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Hip and knee osteoarthritis is ranked as the 11th highest contributor to global disability. Exercise is a core treatment in osteoarthritis. The model for physical activity–related health competence describes possibilities to empower patients to perform physical exercises in the best possible health-promoting manner while taking into account their own physical condition. Face-to-face supervision is the gold standard for exercise guidance.
The aim of this study was to evaluate whether instruction and guidance via a digital app is not inferior to supervision by a physiotherapist with regard to movement quality, control competence for physical training, and exercise-specific self-efficacy.
Patients with clinically diagnosed hip osteoarthritis were recruited via print advertisements, emails and flyers. The intervention consisted of two identical training sessions with one exercise for mobility, two for strength, and one for balance. One session was guided by a physiotherapist and the other was guided by a fully automated tablet computer-based app. Both interventions took place at a university hospital. Outcomes were assessor-rated movement quality, and self-reported questionnaires on exercise-specific self-efficacy and control competence for physical training. Participants were randomly assigned to one of two treatment sequences. One sequence started with the app in the first session followed by the physiotherapist in the second session after a minimum washout phase of 27 days (AP group) and the other sequence occurred in the reverse order (PA group). Noninferiority was defined as a between-treatment effect (gIG)<0.2 in favor of the physiotherapist-guided training, including the upper confidence interval. Participants, assessors, and the statistician were neither blinded to the treatment nor to the treatment sequence.
A total of 54 participants started the first training session (32 women, 22 men; mean age 62.4, SD 8.2 years). The treatment sequence groups were similar in size (PA: n=26; AP: n=28). Seven subjects did not attend the second training session (PA: n=3; AP: n=4). The app was found to be inferior to the physiotherapist in all outcomes considered, except for movement quality of the mobility exercise (gIG –0.13, 95% CI –0.41-0.16). In contrast to the two strengthening exercises in different positions (supine gIG 0.76, 95% CI 0.39-1.13; table gIG 1.19, 95% CI 0.84-1.55), movement quality of the balance exercise was close to noninferiority (gIG 0.15, 95% CI –0.17-0.48). Exercise-specific self-efficacy showed a strong effect in favor of the physiotherapist (gIG 0.84, 95% CI 0.46-1.22). In terms of control competence for physical training, the app was only slightly inferior to the physiotherapist (gIG 0.18, 95% CI –0.14-0.50).
Despite its inferiority in almost all measures of interest, exercise-specific self-efficacy and control competence for physical training did improve in patients who used the digital app. Movement quality was acceptable for exercises that are easy to conduct and instruct. The digital app opens up possibilities as a supplementary tool to support patients in independent home training for less complex exercises; however, it cannot replace a physiotherapist.
German Clinical Trial Register: DRKS00015759; http://www.drks.de/DRKS00015759
Osteoarthritis (OA) is characterized by pathological changes of the joint structure and is accompanied by pain and functional limitations. The global burden of hip and knee OA was ranked as the 11th highest contributor to worldwide disability [
The promotion of physical activity in general, and exercise in particular, is highly relevant for patients with OA, as it has been shown to decrease pain and improve physical functioning [
Supervision seems to be an effective means to promote safe and correct exercise techniques, especially in the initial stages of the disease, and to ensure the right exercise dosage for each individual according to their physical ability and dosage principles from a training science perspective [
As a consequence, exploring alternate cost-efficient forms of delivery modes for people with limited access to therapeutic services appears to be indicated [
Digital interventions offer various types of interfaces and degrees of supervision. First, blended physiotherapy partially replaces face-to-face appointments with a digital app comprising instructions for graded activity, complementary unsupervised exercises, and behavior-change techniques. The physiotherapist plays an active role in patient interaction with regard to exercise content, as well as in supervision of intervention progress [
In recent years, a large number of digital exercise interventions have emerged, but there is a lack of studies comparing apps or digital interventions with human-delivered approaches in the field of chronic diseases. Hsu et al [
In the present study, we evaluated a fully automated digital app that was developed on the basis of a paper-based home exercise program, which emerged as one of the important features of an evidence-based exercise intervention for patients with hip OA [
With regard to the effectiveness of exercise interventions in general, and for digital interventions in particular, a theory-based approach has been shown to optimize the targeted effects [
The PAHCO model considers three subcompetences, each of which specifically helps in coping with demands that arise during the initiation and maintenance of physical exercise in a health-enhancing way.
