Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at, first published .
Postmarketing Follow-Up of a Digital Home Exercise Program for Back, Hip, and Knee Pain: Retrospective Observational Study With a Time-Series and Matched-Pair Analysis

Postmarketing Follow-Up of a Digital Home Exercise Program for Back, Hip, and Knee Pain: Retrospective Observational Study With a Time-Series and Matched-Pair Analysis

Postmarketing Follow-Up of a Digital Home Exercise Program for Back, Hip, and Knee Pain: Retrospective Observational Study With a Time-Series and Matched-Pair Analysis

Original Paper

1Centre for Digital Health Intervention, ETH Zürich, Zürich, Switzerland

2Institute for Implementation Science in Health Care, University of Zürich, Zürich, Switzerland

3School of Medicine, University of St. Gallen, St.Gallen, Switzerland

4University Emergency Center, Medical Center - University of Freiburg, Freiburg, Germany

5Faculty of Medicine, University of Freiburg, Freiburg, Germany

6Vivira Health Lab GmbH, Berlin, Germany

*these authors contributed equally

Corresponding Author:

Leo Benning, MPH, MD

University Emergency Center

Medical Center - University of Freiburg


Freiburg, 79106


Phone: 49 761 270 18409


Background: Musculoskeletal conditions are the main drivers of global disease burden and cause significant direct and indirect health care costs. Digital health applications improve the availability of and access to adequate care. The German health care system established a pathway for the approval of “Digitale Gesundheitsanwendungen” (DiGAs; Digital Health Applications) as collectively funded medical services through the “Digitale-Versorgung-Gesetz” (Digital Health Care Act) in 2019.

Objective: This article presents real-world prescription data collected through the smartphone-based home exercise program “Vivira,” a fully approved DiGA, regarding its effect on self-reported pain intensity and physical inability in patients with unspecific and degenerative pain in the back, hip, and knee.

Methods: This study included 3629 patients (71.8% [2607/3629] female; mean age 47 years, SD 14.2 years). The primary outcome was the self-reported pain score, which was assessed with a verbal numerical rating scale. The secondary outcomes were self-reported function scores. To analyze the primary outcome, we used a 2-sided Skillings-Mack test. For function scores, a time analysis was not feasible; therefore, we calculated matched pairs using the Wilcoxon signed-rank test.

Results: Our results showed significant reductions in self-reported pain intensity after 2, 4, 8, and 12 weeks in the Skillings-Mack test (T3628=5308; P<.001). The changes were within the range of a clinically relevant improvement. Function scores showed a generally positive yet more variable response across the pain areas (back, hip, and knee).

Conclusions: This study presents postmarketing observational data from one of the first DiGAs for unspecific and degenerative musculoskeletal pain. We noted significant improvements in self-reported pain intensity throughout the observation period of 12 weeks, which reached clinical relevance. Additionally, we identified a complex response pattern of the function scores assessed. Lastly, we highlighted the challenges of relevant attrition at follow-up and the potential opportunities for evaluating digital health applications. Although our findings do not have confirmatory power, they illustrate the potential benefits of digital health applications to improve the availability of and access to medical care.

Trial Registration: German Clinical Trials Register DRKS00024051;

J Med Internet Res 2023;25:e43775



Globally, musculoskeletal conditions are among the 10 most important drivers of an increasing disease burden and are common in all age groups [1]. Among these conditions, nonspecific lower back pain accounted for 2.5% of all disability-adjusted life years globally in 2019, an increase of 46.9% compared to the 1990 baseline [1]. In the German health care system, musculoskeletal conditions are among the most frequent chronic conditions [2] and constitute a major cause of chronic pain, physical disability, and decreased quality of life [3]. Consequently, musculoskeletal conditions account for significant direct health care expenses and cause relevant indirect health care expenses. Related work estimates that the cost of lost productivity in the European Union due to musculoskeletal conditions is as high as 2% of the European Union’s gross domestic product [4].

For degenerative and nonspecific musculoskeletal conditions, movement therapy and patient education are considered first-line treatment components of international guidelines for managing musculoskeletal conditions [5,6]. However, access to and availability of movement therapy are limited [7]. Digital home exercise applications intend to substitute or support conventional in-person movement therapy to address this challenge. In this context, we present the first postmarketing follow-up data of the standalone digital therapeutic app “Vivira,” a fully approved “Digitale Gesundheitsanwendung” (DiGA; Digital Health Application) under the “Digitale-Versorgung-Gesetz” (DVG; Digital Health Care Act) in Germany [8,9]. While a comprehensive introduction to the German health care system can be found elsewhere [10], several key elements of the health care system and the DVG legislation should be briefly introduced here. Membership in one of the statutory health insurances is mandatory for all individuals with employment in Germany up to an annually adjusted income threshold. Above this threshold, insured individuals can opt out for private insurance. Similar rules exist for self-employed and some specified groups (eg, federal or state employees), while an extended solidarity-funded coverage applies to family members of regularly insured (eg, children or disabled family members), retired, and unemployed individuals. Premiums are generally defined risk-independent and are based on the insured individual’s gross income. While every individual is free to choose among the different statutory health insurances and no risk-based selection by the insurances is allowed, all statutory health insurances have the legal obligation to cover the same collectively contracted benefits package. This comprehensive insurance system covers approximately 90% of the population in Germany. The DVG from 2019 constituted a significant innovation for the German health care system, as it introduced digital therapeutics into German social law. It included the category of DiGA into the collectively contracted benefits package of the statutory health insurances [11]. Hence, all statutory health insurances have the obligation to reimburse these digital therapeutics when prescribed by a qualified health care professional. To receive market approval as a DiGA, however, the digital therapeutics need to meet quality and safety criteria and need to demonstrate relevant medical effectiveness as outlined by the Bundesinstitut für Arzneimittel und Medizinprodukte (BfArM), a regulatory body for drugs and medical devices in Germany [12]. One important feature of the assessment process is the so-called “fast track” approval, which allows preliminary approval for distribution and reimbursement of the respective DiGA for up to 12 months once the quality and safety criteria are met and the successful scientific evaluation is not yet completed, but deemed likely by the authorities. Over the duration of the preliminary approval period, the manufacturer of the DiGA must provide sufficient evidence for the medical effectiveness of the proposed DiGA. If the demonstration fails, the DiGA is not granted permanent listing and the preliminary market approval is withdrawn.

