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Work productivity of physical therapists providing tele-physical therapy
*Corresponding author: Montakarn Chaikumarn, Department of Physical Therapy, Chulalongkorn University, Bangkok, Thailand. Montakarn.c@chula.ac.th
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Received: ,
Accepted: ,
How to cite this article: Arwa M, Chaikumarn M. Work productivity of physical therapists providing tele-physical therapy. J Musculoskelet Surg Res. 2025;9:475-81. doi: 10.25259/JMSR_215_2025
Abstract
Objectives:
Evidence-based practices demonstrate that tele-physical therapy is comparable to conventional forms of physical therapy sessions, but the work productivity of therapists still needs to be calculated. This study aimed to measure the work productivity of physical therapists who provide tele-physical therapy using time-based metrics of their work efficiency and performance-based revenue data over a 2-month period.
Methods:
A total of 199 licensed physical therapists, with at least 6 months of experience in tele-physical therapy, were contacted online. After obtaining consent, the participants received a data collection form to be filled out for 14 consecutive days in 1 month and then again for 14 consecutive days in the 2nd month. The data collection form consisted of demographic data, including age, sex, qualification, work experience, the record of therapists’ time spent online and with patients in minutes, and their exemplary and typical revenue generation.
Results:
The mean age of physical therapists was 31.77 ± 4.88, with 130 males and 69 females from 21 different cities in Pakistan. The mean average efficiency for the first 2 weeks was 59.17% ± 15.20, and that for the second 2 weeks was 61.22% ± 13.43. Paired sample t-tests showed a significant difference in average efficiency between the two 2-week periods (mean difference = 2.04%, 95% CI [0.45%, 3.64%], P = 0.012). Multiple regression analysis revealed that demographic factors had a slight influence on efficiency.
Conclusion:
The work productivity of therapists providing tele-physical therapy was estimated to be in the range of 59–61%.
Keywords
Efficiency
Physical therapists
Rehabilitation
Tele-physical therapy
Work productivity
INTRODUCTION
Tele-physical therapy, also known as tele-rehabilitation, is defined as a type of rehabilitation provided by a skilled or experienced physical therapist to a patient through an online-based platform, such as a video consultation or a phone call. It not only helps in time and resources management but also makes health facilities accessible in remote areas.[1,2]
The use of telehealth rehabilitation and medicine has a long history. However, it became an evident mode of practice after the 2019 pandemic.[3] Its effectiveness has been tailored in a number of cases for treating pain, functional recovery, patient satisfaction, and quality of life, including various musculoskeletal conditions, chronic pain management, and hip and knee arthroplasties. Clinical evidence suggests that tele-physical therapy and tele-rehabilitation yield results comparable to those of conventional physical therapy sessions.[1,4,5] They tend to reduce pain, improve physical health, and range of motion in a number of musculoskeletal ailments.[6] Several studies have also reported patients’ satisfaction and relatively positive, comparable outcomes of tele-physical therapy.[7,8] It not only reduces the cost of therapy but also solves many problems related to barriers to physical therapy sessions.[9]
The global uptake of telehealth services, both during and since the COVID-19 pandemic, has been significant.[10] The World Health Organization states that more than 70% of countries adopted telemedicine practice during the pandemic, and tele-rehabilitation is an important part of the remote health service delivery. In countries with geographically restricted areas or workforce shortages, tele-physical therapy was an indispensable solution available to maintain continuity of care.[11] Although tele-physical therapy is gaining acceptance, it presents challenges for healthcare providers. Technological barriers, a lack of physical interaction, the absence of standardized treatment protocols, insufficient digital literacy among certain patient populations, and the increase in administrative responsibilities for therapists are among these.[12] However, these factors can lead to therapist fatigue, which may affect their willingness to work and their job performance and motivation.
Broadly speaking, work productivity in healthcare refers to the efficiency with which healthcare professionals perform their responsibilities, measured by the quantity and quality of output over a specified period.[13] Patient load, time per session, administrative demands, technical challenges, and the degree to which the therapist can optimally carry out effective care are all variables affecting productivity in physical therapy.[14] Measuring productivity is the first step in evaluating service delivery efficiency, identifying inefficient workflows, and informing institutional planning.