Based on the aforementioned knowledge deficits, the aim of this crossover study was to evaluate whether exercise instruction and guidance of one training session via a fully automated tablet computer-based app results in comparable benefits in subcompetences of PAHCO in comparison to one session supervised by a physiotherapist in subjects with hip OA. Our hypotheses were as follows: (1) movement quality of exercises guided by the app is not inferior to the movement quality under supervision by a physiotherapist; (2) the effect of the app on control competence is not inferior to the effect of the physiotherapist-guided intervention; and (3) the effects of the app on exercise self-efficacy (as a prerequisite of self-regulation competence) are not inferior to the effects of the physiotherapist-guided intervention.
This study was designed as a randomized 2×2 crossover trial. The participants were randomly assigned to one of two exercise treatment sequences with an allocation ratio of 1:1. The AP sequence started with a training session instructed by a fully automated tablet computer-based digital app, followed by an intervention supervised by a physiotherapist, whereas the PA sequence started with the physiotherapist and the second training intervention was conducted with the app. The washout phase between the two interventions was set to range between 3 and 5 weeks. This time period seemed sufficient to washout relevant treatment effects of a single exercise. Ethical approval was obtained from the ethical committee of Tuebingen University Hospital. The study was registered in the German clinical trial register (DRKS00015759).
Community-dwelling individuals with diagnosed hip OA were recruited via advertisements in regional newspapers, as well as by emails sent to employees of the University of Tuebingen and Tuebingen University Hospital. In addition, flyers were distributed by orthopedic surgeons and physiotherapists. Interested participants were asked to call staff members for further information. According to ethical guidelines, the subjects were fully informed about the positive effects of exercise therapy for hip OA. They were also informed about the structure and details of the app (ie, feedback mechanisms) and the research questions. The screening for eligibility took place in the context of this phone call. Eligible people were then randomly allocated to one of the two treatment sequences and the dates for the two treatment sessions were scheduled. The treatment sequence was blinded until the first treatment session. Both treatments took place at Tuebingen University Hospital. Inclusion and exclusion criteria for participants are described in
Inclusion criteria
50 years and older
self-reported lifetime prevalence of hip osteoarthritis diagnosed by a medical practitioner
informed consent to study participation
Exclusion criteria (general)
comorbidities leading to major impairments in everyday life and representing contraindications for physical activities
self-reported acute illness
significant established osteoporosis requiring treatment, previous spontaneous or low-impact fracture
musculoskeletal surgery at the lower extremity within the last 3 months
regular use of gait aids (eg, walker, crutch)
insufficient German language skills for self-administered questionnaires
previous experience in hip exercise groups
Exclusion criteria (in cases of an artificial joint replacement at the other hip or the knee joints)
artificial joint replacement at the knee or hip joint within the last 6 months, with unstable anchoring or with known radiological signs of implant loosening
current pain at rest or with activity due to artificial joint replacement
luxation as an adverse event of artificial hip joint replacement
acute joint inflammation at the knee or hip joint
The interventions used in this study were extracted from an evidence-based 12-week exercise program that was specifically designed for patients with hip OA [
Participants were asked to comment on perceived exertion and OA-related pain after each set using a 10-point Likert scale. The target value for intensity (last row of
Details of the exercises.
Detail | Pelvic tilt | Hip abduction | Hip extension | Balance |
Name | mobility_seated | strength_supine | strength_table | balance_stance |
Position | Seated | Supine | Table-supported stand | Step position |
Kit | Stool | Mat, elastic band, pillow | Table, weight cuff, upper body padding | Balance pad |
Aim | Pelvic, hip, and lumbar spine mobility; movement learning | Strength endurance | Strength endurance | Balance improvement, fall prevention |
Description | Tilting the pelvis back and forth in the sagittal plane | An elastic band is placed below the knees. The feet are set. The knees are slowly tilted outward in the frontal plane | The upper body rests on the table and is supported by the arms. One leg is angled and slowly led back up in the sagittal plane. After adjusting the intensity, the exercise is performed with either an extended leg or an additional weight cuff | Step position both with open and closed eyes as well as with stable and unstable ground |
Repetitions | 30 | 20 | 25 (if exercise is performed with an additional weight cuff, the number of repetitions is reduced to 15) | 15 seconds |
Sets | 2 | 3 | 3 | 6 |
Intensity | Low, no physical strain | 6-7 after the last repetition, still able to perform the exercise correctly | 6-7 after the last repetition, still able to perform the exercise correctly | Intensity is related to the difficulty of the balance task and is upgraded as long as the exercise can be performed correctly |
The supervisor was a qualified physiotherapist with 5 years of work experience. She was responsible for (1) introducing exercises, (2) correcting deficient or improper execution of exercises, (3) adjusting intensity, and (4) instructing participants in the case of increasing pain. In addition, it was at the physiotherapist’s judgement whether a participant should skip a simple intensity level and start directly with a more demanding variation.
