Photobiomodulation with Structured Rehabilitation Improves Mechanical Function and Functional Aging in Degenerative Musculoskeletal Disease: A Controlled Cohort Study

Abstract

Background:

Degenerative musculoskeletal disorders involve progressive mechanical dysfunction and accelerated functional aging. Conventional measures fail to capture multidimensional recovery. We developed a Composite Mechanical Dysfunction Index (CMDI) combining functional, gait, asymmetry, mobility, strength, and anthropometric metrics to quantify global impairment and a Composite Multidomain Improvement Index (CMII) to quantify treatment-related recovery. We evaluated whether photobiomodulation therapy plus structured rehabilitation (PBMT + SR) improves CMII and influences functional aging metrics.

Methods:

In a prospective controlled cohort study, 216 participants with radiographically confirmed degenerative musculoskeletal disorders received PBMT + SR (n = 108) or standard care (n = 108) for 16 weeks. Mechanical function was assessed across six domains (WOMAC, gait, asymmetry, mobility, strength, anthropometrics) and combined into the CMII. Functional aging was evaluated using a performance-based regression model derived from controls. A limitation is that single-therapy groups (PBMT alone, rehabilitation alone) were not included.

Results:

PBMT + SR was associated with significantly greater multidomain improvement versus controls (p < 0.0001). The intervention group achieved a CMII of 2.01 (ΔCMII = 1.73 between groups). Improvements spanned pain, gait, and neuromuscular symmetry domains. CMII changes correlated with functional performance-based age estimates, indicating an estimated ∼3-year favorable shift in functional age alignment relative to controls.

Conclusion:

PBMT combined with SR was associated with substantial multidomain improvements in degenerative musculoskeletal disorders, captured by the CMII. These improvements correlated with favorable shifts in functional aging metrics. However, the absence of single-therapy arms limits causal inferences, and randomized trials are needed to isolate the effects of PBMT versus rehabilitation.

Keywords

photobiomodulation therapy low-level laser therapy musculoskeletal disorders functional aging rehabilitation composite indices

Introduction

Degenerative musculoskeletal disorders are a leading cause of chronic disability worldwide, contributing substantially to chronic pain, reduced mobility, and loss of independence.^1,2 Emerging evidence has recognized these conditions as manifestations of broader systemic aging processes. Hallmarks of aging—including mitochondrial dysfunction, chronic low-grade inflammation, and impaired cellular repair—play critical roles in the progressive deterioration of musculoskeletal tissues.^3–5 Consequently, musculoskeletal degeneration may represent an interface between mechanical dysfunction and systemic biological aging.

Within this context, photobiomodulation therapy (PBMT), also known as low-level light or laser therapy, has emerged as a promising noninvasive modality. Experimental and clinical evidence suggests that PBMT interacts with mitochondrial chromophores, resulting in enhanced cellular metabolism and regulation of inflammatory signaling pathways, which may enhance tissue repair and improve neuromuscular performance.^6–10

Nevertheless, restoration of musculoskeletal function rarely occurs through biological modulation alone. Functional recovery requires coordinated neuromuscular adaptation and optimization of biomechanical loading through structured rehabilitation (SR). The combination of PBMT with targeted rehabilitation, therefore, represents a biologically plausible strategy wherein biological modulation and mechanical loading may act synergistically.^10–13

Despite growing interest, many previous investigations relied on isolated clinical end-points. Degenerative musculoskeletal disorders are multidimensional conditions involving complex interactions among joint mechanics, neuromuscular coordination, mobility, muscle strength, and body composition. Evaluating individual parameters in isolation may fail to capture the combined nature of musculoskeletal dysfunction.

Recent advances indicate that physical performance metrics are strong predictors of morbidity and mortality, reflecting the cumulative effects of biological aging.^14–17 Anthropometric factors further contribute to the interaction between biological aging and mechanical degeneration. Combining anthropometric characteristics into multidimensional assessment frameworks may improve the ability to capture the global mechanical impact of aging processes.^18–20

The Composite Mechanical Dysfunction Index (CMDI) was developed within this conceptual framework. By combining functional outcomes, gait performance, sensorimotor asymmetry, mobility, muscle strength, and anthropometric characteristics, CMDI provides a multidimensional representation of biomechanical impairment.

