International Journal of Medical and All Body Health Research

Personalized Exercise Prescription in Neuro Physiotherapy: Current Evidence and Future Perspectives

Dr. Jagdish Jaiswal1, Dr. Swapnil Khare2

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Version of record

This is an author-deposited copy. The version of record was originally published elsewhere: Originally published in International Journal of Medical and All Body Health Research, Volume 7, Issue 3, 2026. DOI 10.54660/IJMBHR.2026.7.3.147-155 . Original source: https://www.allmedicaljournal.com/archives/year-2026.vol-7.issue-3.

Abstract

Exercise is central to neurological rehabilitation, yet standardized, one-size-fits-all prescriptions frequently fail to reflect the heterogeneity of impairment, disease trajectory, and personal context that characterize conditions such as stroke, Parkinson's disease (PD), multiple sclerosis (MS), spinal cord injury (SCI), and traumatic brain injury (TBI). This narrative review summarizes current evidence on individualized exercise prescription in neuro physiotherapy and identifies the factors that shape exercise selection, progression, and monitoring. A structured search of PubMed, Google Scholar, and Scopus-indexed literature was conducted, yielding evidence from randomized controlled trials, feasibility studies, systematic reviews, and clinical practice guidelines. Across conditions, individualization is most often anchored to physiological baselines - cardiopulmonary exercise testing, heart-rate reserve, one-repetition maximum, and rating of perceived exertion - with re-assessment at fixed intervals to adjust load. Disease-specific staging systems (Hoehn and Yahr stage, Expanded Disability Status Scale, American Spinal Injury Association Impairment Scale) and patient-level modifiers, including fatigue, cognition, comorbidity burden, self-efficacy, and motivation, further shape prescriptions. Wearable sensors, telerehabilitation platforms, and artificial-intelligence-driven decision-support tools increasingly enable continuous, adaptive personalization, though most such tools remain in feasibility or protocol stages and artificial-intelligence-generated programs still show limited precision and safety-biased conservatism compared with expert clinical judgment. Dose parameters - frequency, intensity, time, and progression - remain inconsistently reported across the literature, constraining reproducibility and evidence synthesis. This review recommends standardized dose reporting (aligned with the FITT-VP framework), wider adoption of physiological-threshold-anchored prescription, and cautious, clinician-supervised integration of digital and AI-based tools to advance truly individualized neuro-physiotherapy practice.

Keywords: Personalized exercise prescriptionNeuro physiotherapyDose-responseNeurorehabilitationStrokeParkinson's diseaseMultiple sclerosisTelerehabilitationArtificial intelligence

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1. Introduction

Exercise-based rehabilitation is one of the most consistently recommended interventions across neurological conditions, supported by decades of evidence linking structured physical activity to improved motor function, fitness, cognition, and quality of life in stroke, Parkinson's disease (PD), multiple sclerosis (MS), spinal cord injury (SCI), and traumatic brain injury (TBI)

(Halabchi et al., 2017; Learmonth & Motl, 2021; Ramaswamy et al., 2018) [29, 41, 52]. Yet this population is fundamentally heterogeneous: two people with an identical diagnosis can differ enormously in impairment severity, disease stage, cardiovascular reserve, cognitive status, and fatigue burden. A prescription that is safe and effective for one may be under-dosed or contraindicated for another. This has driven a shift from standardized, protocol-driven programs toward personalized exercise prescription, in which type, intensity, frequency, and progression are deliberately tailored to a person's baseline function and response to training (Cucinella et al., 2025; Wackerhage & Schoenfeld, 2021) [11, 60].

The rationale rests on two converging lines of evidence: dose-response is not linear, so both under- and over-dosing blunt benefit or provoke harm (Winstein et al., 2019) [61]; and inter-individual variability in training response is well documented even within a single condition, meaning fixed programs systematically under-serve some patients while over-taxing others (Xiao & Ren, 2025) [63]. Cardiovascular rehabilitation has already formalized individualized, threshold-anchored prescription as its clinical standard (Hansen et al., 2021) [30]; neuro physiotherapy is following a similar trajectory, but less consistently.

Personalization here operates on three levels: physiological anchoring (objective baseline testing to set individualized training zones; Gravesteijn et al., 2020) [24], patient-level modification (adjusting for disease stage, comorbidity, fatigue, and psychosocial context; Halabchi et al., 2017) [29], and technology-enabled adaptive personalization (wearables, telerehabilitation, and AI/ML tools that adjust load in near real time; Liew et al., 2025; Mo et al., 2026) [42, 45]. These three levels, and their convergence on a single individualized prescription, are shown in Figure 1.