The exercises were video-supported. Movement speeds were set using an auditory “click” sound and visually by the training partner in the video (see
The app was designed for a 9.7-inch (24.64 cm) Apple iPad and was developed by Ambigate GmbH (Tuebingen, Germany) according to the specifications of the authors. It is not open to the public. In line with the interventional implications of the PAHCO model, the content is basically a combination of practical exercises, cognitive and motor learning, and the processing of personal experience with movement [
Videos and acoustic signals were implemented in the software to guide the different exercises and to support the participant during the exercises. The videos can be divided into different categories: (1) instruction video, (2) exercise video, (3) focus video, and (4) video for intensity adjustment. The characteristics of the videos, such as the perspective or benefits of close-ups, were tested in a pilot study during the app development process. The pilot study was conducted on 13 participants aged 50 years and older. This resulted in the implementation of close-ups in both focus videos and exercise videos. In addition, the camera’s perspective was optimized so that starting positions and movements were optimally visible. In addition to the video structure, the pilot study also focused on the choice of adequate actors. The pilot study showed a tendency for gender and age to play only a minor role. It was much more important for the participants that the exercise was performed by the model in a clearly visible and correct manner. Hence, the actors in the videos are middle-aged, a man and a woman, and represent an average of the healthy population.
Sociodemographic, anthropometric, personal, and OA-related variables were used to characterize the sample, including age, gender, educational level, work-related life situation, previous experience with exercise groups, importance of sport throughout life, and technical affinity, which were collected at baseline. In addition, fear of movement, OA-related symptoms, and physical activity in the preceding 4 weeks were collected at the beginning of the first (T0) and second (T1) treatment sessions.
Movement quality was assessed by two independent raters. Both were research assistants with a bachelor degree in exercise science. They were introduced to the scoring procedure of movement quality using a rating sheet including different categories of movement quality for each exercise. The categories described the starting and ending position of all four exercises, as well as the movement sequence for the exercise. The categories are based on the description of the movement order and do not allow for high variance in the response. However, the aim was to query each movement step of the respective exercise. Raters had to classify whether the execution in a category was fulfilled, partly fulfilled, or not fulfilled, and whether all quality criteria points were met throughout all repetitions and the whole movement execution, temporarily, or not at all. The different categories were weighted according to their relevance. The weighting of the individual categories was based on the therapeutic relevance of the respective category for a correct, effective, and safe execution of the movement.
Most of the categories used a 3-point Likert scale with the grades as defined above. If a category could only be fulfilled or not fulfilled, a dichotomous scale was used. The judgments were based on video and audio recordings of the training sessions. Each session was videotaped from two perspectives, so that each movement could be assessed in the appropriate body axis. The raters were allowed to review the videos using the control unit of the video player, if necessary. The values for each exercise were transformed to a scale of 0-100%. All sets were included and averaged across raters. Different scores were calculated: the average value across all categories was defined as the primary outcome for movement quality, and the four average values for each of the exercises were defined as secondary outcomes for movement quality.
Exercise-specific self-efficacy was measured with a self-assessed questionnaire based on the Multidimensional Self-Efficacy for Exercise Scale [
The assessment of control competence for physical training was conducted in accordance with the self-rating scale developed by Sudeck and Pfeifer [
The sample size was predefined to a minimum of N=40 without a sample size calculation.
A computer-generated randomized order list in blocks of 5 entries for each of the 2 treatment sequences (AP and PA) was created prior to the start of the study by the study personnel. Eligible participants were randomly assigned to the sequences at the end of the initial phone call. The study staff member entered the name of the caller into the list consecutively in the order of the calls. The list was visible to the study personnel with no further concealment. If randomized subjects canceled the first treatment session, the randomization slot was opened again and used for the first new incoming call of an eligible subject. The order in which raters assessed the movement quality of each participant was randomized for each rater and test day separately using the internet tool random.org [
Participants, assessors (raters), and statisticians were not blinded to the treatment sequence or type of intervention. However, participants did not know which treatment sequence they were assigned to before the start of the first training session. The two raters of movement quality were not blinded to treatment sequence or intervention; however, they were not included in any other part of the study, such as data collection or data analysis.