Despite having several articles on the efficiency and efficacy of tele-physical therapy, its cost effectiveness,[15] patients’ satisfaction, remote rehabilitation, and a positive perception of therapists[16] toward tele-rehabilitation, little is known about the work productivity of physical therapists who provide tele sessions.[14,16] Work productivity calculation can serve as a measure of an individual’s performance[17,18] as well as indicate the rate of development and growth. It can also help in monitoring output and improving quality.[19] The current study aimed to fill this critical gap in the literature by finding a quantitative measure of work productivity among physical therapists involved in providing tele-rehabilitation services. By employing the dual-metric approach, researchers measured efficiency levels using both time-based indicators and potential revenues. The current study aimed to operationalize the concept of work productivity as the ratio of the time physical therapists spent with patients to the total amount of time online. The focus of the data collection relied on self-reported measures of performance, including an assessment of results at typical versus excellent levels of productivity and associated revenue. The degree to which demographic and professional variables impact productivity results was assessed. The outcome model will offer a base measure of productivity in tele-physical therapy.[20]
MATERIALS AND METHODS
Design
This longitudinal study was conducted online by distributing questionnaires to physical therapists through email and smartphone applications, courtesy of MARHAM – a rapidly growing online health platform in Pakistan.[21]
Participant recruitment
A non-probability convenience sampling technique was employed to ensure the availability of participants and their willingness to participate. The use of a non-probability convenience sampling method in the present study was due to two reasons: (1) The logistical challenges in reaching a geographically remote and large population of therapists, and (2) the ability to obtain timely and voluntary participation. In exploratory as well as descriptive studies of professional healthcare providers, this technique has always been preferred. Licensed physical therapists registered on MARHAM’s portal, who have provided tele-physical therapy sessions for at least 6 months and have the necessary technology and equipment, were selected. Consent was obtained from all 199 physical therapists before the commencement of the data collection.
Data collection
After receiving data collection permission from MARHAM authorities, the physical therapists registered on the website were contacted through email and a smartphone application, specifically WhatsApp. Access to therapists registered on the MARHAM platform was already ensured through the platform’s internal database, which provided verified emails and phone numbers. The profile of participants included licensure status and documentation of at least 6 months of experience with tele-practice, both of which were authenticated by platform administrators. There were 240 potential participants on the invitation list; 199 gave consent, resulting in a response rate of 82.9%. The researcher explained the research, its potential, and the entire data collection procedure. Data were collected in two rounds from participants over a consecutive 14-day period in each of 2 months. The data collection form was developed by the researcher using Microsoft Word. The form was piloted to a small sample (n = 10) to ensure its validity before being sent to the participants through email. The same tool/form was used in both rounds. The form included demographic data (age, sex, qualification, city, clinical setting, total work experience, and duration of work as a tele-physical therapist), data for calculation of efficiency (total minutes spent online and minutes spent on online consultation), and data for revenue generation estimation (exemplary performance and typical performance). Upon completion of the first 14-day period, the filled forms were obtained from the therapists to maintain a record. After a 2-week gap, the therapists were contacted by the researcher once again to request that they complete the data on the form for the second consecutive 14-day period. Two units were utilized for measuring productivity, i.e., efficiency percentage (ratio of total time spent online waiting for patients and time spent with patients during online sessions), and revenue generation in Pakistan using therapists’ exemplary performance, typical performance, and potential for improvement. Reminder emails and messages were sent to the participants, instructing them to complete the form on a weekly basis.
The current study employed a longitudinal design, utilizing only two 14-day periods, which limited the potential to observe long-term trends or a seasonal effect on productivity. The authors acknowledge that a longer follow-up would likely have strengthened the findings. However, the two-time point study design was deemed most suitable for estimating initial productivity under the resource limitations of the digital health setting where the study hypothesis was conducted.
Data analysis
All analyses were conducted using the Statistical Package for the Social Sciences version 27, with a significance level set at P < 0.05. Descriptive statistics were used to summarize the demographic characteristics of the study participants, as well as their work productivity data. Mean, standard deviation, minimum, and maximum values were calculated for age, city, work experience, and duration of providing tele-rehabilitation sessions. At the same time, frequencies and percentages were obtained for gender, qualification, and type of clinical settings. A paired sample t-test was used to calculate any significant differences between the efficiency percentages, typical performances, exemplary performances, and potential for improvement for both 2-week periods.
In addition, multiple regression analysis was performed to examine the effect of demographic variables on the efficiency percentages over the 2-week period. Before interpreting the regression results, diagnostic tests were used to assess the model assumptions. The variance inflation factor (VIF) was used to diagnose multicollinearity, and values lower than the cut-off of 5 indicated no serious problem. The residuals were checked to be normally distributed, both graphically (with the help of Q-Q plots) and with the Shapiro–Wilk test. Residual-versus-fitted values were used to investigate the homoscedasticity and linearity. There were no substantive violations, thereby validating the regression model. To create an overall measure of productivity theory, the given research proposed the composite productivity index (CPI). The CPI has three variables: Total online time, the time spent with patients in a session, and the number of sessions per day. CPI is computed as follows:
CPI = ([Session hours/Online time]) × (Session count)
It is a formula that depicts the effectiveness of time distribution as well as the amount of work done. CPI was calculated for every participant in two consecutive 2-week periods.
