Exploring factors associated with changes in pain and function following mhealth-based exercise therapy for chronic musculoskeletal pain: a systematic review with meta-analysis and meta-regression

Citation: Rodríguez-Sánchez-Laulhé, P.; Heredia-Rizo, A.M.; Salas-González, J.; Piña-Pozo, F.; Fernández-Seguín, L.M.; García-Muñoz, C. Exploring Factors Associated with Changes in Pain and Function Following mHealth-Based Exercise Therapy for Chronic Musculoskeletal Pain: A Systematic Review with Meta-Analysis and Meta-Regression. Appl. Sci. 2024,14, 6632. https://doi.org/10.3390/ app14156632 Academic Editor: Arkady Voloshin Received: 3 July 2024 Revised: 22 July 2024 Accepted: 25 July 2024 Published: 29 July 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). applied sciences Systematic Review Exploring Factors Associated with Changes in Pain and Function Following mHealth-Based Exercise Therapy for Chronic Musculoskeletal Pain: A Systematic Review with Meta-Analysis and Meta-Regression Pablo Rodríguez-Sánchez-Laulhé1,2 , Alberto Marcos Heredia-Rizo 2,3,* , Jesús Salas-González 1,2, Fernando Piña-Pozo 1, Lourdes María Fernández-Seguín2,3,* and Cristina García-Muñoz 2,4 1Departamento de Fisioterapia, Facultad de Enfermería, Fisioterapia y Podología, Universidad de Sevilla, 41009 Seville, Spain; [email protected] (P.R.-S.-L.); [email protected] (J.S.-G.); [email protected] (F.P.-P.) 2CTS 1110: Understanding Movement and Self in Health from Science (UMSS) Research Group, 41009 Andalusia, Spain; [email protected] 3Instituto de Biomedicina de Sevilla, IBiS, Departamento de Fisioterapia, Universidad de Sevilla, 41013 Seville, Spain 4Departamento de Salud, Universidad Loyola Andalucía, 41704 Seville, Spain *Correspondence: [email protected] (A.M.H.-R.); [email protected] (L.M.F.-S.) Abstract: Exercise therapy is the first-line intervention recommended for those with chronic musculoskeletal pain (CMP). Smartphone technologies (mHealth) represent a feasible means for exercise prescription and individualization. This systematic review with meta-analysis aimed to identify factors associated with changes in pain and function following mHealth-based exercise therapy in patients with CMP. CINAHL (via EBSCOhost), Embase, PubMed, Scopus, and SPORTdiscus were searched from inception to February 2023. Observational and controlled clinical trials with correlation or regression analysis of factors associated with the effect of mHealth exercise interventions on pain and function were included. The risk of bias, completeness of interventions, spin of information, and certainty in the evidence were evaluated. Eight studies with 51,755 participants were included. Reduced pain intensity after intervention was associated with higher physical function: r (95% CI) = − 0.55 ( − 0.67 to − 0.41); I 2 = 86%, Tau 2 = 0.02; p< 0.01. Meta-regression identified the Body Mass Index (BMI), exercise dose, and completion rate as potential moderators between changes in pain and physical function following mHealth exercise therapy. No association was found between pain and anxiety: r (95% CI) = 0.15 ( − 0.08 to 0.37); I 2 = 87%, Tau 2 = 0.02; p= 0.19. Very low certainty in the evidence was observed due to serious concerns regarding the risk of bias, inconsistency, and indirectness. The limited available evidence detracts from the clinical interpretation of the findings. Keywords: chronic pain; exercise; mHealth; meta-analysis; systematic review; telehealth 1. Introduction Two out of three adults may require rehabilitation at some point during the course of a musculoskeletal disorder [ 1 ], and this figure is expected to increase [ 1 , 2 ]. Spinal pain, together with knee and hip osteoarthritis (OA), ranks among the most prevalent and limiting musculoskeletal conditions [ 3 ] for which exercise is usually recommended as a first-line care approach [ 4 , 5 ]. Exercise-based interventions have been shown to enhance symptoms and decrease disability in individuals with chronic musculoskeletal pain [6–9]. However, exercise therapy is often omitted or sub-optimally prescribed [ 5 , 10 ]; thus, the impact of exercise on patients with knee OA, low back pain, or neck pain tends to be small to moderate [ 7 , 11 , 12 ] and not clinically relevant [ 8 , 11 ]. Indeed, more than 50% Appl. Sci. 2024,14, 6632. https://doi.org/10.3390/app14156632 https://www.mdpi.com/journal/applsci Appl. Sci. 2024,14, 6632 2 of 21 of patients may not respond positively to exercise [ 13 ] aiming to reduce pain [ 14 , 15 ] or improve function [ 14 ]. Exercise therapy is not a “one-size-fit-all” solution, and many clinical, contextual, and psychosocial factors can modulate its effects. Previous research suggests that aspects such as age, pain intensity, treatment expectations, and employment status [ 16 , 17 ] can have an impact on the effect of exercise. For example, higher levels of pain and disability at baseline have been correlated with better outcomes following exercise-based interventions [18]. Exercise programs need to be both meaningful and achievable [ 19 ] to increase treatment adherence [ 20 , 21 ] and facilitate successful rehabilitation [ 19 ]. Exercise should be tailored to the individual’s characteristics, preferences, and capabilities [ 19 , 22 ]. In this context, new technologies offer opportunities to enhance prescription in the form of telerehabilitation [ 5 , 23 ]. Current evidence supports telerehabilitation as a cost-saving resource for the management of chronic musculoskeletal pain [ 24 , 25 ]. It represents a good alternative to simplify access to clinical care [ 26 ], adapt exercise regimes, and monitor patients’ progression and engagement with the intervention [ 7 , 27 , 28 ]. The widespread use of mobile devices [ 29 ] makes mobile apps easily accessible across healthcare [ 30 , 31 ]. mHealth, defined as ‘health practice supported by mobile devices’ [ 32 ], is showing an increasing trend in providing self-management strategies for people with musculoskeletal pain [ 30 , 33 ]. Specifically, digital interventions include motivating and enjoyable interventions through treatment gamification. These features applied gaming elements to maintain patient adherence and engagement with therapy, which can improve