Baseline sociodemographic, activity-related, and clinical characteristics are summarized for the overall study population and for each of the two treatment sequences separately (PA, AP). Categorical data are presented as absolute numbers and percentages, and continuous data are presented as mean (SD) or median (IQR), as appropriate. Differences between treatment sequences were compared using Pearson chi-square test for categorical data, unpaired Student
This study used a 2×2 crossover design to test for noninferiority of the app versus physiotherapist with respect to the primary and secondary outcome measures as outlined above. The following effects must be considered in a crossover design: direct treatment effect (τ), period effect (π), carryover effect (λ), and random subject effect (nested within sequence of treatment order). Therefore, a mixed-model analysis of variance (ANOVA) was used with the fixed factors treatment (physiotherapist, app), period (T1, T2), and treatment sequence (PA, AP), with the latter indicating potential carryover effects.
Treatment effects were averaged over the levels of period and treatment sequence. They were used to calculate crossover standardized effect sizes comparable to independent group designs that were adjusted for medium sample sizes (Hedges gIG) [
Noninferiority of the app to physiotherapist-guided treatment was established if the upper limit of the 95% CI of gIG was entirely below the predefined noninferiority margin, Δ=0.2 (gIG + 95% CI
Outcome measures of movement quality were assessed only once during each intervention and were not further adjusted for any variable. Outcome measures of exercise-specific self-efficacy and control competence for physical training were assessed directly prior to and after each treatment for both periods (T1, T2). Post-pre differences (ie, change from baseline) were used as input variables for the mixed-model ANOVA for these measures.
Rater agreement was assessed by calculating the percentage of exercise-related movement quality categories in which raters completely agreed. Values were averaged across all sets for each exercise and were considered separately for the app and physiotherapist treatments.
If the mixed-model ANOVA exhibited a significant carryover effect, an additional analysis of treatment effects was conducted for period T1 only with a simple
The level of statistical significance was set at the conventional level of α=.05. All data were analyzed using SPSS IBM Version 25 and R version 3.6.1.
Recruitment started in November 2018. Sixty-eight people contacted the study staff by phone, and 59 were deemed eligible and received a follow-up email, including written study information, details on measuring time points, and travel directions. However, five potential participants cancelled the first training appointment due to physical problems or overlapping appointments. Finally, 54 people completed the first training session. Seven participants could not attend the second session. Further details on participants flow are depicted in
Study flowchart. A: app; P: physiotherapist; AP: app-guided followed by physiotherapist-guided sequence; PA: physiotherapist-guided followed by app-guided sequence; ESE: exercise-specific self-efficacy; CCPT: control competence for physical training; ANOVA: analysis of variance.
Baseline characteristics are presented in
Baseline data for the complete sample differentiated according to treatment sequence.