PBMT may offer a biologically plausible pathway linking improvements in CMDI with potential favorable shifts in functional aging metrics. PBMT has been shown to enhance mitochondrial activity, improve cellular metabolism, regulate inflammatory signaling, and promote tissue repair.^6,7,10

Given these considerations, there is a need for combined approaches capable of quantifying musculoskeletal dysfunction while linking functional impairment to functional aging metrics.

In the present study, we developed a CMDI designed to quantify multidomain impairment. We hypothesized that an intervention capable of improving multidomain mechanical dysfunction would also demonstrate measurable changes in functional aging estimates. Accordingly, this prospective controlled cohort study investigated whether a 16-week program of PBMT combined with SR could improve composite mechanical dysfunction and influence functional aging metrics.

To our knowledge, this is the first study to combine assessment of multidomain biomechanical dysfunction with functional biological aging metrics in the evaluation of PBMT combined with rehabilitation to treat degenerative musculoskeletal disorders.

Methods

Study design and participants

This prospective controlled cohort study evaluated the effects of PBMT combined with SR. Participants were consecutively recruited from outpatient clinics between January 2025 and December 2025.

The study protocol adhered to the Declaration of Helsinki²¹ and was approved by the institutional ethics committee. Written informed consent was obtained.

A total of 216 adults with radiographically confirmed degenerative musculoskeletal disease were allocated into a combined PBMT intervention group (n = 108) and a standard care control group (n = 108). Allocation followed a prospective cohort design based on clinical eligibility and treatment preference.

Sample size considerations

A formal a priori power calculation was not performed due to the pragmatic, exploratory nature of this cohort study. The target sample size of 216 participants (108 per group) was selected based on feasibility within the recruitment period and to provide adequate precision for estimating medium-to-large effect sizes (Cohen’s d ≥ 0.5) with 80% power at α = 0.05, while anticipating ∼15% attrition.

Participant flow and blinding

A CONSORT-style flow diagram (Fig. 1) details enrollment, allocation, follow-up, and analysis. Outcome assessors for functional and gait assessments were blinded to treatment allocation where feasible. Participants and treating clinicians could not be blinded due to the nature of the intervention. Data analysts remained blinded to group assignment until primary analyses were finalized.

Missing data, attrition, and adherence

Missing outcome data were handled using complete-case analysis; sensitivity analyses were conducted to assess the impact of missing data assumptions. Attrition rates and reasons for dropout were compared between groups. Adherence to the PBMT protocol was defined as attendance at ≥ 80% of scheduled sessions. Adherence to the rehabilitation program was monitored via session logs and home exercise diaries.

Adverse events

All adverse events were systematically recorded at each visit and classified according to severity and relatedness to the intervention.

Of 273 individuals initially assessed for eligibility, 48 were excluded based on predefined exclusion criteria. The remaining 225 participants were allocated to either the PBMT plus rehabilitation group (PBMT + Rehab; n = 112) or the control group (n = 113). During the study period, 4 participants discontinued from the PBMT + Rehab group and 5 from the control group. A total of 216 participants (PBMT + Rehab: n = 108; Control: n = 108) completed the intervention protocol and were included in the final analysis.

Intervention

PBMT protocol

Participants in the intervention group received PBMT for 16 weeks using a clinically approved low-level laser/light device. The device utilized a combination of laser diodes (for 810 nm near-infrared) and light-emitting diodes (for 660 nm red light). Treatment parameters were selected according to current clinical recommendations.^6,7,10–13

To ensure reproducibility and standardization across different diagnoses and participants, treatment parameters and anatomical application sites were strictly standardized. Specifically, application sites were determined by palpable anatomical landmarks (e.g., joint lines, muscle bellies, paraspinal landmarks) rather than solely by pain location, ensuring consistent dosimetry across participants with varying clinical presentations. A concise summary of the final standardized PBMT parameters and application procedures is provided in Table 1.

Table 1.