RESULTS
Therapists’ characteristics
A total of 199 licensed physical therapists from 42 cities in Pakistan were recruited for this study, courtesy of the MARHAM authorities. The mean age of participants was 31.77 (±4.88). Out of the 199 participants, 130 were males and 69 were females. Approximately 52.2% (104) of the participants belonged to Lahore city. The mean of total work experience was 7.12 years (±4.77), and the mean of duration of work as a tele-physical therapist was 2.32 years (±1.29). One hundred twenty-three participants held a master’s level of qualification, 7 were PhDs, and 69 had a bachelor’s degree only. Most of the participants were practicing through private clinics and hospitals. The mean efficiency percentage of the first 2 weeks was 59.1% (±15.2), and that of the second 2 weeks was 61.2% (±13.4) [Table 1].
| Demographic data | Mean (±SD) |
|---|---|
| Age | 31.77 (±4.88) |
| Sex | Male therapists=130 Female therapists=69 |
| Qualification | Bachelors=69 Masters=123 Ph.D=7 |
| Total clinical experience in years | 7.12 (±4.77) |
| Duration of work as a tele-physical therapist | 2.32 (±1.29) |
| Clinical setting | Government hospital=39 Private hospital=65 Private clinical=64 Private space (home)=31 |
SD: Standard deviation.
Paired sample t-test
Paired sample t-tests showed significant differences between the values of efficiency percentages, exemplary performances, and potential for improvement for both 2 weeks, whereas typical performances showed no significant difference [Table 2].
| Pairs | Mean | SD | t | df | P-value |
|---|---|---|---|---|---|
| Efficiency percentage for the first 2 weeks-efficiency percentage for the second 2 weeks | −2.04286 | 11.31695 | −2.546 | 198 | 0.012* |
| Typical performance for the first 2 weeks-typical performance for the second 2 weeks | 44.22111 | 792.61380 | 0.787 | 198 | 0.432 |
| Exemplary performance for the first 2 weeks-exemplary performance for the second 2 weeks | 493.71859 | 3188.75443 | 2.184 | 198 | 0.030* |
| Potential for improvement for the first 2 weeks-potential for improvement for the second 2 weeks | 0.15663 | 1.09660 | 2.015 | 198 | 0.045* |
Multiple regression analysis
The dependent variables, i.e., efficiency percentages for the first 2 weeks and the second 2 weeks, were regressed on predicting variables of age, sex, qualification, total work experience in years, duration of work as a tele-physical therapist, and clinical settings. All six independent variables had a positive impact on efficiency percentages for the first 2 weeks, F (6,192) = 2.700, P = 0.015, and for the second 2 weeks, F (6,192) = 2.373, P = 0.031. Moreover, the R2 values of 0.078 and 0.069 indicate that the model explains 7.8% of the variance in efficiency percentages for the first 2 weeks and 6.9% of the variance in efficiency percentages for the second 2 weeks. The regression diagnostic test was performed systematically to conclude that the model was acceptable concerning multicollinearity, with the VIFs all <2.0. Moreover, no partial outliers or violations of the assumption of normality were identified, and no sign of heteroscedasticity was reported. These results support the validity of the underlying model assumptions [Tables 3 and 4].
| First 2-week period | ||||
|---|---|---|---|---|
| Regression weights | B | t | P-value | 95% CI (lower, upper) |
| Age →EP Sex →EP Qualification →EP Total work experience →EP Duration of work as tele-physical therapist →EP Clinical settings →EP |
0.992 1.858 5.646 −1.136 −0.444 2.024 |
1.641 0.830 2.594 −1.955 −0.381 1.805 |
0.102 0.407 0.010* 0.052 0.0704 0.073 |
(−0.196, 2.180) (−2.337, 6.053) (1.357, 9.937) (−2.281, 0.009) (−2.752, 1.844) (−0.187, 4.235) |
| R=0.078 F (6, 192)=2.700 |
||||
| Second 2-week period | ||||
|---|---|---|---|---|
| Regression weights | B | t | P-value | 95% CI (lower, upper) |
| Age →EP Sex →EP Qualification →EP Total work experience →EP Duration of work as tele-physical therapist →EP Clinical settings →EP |
1.072 1.22 1.305 −0.744 −2.085 2.647 |
2.001 0.061 0.676 −1.444 −2.016 2.661 |
0.047* 0.951 0.500 0.151 0.045* 0.008* |
(0.018, 2.128) (−2.856, 5.020) (−2.376, 5.086) (−1.651, 0.385) (−3.112, −0.048) (0.678, 3.504) |
| R=0.069 F (6, 192)=2.373 |
||||
CPI
Two measures, both time efficiency (time spent in session ÷ online time) and session count, were integrated to obtain a CPI for both data-collection periods. The mean CPI was 5.37 (SD = 2.64) during the first 2 weeks, but it increased to 6.36 (SD = 2.38) in the second 2 weeks. A paired-sample t-test revealed that the productivity of the composite of the two periods differs significantly (t [198] = 4.476, P < 0.001), which points to a productive increase in the work of the therapist within the study interval.