Characteristic | Total (N=54) | PAa (n=26) | APb (n=28) | |||
Age (years), mean (SD) |
|
62.4 (8.2) | 62.5 (8.0) | 62.3 (8.5) | .91 | |
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|
|
.74 | ||
Female | 32 (59) | 16 (61) | 16 (57) |
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||
Male | 22 (41) | 10 (39) | 12 (43) |
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||
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|
|
.19 | ||
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Academic education | 22 (41) | 8 (31) | 14 (50) |
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|
|
Vocational education | 31 (57) | 18 (69) | 13 (46) |
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|
|
No vocational education | 1 (2) | 0 (0) | 1 (4) |
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|
.44 | ||
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Employed | 32 (59) | 14 (54) | 18 (64) |
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|
Retired | 22 (41) | 12 (46) | 10 (36) |
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|
Technical affinityc, mean (SD) |
|
2.87 (0.4) | 2.90 (0.4) | 2.84 (0.5) | .65 | |
Previous experience with similar exercises in group sessionsd, median (IQR) |
|
3.00 (2-3.25) | 3.00 (2.0-3.0) | 3.00 (2.0-4.0) | .31 | |
Daily activity (minutes of cycling and walking/week), median (IQR) |
|
215 (38-398) | 215 (19-349) | 225 (60-518) | .49 | |
Sports activity (minutes/week), median (IQR) |
|
209 (59-331) | 229 (71-381) | 184 (0-308) | .26 | |
Fear of movemente, median (IQR) |
|
9.0 (8.0-12.0) | 9.0 (8.0-13.3) | 9.5 (8.3-10.8) | .84 | |
WOMACf pain, mean (SD) |
|
31.4 (16.0) | 31.4 (16.2) | 31.4 (16.1) | .99 | |
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||
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Pain, mean (SD) | 62.7 (15.5) | 62.7 (16.2) | 62.7 (15.1) | .99 | |
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Symptoms, mean (SD) | 57.8 (17.6) | 58.4 (16.4) | 57.3 (19.4) | .83 | |
|
ADLh, median (IQR) | 66.9 (54.4-82.7) | 64.7 (54.4-77.9) | 72.8 (55.2-86.8) | .26 | |
|
Sport recreation, mean (SD) | 54.2 (22.7) | 49.0 (21.7) | 58.9 (23.0) | .11 | |
QLi, median (IQR) | 43.8 (29.7-62.5) | 31.3 (25.0-50.0) | 50.0 (37.5-62.5) | .03 | ||
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Overall, mean (SD) | 6.13 (1.4) | 5.70 (1.1) | 6.52 (1.5) | .03 | |
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Task, mean (SD) | 6.04 (1.6) | 5.85 (1.3) | 6.21 (1.8) | .40 | |
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Cope, mean (SD) | 5.53 (2.0) | 4.88 (1.6) | 6.13 (2.1) | .02 | |
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Schedule, median (IQR) | 6.83 (5.7-8.1) | 6.0 (5.7-7.3) | 7.33 (6.3-8.9) | .02 | |
CCPTk, mean (SD) |
|
2.67 (0.6) | 2.59 (0.5) | 2.74 (0.6) | .35 |
aPA: physiotherapist-guided followed by app-guided sequence.
bAP: app-guided followed by physiotherapist-guided sequence.
cScored on a 5-point scale from 1 (not true at all) to 5 (fully true); n=3 missing values.
dScored on a 5-point scale from 1 (substantial experience) to 5 (minimal experience).
eScored from 6 (no fear) to 24 (extreme fear).
fWOMAC: Western Ontario and McMaster Universities Osteoarthritis Index; pain subscale transformed values from 0 (no pain) to 100 (extreme pain).
gHOOS: Hip Disability and Osteoarthritis Outcome Score; transformed values from 0 (extreme impairment) to 100 (no impairment).
hADL: activities of daily living.
iQL: hip-related quality of life.
jESE: exercise-specific self-efficacy; rated on a scale from 0 (not at all safe) to 10 (absolutely safe).
kCCPT: control competence for physical training; scored on a scale from 1 (totally disagree) to 4 (totally agree).
Results for movement quality are summarized in
Effects of treatment on movement quality (MQ).
Outcomea | Estimated mean (95% CI) | Analysis of variance mixed model | Effect size, gIGb |
nic | ||||
|
Physiotherapist | App | πd ( |
λe ( |
τdf (95% CI) |
|
|
|
Primary: MQ_overall | 88.3 (87.2-89.5) | 85.9 (84.8-87.0) | .002 | .37 | 2.41 (1.21-3.61) | 0.59 (0.29-0.89) | 0 | |
Secondary: MQ_mobility_seated | 83.5 (80.7-86.3) | 84.8 (82.0-87.6) | .01 | .86 | –1.27 (–4.27-1.72) | –0.13 (–0.41-0.16) | 1 | |
Secondary: MQ_strength_supine | 91.6 (90.2-93.2) | 87.9 (86.5-89.2) | .12 | .80 | 3.76 (2.01-5.50) | 0.75 (0.39-1.13) | 0 | |
Secondary: MQ_strength_table | 87.7 (86.2-89.1) | 81.4 (80.0-82.8) | .003 | .04 | 6.25 (4.79-7.72) | 1.19 (0.84-1.55) | 0 | |
Secondary: MQ_balance_stance | 90.5 (89.1-91.9) | 89.7 (88.3-91.1) | .58 | .28 | 0.78 (–0.93-2.49) | 0.15 (–0.17-0.48) | 0 |
a Movement quality (MQ) with 0-100% of quality criteria points fulfilled.
bHedges gIG.
cni: noninferiority for app (“1” if gIG + 95% CI<0.2; else “0”).
dπ: period effect.
eλ: carryover effect.
fτd: treatment effect differences averaged over the levels of period and sequence; positive values indicate a beneficial effect for the physiotherapist.