Although the regulatory requirements welcome innovative and real-world evidence-based approaches toward evidence generation, all successful attempts at receiving permanent market approval have so far relied on conventional randomized controlled trials [13,14]. Nonetheless, there is a growing interest in real-world observation data from permanently listed DiGAs to better understand prescription, use, and outcome data under nontrial conditions. This study hence aimed to assess the effects of the DiGA Vivira on self-reported pain intensity and function scores in a real-world setting.


We performed a retrospective observational study based on self-reported pain scores, function scores, and retention data. Besides these outcomes, patients also reported demographic information (age and sex), pain area, and pain duration at baseline. We used data reported by the patients between October 20, 2020, and June 22, 2021, and included all available software versions of Vivira. All patients consented to the use of their data in this study under article 4 of the “Digitale-Gesundheitsanwendungen-Verordnung” (DiGAV; Digital Health Applications Act). All collected data were stored according to the German Data Protection Regulations (Datenschutz-Grundverordnung). We registered the study with the German Center for Clinical Trials (Deutsches Register Klinischer Studien, reference DRKS00024051). Enrollment for the treatment with Vivira was solely at the respective physician’s discretion and without any control from the manufacturer. The inclusion criteria are presented in Textbox 1.

According to the inclusion criteria, we included 3629 patients who could be analyzed with at least one completed assessment after enrollment.

Inclusion criteria.
  1. Enrollment after preliminary approval of the home exercise program Vivira as a “Digitale Gesundheitsanwendungen” (DiGA; Digital Health Application).
  2. A reported initial pain score on a verbal numerical rating scale (range 0-10) of >0.
  3. Any reported pain duration (acute, subacute, or chronic).
  4. Completion of >0 exercises during participation.
  5. Presence of at least two patient-reported data entries.
Textbox 1. Inclusion criteria.

Ethics Approval

The study and the underlying evaluation concept received approval from the Ethics Committee of the Medical Association of the state of Baden-Württemberg (Ethikkommission der Landesärztekammer Baden-Württemberg, F-2021-010).

Physical Exercise Composition and Progression Modules

Upon enrollment, the app prompts participants to complete an initial assessment, which assesses the current functional state (ie, limitations in strength, mobility, and coordination) through a series of exercises that participants can either complete or fail to complete (ie, a binary assessment through different movement exercises). Specific extensions to the assessment account for participants’ pre-existing movement limitations (eg, inability to complete assessment prompts requiring 90° flexion of the hip and knee joints, inability to maintain a stable resting position, or inability to sit on the heels). The completion and noncompletion of each test are assigned weights that allow the computation of function scores for strength, mobility, and coordination. The definition of each weight is based on an interdisciplinary expert panel of orthopedic surgeons and physiotherapists. The underlying principle of the initial assessment follows the therapeutic concept of regional interdependence, which has been described in detail elsewhere [15,16]. In brief, it formalizes the clinical observation that in the context of musculoskeletal conditions, therapeutic interventions applied to one anatomical region can have positive effects on pain and range of motion in other anatomical regions.

Once a participant completes the initial assessment, the app automatically composes an individualized set of 4 exercises from a repository of 120 different exercises. Every exercise includes a 2-dimensional progression module. The exercise intensity is increased gradually (ie, increases in the number of repetitions and the duration of exercises) before the complexity of the exercise is increased (ie, adding a sequence of exercise changes or adding another movement component). The app modifies the intensity, complexity, and composition of the exercise according to participant feedback. Participants are required to provide binary feedback after each exercise to ensure that a prompted exercise neither triggers new pain sensations, nor exacerbates existing pain before being prompted with the next exercise. This feedback guides the automated customization process of the exercise program. Figure 1 illustrates the user interface of the app.

Figure 1. User interface of Vivira. (A) The home screen prompts patients to enter their daily exercise program. (B) Four exercises, composed on the basis of close patient-feedback loops, are displayed and can be entered in any order. (C) Prior to the start of each exercise, video- and text-based instructions explain each exercise in detail, highlight important components of each exercise, and provide background information. (D) During each exercise, a video loop repeats the exercise instructions and displays the number of repetitions or, if applicable, a timer. (E-G) After completion of all 4 daily exercises, the program collects patient feedback (not shown here) and returns to the home screen.

Self-Reported Outcome Measures

The app collects the current and self-assessed pain intensity based on a verbal numerical rating scale (VNRS) [17] once per week.

Function scores are based on the initial assessment of the functional state, as outlined above. A reassessment is prompted to participants every 4 weeks as a virtual follow-up. Upon completion, an updated functional state along the 3 dimensions of strength, mobility, and coordination is provided. Additionally, a composite (total) score is computed.

