PhD: Individually tailored digital-motor outcomes in real life

Medicine Made to Measure (MMM)

• Doctoral network
• Funded by the Marie Curie Actions Scheme
• 10 PhD candidates
• 22 participating organisations

• Aim: to develop
• Antisense oligonucleotide treatments (ASOs)
• Tailored to single patients with nano-rare disease mutations

Rare diseases

• A rare disease is any disease with a prevalence of <0.05%.

• There are >6000 rare diseases.

• Taken together, their prevalence is ~4%.

• >70% have genetic causes.

• Nano-rare mutations: Worldwide <30 patients.

(approximate numbers, for Europe)

“rare individually, common collectively”

Supervisors

Dr. Sabato Mellone
University of Bologna

Prof. Dr. Matthis Synofzik
University of Tübingen

Prof. Dr. Mats Karlsson
University of Uppsala

Co-supervisors

Carlo Tacconi
mHealth Technologies s.r.l., Bologna

Prof. Dr. Lorenzo Chiari
University of Bologna

Secondments

• Uppsala, Sweden
• Aug 2025 - Sep 2025

• Tübingen, Germany
• Sep 2024 - Nov 2024
• Three more months in the coming years

Individualized ASOs for nano-rare mutations:
Trial design challenges

• ASO treatment is taylor-made for the mutation
• Tiny number of patients worldwide
• ASO treatment has slow, disease-modifying effect

• Traditional clinical trial designs infeasible
• Traditional N-of-1 trial designs infeasible

Spinocerebellar Ataxia (SCA)

• Inherited neurodegenerative disease
• Affects the cerebellum and spinal cord
• Rare disease: ≤5 people per 100,000

PhD Objective

• Robust assessment of motor performance in genetic ataxias
• Based on body-worn sensor recordings in real-life settings
• Sensitive to moderate-term change in a single patient

Deliverables

Inertial measurement units

• Can be attached to various body parts
• Measure movement
• Accelerometer
• Gyroscope
• (Magnetometer, Barometer, …)

IMU hardware suppliers:

	mHealth Technologies
	APDM
	Any smartphone can be used as IMU

Sensor configurations

Lower back and feet

Lower back only

Gait analysis pipelines

Software	Mobility Lab	gaitmap	mobgap
Organization
Configuration	Feet & trunk	Feet only	Trunk only
Open source	✗	✓	✓
Validated*	✓	✗	✓
Stride DMOs	58	12	4

*Note:
Clinical validation is outcome- and population-specific.
Only Mobility Lab has been validated in ataxias.

Gait measures

Gait cycle analysis

Double support

Spatial and temporal analysis

Elevation at mid-swing

Foot contact angles

Circumduction

Lateral step variability

Gait cycle analysis

Double support

Spatial and temporal analysis

Elevation at mid-swing

Foot contact angles

Circumduction

Lateral step variability

Walking bouts

• Continuous sequences of steps
• Delimited by periods of non-walking

Walking segments

Split long walking bouts evenly into segments ≥25 strides.

• Better temporal resolution and homogeneity

Additional features

Stride-to-stride comparisons

Discrete transform coefficients

Complexity measures

Feature aggregation

• Result: Many candidate outcomes, each with one value per subject and session/condition

Feature transformation

Transform features on the per-stride, per-segment, or per-session/condition level.

Environmental context effects

• Real-life context factors are known to affect gait features
• Indoors vs. outdoors
• Type or absence of shoes
• Slope, stairs
• Ground texture
• Weather
• …

• Exogenous variables, introducing variance
• Estimateable to some degree from signal-derived proxies

Context stratification

Context compensation

Dataset

• Gait recordings from Tübingen University Hospital
• 27 genetic spinocerebellar (pre-)ataxia patients
• 36 healthy controls
• Lab-based & real-life
• Yearly follow-ups over several years

• Patients further categorized into:
• 6 pre-symptomatic (SARA 2 ± 1)
• 21 symptomatic (SARA 10 ± 3)

• In total:
• ~200 visits
• ~20000 walking segments
• ~570,000 gait cycles

Other potential datasets

• PROSPAX
• Lab-based movement recordings only
• ≥74 ARSACS (spastic ataxi) patients
• ≥113 SPG7 (spastic paraplegia) patients
• ≥50 healthy controls

• Planned prospective study
• ≥10 ataxia (SCA or AT) patients

Outcomes

• Clinician- / patient-rated scores (per visit)
• SARA
• ABC
• Others, but incomplete (SPRS, INAS, PGI, ADL)

• Digital motor outcomes
• Large space of combinations, small fraction suitable

Further covariates

• Demographics (age, gender)
• Diagnosis / gene affected
• Anthropometrics at first visit (weight, height, handedness, …)
• Estimated age of disease onset (only 19 of 27 patients)

SARA score

ABC score

• Traditionally 16 items
• In the Tübingen dataset: extended to 41 items

Disease onset estimates

Tezenas du Montcel et al. 2014

Data: Per-visit level

SARA scores in healthy subjects simulated (based on Shaafi Kabiri et al. 2018)

Data: Per-visit level

Data: Per-visit level

Qualitative characteristics

• Generally constant over time in the absence of disease
• Effect of age, if present, is small
• Roughly linear rise at disease onset
• Theoretical saturation not/barely represented in the dataset
• Clinical scales bounds not reached
• DMOs said to saturate around SARA 12-15

Data: Per-segment level

Cross-sectional testing

(lab based)

(real life)

Longitudinal paired-samples testing (Seemann et al.)

Seemann et al. 2025

Longitudinal paired-samples testing (Seemann et al.)