DISCUSSION
This longitudinal study reports the calculation of work productivity among physical therapists who provide tele-physical therapy or rehabilitation through the MARHAM platform. Despite increasing research literature on digitalization of all health fields, its efficacy, and barriers, this is one of the few studies that directly measure the work productivity of physical therapists in a telehealth environment, including both time-based efficiency and forecasted revenue. However, this dual-unit measurement provides a more comprehensive view of one’s productivity than simple time-use data. The findings can be used by health administrators and digital health platforms in their efforts to optimize staff performance. The current study was limited to one platform only because of easy access, cooperative behavior from the administration, and obtaining ethical clearance for data collection from the institutional head. If more than one platform were utilized in data collection, the researcher would have faced much more time constraints and tremendous logistical challenges.
The unit of time was calculated based on the total minutes spent online and the time spent in online sessions with patients over a 2-week period, twice in a 2-month period. The ratio was calculated, resulting in an efficiency percentage for each day. The purpose of collecting data for 14 days over 2 months was to minimize any bias, such as a heavier or lighter load of patients at a particular time period. The mean efficiency percentage for the first 2 weeks was calculated to be 59.17% (±15.20) and for the second 2 weeks, 61.22% (±13.43). The paired sample t-test showed a significant difference between these values. A 2-month time frame of data collection was considered appropriate to offer practical monitoring and minimize participant attrition and still allow quantifying the productivity during two consecutive intervals.
Most of the therapists were from Lahore and practiced at private hospitals and clinics. The measures of typical performance (average amount of money earned through online consultations, exemplary performance (highest amount of money earned through online consultations), and potential for improvement (ratio between typical and exemplary performances) were based on a modified version of Gilbert’s method.[22] Moreover, multiple regression analysis demonstrated that during the first 2 weeks, all the demographic factors showed positive influence on the work productivity of physical therapists during tele sessions, with qualification being the most significant whereas age, clinical settings, and duration of work as a tele-physical therapist showed significant influence on the work productivity during the second 2 weeks. Certain regression weights showed negative signs in the analysis. This depicts that these factors are negatively affecting productivity. Work experience of a longer length is correlated with less efficiency, which may be indicative of difficulty adjusting to telehealth platforms or perhaps a distinct work style.[23] A similar pattern is observed for the duration of work as a tele-physical therapist, as the negative coefficient suggests that ongoing tele-physical therapy does not always lead to improved efficiency, which may stem from burnout or unique role demands. These results highlight the importance of further investigating how both experience and the duration of tele-physical therapy influence outcomes, particularly by considering background factors such as adaptation to new technologies or the distribution of work burdens. The study’s findings serve as a valuable reference point for therapists in online rehabilitation, particularly in low- and middle-income countries.
The moderate efficiency percentages that are noted in this study may be due to the flexible yet limited nature of tele-physical therapy. Some responses received by the researchers regarding tele-physical therapy sessions include variability in daily patient caseloads, connectivity, and administrative burdens. Unlike other clinical settings that have well-organized patient handoffs or transfers, technical configurations or patient-specific issues may exist that affect the efficiency of treatment time during tele-sessions. It is hypothesized that such factors could account for the trend of slightly lower levels of productivity research compared to face-to-face activities. For this reason, more therapists’ familiarity with digital devices and home settings may also contribute to performance fluctuations.[24] It is also possible that daily productivity monitoring by therapists may introduce the Hawthorne effect, where individuals alter their habitual activity to align with their normalized representation of optimal performance.[25] The corresponding behavioral change can involve recalculating the schedule patterns or adjusting the way time is reported, thereby increasing the perceived treatment efficiency. This performance bias/self-reporting bias is common in behavioral research that involves self-tracking. The use of the CPI yielded a more comprehensive picture of therapists’ efficiency, as it combined time utilization with service delivery into a single, understandable indicator. This statistically significant increase in the CPI during the two study periods implies not only improved time management but also increased comfort and familiarity with tele-therapy provision, providing a practical framework for benchmarking the productivity of heterogeneous digital rehabilitation platforms.