Statistical Methods

The hypothesis test used for self-reported pain intensity was the 2-sided Skillings-Mack test, which is particularly useful for an unbalanced and incomplete block design or in the presence of missing data due to design or missing at random. For function scores, a meaningful time analysis was not feasible due to high attrition for completed movement assessments and, therefore, we calculated matched pairs. We used the Wilcoxon signed-rank test as the hypothesis test and calculated the IQR. We used median intervals (days) between completed functional reports to form cohorts, which we referred to as “first-to-second entry,” “first-to-third entry,” and “first-to-fourth entry.” All participants were matched to themselves at baseline at the respective time of each entry. We used the Bonferroni method to control for family-wise errors and report corrected alpha levels for the Skillings-Mack test and Wilcoxon signed-rank test.

We performed chi-square tests to investigate the differences at baseline in pain area (ie, upper back, lower back, hip, or knee) and pain duration (ie, acute, subacute, or chronic) among participant age groups. TTo illustrate standardized residuals for each chi-square test, we presented mosaic plots. We recorded overall pain scores and classified pain duration at the time of enrollment according to global consensus [18,19].

User Statistics

A total of 3629 patients met the inclusion criteria and provided at least two data points needed for comparison with an intraindividual control over 12 weeks. We formed age groups to investigate differences between age groups in pain duration and pain area. Table 1 presents the baseline characteristics of the patients.

Using chi-square tests, the number of female or male patients did not significantly differ over 4 assessments (χ24=1.9; P=.75). Additionally, we investigated whether the 2 main demographic features of age group and sex influenced pain duration and pain area. We observed a significant association between pain duration and age group (χ210=24.36; P<.001). Moreover, we observed a significant association between sex and both pain duration (χ22=12.09; P=.002) and pain area (χ23=33.73; P<.001). Figure 2 illustrates these findings, and Multimedia Appendix 1, Multimedia Appendix 2, Multimedia Appendix 3, and Multimedia Appendix 4 further describe these results using contingency tables.

Table 1. Baseline characteristics of the study population.
Age group and sexTotal patients, nPain duration, nReported pain area, n

AcuteSubacuteChronicLower backUpper backHipKnee
18-35 years89112418058739734346105


36-45 years6721021224483132573666


46-55 years95410618766142530297130


56-65 years8137314759338622975123


66-75 years2482149178101652854


>75 years5149382013612


Figure 2. Mosaic plot illustrating the baseline distribution for age (A and B) or sex (C and D) by pain duration or pain area. The size of the square illustrates the number of observations. A larger square indicates the size of the observations regarding age or sex with the corresponding pain duration or pain area. The color indicates which direction this specific observation differs from the expected observation denoted by the standardized residuals. The color depth indicates how strongly the specific observation differs from the expected observation denoted by the standardized residuals.

Assessment of Patient-Reported Pain Intensity

Prior to the formation of indication-specific strata, we noted a substantial reduction in pain scores across 2, 4, 8, and 12 weeks (T3628=5308; P<.001). The mean pain intensity values (out of 10) at baseline, 2 weeks, 4 weeks, 8 weeks, and 12 weeks were 5.42 (SD 1.79), 4.36 (SD 2.21), 3.99 (SD 2.22), 3.84 (SD 2.27), and 3.48 (SD 2.36), respectively. Figure 3 and Table 2 illustrate these differences and report additional stratum-specific (ie, for different pain areas and pain durations) results.

Figure 3. Average self-reported pain score for each retention time period for all pain areas, specific pain areas, and pain areas and durations. The center line (green) indicates the median, boxplot limits indicate the upper and lower quartiles, whiskers indicate the 1.5× interquartile range, and points indicate outliers. *P<.05, **P<.005, ***P<.0005 (Skillings-Mack test).
Table 2. Self-reported pain scores and changes across indication subsets and reported pain durations by retained days.
Pain areaInitialWeek 2Week 4Week 8Week 12Skillings-Mack test

nMean (SD)nMean (SD)nMean (SD)nMean (SD)nMean (SD)Test values (df)Pain reduction, %
All36295.42 (1.79)17764.36 (2.21)13303.99 (2.22)8203.84 (2.27)4583.48 (2.36)5308.10 (3628)a−35.86
Lower back16425.43 (1.76)8124.44 (2.20)6014.08 (2.22)3764.02 (2.23)2073.78 (2.45)2369.86 (1641)a−30.39

Acute2314.87 (1.86)1173.62 (2.28)662.85 (2.35)422.57 (2.17)192.09 (1.97)304.46 (230)b−57.10

Subacute3145.21 (1.64)1494.30 (2.15)1123.98 (2.34)644.03 (2.22)314.39 (2.52)423.39 (313)c−15.76

Chronic10975.62 (1.74)5464.66 (2.16)4234.29 (2.11)2704.22 (2.18)1573.86 (2.43)1620.33 (1096)a−31.20
Upper back12095.59 (1.78)5734.51 (2.23)4194.20 (2.26)2494.09 (2.27)1293.58 (2.34)1717.08 (1208)a−35.98

Acute1404.50 (1.67)612.88 (2.03)522.63 (2.00)282.71 (2.39)182.55 (3.01)171.14 (139)d−43.43

Subacute2075.40 (1.69)944.23 (2.29)753.84 (1.83)353.00 (1.44)222.60 (1.71)285.51 (206)c−51.86

Chronic8625.82 (1.75)4184.82 (2.13)2924.56 (2.29)1864.50 (2.24)893.98 (2.21)1216.06 (861)a−31.57
Hip2885.36 (1.75)1594.35 (2.20)1223.80 (1.98)733.42 (2.16)453.00 (2.16)441.23 (287)a−44.04

Acute244.63 (1.95)113.18 (1.66)143.54 (2.15)73.00 (2.45)44.50 (0.71)34.01 (23)d−2.70

