\).

Markov property

\[P(X_{t+1} = j \vert X_t = i, X_{t-1} = i_{t-1}, \dots X_0 = i_0) = P(X_{t+1} = j \vert X_t = i)\]

Markov Models

Transition Matrices

These probabilities, denoted \(p_{ij}\) are called transition probabilities.

\[ P = \begin{bmatrix} p_{11} & \cdots & p_{1K} \\ \vdots & \ddots & \vdots \\ p_{K1} & \cdots & p_{KK} \\ \end{bmatrix} \]

Formally: \(p_{ij} = P(X_{t+1} = j \vert X_t = i)\)

Markov Models

Discrete Time Models

  • We capture time-varying transition probabilities using multinomial logistic regression.
  1. Model Specification (for \(K=3\) states with state \(K\) as reference):
    • Log-odds of transitioning to state \(j\) vs. state \(K\):

\[ log \left( \frac{P(Y = j \vert x)}{P(Y = k \vert x)} \right) = \underbrace{\alpha}_{\text{intercept}} + \underbrace{\beta_j^T \:X}_{\text{covariate effects}} \qquad \text{for } j = 1, \dots K-1 \]

Data Analysis

Data Analysis

Our Dataset

Figure 1: Cognitive transitions across time

Data Analysis

Modelling

fit_a <- nnet::multinom(
  formula = status ~ Gender + Education_tri + Depression + (Age * Total_p) + status_prev, 
  data = data_stack, family = multinomial, trace = FALSE)

fit_b <- nnet::multinom(
  formula = status ~ Gender + Education_tri + Depression + (Age * Total_p) + status_prev, 
  data = data_stack |> mutate(status = relevel(status, ref = "2")), 
  family = multinomial, trace = FALSE)

fit_results <- rbind(tidy_output(fit = fit_a), tidy_output(fit = fit_b))

fit_results |> head() |> output_model()
transition term estimate p.value conf.low conf.high
NC - MCI Being female 0.941 0.703 0.690 1.284
NC - MCI High school degree vs. No education 0.446 0.000 0.360 0.551
NC - MCI Further education vs. No education 0.310 0.000 0.264 0.363
NC - MCI Depression 1.147 0.000 1.067 1.233
NC - MCI Age 1.008 0.006 1.002 1.014
NC - MCI Procrastination (2020) 0.921 0.000 0.882 0.962

Data Analysis

Odds Ratios

Figure 2: Odds Ratio from model

Data Analysis

Predictions

Figure 3: Predicted transition probabilities for different age cohorts

Conclusions

  • Preliminary evidence that procrastination may function as an early behavioural marker of cognitive decline, particularly in older age
  • As a modifiable and measurable construct, procrastination holds promise as a target for early detection and preventative intervention

References

1. Nichols, E., Steinmetz, J. D., Vollset, S. E., Fukutaki, K., Chalek, J., Abd-Allah, F., Abdoli, A., Abualhasan, A., Abu-Gharbieh, E., Akram, T. T., et al. (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the global burden of disease study 2019. The Lancet Public Health, 7(2), e105–e125. https://doi.org/10.1016/S2468-2667(21)00249-8
2. Cooper, C., Sommerlad, A., Lyketsos, C. G., & Livingston, G. (2015). Modifiable predictors of dementia in mild cognitive impairment: A systematic review and meta-analysis. American Journal of Psychiatry, 172(4), 323–334. https://doi.org/10.1176/appi.ajp.2014.14070878
3. Shigemizu, D., Akiyama, S., Higaki, S., Sugimoto, T., Sakurai, T., Boroevich, K. A., Sharma, A., Tsunoda, T., Ochiya, T., Niida, S., & Ozaki, K. (2020). Prognosis prediction model for conversion from mild cognitive impairment to alzheimer’s disease created by integrative analysis of multi-omics data. Alzheimer’s Research & Therapy, 12, 1–12. https://doi.org/10.1186/s13195-020-00716-0
4. Tschanz, J., Welsh-Bohmer, K., Lyketsos, C., Corcoran, C., Green, R. C., Hayden, K., Norton, M. C., Zandi, P., Toone, L., West, N., et al. (2006). Conversion to dementia from mild cognitive disorder: The cache county study. Neurology, 67(2), 229–234. https://doi.org/10.1212/01.wnl.0000224748.48011.84
5. Dalen, J. W. van, Wanrooij, L. L. van, Charante, E. P. M. van, Brayne, C., Gool, W. A. van, & Richard, E. (2018). Association of apathy with risk of incident dementia: A systematic review and meta-analysis. JAMA Psychiatry, 75(10), 1012–1021. https://doi.org/10.1001/jamapsychiatry.2018.1877

Wrapping up

Thank you

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