PhD: Individually tailored digital-motor outcomes in real life
First annual review

Medicine Made to Measure (MMM)

Individualized ASOs for nano-rare mutations:
Trial design challenges

• ASO treatment is taylor-made for the mutation

• Tiny number of patients worldwide (<30)
• Traditional clinical trial designs infeasible

• ASO treatment has slow, disease-modifying effect
• Traditional N-of-1 trial designs infeasible

Spinocerebellar Ataxia (SCA)

• Inherited neurodegenerative disease
• >40 identified subtypes
• Rare disease: ≤5 people per 100,000
• Affects the cerebellum and spinal cord

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Digital motor outcomes (DMOs)

Main dataset

• Gait recordings from Tübingen University Hospital
• 27 genetic spinocerebellar (pre-)ataxia patients
• 36 healthy controls
• Yearly follow-ups over several years
• 3 inertial measurement units (IMUs): Feet and trunk
• Lab-based & real-life walking

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

• Other outcomes and covariates:
• Clinician-/patient-rated scores (SARA, ABC, …)
• Basic demographics, anthropometrics, affected gene, …

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.

Data: Per-segment level

Data: Per-visit level

Objectives

Enable single-patient ASO trial design, leveraging the large number of DMOs and walking segments in lieu of large N.

1. Identify robust and informative DMOs
• under environment/context-aware analysis
2. Model disease severity longitudinally as a latent variable
• To predict individual disease progression
3. Perform a prospective validation study
• (approx. 10 patients, 1 year duration)

Extracting additional DMOs

Context stratification

Keywords	Covariate	Threshold
slow / fast	Gait speed	1.2m/s
sporadic / continuous	Number of steps in a 1-minute window	45
straight / curvy	Number of turns in a 1-minute window	1
short / long	Walking bout duration	30s

Cross-sectional testing

Longitudinal paired-samples testing

Outcome ranking

outcome	ess	rb_lgt			rb_crs			ρ_ΔΔ_2y		spread
		1y	2y	3y	sym	sara<8	sara≥8	sara	-abc
test_dw_instance_compound_5	39	.86	.75	.96	.68	.28	.67	.32	.12
test_dw_instance_compound_3	45	.82	.66	.93	.59	.18	.62	.26	.26
test_dw_instance_compound_2	55	.77	.78	.78	.49	.36	.48	.58	.21
test_dw_instance_compound_4	66	.79	.65	.36	.75	.48	.52	.33	.2
agg_adjacent_swings_resampled_sensor_lumbar_acc_x_r_d1_abs_μ /σ /curvy_long/μ	74	.71	.45	.76	.52	.32	.46	-.5	-.06	.47
agg_adjacent_swings_resampled_sensor_lumbar_acc_x_r_μ /σ /curvy_long/μ	83	.62	.62	.67	.54	.24	.45	-.32	.0055	.45
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_μ /σ /long /μ	86	.68	.46	.54	.5	.066	.42	-.036	.2	.52
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_d1_abs_μ/σ /long /μ	88	.72	.4	.41	.49	.09	.4	.062	.32	.57
agg_adjacent_swings_resampled_sensor_lumbar_acc_x_r_abs_μ /σ /curvy_long/μ	88	.58	.66	.65	.54	.24	.45	-.3	.21	.42
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_d3_abs_μ/σ /long /μ	89	.71	.42	.38	.48	.067	.41	.11	.093	.54
stance_duration_μ /cv/curvy_long/μ	90	.65	.76	.38	.63	.29	.52	.25	-.43	.45
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_d2_abs_μ/σ /long /μ	93	.71	.44	.38	.48	.067	.4	.11	.21	.54
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_d1_abs_μ/μ /long /μ	94	.62	.52	.43	.54	.12	.42	.16	.31	.44
double_support_apdm_μ /σ /curvy_long/μ	95	.69	.46	.49	.72	.42	.46	.13	-.37	.36
coeff_swings_sensor_acc_dft_x_1_abs_μ /cv/long /μ	95	.76	.51	.43	.71	.43	.44	.1	.044	.41
agg_adjacent_swings_resampled_sensor_lumbar_acc_x_r_abs_d2_abs_μ/μ /curvy_long/μ	96	.66	.5	.56	.63	.32	.49	-.24	.088	.42
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_d3_abs_μ/μ /long /μ	97	.65	.49	.43	.54	.12	.42	.24	.35	.46
swing_apdm_μ /cv/curvy_long/μ	98	.72	.58	.49	.7	.36	.51	.15	-.32	.36
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_d2_abs_μ/μ /long /μ	98	.64	.51	.41	.53	.11	.42	.22	.31	.43
agg_adjacent_swings_resampled_sensor_lumbar_acc_z_r_abs_d3_abs_μ/γ /short /μ	98	-.69	-.53	-.41	-.48	-.28	-.48	-.57	-.15	.69
agg_adjacent_swings_resampled_sensor_lumbar_acc_x_r_abs_d1_abs_μ/σ /curvy_long/μ	100	.66	.45	.69	.51	.31	.44	-.49	.071	.42
initial_plus_mid_swing_apdm_μ /cv/curvy_long/μ	100	.63	.69	.6	.67	.42	.42	-.097	.082	.38
agg_adjacent_swings_resampled_sensor_lumbar_gyr_z_r_abs_μ /cv/long /μ	100	.57	.56	.47	.5	.072	.41	.1	.15	.57
gait_speed_apdm_μ /σ /curvy_long/μ	100	.57	.73	.34	.44	.21	.45	.39	0	.42
(…)
sara_sim_1	103	.53	.56	.76	1	.85	1	1	.077
(…)