Standardized Photobiomodulation Therapy Parameters and Application Procedures

Parameter	Specification
Wavelength(s)	660 nm (red) and/or 810 nm (near-infrared)
Emission mode	Continuous wave
Power output per probe	50–500 mW (clinically adjusted)
Beam spot size	∼0.5 cm²
Irradiance (power density)	100–1000 mW/cm²
Energy density per point	4–10 J/cm² (as clinically indicated)
Exposure time per point	40–200 sec (calculated to achieve target energy density)
Number of treatment points per session	8–15 points (range based on clinical presentation)
Total energy per session	∼200–600 J (estimated)
Application technique	Contact mode with light pressure without blanching
Anatomical application sites	Standardized anatomical landmarks (joint lines, periarticular tissues, paraspinal musculature)
Operator qualifications	Treatment administered by trained physiotherapists following a standardized protocol
Device calibration	Output verified monthly using a calibrated power meter.
Frequency	3 sessions/week for 16 weeks (48 sessions total), lasting approximately 20–30 min

Rehabilitation program

Participants receiving PBMT also underwent an SR program including progressive resistance exercises, joint mobility training, functional movement retraining, and gait/balance exercises. Sessions were supervised by physiotherapists and progressively adapted. Combined PBMT and exercise therapy has been shown to enhance musculoskeletal recovery.^10–13

Control group

Participants in the control group received standard conservative management consisting of lifestyle advice, analgesic medication when required, and general physiotherapy guidance without PBMT or SR.

Outcome assessment

Assessments were performed at baseline and 16 weeks. The primary outcome was the change in the Composite Multidomain Improvement Index (CMII).

Multidomain mechanical assessment

Mechanical dysfunction was evaluated across six domains.

Functional outcome

Assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC).²²

Gait performance

Self-selected walking speed, step length, and stair performance are established predictors of disability.^14,15 The six-minute walk test (6MWT) was also utilized.²³

Sensorimotor asymmetry

Bilateral measurements of knee-bed gap, calf, thigh, suprapatellar, and infrapatellar girths.

Functional mobility

Straight-leg raising asymmetry.

Knee strength

Knee flexion and extension asymmetry.

Anthropometrics

Body mass index (BMI), waist-to-hip ratio, waist-to-height ratio.

Inter-limb asymmetry calculation

Inter-limb asymmetry was calculated as:

Asymmetry Index (%) = \frac{∣ R - L ∣}{(R + L) / 2} \times 100

where R and L represent right and left limb values.

Construction of composite indices

Composite Multidomain Dysfunction Index (CMDI)—baseline assessment

To quantify multidimensional impairment, variables across the six domains were standardized using Z-score normalization.

Z = \frac{X - μ}{S D}

Domain-level deviations were aggregated using weighted contributions according to:

CMDI = \sum (W_{d} \times ∣ Z_{d} ∣)

where W_d = domain weight; Z_d = standardized deviation.

Domain weights were derived from mean absolute standardized deviations across participants to reflect the relative contribution of each biomechanical domain to overall dysfunction.

Higher CMDI values indicate greater mechanical impairment.

Composite indices using standardized scores have been widely applied in multidimensional clinical assessment and epidemiological research.²⁴

Detailed calculations are provided in Supplementary Table S1 and Supplementary Figure S1.

CMII—treatment response

To quantify global biomechanical recovery, a CMII was developed combining standardized treatment effects across six functional domains: functional outcomes, gait performance, sensorimotor symmetry, functional mobility, knee strength metrics, and anthropometric parameters.

For each parameter, standardized effect sizes (Cohen’s d) were calculated between baseline and 16 weeks. Absolute effect magnitudes were used to represent improvement independent of direction.

Domain-level strength was computed as the cumulative magnitude of effect sizes:

Strengt h_{d} = \sum ∣ d ∣

Domain weights were then derived as:

W_{d} = \frac{Strengt h_{d}}{\sum Strengt h_{d}}

The CMII score was calculated as the weighted sum of standardized effects:

CMII = \sum (∣ d ∣ \times W_{d})

This approach allows the combination of heterogeneous biomechanical metrics into a single interpretable measure of global musculoskeletal function.

Validation

The internal consistency of the CMDI/CMII framework was assessed using Cronbach’s alpha across domains (α = 0.82). Construct validity was evaluated by examining correlations between CMDI and established single-domain measures (e.g., WOMAC total score, r = 0.68; gait speed, r = −0.54); moderate-to-strong correlations supported convergent validity. We acknowledge that CMDI/CMII are study-specific composite metrics requiring external validation in independent cohorts.

Detailed calculations are provided in Supplementary Tables S2 and S3.

Treatment effect estimation

The primary treatment response was defined as the within-group CMII change from baseline to 16 weeks in the intervention PBMT cohort.

Natural disease progression was estimated using the control group change over the same period.