Violin plots (mirrored estimated kernel density plot on each side of the boxplot, tails are trimmed to the range of the data) to visualize the distribution of movement quality (MQ) depending on the treatment sequence and type of intervention. AP: app-guided followed by physiotherapist-guided sequence; PA: physiotherapist-guided followed by app-guided sequence.
Effect sizes, gIG (95% CI), related to the noninferiority margin for movement quality (MQ).
The overall movement quality score, as well as the movement quality scores for the mobility exercise and strength exercise (table), were significantly better in period 2 (T2) than in period 1 (T1). A statistically significant carryover effect was detected in favor of treatment sequence PA for the movement quality of the table strength exercise.
Total agreement between the two raters varied between 79% and 91%. Apart from movement quality for seated mobility, agreement was 3-5% better with the physiotherapist. The highest rater agreement of the assessors was found for the strength exercise in the supine position. Detailed results for movement quality rater agreement are shown in
The results of sensitivity analysis after excluding the movement quality rating categories with unsatisfactory interrater reliability are shown in
The results for exercise-specific self-efficacy are shown in
Overall exercise-specific self-efficacy showed a large effect of the physiotherapist versus the app. Medium effects were found in the exercise-specific self-efficacy subcategories coping and schedule, and a small effect was seen in the subcategory task (
Effects of treatment on exercise-specific self-efficacy (ESE) and control competence for physical training (CCPT) (N=54).
Variable | Estimated mean (95% CI) | Analysis of variance mixed model | Effect size, gIGa (95% CI) | nib | Cronbach α | |||||||
|
Physiotherapist | App | πc ( |
λd ( |
τde (95% CI) |
|
|
|
||||
Primary: ESEf_all_ Δ | 1.85 (1.56-2.14) | 0.95 (0.67-1.24) | <.001 | .002 | 0.90 (0.52-1.27) | 0.84 (0.46-1.22) | 0 | .89 | ||||
Secondary: ESE_task_ Δ | 1.96 (1.55-2.37) | 1.23 (0.83-1.63) | <.001 | .03 | 0.73 (0.13-1.33) | 0.49 (0.10-0.88) | 0 | .79 | ||||
Secondary: ESE_cope_ Δ | 2.16 (1.75-2.57) | 1.06 (0.67-1.46) | <.001 | .03 | 1.10 (0.60-1.60) | 0.74 (0.38-1.10) | 0 | .89 | ||||
Secondary: ESE_schedule_ Δ | 1.41 (1.07-1.75) | 0.57 (0.23-0.90) | <.001 | .001 | 0.84 (0.36-1.33) | 0.68 (0.28-1.09) | 0 | .91 | ||||
Primary: CCPTg_ Δ | 0.31 (0.17-0.45) | 0.22 (0.08-0.36) | .25 | .36 | 0.09 (–0.08-0.26) | 0.18 (–0.14-0.50) | 0 | .94 |
aHedges gIG.
bni: noninferiority for app (“1” if gIG + 95% CI<0.2; else “0”).
cπ: period effect.
dλ: carryover effect.
eτd: treatment effect differences averaged over the levels of period and sequence; positive values indicate a beneficial effect for physiotherapist.
fESE: exercise-specific self-efficacy; rated on a scale of 0 (not at all safe) to 10 (absolutely safe).
gCCPT: control competence for physical training; rated on a scale of 1 (totally disagree) to 4 (totally agree).
Violin plots (mirrored estimated kernel density plot on each side of the boxplot, tails are trimmed to the range of the data) for visualization of distribution of exercise-specific self-efficacy (ESE) depending on treatment sequence and type of intervention. ESE values range from 0 (not at all safe) to 10 (absolutely safe). AP: app-guided followed by physiotherapist-guided sequence; PA: physiotherapist-guided followed by app-guided sequence.
Effect sizes, gIG (95% CI), related to the noninferiority margin for exercise-specific self-efficacy (ESE, overall and with the subdimensions task, cope, and schedule) and control competence for physical training (CCPT). A: app; P: physiotherapist.