Subacute665.06 (1.61)383.97 (2.20)263.35 (1.96)122.50 (2.17)102.00 (1.63)97.65 (65)d−60.48

Chronic1985.55 (1.74)1104.60 (2.20)824.00 (1.96)543.69 (2.12)313.21 (2.32)298.20 (197)a−42.16
Knee4904.97 (1.85)2323.71 (2.09)1883.41 (2.20)1223.20 (2.31)772.87 (2.15)744.03 (489)a−42.19

Acute354.46 (1.72)152.73 (1.44)142.79 (1.72)92.13 (1.81)71.60 (1.82)45.47 (34)d−64.10

Subacute1074.63 (1.73)522.85 (1.69)362.88 (2.21)231.89 (1.56)132.11 (1.45)138.91 (106)d−54.37

Chronic3485.12 (1.88)1654.07 (2.16)1383.61 (2.22)903.63 (2.37)573.20 (2.26)544.55 (347)a−37.51

aAdjusted P<.0005 (calculated using Bonferroni correction).

bAdjusted P<.05 (calculated using Bonferroni correction).

cAdjusted P<.005 (calculated using Bonferroni correction).

dAdjusted P>.05 (calculated using Bonferroni correction).

Patient-Reported Functional Assessment

Chronic conditions appeared to improve along the dimensions of strength, mobility, and coordination, as well as the total score (Tables 3-6). This finding was consistent over all intervals of submitted function scores assessed (Multimedia Appendix 5 and Multimedia Appendix 6). Within chronic conditions, only patients with chronic hip pain did not achieve significant improvements in mobility and coordination across any completed submission of function scores (Multimedia Appendix 5 and Multimedia Appendix 6). Overall, the strength score showed significant improvements across most pain areas studied. However, patients with acute lower back pain, acute upper back pain, and acute hip pain did not show significant improvements in strength scores between their first and second assessments of function scores (Tables 3-6).

Table 3. Self-reported total function scores and changes across indication subsets and reported pain durations by retained days.
Reported pain area and durationRetained days, value (IQR)Initial, value (IQR)Last, value (IQR)P valuea
Lower back    

Acute (n=50)29 (14-32)67 (50-77)73 (60-80).0028b

Subacute (n=74)29 (17-33)63 (47-73)67 (53-80)<.0001c

Chronic (n=326)29 (23-32)57 (40-73)67 (50-80)<.0001c
Upper back    

Acute (n=29)30 (10-33)67 (53-73)77 (60-87).0001c

Subacute (n=51)29 (28-36)60 (37-77)63 (43-80).0120b

Chronic (n=226)29 (17-33)53 (33-67)57 (43-73)<.0001c

Acute (n=12)28 (25.5-29.5)65 (48.5-70)71.5 (57-88).0566d

Subacute (n=22)27.5 (13-30)55 (43-67)67 (50-80).0021b

Chronic (n=70)29 (28-32)63 (50-73)67 (53-80).0025b

Acute (n=12)29 (28-36)53 (45-65)71.5 (53.5-80).0371d

Subacute (n=32)28.5 (23-32.5)63 (55-73)77 (63-87)<.0001c

Chronic (n=107)30 (27-35)60 (47-70)67 (53-80)<.0001c

aAdjusted for family-wise error using the Bonferroni method.



dNot significant.

Table 4. Self-reported strength scores and changes across indication subsets and reported pain durations by retained days.
Reported pain area and durationRetained days, value (IQR)Initial, value (IQR)Last, value (IQR)P valuea
Lower back    

Acute (n=50)29 (14-32)60 (40-80)70 (50-80).0215b

Subacute (n=74)29 (17-33)60 (30-70)60 (40-90).0010c

Chronic (n=326)29 (23-32)50 (30-70)60 (40-80)<.0001d
Upper back    

Acute (n=29)30 (10-33)60 (50-80)60 (60-100).0198b

Subacute (n=51)29 (28-36)50 (20-80)60 (40-90).0076e

Chronic (n=226)29 (17-33)50 (20-80)60 (40-80)<.0001d

Acute (n=12)28 (25.5-29.5)45 (40-70)75 (45-85).1270b

Subacute (n=22)27.5 (13-30)55 (20-60)65 (40-100).0001d

Chronic (n=70)29 (28-32)60 (40-80)70 (50-100).0093e

Acute (n=12)29 (28-36)45 (20-70)60 (45-100).0156e

Subacute (n=32)28.5 (23-32.5)80 (60-80)90 (60-100).0066e

Chronic (n=107)30 (27-35)60 (40-80)70 (50-100).0006c

aAdjusted for family-wise error using the Bonferroni method.

bNot significant.




Table 5. Self-reported mobility scores and changes across indication subsets and reported pain durations by retained days.
Reported pain area and durationRetained days, value (IQR)Initial, value (IQR)Last, value (IQR)P valuea
Lower back    

Acute (n=50)29 (14-32)70 (55-80)75 (65-80).0297b

Subacute (n=74)29 (17-33)67,5 (45-80)70 (55-80).0006c

Chronic (n=326)29 (23-32)60 (45-75)70 (50-80)<.0001d
Upper back    

Acute (n=29)30 (10-33)65 (55-75)80 (60-90).0001d

Subacute (n=51)29 (28-36)60 (40-75)55 (45-80).1191b

Chronic (n=226)29 (17-33)50 (35-70)55 (40-75)<.0001d

Acute (n=12)28 (25.5-29.5)67.5 (52.5-85)67.5 (60-87.5).2578b

Subacute (n=22)27.5 (13-30)57.5 (50-70)62.5 (50-80).0251b

Chronic (n=70)29 (28-32)60 (50-70)65 (50-75).0201b

Acute (n=12)29 (28-36)60 (52.5-70)70 (52.5-82.5).1426b

Subacute (n=32)28.5 (23-32.5)60 (50-70)72.5 (60-82.5)<.0001d

Chronic (n=107)30 (27-35)60 (45-70)65 (50-80)<.0001d

aAdjusted for family-wise error using the Bonferroni method.

bNot significant.