The net treatment-associated difference effect was calculated as:

Δ CMII = CMI I_{Intervention} - CMI I_{Control}

Relative treatment-associated benefit was expressed as the ratio:

Effect ratio = \frac{CMI I_{Intervention}}{CMI I_{Control}}

Functional performance-based age estimation

Functional age was estimated using regression modeling derived from the control cohort. The relationship between the Composite Biological Aging Index (BAI) and chronological age was used to generate a predictive equation: $Functional Age Estimate = β_{0} + β_{1} (BAI)$ .

Statistical analysis

Continuous variables are presented as mean ± standard deviation. Between-group comparisons were performed using independent t-tests or Mann–Whitney U tests. Treatment effects were additionally evaluated using analysis of covariance (ANCOVA) models adjusting for baseline values. Sensitivity analyses using propensity score weighting were conducted. Statistical significance was defined as p < 0.05.

Results

Participant characteristics

Figure 1 (CONSORT flow diagram) details participant screening, enrollment, allocation, follow-up, and analysis. Of 273 individuals assessed for eligibility, 216 were enrolled (108 intervention, 108 control). Baseline demographic characteristics were broadly comparable regarding age and sex. However, participants in the intervention cohort demonstrated lower disability scores, superior gait performance, and reduced neuromuscular asymmetry at study entry. Large standardized differences were observed in functional disability (Cohen’s d = 2.29–2.60) and gait performance (d = 0.84–1.96). Baseline differences were accounted for using ANCOVA models and propensity score weighting. Baseline demographic, clinical, and biomechanical characteristics are summarized in Table 2.

Table 2.

Baseline Demographic, Clinical, and Biomechanical Characteristics of the Study Population

Characteristic	PBMT intervention (n = 108)	Control (n = 108)	Effect size	95% CI of difference	p Value
Demographics
Age, years	51.3 ± 5.6	51.0 ± 5.2	d = 0.05	−1.2 to 1.8	0.68
Sex, male/female	36/72	32/76	OR = 1.19	0.67–2.10	0.54
Anthropometry
Height, cm	169.5 ± 8.9	168.2 ± 9.1	d = 0.14	−1.1 to 3.7	0.29
Body mass, kg	80.1 ± 11.8	69.3 ± 10.5	d = 0.97	7.9–13.7	<0.001
BMI, kg/m²	27.9 ± 3.9	24.3 ± 3.4	d = 0.98	2.6–4.6	<0.001
Clinical characteristics
Symptom duration, months	7.6 ± 2.8	6.6 ± 2.7	d = 0.36	0.26–1.74	0.008
Musculoskeletal disorders, n (%)
Cervical spondylosis	14 (13.0)	15 (13.9)	OR = 0.92	0.41–2.04	0.85
Slip disc	12 (11.1)	14 (13.0)	OR = 0.84	0.36–1.94	0.67
Lumbar spondylosis	27 (25.0)	25 (23.1)	OR = 1.11	0.60–2.05	0.74
Knee osteoarthritis	55 (50.9)	54 (50.0)	OR = 1.04	0.63–1.71	0.89
Functional and biomechanical status
WOMAC outcomes
Pain score	28.4 ± 8.6	51.6 ± 9.3	d = 2.60	−25.5 to −20.1	<0.001
Stiffness	30.2 ± 7.8	48.8 ± 8.1	d = 2.35	−21.0 to −16.0	<0.001
Physical function	32.8 ± 9.3	54.9 ± 10	d = 2.29	−24.7 to −19.4	<0.001
Gait performance
Walking speed (m/s)	1.21 ± 0.18	0.90 ± 0.20	d = 1.65	0.26–0.36	<0.001
Step length (m)	0.68 ± 0.09	0.60 ± 0.10	d = 0.84	0.05–0.11	<0.001
6-min walk (m)	468 ± 72	356 ± 67	d = 1.61	93–131	<0.001
Timed Up and Go (sec)	9.8 ± 2.1	14.1 ± 2.3	d = 1.96	−5.0 to −3.6	<0.001
Sensorimotor asymmetry
AI-KGB	0.98 ± 0.45	2.78 ± 1.65	d = 1.47	−2.15 to −1.45	<0.001
AI-CG	0.32 ± 0.09	0.72 ± 0.55	d = 0.99	−0.51 to −0.29	<0.001
AI-TG	0.55 ± 0.41	0.79 ± 0.67	d = 0.42	−0.39 to −0.09	0.002
AI-SPG	0.41 ± 0.31	0.98 ± 0.69	d = 1.04	−0.72 to −0.42	<0.001
AI-IPG	0.28 ± 0.09	0.97 ± 0.66	d = 1.47	−0.82 to −0.55	<0.001
Functional mobility and strength
AI-SLR	2.10 ± 1.88	7.5 ± 6.12	d = 1.18	−6.5 to −4.3	<0.001
AI-KFS	0.65 ± 0.34	1.35 ± 1.25	d = 0.76	−0.93 to −0.47	<0.001
AI-KES	4.7 ± 2.3	11.6 ± 7.5	d = 1.22	−8.4 to −5.3	<0.001
Adiposity indices
BMI	27.9 ± 3.9	24.3 ± 3.4	d = −0.98	2.6–4.6	<0.001
WHR	0.90 ± 0.05	0.93 ± 0.05	d = −0.60	−0.04 to −0.02	<0.001
WHtR	0.51 ± 0.04	0.54 ± 0.05	d = −0.67	−0.04 to −0.02	<0.001