The results of the current study were consistent with those obtained by Bohannon, who calculated the productivity of 11 physical therapists over a 20-day period in an acute care hospital.[26] He developed a standard centered on the exemplary performances of individual therapists. According to Bohannon, data on the potential for improvement in each therapist could enhance productivity, which would eventually result in a better efficiency of the department as a whole, yielding at least $250,000 more than the average sum of dollars earned. The findings of the current study differ from Bohannon’s in that his was limited to in-person settings, whereas ours expands the idea into telehealth at a time, which is useful for all current rehabilitation workflows.
Similarly, Allen used work sampling as a practical application to assess the activities and workload of physical therapists at a teaching hospital in Canada.[27] He focused on work sampling to measure the time spent by physiotherapists and underlined the relevance of time-related measures for determining the workforce. To understand the full scope of therapists’ responsibilities, he estimated the time spent by therapists on activities other than patient treatment. He also compared his observation of treatment time with the Canadian Schedule of Unit Values. The study found that the standard developed by the Nova Scotia government of 50000 working units per annum helped justify staffing levels at the physiotherapy department. Furthermore, a productivity indicator was developed using weighted units expressed as a percentage of the total hours worked or paid.
Study limitation
The current study was conducted over a limited time span. Long-term data recording is further required to report productivity efficiently. Another drawback is with regard to external validity. External variables, such as the number of patients served each day, variations in the system or network, and scheduling algorithms (each of which has the potential to impact the flow and productivity of sessions), were not controlled. The sample was large and covered different geographic settings, but it was limited to one digital platform and lacked a comparison or control group of therapists providing face-to-face services. Moreover, the productivity metrics are self-reported and may lead to a higher probability of reporting bias. Causal inferences are also hampered because there is no baseline data on the intervention. Future studies should utilize objective measures of workload, employ matched controls, and extend the study duration to enhance generalizability.
Implications of research
The current research suggests a methodological approach to productivity benchmarking in tele-physical therapy. The empirical findings demonstrate that measures of efficiency are effective in optimizing therapist work schedules, equalizing caseloads, and staffing placement in digital environments. At the same time, determining demographic factors that predict productivity provides practical insights for training, deployment, and quality-of-care programs, especially in resource-constrained environments. Finally, the results support the future interest of automated productivity monitoring in an attempt to improve reporting bias and enhance accuracy.
CONCLUSION
This study calculated the work productivity of physical therapists providing tele-physical therapy through MARHAM by units of time (Efficiency percentage for both 2 weeks). The unit of time resulted in mean efficiency percentages of 59.17% and 61.22% for physical therapists during online sessions. The impact of demographic data on therapists’ productivity was found to be modest.
Recommendations
Future research should utilize objective measures of workload derived directly from telehealth networks, such as automatic session logs, time reports related to patient interactions, and caseload statistical reports, in addition to employing self-report questionnaires. In addition, collecting data over a span of more than 6 months (or even a year) would allow for identifying longitudinal trends and measuring the adaptability of the therapists. There is a need to combine various sources of productivity measures in future research to achieve greater transparency in telehealth outcomes. In particular, assessments based on patients (their satisfaction ratings, no-show rates, treatment adherence data, and functional outcomes) should be incorporated into the assessments that physicians can report to provide an integrated measurement of productivity and quality of therapy in virtual care platforms. Last but not least, a comparative discussion of telehealth and face-to-face productivity must be attempted to put digital rehabilitation workflows into perspective.
Acknowledgment:
The authors would like to thank the participants and the team of MARHAM for their assistance in the data collection procedure.
Authors’ contribution:
MC: Contributed to the manuscript preparation and its critical revision. MA: Contributed to the concept, design, data acquisition, and statistical analysis. All authors have critically reviewed and approved the final draft and are responsible for the manuscript’s content and similarity index.
Ethical approval:
The research/study was approved by the Institutional Review Board at the Research Ethics Review Committee for Research Involving Human Research Participants, Group 1, Chulalongkorn University, number COA: 218/66, dated July 14, 2023.
Declaration of patient consent:
Patient’s consent is not required, as there are no patients in this study.
Use of artificial intelligence (AI)-assisted technology for manuscript preparation:
The authors confirm that there was no use of AI-assisted technology for assisting in the writing or editing of the manuscript, and no images were manipulated using AI.
Conflicts of interest:
There are no conflicting relationships or activities.
Financial support and sponsorship: This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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