Table 6. Self-reported coordination scores and changes across indication subsets and reported pain durations by retained days.
Reported pain area and durationRetained days, value (IQR)Initial, value (IQR)Last, value (IQR)P valuea
Lower back    

Acute (n=50)29 (14-32)70 (60-80)80 (60-80).1368b

Subacute (n=74)29 (17-33)70 (50-80)80 (50-80).0766b

Chronic (n=326)29 (23-32)65 (40-80)80 (50-90)<.0001c
Upper back    

Acute (n=29)30 (10-33)80 (60-80)80 (80-100).2664b

Subacute (n=51)29 (28-36)60 (40-80)70 (40-80).2129b

Chronic (n=226)29 (17-33)60 (40-80)60 (40-80).0005d

Acute (n=12)28 (25.5-29.5)60 (55-80)70 (55-95).5000b

Subacute (n=22)27.5 (13-30)60 (30-80)65 (40-80).1396b

Chronic (n=70)29 (28-32)60 (40-80)60 (50-80).2875b

Acute (n=12)29 (28-36)55 (35-60)60 (55-60).1562b

Subacute (n=32)28.5 (23-32.5)60 (50-80)80 (60-85).0066e

Chronic (n=107)30 (27-35)60 (40-80)60 (40-80).0026e

aAdjusted for family-wise error using the Bonferroni method.

bNot significant.




The continuation of exercise and the consequent submission of further function scores led to significant improvements in the strength score for only patients with acute lower back pain (Multimedia Appendix 5). Similarly, mobility and coordination scores improved particularly well in patients with chronic conditions, but failed to improve significantly in patients with chronic hip pain. Yet, compared with the pain scores, we did not see a leveling off of the improvements after the first reported interval and saw continuous improvements, particularly in the mobility and strength scores (Multimedia Appendix 5 and Multimedia Appendix 6).

Assessment of Retention

Our analysis showed that the home exercise app Vivira achieved overall retention rates of 36.6% (601/1642) for lower back pain, 34.7% (419/1209) for upper back pain, 42.4% (122/288) for hip pain, and 38.4% (188/490) for knee pain after 4 weeks (Figure 4; Multimedia Appendix 7). After 12 weeks, the retention rates ranged from 8% (acute lower back pain and chronic upper back pain) to 20% (acute knee pain), with an average of 14% (Multimedia Appendix 7).

Figure 4. Retention rates for different pain areas and durations. A more detailed overview is provided in Multimedia Appendix 7.

Principal Findings

The data showed improvements in the primary outcome, as indicated by a significant decrease in overall pain intensity, and most of the secondary outcomes (pain area; pain area by pain duration, as assessed with a VNRS; and function scores). A reduction in acute pain intensity was only observed in patients with lower back pain, while we observed no significant changes in the remaining patients. Under the assumption that providers did not prescribe Vivira for conditions not covered by the approved spectrum of conditions, we hypothesized that most acute pain episodes in the hip and knee reflected acute exacerbations of pre-existing structural and degenerative conditions (eg, activated osteoarthritis) that cannot be addressed sufficiently with only a self-directed home exercise program. Yet, conclusions based on the limited sample size of the hip and knee groups warrant careful consideration.

A statistically significant change in any patient-reported outcome does not per se reflect a clinically meaningful effect. It, therefore, needs to be discussed whether changes in pain scores also reflect a clinically significant change. For acute pain, related work has established a robust equivalence of different pain scores and its response to different therapeutic interventions. Holdgate et al [20] estimated pain score reductions between 1.2 and 1.6 points on a VNRS to be within a minimum clinically significant difference. For chronic pain, a broad consensus has been established that a clinically important difference can be assumed if pain reduction of >30% from the initial pain intensity is achieved [21,22]. Applying these thresholds to the data at hand, we conclude that the achieved pain score reductions after 2, 4, 8, and 12 weeks (reduction of 1.94 points) are well in the range of the minimum clinically significant difference. After 12 weeks, a >30% reduction in the pain intensity was seen in all pain areas, and most pain areas (upper back, hip, and knee) also crossed the clinically important difference threshold. However, it remains to be discussed why the effect of the app levels off to a steady state after the early use phase. We hypothesize that the mainly unspecific and degenerative musculoskeletal conditions are effectively addressed by a constant exercise intensity that can be maintained over a long period, as shown in the existing literature [23,24]. Additionally, we used interdisciplinary expert consensus from a panel of orthopedic surgeons and physiotherapists, as well as data from a randomized controlled trial of the home exercise program to assess the plausibility of the results from this study [25]. However, owing to the high attrition in the data set, careful interpretation of potential biases is warranted.