As shown in
Violin plots (mirrored estimated kernel density plot on each side of the boxplot, tails are trimmed to the range of the data) for visualization of distribution of control competence for physical training (CCPT) depending on treatment sequence and type of intervention. CCPT values range from 1 (totally disagree) to 4 (totally agree). AP: app followed by physiotherapist sequence; PA: physiotherapist followed by app sequence.
Outcomes with significant carryover effects were additionally analyzed for period 1 only, and are represented in
Carryover effects for period 1 only (Student t test for unpaired samples).
Variable | Mean (SD) change from baseline | Effect size, |
nic | ||
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Physiotherapist (n=26) | App (n=28) |
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Primary: ESEd_all_Δ | 2.77 (1.1) | 1.15 (1.4) | 1.62 (0.94-2.31) | 1.27 (0.68-1.86) | 0 |
Secondary: ESE_task_Δ | 2.96 (1.4) | 1.62 (2.0) | 1.34 (0.39-2.29) | 0.76 (0.20-1.32) | 0 |
Secondary: ESE_cope_Δ | 3.10 (1.8) | 1.18 (1.6) | 1.92 (1.01-2.83) | 1.14 (0.56-1.72) | 0 |
Secondary: ESE_schedule_Δ | 2.26 (1.3) | 0.64 (1.6) | 1.62 (0.83-2.40) | 1.10 (0.52-1.68) | 0 |
Secondary: MQe_strength_table | 87. 8 (4.8) | 79.0 (7.2) | 8.7 (5.38, 12.10) | 1.4 (0.79-2.00) | 0 |
aτd: treatment effect differences averaged over the levels of period and sequence; positive values indicate a beneficial effect for physiotherapist.
bHedges
cni: noninferiority for app (“1” if gIG + 95% CI<0.2; else “0”).
dESE: exercise-specific self-efficacy; values ranging from 0 (not at all safe) to 10 (absolutely safe).
eMQ: movement quality.
No harms or unintended effects occurred during the study.
Digital home training programs that help people support their training routines are urgently needed in the current world of decreasing physical activity. This is particularly true for patients suffering from OA. It is well known that exercises are efficient to decrease pain and increase physical functioning in OA [
To the best of our knowledge, there is no app specifically designed for hip OA patients that combines the transfer of knowledge, exercise instructions, and processing of personal experience with movement. Furthermore, there are no studies comparing the effectiveness of these interventions provided by humans and apps. Therefore, the aim of this study was to investigate whether digital exercise instruction and guidance leads to benefits in PAHCO’s subcompetences in a manner that is comparable to physiotherapist care for patients with hip OA.
Movement quality of exercise execution was one important outcome in our study. Exercising in groups of two, in which the partner is alternately practicing and observing, shows important effects on learning success, especially for practical implementation with patients [
Self-efficacy is an important key to modifying behavior [
We found carryover effects for all outcomes related to the self-administered exercise-specific self-efficacy score, but only for one movement quality outcome. The subjective confidence of being able to show a desired behavior seems to be influenced by a personal supervisor much more than the objective ability to perform a movement. This finding underlines the importance of using both subjective and objective outcome measures if PAHCO is to be improved by a special intervention.
The effect size for control competence for physical training in our study was smaller than the critical value of 0.2, and the criterion for noninferiority was only missed because of the 95% CI exceeding the noninferiority margin. Control competence for physical training is quantified using items that are directly related to the competence to control training intensity in the required way (ie, “I know how I can best increase my strength in the leg and hip area with physical training” or “I am able to adjust my training effort well to my physical condition”). These items are comparable to the exercise-specific self-efficacy subscale task to some extent, with items such as “…I am certain that I will be able to carry out exercises with the right technique.” The app was inferior to the physiotherapist in this subscale. However, only a small effect size was obtained in comparison to all other exercise-specific self-efficacy scales that showed medium or large effect sizes.