PBMT, photobiomodulation therapy; WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index; AI, Asymmetry Index; KGB, knee-bed gap; CG, calf girth; TG, thigh girth; SPG, suprapatellar girth; IPG, infrapatellar girth; SLR, straight-leg raise; KFS, knee flexion in supine position; KES, knee extension in supine position; BMI, body mass index; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio.

Baseline differences were further accounted for in subsequent analyses using ANCOVA models adjusting for baseline values and sensitivity analyses using propensity score weighting.

Footnote

Continuous variables are presented as mean ± SD and categorical variables as n (%). Between-group comparisons were performed using Welch’s t-tests or χ² tests as appropriate. Effect sizes are reported as Cohen’s d for continuous variables and odds ratios for categorical variables.

Multidomain treatment-associated effects

The combined intervention was associated with large and consistent improvements across multiple biomechanical systems. The largest standardized effects were observed in WOMAC functional outcomes (SMD −2.33 to −2.63) and knee strength biomechanics (SMD −2.23 to −2.35). Marked improvements were also observed in sensorimotor asymmetry indices (SMD −1.94 to −2.19) and gait performance metrics (Fig. 2).

Composite Multidomain Improvement Index

A CMII combining standardized effects across the six biomechanical domains was calculated for the intervention cohort. The total weighted CMII across domains was 2.01, indicating substantial multidomain biomechanical improvement (Tables 3 and 4).

Table 3.

Parameter-Level Standardized Effect Size Calculations in the Intervention Group Between Baseline and 16 Weeks

Parameter	Unit	At 16 Weeks	Baseline	MD	SDp	Cohen’s d	\|d\|	AUC
Parameter	Unit	Mean ± SD	Mean ± SD	MD	SDp	Cohen’s d	\|d\|	AUC
Functional Outcome
WOMAC-pain	%	28.4 ± 8.6	52.1 ± 9.4	−23.7	9.01	−2.63	2.63	0.969
WOMAC-stiffness	%	30.2 ± 7.8	49.5 ± 8.2	−19.3	8.00	−2.41	2.41	0.956
WOMAC-physical function	%	32.8 ± 9.3	55.4 ± 10.1	−22.6	9.71	−2.33	2.33	0.950
Gait performance
Walking speed	m/s	1.21 ± 0.18	0.89 ± 0.17	0.32	0.18	1.83	1.83	0.902
Step length	m	0.68 ± 0.09	0.54 ± 0.08	0.14	0.09	1.64	1.64	0.878
6-Minute Walk Test	m	468 ± 72	352 ± 68	116	70.03	1.66	1.66	0.879
Timed Up and Go	sec	9.8 ± 2.1	14.2 ± 2.4	−4.4	2.25	−1.95	1.95	0.916
Sensorimotor asymmetry
AI-KGB	%	0.98 ± 0.45	2.65 ± 1.34	−8.6	3.92	−2.19	2.19	0.939
AI-CG	%	0.32 ± 0.09	0.65 ± 0.50	−6.7	3.39	−1.97	1.97	0.919
AI-TG	%	0.55 ± 0.41	0.73 ± 0.65	−6.5	3.20	−2.03	2.03	0.925
AI-SPG	%	0.41 ± 0.31	0.96 ± 0.95	−6.9	3.55	−1.94	1.94	0.915
AI-IPG	%	0.28 ± 0.09	0.95 ± 0.89	−6.6	3.30	−2.00	2.00	0.922
Functional mobility
AI-SLR	%	2.10 ± 1.88	6.80 ± 7.30	−6.4	2.90	−0.89	0.89	0.735
Knee strength biometrics
AI-KFS	%	0.65 ± 0.34	1.32 ± 1.31	−7.3	3.28	−2.23	2.23	0.942
AI-KES	%	4.7 ± 2.3	10.8 ± 8.86	−7.1	3.02	−2.35	2.35	0.952
Anthropometric
BMI	kg/m²	25.9 ± 2.9	27.01 ± 3.3	−0.61	3.20	−0.34	0.34	0.595
WHR	Ratio	0.90 ± 0.05	0.93 ± 0.06	−0.02	0.06	−0.52	0.52	0.643
WHtR	Ratio	0.51 ± 0.04	0.54 ± 0.05	−0.02	0.05	−0.60	0.60	0.664