Secondary Results

In line with the improvements in pain intensity, we saw significant improvements in the function scores for most indication subgroups and pain durations. These improvements were particularly emphasized for chronic conditions. Interestingly, the responses for hip conditions in general and acute hip pain in particular were not of a relevant magnitude, except for the subacute and chronic strength scores. We attribute this to the fact that most patients in this category had osteoarthritis or other degenerative conditions of the hip joint, which are typically associated with a much greater limitation in the range of movement compared to, for example, degenerative conditions of the knee. Additionally, an episode of acute pain in any degenerative musculoskeletal condition likely reflects an exacerbation, and an exercise program might not provide the ideal therapeutic intervention for this context. Additionally, we are aware of the small sample size of this subgroup and hence consider the explanatory power of this subgroup analysis as greatly limited. A second noteworthy aspect centers around the assessment of coordination, which only demonstrated significant improvements in patients with chronic lower back pain, chronic upper back pain, and chronic knee pain. In comparison with the strength and mobility assessments, which showed significant improvements across most indication subgroups and pain durations, the limited performance of the coordination dimension reflects either an insufficient stimulus to improve coordination through the individualized exercise program or a much more consolidated deficit in coordination that lags behind the responses in the strength and mobility scores. In line with the principles of regional interdependence, we consider the latter plausible [15,16]. Consequently, we saw more sustained responses in the coordination score for prolonged use phases among patients with chronic pain across all pain areas (Multimedia Appendix 5 and Multimedia Appendix 6). Another aspect addresses the patterns of improvement over the time of use. In contrast to pain score reductions, which leveled off after the early use phase and were primarily maintained during the subsequent maintenance use phase, we saw a continued improvement in the function scores reported (Multimedia Appendix 5 and Multimedia Appendix 6).

Strengths and Limitations

The strengths of this study are the large real-world prescription data set and the use of the first prescription-based postmarketing data available from a DiGA for musculoskeletal conditions within the regulatory framework of the DVG. The findings provide insights into the clinical effects expected in a real-world care setting and highlight the methodological challenges of complex patient-reported data sets. The importance of these data for the thorough assessment of novel and digital therapeutics has been underscored by the introduction of the United States Twenty-First Century Cures Act in 2016 and the communicated position by the European Medicines Agency [26,27]. Yet, there are some relevant limitations in our study that primarily affect the external validity of our findings. First, the enrollment was assumed to follow a relative self-selection mechanism, which introduced a relevant selection bias that we could not control, given the study design. Second, and although our data showed above-average retention rates, we noted a relevant loss to follow-up across all strata, which is probably of differential nature. Yet, this is not unexpected, as related work has also reported a significant decline in participation in digital health applications [28,29], and we consider it an adequate reflection of the current real-world pattern of use. Additional limitations due to the real-world setting are that we were not able to record any medical history from participants regarding other events that may have affected the initial pain, the development of pain intensity, or the potential effects of the measurements (including but not limited to the occurrence of other physical or psychological diseases) or maturation of the patients (eg, coming to terms with constant or chronic pain and developing coping mechanisms that might influence the perception of pain intensity). Furthermore, we could not measure how familiar the patients were with digital interventions. Since patients can also improve owing to other uncontrollable factors, we need to acknowledge a potential regression toward the mean. As in most observational studies, measuring outcomes may influence the outcomes. However, since the measurements were included in the intervention itself, we would argue that this effect is smaller in this study compared with other work in which measurements were conducted outside of the intervention (eg, pre-post examinations of physicians). Lastly, we consider the consensus-based discrete transformation of the binary results of the movement assessment as methodologically challenging, and thus, a quantitative validation is required.

Comparison With Prior Work

This work complements preliminary use data of the same digital home exercise program published elsewhere [30]. While the preliminary data also demonstrated a significant and clinically relevant reduction in pain intensity prior to stratification, this study allows a more robust interpretation at the indication-specific level and shows significant improvements in pain intensity for patients with upper and lower back pain, as well as for subpopulations with subacute and chronic hip and knee pain. Although the retention rates in this analysis are considerably higher than the rates in the preliminary analysis, this study suffers from high and probably differential loss to follow-up, which may result in selection bias. Retention, which is required to enable a sufficiently granular analysis of the use and outcome data, is a well-described problem of digital therapeutics. Baumel et al [29] reported an average 30-day retention rate of 3.3% (IQR 6.2%) for all digital health applications examined. Although their analysis was limited to digital therapeutics for mental health conditions and included only those applications that were freely available on the internet and in established online stores (ie, Google Play Store), it exemplifies the stereotypical retention curve of many digital therapeutics well and underscores the common challenge of increasing the retention rate for digital therapeutics. In comparison to these data, our study showed above-average retention rates (Figure 4; Multimedia Appendix 7). We know, however, that free-to-use digital health applications likely have different interaction and retention dynamics than DiGAs and comparable prescription digital health applications. Pratap et al [28] identified (1) required prescription by a physician or psychotherapist, (2) presence of at least one specified condition, and (3) middle to old age as factors that contribute to higher retention rates. From our perspective, all factors were met for our investigation of Vivira. We, therefore, assume that average retention rates are likely to be significantly higher among DiGAs than among free-to-use digital health applications, although further research needs to yield the required evidence. In addition, patient perception of effectiveness and gamification elements can probably contribute to a higher retention rate, although the available evidence in this field needs to be substantiated further [31,32].


Digital therapeutics can offer accessible and readily available therapeutic means at scale to effectively address the increasing demand for care arising from unspecific and degenerative musculoskeletal conditions. This work presents the first postmarketing data to demonstrate the real-world effects of a digital prescription home exercise program under the DVG for a broad spectrum of unspecific and degenerative musculoskeletal conditions. The demonstration of statistically significant and clinically relevant effects is crucial to establish digital therapeutics as a therapeutic option in the field of musculoskeletal health. As reported in this study, complex user-reported observational data pose analytical challenges and have not yet become a standard feature in the evaluation process of digital therapeutics. Nevertheless, these data will likely complement confirmatory trial data for the clinical and regulatory assessment of the effectiveness of digital therapeutics.