The characteristics of our sample are typical characteristics of OA patients, indicating the good generalizability of the findings. The average age and the gender distribution correspond to the risk profile of the disease [
No adverse events were reported for any of the interventions. However, safety aspects are extremely relevant for fully automated computer-based interventions as there is no health care professional controlling for nonphysiological or even harmful execution of exercises. The app tested in this study used personalized closed feedback loops to adapt exercise instructions and dosage according to the user’s feedback on pain and physical exhaustion. Nonetheless, it has been shown that movement quality was inferior by using the app when it comes to more complex exercises. Future studies with longer intervention periods should therefore evaluate if minor movement competence goes along with higher pain levels, more adverse events, or poorer health outcomes. Proof of safety and medical benefit or patient-relevant structural and process improvements are mandatory aspects for approval of an “app on prescription” in Germany [
Results of this study show ceiling effects for the variables self-efficacy and control competence. The study population already had very good values at baseline, thereby reducing the possibility for change. To be able to assess effects in a more differentiated way, samples of future studies should have lower initial values in this outcome dimension. To investigate outcome effects in a more vulnerable population, further research should also focus on a sample that is less physically active, has more severe symptoms, feels greater barriers to technology, and has a greater fear of movement. This limitation and the above-mentioned restrictions with regard to the external validity of the study results may be caused by a potential recruiting bias, which may have affected the outcomes as well. Subjects were fully informed about the rationale and aim of the study in the context of recruitment and inclusion. It therefore cannot be ruled out that this information may have had an effect on user self-selection and expectation, and may have therefore biased the results.
This study also has some methodological issues that should be discussed. Five outcomes of the study showed carryover effects in favor of the physiotherapist, four of which were related to exercise-specific self-efficacy. Although a washout phase with a minimum of 3 weeks was conducted between training sessions, this phase was not long enough to eliminate positive treatment effects. The session with the physiotherapist induced long-lasting effects that sustained during the washout phase. As a consequence, a sensitivity analysis was conducted and results of this sensitivity analysis led to similar but even more pronounced statements related to the inferiority of the app versus the physiotherapist. We are aware that the practice of analyzing data from the first study period as if it had been obtained from a conventional parallel-group design has been shown to be potentially strongly anticonservative [
Exercise-specific self-efficacy and control competence for physical training were assessed prior to and after each intervention, and change from baseline was used as a dependent variable for each intervention. There is little agreement in the current literature as to whether or how to introduce period-specific baseline measurements into the model. The use of change from baseline is discouraged due to its poor type I error rate control and lower power than other methods, yet under the assumption of no carryover [
Aside from carryover effects, period effects were present in almost all measures. Effects of differences between pre and post values of the training session were larger in period 1, whereas movement quality assessed while exercising was better in period 2. This seems conclusive, as the possibility of improvement within one training session may have a saturation effect partially due to ceiling effects, whereas the better movement quality in period 2 may be related to a learning effect on exercise execution from period 1. As period effects do not affect comparison between groups, this limitation does not seem crucial for the interpretation of the results.
Despite the absence of noninferiority to the physiotherapist in almost all measures of interest, exercise-specific self-efficacy and control competence for physical training also improve using an app, and movement quality is acceptable for exercises that are easy to perform. However, relevant differences in movement quality are present in challenging tasks. The digital app therefore opens up possibilities to take on the role of a supplementary tool to support the patient in independent home training for less complex exercises. Nevertheless, it cannot replace a physiotherapist with an equivalent effect.
Screenshot of the exercise video “Pelvic tilt.”.
Screenshot of the exercise video “Strengthening of the hip abductors.".
Screenshot of the exercise video “Strengthening of hip extensors.”.
Screenshot of the exercise video "Balance task.".
App components and theoretical background of the interaction principles.
Exemplary decision path of the software algorithm based on the exercise to strengthen the hip abductors.
Rater agreement for movement quality, averaged across all categories and sets for each exercise and intervention in percent. P: physiotherapist; A: app.
Sensitivity analysis for movement quality after excluding categories with poor interrater reliability.
analysis of variance
app-guided followed by physiotherapist-guided sequence
osteoarthritis
physiotherapist-guided followed by app-guided sequence
physical activity-related health competence
The authors wish to thank the study participants. They would also like to express their thanks to the scientific assistants Simone Schweda, Hendrik Maier, Lisa-Marie Krehl, Hannah Hofmann, and Phillip Weber, and colleagues Georg Haupt and Dr. Katharina Bernecker for their help in data collection and analysis. The project is funded by the Leibniz-Wissenschafts Campus Tuebingen “Cognitive Interface” with funds from the Ministry of Science, Research and the Arts Baden-Wuerttemberg. Tuebingen University Hospital is the owner of the software; however, the software is not open to the public.
None declared.