Fig 1: Conceptual framework of personalized exercise prescription in neuro physiotherapy, showing the three levels synthesized in this review - physiological anchoring, patient-level modification, and technology-enabled adaptation - converging on an individualized prescription.

This narrative review has two objectives: (1) to summarize current evidence on individualized exercise prescription across stroke, PD, MS, SCI, and TBI; and (2) to identify the factors that influence exercise selection, progression, and monitoring, highlighting gaps for future research.

2. Methods

This is a narrative review rather than a formal systematic review, given the breadth of synthesizing evidence across five neurological populations alongside qualitative prescription factors. The approach followed four stages (Alsultan et al., 2024) [1]: (1) problem formulation around the two stated objectives; (2) literature search across PubMed/MEDLINE, Google Scholar, and Scopus-indexed sources, combining three concept blocks - exercise-prescription terms (exercise therapy, personalized/individualized exercise, precision rehabilitation), neurological-condition terms (stroke, Parkinson disease, multiple sclerosis, spinal cord injury, traumatic brain injury), and personalization/technology terms (precision medicine, algorithm, biomarker, wearable, dose-response) - combined with AND, supplemented by citation snowballing; (3) data evaluation for relevance to individualized or dose-specific prescription, including RCTs, feasibility studies, cohort studies, protocols, and systematic/network/umbrella reviews; and (4) data analysis, extracting population, FITT-VP intervention characteristics (Figure 2), personalization mechanism, monitoring/adherence method, outcomes, and reported gaps, synthesized thematically given the heterogeneity of designs and outcome measures across the literature (Bishnoi et al., 2022) [5].

Fig 2: The FITT-VP framework (Frequency, Intensity, Time, Type, Volume, Progression) used as the standard data-extraction and reporting structure throughout this review.

3. Results: Current Evidence on Personalized Exercise

3.1. Overview of the evidence base

The literature spans stroke, PD, MS, SCI, and TBI, with a growing share of recent publications addressing AI-assisted and sensor-driven personalization specifically (Liew et al., 2025; Babić et al., 2026) [42, 2]. Study designs range from single-case reports to large network meta-analyses, and dose reporting is inconsistent throughout: an umbrella review of 144 stroke meta-analyses found intensity and progression parameters extractable in only a minority of primary studies (Du et al., 2025) [17], and a broader review of exercise-training principles found progression protocols reported completely in 0 of 25 systematic reviews assessed (Morrison et al., 2026) [46]. This reporting gap recurs throughout the sections below and is revisited in the Discussion.

3.2. Physiological-threshold-anchored prescription

The most consistent form of personalization is anchoring intensity to an individually determined physiological threshold rather than age-predicted values. In MS, trial protocols set resistance-training loads at 60-80% of one-repetition maximum and HIIT targets at 80-95% of measured peak heart rate, re-assessed at the trial midpoint and adjusted for disability level (Gravesteijn et al., 2020; Kupjetz et al., 2023) [24, 38]. In PD, Griffith, McKee, et al. (2025) [25] argue explicitly for cardiopulmonary-exercise-test-based rather than estimated intensity prescription, and a companion trial found that autonomic-dysfunction status altered the functional response to a standardized endurance program, supporting further stratification (Griffith, Mehta, et al., 2025) [26]. Individualized load-setting extends to blood-flow-restriction protocols, which set occlusion pressure itself (typically 50-70% of arterial occlusion pressure) on the rationale that fixed cuff pressures produce inconsistent stimulus across differing limb morphology (Jønsson et al., 2024) [34]. Threshold-anchored dose parameters for each condition are compiled in Table 1 (Section 3.3).

3.3. Condition-specific dose-response evidence

Dose-response relationships have been studied most extensively in stroke: Winstein et al. (2019) [61] argue that many negative upper-limb trials likely reflect insufficient rather than ineffective dosing, and a step-count/aerobic-minute roadmap has been proposed to standardize dose reporting going forward (Peters et al., 2022) [50]. Network meta-analyses further show that different modalities suit different target outcomes - dual-task training for quality of life, resistance and robot-assisted training for sustained walking capacity - supporting a "match modality to outcome" model rather than one optimal prescription (Jiang, Ding, Ma, et al., 2025) [33].