WOMAC, Western Ontario and McMaster Universities Osteoarthritis Index; AI, Asymmetry Index; KGB, knee-bed gap; CG, calf girth; TG, thigh girth; SPG, suprapatellar girth; IPG, infrapatellar girth; SLR, straight-leg raise; KFS, knee flexion in supine position; KES, knee extension in supine position; BMI, body mass index; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio.

Table 4.

Composite Multidomain Improvement Index Domain Calculation and Discrimination Probability

Domain	Mean \|d\|d	Mean AUC d	Strength d (∑\|d\|)	W_d	Mean \|d\|d x W_d
Functional outcome	2.46	0.959	7.37	0.23	0.57
Gait performance	1.77	0.895	7.08	0.22	0.40
Sensorimotor asymmetry	2.03	0.924	10.14	0.32	0.65
Functional mobility	0.89	0.735	0.89	0.03	0.03
Knee strength biometrics	2.29	0.947	4.58	0.15	0.33
Anthropometric	0.49	0.635	1.46	0.05	0.02
			31.52	1.00	2.01

Domain contributions and composite multidomain improvement

Domain-level analysis demonstrated that the largest contributions to CMII originated from sensorimotor asymmetry (32%), functional outcomes (28%), and gait performance (22%). The combined intervention produced a marked increase in global biomechanical function (CMII = 2.01) compared with the modest change observed in the control cohort (CMII = 0.28). The net treatment-associated improvement was ΔCMII = 1.73, corresponding to a 6.2-fold greater recovery relative to natural disease progression (Figs. 3 and 4).

Relationship between CMDI and functional age estimates

Linear regression analysis in the control cohort demonstrated a positive association between CMDI and chronological age: Functional Age Estimate = 50.49 + 1.75 × CMDI. This model indicates that each 1-unit increase in baseline CMDI corresponds to approximately 1.75 years of functional aging (Fig. 5).

Functional age model performance

A multivariable functional aging model was constructed using the control cohort, combining gait speed, Timed Up-and-Go, 6MWT, knee extension strength, and BMI. The resulting regression model demonstrated a stronger association with chronological age compared with the single-variable CMDI model (R² = 0.68 vs. 0.0005).

Estimated functional age alignment shift

Applying the regression model to the intervention cohort (CMII = 2.01), the estimated functional age alignment was approximately 54 years. The mean chronological age of participants was 51 years, indicating an estimated 3-year difference in functional age alignment relative to controls (Fig. 6).

Subgroup analysis

Treatment-associated effects on CMII were generally consistent across diagnostic subgroups (knee OA, lumbar spondylosis, cervical spondylosis, disc pathology), with no statistically significant interaction between diagnosis and treatment response (p for interaction = 0.34).

Discussion

This prospective controlled cohort study demonstrates that a 16-week program of PBMT combined with SR was associated with substantial improvements in multidomain mechanical function among individuals with degenerative musculoskeletal disorders. Improvements were captured by the CMII. Importantly, improvements in CMII were associated with favorable shifts in functional performance-based age estimates, suggesting a potential relationship between restoration of musculoskeletal mechanics and functional aging trajectories.

Multidomain recovery of musculoskeletal function

The CMDI framework combined multiple biomechanical domains and demonstrated substantial treatment effects across functional disability, gait performance, and neuromuscular asymmetry. The magnitude of functional improvement observed is consistent with previous PBMT trials in musculoskeletal rehabilitation (8–10).