Other activities

Periods abroad

Training & conferences

• University courses:
• • Generalized linear latent variable models (Prof. Silvia Cagnone)
  • Models for panel data (Prof. Luca Trapin)
  • Advanced time series analysis (Prof. Alessandra Luati)

• Conferences:
• MobEx (Bologna, Jan 2025)

• Workshops & summer schools:
• EURORDIS Open Academy (Barcelona, Jun 2024)
• Uppsala Pharmacometric Summer School (Uppsala, Aug 2025)
• MMM training workshop: “Rare Neurological Diseases: Clinical and Genetic Background, Research and Methodologies” (online, Sep 2024)
• AstraZeneca training school: “The guide to antisense therapy development with a focus on individualized treatments” (Göteborg, Apr 2025)
• FITT gGmbH Business Training:
• “Project management” (Barcelona, Jun 2024)
• “Self-marketing” (Tübingen, Dec 2024)
• “Innovation incubator” (Tübingen, Dec 2024)
• “Business skills” (Göteborg, Apr 2025)
• Grasshopper Films GbR Media Training (online, spring 2025)

Prospective study

Postponed:

• Further analysis crucial to determine optimal protocol:
• Single-sensor pipeline ()
• Robust statistical modeling

• Personnel allocation uncertain in Tübingen
• Tübingen collaborates with Pisa:
• Parallel data collection there?
• Suggestions welcome!

Disease progression model

In progress:

1.	Outcome pre-selection Screening/ranking, continually refined Machine learning (e.g. random forest)
2.	Cross-sectional latent disease severity model Relate outcomes to true disease severity Further narrow down most informative outcomes
3.	Longitudinal disease progression model Predict untreated disease course Estimate trial parameters

Sensor configurations

SARA score

Disease onset estimates

Data: Per-visit level

Outcome ranking: Metrics

ess	1-year estimated sample size (pooled from 1-, 2-, 3-year)
rb_lgt	Longitudinal effect sizes (Wilcoxon signed-rank rank biserial)
rb_crs	Cross-sectional effect sizes (Mann-Whitney rank biserial)
ρ_ΔΔ_2y	2-year change correlation (Spearman)
spread	Measure of within-visit variation

Outcome ranking: Filtering

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.

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\[ \min_\beta \sum_{i, j} \left( \left( t_{i j} - \bar{t}_{i \cdot} \right) \cdot I_{\mathrm{symptomatic}}(i) - \sum_{k} \left( y_{k i j} - \bar{y}_{k i \cdot} \right) \cdot \beta_k \right)^2 + \Omega(\beta) \]

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