In PD, Bayesian network meta-analyses have begun quantifying optimal dose (in MET-minutes/week) as well as modality for balance and gait outcomes (Fan et al., 2025; Xie et al., 2025) [19, 64], and a prospective cohort study links standardized exercise volume to peripheral biomarkers and brain connectivity (Rotondo et al., 2024) [55]. In MS, dose-response meta-analysis shows training effects scale with dose only up to a point (Corrini et al., 2023) [10], while a descriptive review of MS balance training found components, progression, and intensity so inconsistently reported that true dose-response synthesis was not possible (Brincks et al., 2023) [6] - a direct illustration of the reporting gap from Section 3.1. In SCI, upper-body ergometer rowing significantly improved peak aerobic power but not resting cardiometabolic markers over six months, suggesting the smaller active muscle mass in this population may need a different volume threshold than lower-body-referenced guidelines assume (Hansen et al., 2024) [31]. In TBI/concussion, the sub-symptom-threshold paradigm titrates intensity to the heart rate at which symptoms first emerge rather than to a fixed percentage of predicted maximum (Valaas et al., 2023) [59], and adherence to prescribed dose has itself been linked to faster concussion recovery (Chizuk et al., 2022) [8]. Table 1 summarizes representative FITT-VP parameters across all five conditions.

Table 1: Summary FITT-VP Dose Parameters by Neurological Condition Condition Frequency Intensity Time / Duration Progression Key Sources Stroke 2-5 sessions/week Moderate-vigorous (40- 80% HRR); HIIT protocols increasingly used 20-60 min/session; 3 weeks-6 months total Stepwise increases in load, duration, or task complexity; step-count/aerobic-minute roadmaps Winstein et al. (2019); Lang et al. (2016); Peters et al.

Parkinson's disease 2-7 sessions/week (endurance ~5×/week; strength/balance ~2×/week)

Moderate-vigorous (60- 85% HR reserve/max); CPET-guided where available 30-90 min/session; up to 16 months in long programs Individually tailored goal-setting; dose quantified in MET-min/week in recent network meta-analyses Ramaswamy et al. (2018); Gilby et al. (2025); Fan et al.

Multiple sclerosis 2-3 sessions/week PRT: 60-80% 1RM; HIIT: 80-95% peak HR; continuous training: 60-80% HRmax 16-48 weeks program length; sessions ~20-60 min Load/volume increased every 2 weeks (PRT); intensity re-set from repeat CPET at trial midpoint Gravesteijn et al. (2020); Kupjetz et al. (2023); Riemenschneider et al.

Spinal cord injury Variable; 3-5 sessions/week in supervised protocols Individualized to available active muscle mass; upper-body ergometry common 12–16-week interventions typical Volume/resistance progressed against functional and metabolic markers Hansen et al. (2024); Gorgey et al. (2017); Carey et al.

Traumatic brain injury / concussion 5 sessions/week in aerobic-exercise trials Sub-symptom-threshold: individualized to the heart rate at which symptoms first emerge 30 min/session; 6–12-week programs Prescription updated every ~3 weeks from repeat symptom-limited exercise testing Valaas et al. (2023); Mercier et al. (2020); Chizuk et al.

Note: HRR = heart-rate reserve; HRmax = maximum heart rate; PRT = progressive resistance training; HIIT = high-intensity interval training; 1RM = one-repetition maximum; CPET = cardiopulmonary exercise testing. Ranges reflect the studies reviewed and are illustrative rather than prescriptive; individual dose should be set from patient-specific baseline testing.

3.4. Patient-specific factors influencing exercise selection, progression, and monitoring Beyond physiological thresholds, several patient-level factors consistently shape prescription. Disease staging (EDSS in MS, Hoehn and Yahr in PD, AIS grade in SCI) functions as a de facto personalization variable, with the specific tailoring mechanisms for each disability band identified as a core extraction target in an updated MS guideline protocol (Declerck et al., 2026) [15]. Comorbidity: the PERFORM trial was designed explicitly around people with co-occurring long-term conditions, reflecting that single-diagnosis guidelines poorly serve multimorbid patients (Simpson et al., 2024) [57]. Fatigue is repeatedly identified as a primary dose-limiting factor, particularly in MS, where agility-based training has been compared directly against strength/endurance training for its differential effect on fatigability (Wolf et al., 2023) [62]. Cognition and psychosocial context: human-centered design work found that effective personalization required accounting for social support, cognitive status, depression, and fear of movement, not physiological variables alone (Cucinella et al., 2025) [11]. Self-efficacy and motivation recur as both determinants of, and targets for, personalized prescription, with specific motivational drivers and barriers identified for individual counselling (Cools et al., 2025) [9]. Metabolic status: outside neurology proper, individualized prescriptions incorporating fixed, adaptive, and response factors under a two-way patient-clinician communication model outperform standardized prescriptions for metabolic outcomes (Kim, Choe, & Sung, 2024) [35] - a model directly transferable to neuro-physiotherapy populations with high metabolic-syndrome prevalence. Table 2 summarizes these factors alongside their typical personalization response.