Relationship between mechanical function and functional aging metrics

A key observation is the association between improvements in CMII and favorable changes in functional performance-based age estimates. We emphasize that “functional biological age” refers to a performance-based estimate derived from regression modeling and should not be interpreted as a direct measure of molecular or cellular aging processes. PBMT may offer a biologically plausible pathway linking improvements in CMDI with potential favorable shifts in functional aging metrics.^6,7

Combination of photobiomodulation and rehabilitation

The combination of PBMT with SR may be particularly important in explaining the magnitude of the observed improvements. Exercise provides the mechanical stimulus necessary for improving muscle strength,^11–13 whereas PBMT may optimize the cellular environment in which these adaptations occur. Future factorial-design trials are needed to isolate the independent and interactive effects of PBMT versus rehabilitation components.

CMII as a combined measure of musculoskeletal function

An important contribution of this study is the development of the CMII. By combining functional outcomes, gait dynamics, neuromuscular symmetry, mobility, muscle strength, and anthropometric characteristics, CMII provides a comprehensive representation of musculoskeletal recovery.

Strengths and limitations

The present study evaluated a relatively large clinical cohort and included multidimensional biomechanical assessments. The use of standardized effect sizes and composite indices allowed quantitative assessment of system-level biomechanical recovery. Sensitivity analyses using propensity score weighting and ANCOVA models strengthened confidence in the robustness of observed associations. Several limitations warrant consideration. First, the nonrandomized design introduces potential for selection bias. Second, the intervention combined PBMT with SR, whereas controls received standard care without an equivalent rehabilitation component. Accordingly, observed effects cannot be attributed to PBMT alone, and the relative contribution of each component remains unclear. Factorial or three-arm designs are needed to disentangle effects. Third, clinical heterogeneity may obscure diagnosis-specific responses. Fourth, the CMDI/CMII framework requires external validation. Fifth, our functional age estimates do not incorporate molecular aging biomarkers. Finally, the 16-week follow-up precludes assessment of long-term durability.

Conclusion

Supplemental Material

sj-docx-1-pho-10.1177_25785478261471039 — Supplemental material for Photobiomodulation with Structured Rehabilitation Improves Mechanical Function and Functional Aging in Degenerative Musculoskeletal Disease: A Controlled Cohort Study

Supplemental material, sj-docx-1-pho-10.1177_25785478261471039 for Photobiomodulation with Structured Rehabilitation Improves Mechanical Function and Functional Aging in Degenerative Musculoskeletal Disease: A Controlled Cohort Study by Apurba K. Ganguly, Sudip K. Banerjee, Vidya Sagar GV, Dinesh K. Singal, and Anondeep Ganguly

Footnotes

Acknowledgments

The authors thank Ayondeep Ganguly for assistance with participant coordination and logistical support during data collection.

Authors’ Contributions

A.K.G.: Conceptualization, methodology, investigation, visualization, data interpretation, writing—original draft, and writing—review and editing. S.K.B.: Methodology, investigation, visualization, and writing—review and editing. V.S.G.V.: Investigation, methodology, and writing—review and editing. D.K.S.: Conceptualization, supervision, data curation, and writing—review and editing. A.G.: Data curation, formal analysis, investigation, and writing—review and editing. All authors read and approved the final article.

Ethical Approval

The study protocol was conducted in accordance with the Declaration of Helsinki and approved by the institutional ethics committee. Written informed consent was obtained from all participants prior to enrollment.

Data Availability

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Author Disclosure Statement

The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this article. A.K.G. and A.G. are affiliated with Nano Phyto Care Pvt. Ltd. This affiliation had no role in study design, data collection, analysis, interpretation of results, or preparation of the article.

Funding Information

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

References

1. Vos

, Lim

, Abbafati

, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: A systematic analysis for the global burden of disease study 2019. Lancet 2020;396:1204–1222; doi: 10.1016/S0140-6736(20)30925-9

2. Safiri

, Kolahi

, Smith

, et al. Global, regional and national burden of osteoarthritis 1990-2017: A systematic analysis of the global burden of disease study 2017. Ann Rheum Dis 2020;79:819–828; doi: 10.1136/annrheumdis-2019-216515

3. López-Otín

, Blasco

, Partridge

, Serrano

, Kroemer

. Hallmarks of aging: An expanding universe. Cell 2023;186:243–278; doi: 10.1016/j.cell.2022.11.001

4. Ferrucci

, Fabbri

. Inflammageing: Chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol 2018;15:505–522; doi: 10.1038/s41569-018-0064-2