The authors acknowledge the work of Markus Klingenberg, who developed the therapy concept of the medical software device assessed in this study. This includes the digital implementation of the functional therapeutic approach, the device’s software-patient feedback interface, and the device’s exercise progression algorithm.

Authors' Contributions

GWT contributed to data analysis, visualization, and interpretation, and drafting of the manuscript. TK provided methodological guidance and reviewed the manuscript. FPH contributed to the development of the study concept, data analysis and interpretation, and manuscript composition. LB led the development of the study concept, prepared the data collection, contributed to the data analysis and interpretation, and contributed to the manuscript draft. All authors thoroughly reviewed the manuscript.

Conflicts of Interest

GWT and TK are affiliated with the Centre for Digital Health Interventions (CDHI), a joint initiative of the Institute for Implementation Science in Health Care, University of Zurich; the Department of Management, Technology, and Economics at Swiss Federal Institute of Technology in Zürich; and the Institute of Technology Management and School of Medicine at the University of St Gallen. CDHI is funded in part by the Swiss health insurer CSS. CSS was not involved in this study. TK is also a co-founder of Pathmate Technologies, a university spin-off company that creates and delivers digital clinical pathways. However, Pathmate Technologies was not involved in this study. FPH states no conflicts of interest. LB is affiliated with Vivira Health Lab GmbH, the developer of the software examined, and received funding through the Berta-Ottenstein-Programme for Clinician Scientists from the Faculty of Medicine, University of Freiburg.

Multimedia Appendix 1

Cross table from the chi-square test for pain duration by age.

DOCX File , 18 KB

Multimedia Appendix 2

Cross table from the chi-square test for pain area by age.

DOCX File , 18 KB

Multimedia Appendix 3

Cross table from the chi-square test for pain duration by sex.

DOCX File , 16 KB

Multimedia Appendix 4

Cross table from the chi-square test for pain area by sex.

DOCX File , 16 KB

Multimedia Appendix 5

Self-reported functional scores and changes across indication subsets and reported pain durations by retained days in matched comparisons between the first and third completed functional assessments.

DOCX File , 21 KB

Multimedia Appendix 6

Self-reported functional scores and changes across indication subsets and reported pain durations by retained days in matched comparisons between the first and fourth completed functional assessments.

DOCX File , 21 KB

Multimedia Appendix 7

User retention across indication subsets and reported pain durations.