Table 2: Patient-Specific Factors Influencing Exercise Selection, Progression, and Monitoring Factor Clinical Relevance Example Personalization Response Representative Sources Disability severity / disease staging Determines baseline capacity and safety ceiling Stratify prescription by EDSS (MS), Hoehn & Yahr stage (PD), or AIS grade (SCI)

Declerck et al. (2026); Jønsson et al.

Comorbidity / multimorbidity Single-diagnosis guidelines may not fit multimorbid patients Multi-condition-aware protocols (e.g., PERFORM trial) Simpson et al. (2024)

Fatigue Primary dose-limiting factor, especially in MS Modality choice weighted toward lower fatigability (e.g., MAT vs. SET); fatigue as primary outcome Wolf et al. (2023); Bailey et al. (2026) Cognition / psychosocial context Shapes engagement with technology-based and cognitively demanding tasks Adjust for MoCA score, depression, fear of movement, family/social support Cucinella et al. (2025); De Meo et al.

Self-efficacy and motivation Predicts initiation and adherence, independent of physical capacity Personalized, realizable goal-setting; motivational counselling on individual barriers Cools et al. (2025); Gangwani et al.

Metabolic / systemic status Influences intensity tolerance and cardiometabolic risk Two-way communication model allowing patient-selected parameters within a clinician-set range Kim, Choe, and Sung (2024)

Note: EDSS = Expanded Disability Status Scale; AIS = American Spinal Injury Association Impairment Scale; MoCA = Montreal Cognitive Assessment; MAT = multimodal agility-based training; SET = strength and endurance training.

3.5. Technology-enabled personalization: wearables, telerehabilitation, and artificial intelligence The most active area of recent growth is digital technology enabling continuous, adaptive personalization. Wearables close the feedback loop directly: a PD dynamic-cycling system adjusted resistance every three sessions from measured pedaling effort and cadence entropy (Kim, Smith, et al., 2024) [36]. Telerehabilitation achieves outcomes broadly comparable to face-to-face physiotherapy, though intensity monitoring remains its weakest methodological link (Munoz-Tomas et al., 2023) [47]. Digital apps providing personalized exercise videos show a significant positive effect on physical function in MS and PD, though evidence certainty is rated low-to-very-low (Davergne et al., 2022) [13]. Artificial intelligence represents both the frontier of personalization and its greatest current limitation. Liew et al. (2025) [42] set out a roadmap for AI-driven precision neurorehabilitation, while Mo et al. (2026) [45] prospectively tested a machine-learning framework that individualized a twelve-week prescription from BMI and fitness data, reporting superior outcomes to static guidelines (a 23.5% reduction in overweight prevalence). Direct evaluations of generative-AI tools urge caution, however: a systematic review found large language models align with foundational FITT principles but consistently lack the specificity, progression logic, and real-time adaptability of expert prescribers, and in some cases show bias against older adults and people with disability (Puce et al., 2025) [51] - a conclusion echoed by a focused evaluation of GPT-4- generated programs (Dergaa et al., 2023) [16]. Given the narrower safety margins in neuro physiotherapy, AI is currently best treated as a supplement to, not a replacement for, clinician judgment. The assess-prescribe-monitor-adjust cycle common to these technologies is depicted in Figure 3.

Fig 3: The technology-enabled personalization feedback loop: baseline assessment informs an initial prescription, which is continuously monitored (via wearables, apps, or AI-based tools), triggering re-assessment and an adjusted prescription. Table 3: Technology-Enabled Personalization Tools and Current Evidence Maturity Tool Category Function in Personalization Evidence Maturity Representative Sources Wearable sensors (HR monitors, smart insoles, accelerometers)

Continuous monitoring; closes the feedback loop for real-time load adjustment Feasibility to early clinical use Kim, Smith, et al. (2024); D'Arco et al. (2025); Klatt et al. (2026)

Telerehabilitation platforms Remote delivery of individualized exercise with variable supervision Comparable outcomes to face-to-face care in systematic reviews; dose monitoring remains weak Munoz-Tomas et al. (2023); Matamala-Gomez et al. (2020); Özden et al. (2022)

Digital apps / SMS-based reminders Support adherence and deliver personalized exercise video content Positive effect on adherence and function; certainty of evidence low-moderate Davergne et al. (2022); Lang et al.

Machine-learning prescription frameworks Data-driven individualization of dose from fitness/biomarker inputs Promising in controlled cohorts; not yet validated at scale in neuro populations Mo et al. (2026); Ye et al. (2025); Faria et al. (2024)

Generative AI (e.g., large language models)

Free-form prescription generation from patient description Immature: safety-biased, lacks progression precision, shows demographic bias Puce et al. (2025); Dergaa et al.