Seemann et al. 2025

Pooled sample size estimates

ESSs are unstable under varying:
• Year pair selection criteria
• Time between baseline and follow-up year

• Pool 1-, 2-, and 3-year effect sizes
• With inverse variance weighting
• Pre-normalize to 1-year by linear interpolation
• Assumes outcomes change linearly on average

Outcome ranking

Outcome ranking (mobgap)

Outcome ranking (gaitmap)

TODO: Incorporate gaitmap

Outcome ranking: Metrics

Outcome ranking: Filtering

Panel model

\[ y_{ij} = \beta_1\mathrm{BOn}_{ij} + \beta_2\mathrm{BSat}_{ij} + \beta_3\mathrm{BOn}_{ij}\left(\mathrm{EDO}_\mathrm{o}\right)_{i} + \beta_4\mathrm{BOn}_{ij}\mathrm{Gender}_{i} + \beta_4\mathrm{BOn}_{ij}\mathrm{BMI}_{i} \]

• Fixed-effects panel model (= linear model with de-meaning)
• Done as a course exercise
• Non-parametric bootstrap & Monte Carlo methods employed

Fit results

Simulating outcome data

Simulating outcome data

Assumption violations

Why composite measures?

• Many candidate outcomes, capturing variance from:
• Various aspects of disease severity
• Various nuisance factors

• Combining them into a composite score could:
• Leverage orthogonal information from different features
• Average out some of the unwanted variance
• Similar to a test battery for a clinical score

Optimized composite measures

• Idea: Formulate the search for the ideal combination of outcomes as an optimization problem.
• E.g. Find linear combination that minimizes some loss.
• Loss: E.g. Composite change vs. disease time change.

• Result \(\theta = \beta y\) is a generated regressor – likely suboptimal compared to a jointly estimated latent variable model.

Factor analysis

• The “original” latent variable model
• Distills a set of observed variables into a few conceptual factors

Coni et al. 2019

• Pilot test on visit level resulted in subpar composites

Cross-sectional latent disease severity modeling

• Model disease severity itself
• Using a non-linear, single-factor, latent variable model

1. Cross-sectionally, on the segment level
2. Use established relationships between features and severity for a longitudinal model

• This has been done with success recently by Hamdan et al.
• On lab-based, visit-level features + SARA score

Item characteristic curves (Hamdan et al.)

Hamdan et al. 2024

Fisher information (Hamdan et al.)

Hamdan et al. 2024

ccIRT model performance (Hamdan et al.)

Hamdan et al. 2025

ccIRT model performance (Hamdan et al.)

Hamdan et al. 2025

Model overview

• Bayesian non-linear single-factor model
• “Cross-sectional”, i.e. considers each visit as an individual
• Relates segment-level features to the latent disease severity
• By fitting a non-linear function per feature
• No incorporation of clinical scores

\[ y_{ij} = f\left(s_{v(i)}, \gamma_j\right) \, \beta_j + \alpha_j + \epsilon_{ij} \qquad \epsilon_{ij} \sim t_\nu\left(0, \sigma^2_j\right), \, s_v \sim \mathcal{N}(0, 1) \]

Choice of \(f\)

\[ \begin{align} \mathrm{exprel}(x) &= \frac{e^x - 1}{x} \\ \mathrm{tilt}(x, \gamma) &= x \, \mathrm{exprel}(\gamma x) \\ f(x, \gamma) &= \mathrm{tilt}(x, \gamma) \, \mathrm{scale}(\gamma) + \mathrm{offset}(\gamma) \end{align} \]

Where \(\mathrm{scale(\gamma)}\) and \(\mathrm{offset}(\gamma)\) are deterministic functions of \(\gamma\) chosen such that \[ x \sim \mathcal{N}(0, 1) \Rightarrow \mathrm{E}\left[f(x, \gamma)\right] = 0, \mathrm{Var}\left[f(x, \gamma)\right] = 1 \]

• \(\gamma\) becomes a shape parameter that does not affect the offset and scale of \(x\)

Choice of \(f\)

Choice of residual prior

\[ \epsilon_{ij} \sim t_\nu\left(0, \sigma^2_j\right) \]

Where

And e.g. \[ \mu_{\mu_\sigma} = 0.8 \qquad \sigma_{\mu_\sigma} = 1.5 \qquad \mu_{\sigma_\sigma} = 0.3 \qquad \sigma_{\sigma_\sigma} = 1.6 \]

• Hierarchical prior with feature-specific scale
• \(t\)-distributed residuals for robustness against outliers

Choice of parameter priors

\[ \begin{align} \alpha_j &\sim \mathcal{N}(0, \sigma^2_\alpha) \\ \beta_j &= \mathrm{softplus}(\eta_j, \delta) \qquad \eta_j \sim \mathcal{N}(\mu_\eta, \sigma^2_\eta) \\ \gamma_j &\sim \mathcal{N}(0, \sigma^2_\gamma) \end{align} \]

Where e.g. \[ \sigma_\alpha = 0.5 \qquad \mu_\eta = -1 \qquad \sigma_\eta = 0.7 \qquad \sigma_\gamma = 0.5 \qquad \delta = 1 \]

And \[ \mathrm{softplus}(x, \delta) = \max(x, 0) + \log\left(1 + e^{-\frac{|x|}{\delta}}\right) \, \delta \]

softplus function

Latent disease progression model

• Genetics-based disease onset estimates as priors?
• Pre-symptomatic patients? (censoring?)
• Outcomes not linear in \(\theta\)?
• Context covariates? (let them affect variance of \(\epsilon\)?)

TODO: Update this draft