DOCX File , 18 KB

  1. GBD 2019 Diseases and Injuries Collaborators. 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 Oct 17;396(10258):1204-1222 [FREE Full text] [CrossRef] [Medline]
  2. Fuchs J, Rabenberg M, Scheidt-Nave C. [Prevalence of selected musculoskeletal conditions in Germany: results of the German Health Interview and Examination Survey for Adults (DEGS1)]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2013 May 25;56(5-6):678-686 [FREE Full text] [CrossRef] [Medline]
  3. Woolf AD, Erwin J, March L. The need to address the burden of musculoskeletal conditions. Best Pract Res Clin Rheumatol 2012 Apr;26(2):183-224 [FREE Full text] [CrossRef] [Medline]
  4. Bevan S. Economic impact of musculoskeletal disorders (MSDs) on work in Europe. Best Pract Res Clin Rheumatol 2015 Jun;29(3):356-373. [CrossRef] [Medline]
  5. Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, Lancet Low Back Pain Series Working Group. What low back pain is and why we need to pay attention. Lancet 2018 Jun 09;391(10137):2356-2367 [FREE Full text] [CrossRef] [Medline]
  6. Smith BE, Hendrick P, Bateman M, Holden S, Littlewood C, Smith TO, et al. Musculoskeletal pain and exercise-challenging existing paradigms and introducing new. Br J Sports Med 2019 Jul 20;53(14):907-912 [FREE Full text] [CrossRef] [Medline]
  7. Rommel A, Kroll L. Individual and regional determinants for physical therapy utilization in Germany: Multilevel analysis of national survey data. Phys Ther 2017 May 01;97(5):512-523. [CrossRef] [Medline]
  8. Grinblat R. The Role of Law in Creating Space for Innovation: An Example from the Healthcare Sector in Germany. In: Nestle V, Glauner P, Plugmann P, editors. Creating Innovation Spaces. Management for Professionals. Cham: Springer; 2021:209-219.
  9. Walzer S. Digital Healthcare in Germany: Market Access for Innovations. Cham: Springer; 2022.
  10. Busse R, Blümel M, Knieps F, Bärnighausen T. Statutory health insurance in Germany: a health system shaped by 135 years of solidarity, self-governance, and competition. Lancet 2017 Aug 26;390(10097):882-897. [CrossRef] [Medline]
  11. Gerke S, Stern AD, Minssen T. Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries. NPJ Digit Med 2020 Jul 10;3(1):94. [CrossRef]
  12. Lauer W, Löbker W, Höfgen B. [Digital health applications (DiGA): assessment of reimbursability by means of the "DiGA Fast Track" procedure at the Federal Institute for Drugs and Medical Devices (BfArM)]. Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2021 Oct 16;64(10):1232-1240 [FREE Full text] [CrossRef] [Medline]
  13. Stern AD, Brönneke J, Debatin JF, Hagen J, Matthies H, Patel S, et al. Advancing digital health applications: priorities for innovation in real-world evidence generation. The Lancet Digital Health 2022 Mar;4(3):e200-e206 [FREE Full text] [CrossRef]
  14. Gensorowsky D, Witte J, Batram M, Greiner W. Market access and value-based pricing of digital health applications in Germany. Cost Eff Resour Alloc 2022 Jun 13;20(1):25 [FREE Full text] [CrossRef] [Medline]
  15. Sueki DG, Cleland JA, Wainner RS. A regional interdependence model of musculoskeletal dysfunction: research, mechanisms, and clinical implications. J Man Manip Ther 2013 May 12;21(2):90-102 [FREE Full text] [CrossRef] [Medline]
  16. Wainner RS, Whitman JM, Cleland JA, Flynn TW. Regional interdependence: a musculoskeletal examination model whose time has come. J Orthop Sports Phys Ther 2007 Nov;37(11):658-660 [FREE Full text] [CrossRef] [Medline]
  17. Aicher B, Peil H, Peil B, Diener H. Pain measurement: Visual Analogue Scale (VAS) and Verbal Rating Scale (VRS) in clinical trials with OTC analgesics in headache. Cephalalgia 2012 Feb 15;32(3):185-197. [CrossRef] [Medline]
  18. Nugraha B, Gutenbrunner C, Barke A, Karst M, Schiller J, Schäfer P, IASP Taskforce for the Classification of Chronic Pain. The IASP classification of chronic pain for ICD-11: functioning properties of chronic pain. Pain 2019 Jan;160(1):88-94. [CrossRef] [Medline]
  19. Merskey H. The definition of pain. Eur. Psychiatr 2020 Apr 16;6(4):153-159 [FREE Full text] [CrossRef]
  20. Holdgate A, Asha S, Craig J, Thompson J. Comparison of a verbal numeric rating scale with the visual analogue scale for the measurement of acute pain. Emerg Med (Fremantle) 2003;15(5-6):441-446. [CrossRef] [Medline]
  21. Farrar J, Portenoy R, Berlin J, Kinman J, Strom B. Defining the clinically important difference in pain outcome measures. Pain 2000 Dec 01;88(3):287-294. [CrossRef] [Medline]
  22. Ostelo RWJG, Deyo RA, Stratford P, Waddell G, Croft P, Von Korff M, et al. Interpreting change scores for pain and functional status in low back pain. Spine 2008;33(1):90-94. [CrossRef]
  23. Jakobsen MD, Sundstrup E, Brandt M, Andersen LL. Factors affecting pain relief in response to physical exercise interventions among healthcare workers. Scand J Med Sci Sports 2017 Dec 28;27(12):1854-1863 [FREE Full text] [CrossRef] [Medline]
  24. Geneen L, Moore R, Clarke C, Martin D, Colvin L, Smith B. Physical activity and exercise for chronic pain in adults: an overview of Cochrane Reviews. Cochrane Database Syst Rev 2017 Jan 14;1(1):CD011279 [FREE Full text] [CrossRef] [Medline]
  25. Weise H, Zenner B, Schmiedchen B, Benning L, Bulitta M, Schmitz D, et al. The effect of an app-based home exercise program on self-reported pain intensity in unspecific and degenerative back pain: Pragmatic open-label randomized controlled trial. J Med Internet Res 2022 Oct 28;24(10):e41899 [FREE Full text] [CrossRef] [Medline]
  26. Arlett P, Kjaer J, Broich K, Cooke E. Real-world evidence in EU medicines regulation: Enabling use and establishing value. Clin Pharmacol Ther 2022 Jan 19;111(1):21-23 [FREE Full text] [CrossRef] [Medline]
  27. Brown JP, Wing K, Evans SJ, Bhaskaran K, Smeeth L, Douglas IJ. Use of real-world evidence in postmarketing medicines regulation in the European Union: a systematic assessment of European Medicines Agency referrals 2013-2017. BMJ Open 2019 Oct 28;9(10):e028133 [FREE Full text] [CrossRef] [Medline]
  28. Pratap A, Neto EC, Snyder P, Stepnowsky C, Elhadad N, Grant D, et al. Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants. NPJ Digit Med 2020 Feb 17;3(1):21 [FREE Full text] [CrossRef] [Medline]
  29. Baumel A, Muench F, Edan S, Kane JM. Objective user engagement with mental health apps: Systematic search and panel-based usage analysis. J Med Internet Res 2019 Sep 25;21(9):e14567 [FREE Full text] [CrossRef] [Medline]
  30. Teepe GW, Kowatsch T, Hans FP, Benning L. Preliminary use and outcome data of a digital home exercise program for back, hip, and knee pain: Retrospective observational study with a time series and matched analysis. JMIR Mhealth Uhealth 2022 Dec 02;10(12):e38649 [FREE Full text] [CrossRef] [Medline]
  31. Gentry SV, Gauthier A, L'Estrade Ehrstrom B, Wortley D, Lilienthal A, Tudor Car L, et al. Serious gaming and gamification education in health professions: Systematic review. J Med Internet Res 2019 Mar 28;21(3):e12994 [FREE Full text] [CrossRef] [Medline]
  32. Looyestyn J, Kernot J, Boshoff K, Ryan J, Edney S, Maher C. Does gamification increase engagement with online programs? A systematic review. PLoS One 2017;12(3):e0173403 [FREE Full text] [CrossRef] [Medline]

DiGA: Digitale Gesundheitsanwendung (Digital Health Application)
DVG: Digitale-Versorgung-Gesetz (Digital Health Care Act)
VNRS: verbal numerical rating scale

Edited by G Eysenbach; submitted 24.10.22; peer-reviewed by H Mühlan, M Swartz, PhD, MPH, RD, LD; comments to author 15.12.22; revised version received 05.01.23; accepted 11.01.23; published 27.02.23


©Gisbert Wilhelm Teepe, Tobias Kowatsch, Felix Patricius Hans, Leo Benning. Originally published in the Journal of Medical Internet Research (, 27.02.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on, as well as this copyright and license information must be included.