Biomarker-based subgroup clustering (e.g., HRV)

Stratifies patients into physiological response subgroups Narrower but more clinically mature application of AI Basri & Turki (2025); Ezzdine et al. (2025) Note: HRV = heart rate variability. "Evidence maturity" reflects the study designs and sample sizes reviewed in Section 3.5, not a formal GRADE assessment.

3.6. Adherence, monitoring, and real-world

implementation Personalization is only clinically meaningful if the prescribed dose is actually delivered - and adherence data here are sobering. In the BRiMS feasibility trial for secondary progressive MS, only 21% of participants logged at least 100 of the 120 weekly minutes advised, despite individualized, fortnightly-reviewed prescriptions (Gunn et al., 2019) [27]. Family and caregiver support repeatedly emerge as one of the strongest determinants of whether a prescribed home program is actually completed (Zhang et al., 2023) [66]. A scoping review of adherence in stroke survivors situates these findings within a broader pattern: barriers cluster around programme complexity, motivation, and the practicality of monitoring, suggesting adherence itself should be treated as a personalization target rather than a downstream outcome (Gwynne-Mayer et al., 2025) [28].

4. Discussion

4.1. Personalization is real but unevenly operationalized. The field has moved beyond a single standardized prescription per diagnosis, with physiological-threshold anchoring now common in well-designed trials (Section 3.2). But rigor is far from universal: many feasibility studies describe individualization only qualitatively ("tailored to patient tolerance"), consistent with the broader finding that individualization as a training principle is reported completely in only a small minority of published exercise systematic reviews (Morrison et al., 2026) [46].

4.2. Dose reporting is the field's central methodological

The gap identified in Section 3.1 recurs independently across every condition - in MS balance training (Brincks et al., 2023) [6], the stroke umbrella review (Du et al., 2025) [17], and elsewhere. Without consistent FITT-VP reporting, neither clinicians nor future reviewers can reliably reproduce published "personalized" protocols.

4.3. Patient-specific factors matter as much as

physiological thresholds, but are measured far less consistently.

Fatigue, cognition, comorbidity, and self-efficacy (Section 3.4, Table 2) are routinely cited as modifiers, yet no equivalent standardized instrument exists for weighting them the way heart rate or 1RM anchor physiological dosing. Emerging core-outcome-set initiatives (Ramdharry et al., 2021) [53] are an early step toward closing this gap.

4.4. Artificial intelligence is a genuinely emerging tool, not

Studies showcasing AI's potential (Mo et al., 2026) [45] sit alongside evaluations concluding that generative-AI tools still lack a clinician's specificity and real-time adaptability, and can carry demographic bias (Section 3.5). AI is best treated as a supplement to clinical judgment for now, particularly given the narrower safety margins in neuro physiotherapy (autonomic dysfunction in PD, symptom provocation in TBI, cardiovascular risk in SCI).

4.5. Adherence should be treated as a personalization

A precisely individualized prescription that is not delivered has no clinical effect; caregiver support and monitoring accuracy (Section 3.6) are at least as consequential to outcomes as dose precision itself.

1. Routine FITT-VP-structured dose reporting in clinical

and published records 2. wider adoption of physiological-threshold-anchored prescription with defined re-assessment intervals, where resources allow 3. systematic, tool-supported incorporation of patient-specific modifiers rather than informal clinical impression 4. cautious, clinician-supervised use of wearable and AI-based tools for monitoring and stratification - short of autonomous prescription - until current precision and bias concerns are resolved.

5. Conclusion

Personalized exercise prescription is increasingly central to effective neuro physiotherapy. Physiological-threshold-anchored dosing, staging-based tailoring, and technology-enabled monitoring are now well represented across stroke, PD, MS, SCI, and TBI research. At the same time, inconsistent dose reporting, the lack of standardized tools for weighting patient-specific modifiers, immature AI prescription technology, and modest real-world adherence together limit how far this evidence currently translates into reproducible clinical guidance. Advancing the field depends less on further isolated efficacy trials and more on standardizing how dose, personalization mechanism, and adherence are defined, measured, and reported.

Declarations

Funding: No funding received Conflict of interest: Nill

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  63. doi:10.54660/IJMBHR.2026.7.3.147-155. Copyright and License published articles. This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution Non Commercial-Share A like 4.0 International (CC BY-NC-SA 4.0) License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. https://doi.org/10.54660/IJMBHR.2026.7.3.147-155

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