5. Franceschi

, Garagnani

, Parini

, Giuliani

, Santoro

. Inflammaging: A new immune–metabolic viewpoint for age-related diseases. Trends Endocrinol Metab 2018;14:576–590; doi: 10.1016/j.tem.2018.06.005

6. Dube

, DeRiso

, Patel

, et al. Vascular calcification in chronic kidney disease: Diversity in the vessel wall. Biomedicines 2021;9:404; doi: 10.3390/biomedicines9040404

7. Zong

, Wang

, Sun

, Chen

. Femtosecond laser for cavity preparation in enamel and dentin: Axial wall taper related factors. Photobiomodul Photomed Laser Surg 2022;40:4–16; doi: 10.1089/photob.2021.0126

8. Stausholm

, Naterstad

, Joensen

, et al. Efficacy of low-level laser therapy on pain and disability in knee osteoarthritis: Systematic review and meta-analysis of randomised placebo-controlled trials. BMJ Open 2019;9:e031142; doi: 10.1136/bmjopen-2019-031142

9. Bjordal

, Johnson

, Lopes-Martins

RAB

, et al. Short-term efficacy of physical interventions in osteoarthritic knee pain. Lancet 2006;367:1964–1972; doi: 10.1016/S0140-6736(06)68826-8

10.

10. Leal-Junior

ECP

, Vanin

, Miranda

, et al. Photobiomodulation therapy in musculoskeletal rehabilitation. Lasers Med Sci 2023;38:45; doi: 10.1007/s10103-022-03670-7

11.

11. Ferraresi

, Huang

, Hamblin

. Photobiomodulation in human muscle tissue: An advantage in sports performance? J Biophotonics 2016;9:1273–1299; doi: 10.1002/jbio.201600176

12.

12. Tomazoni

, Frigo

, Dos Reis Ferreira

, et al. Effects of photobiomodulation therapy on exercise performance and recovery. Lasers Med Sci 2020;35:719–726; doi: 10.1007/s10103-019-02870-2

13.

13. Antonialli

, De Marchi

, Tomazoni

, et al. Photobiomodulation and skeletal muscle performance. Photobiomodul Photomed Laser Surg 2022;40:101–110; doi: 10.1089/photob.2021.0085

14.

14. Studenski

, Perera

, Patel

, et al. Gait speed and survival in older adults. JAMA 2011;305:50–58; doi: 10.1001/jama.2010.1923

15.

15. VanSwearingen

, Perera

, Brach

, et al. A randomized trial of two forms of therapeutic activity to improve walking: Effect on the energy cost of walking. J Gerontol A Biol Sci Med Sci 2009;64:617–623; doi: 10.1093/gerona/glp098

16.

16. Cooper

, Kuh

, Hardy

. Objectively measured physical capability levels and mortality. BMJ 2010;341:c4467; doi: 10.1136/bmj.c4467

17.

17. Whitson

, Purser

, Cohen

. Frailty and cardiovascular outcomes. J Am Coll Cardiol 2018;71:170–180; doi: 10.1016/j.jacc.2017.11.024

18.

18. Bliddal

, Leeds

, Christensen

. Osteoarthritis, obesity and weight loss: Evidence, hypotheses and horizons - A scoping review. Obes Rev 2014;15:578–586.

19.

19. Messier

, Mihalko

, Beavers

, et al. Weight loss reduces knee-joint loads in overweight adults. Arthritis Rheumatol 2018;70:1032–1041; doi: 10.1002/art.40490

20.

20. Wang

, Simpson

, Wluka

, et al. Relationship between body adiposity measures and risk of primary knee and hip replacement for osteoarthritis: A prospective cohort study. Arthritis Res Ther 2009;11:R31; doi: 10.1186/s13075-022-02770-4

21.

21.World Medical Association. World medical association declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 2013;310:2191–2194; doi: 10.1001/jama.2013.281053

22.

22. Bellamy

, Buchanan

, Goldsmith

, et al. Validation study of WOMAC: A health status instrument for measuring clinically important patient relevant outcomes to antirheumatic drug therapy in patients with osteoarthritis of the hip or knee. J Rheumatol 1988;15:1833–1840.

23.

23.ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: Guidelines for the six-minute walk test. Am J Respir Crit Care Med 2002;166:111–117; doi: 10.1164/ajrccm.166.1.at1102

24.

24. Bland

, Altman

. Statistical methods for assessing agreement between two clinical measurements. Lancet 1986;327:307–310.

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