clinical outcomes [ 28 , 34 ]. mHealth is clinically feasible and effective in implementing exercise therapy [ 1 , 35 , 36 ], and it has proven beneficial in enhancing recovery [ 35 – 38 ] and saving healthcare resources [ 27 ] among different musculoskeletal disorders. The impact of mHealth on improving pain and function in those with chronic pain may vary based on the particular condition, the specific mHealth modality, and the individuals’ characteristics [ 35 , 36 ]. A better understanding of the factors associated with the outcome following exercise therapy in patients with chronic pain is warranted to identify potential responders [ 13 ]. Exploratory analyses suggest that psychological [ 39 ], racial, and ethnic factors [ 40 ] can influence the effect of digitally delivered exercise interventions in adults with musculoskeletal pain. This systematic review aims to gather evidence on factors associated with changes in pain and function following mHealth-based exercise therapy in individuals diagnosed with chronic musculoskeletal pain. Possible associations between changes in pain and physical function will be also explored. 2. Materials and Methods The study protocol was prospectively registered at Open Science Framework https: //osf.io/fywhc (accessed on 25 February 2023) and has followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement and the PRISMA for abstracts [41,42]. 2.1. Deviations from the Review Protocol There were some minor deviations from the intended protocol. The construct ‘quality of life’ was omitted from the analysis, as it was assessed in a single study [ 43 ]. The severity of anxiety or depressive symptoms was included among possible factors associated with the effect of mHealth [ 40 , 43 – 46 ]. In addition, the quality of mHealth reporting was assessed using the mHealth evidence reporting and assessment (mERA) checklist [ 47 ] in order to improve clarity and completeness in how mHealth interventions were described. Appl. Sci. 2024,14, 6632 3 of 21 2.2. Data Sources and Search Strategies A reviewer (PRSL) searched CINAHL (via EBSCOhost), Embase, PubMed, SPORTdiscus (via EBSCOhost), and Scopus from inception to February 2023. Filters for document type, source type, and language were imposed. Manual searches were conducted in reviews related to the topic of interest to search for eligible records not retrieved from electronic databases. The complete search strategies are detailed in Supplementary File S1. 2.3. Eligibility Criteria The PECOs (Population, Exposure, Comparator, Outcomes, Study design) framework was followed to establish the eligibility criteria [48]. Inclusion criteria: - P (population): adults (>18 years) diagnosed with chronic musculoskeletal pain or chronic spine pain according to the ACTTION-APS Pain Taxonomy [ 49 ]. This includes individuals with chronic low back pain, lumbosacral radiculopathy, fibromyalgia and myofascial widespread pain, gout, osteoarthritis, rheumatoid arthritis, and spondyloarthropathies. - E (exposure): participants underwent an mHealth-based exercise program, alone or within a multimodal intervention. mHealth was defined as ‘health practice supported by mobile devices such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices’ [32]. - C (comparator): any control group, with no restriction, or no control group. - O (outcomes): pain-related measures (e.g., pain intensity) and physical function. - S (study design): observational studies and controlled clinical trials that provide results from a correlation or regression analysis between clinical or demographic variables and the impact of mHealth exercise-based therapy on pain and function. Exclusion criteria: - Patients with chronic pain were analyzed together with participants with other chronic disorders. - Studies where exercise was provided using a digitally delivered modality other than mHealth, e.g., web-based tools. - Participants with cancer-related pain or with pain associated with the central or peripheral nervous systems. - Cluster analysis. - Outcome measures were evaluated before and after surgery, e.g., knee arthroplasty. - Studies written in a language other than English or Spanish. - Reviews, editorials, letters, commentaries, thesis dissertations, grey literature, and conference abstracts. 2.4. Study Selection One researcher (PRSL) conducted the selection process using the Mendeley desktop software, v2.72.0 (Elsevier Inc., New York, NY, USA). Duplicate records were automatically removed and manually checked. The remaining studies were screened based on the title and abstract. Then, two independent reviewers (PRSL and JSG) revised the full text of eligible records and those without an abstract. A consensus with a third reviewer (AMHR) was needed in seven studies to solve disagreements. A manual search was conducted in the list of references of systematic reviews on the topic of interest. 2.5. Data Extraction and Synthesis Three reviewers (PRSL, AMHR, and LMFS) independently extracted the following information using a standardized form: first author, year of publication, and country; study design; number of participants, including sex distribution, mean age, body mass index, and race/ethnicity; educational stage; employment, family, and financial status; medical diagnosis; duration of symptoms and previous history of chronic pain; type of Appl. Sci. 2024,14, 6632 4 of 21 mHealth intervention; characteristics of mHealth and control groups; outcome measures; mHealth completion rate (number of participants who completed the intervention vs. those initially recruited); and main findings (e.g., correlation analysis). For a detailed description of the mHealth intervention, additional information was collected: the type of devices used; professional monitoring system; software company; study setting; financial support; format of exercise presentation; type of exercises; adjusting program; weekly doses; and engagement intervention rate, which was estimated as the number of completed sessions vs. expected sessions. One researcher (CGM) contacted two corresponding authors via email [ 40 , 45 ] when information was not reported or needed clarification. Both corresponding authors replied. 2.6. Risk of Bias Appraisal Two independent reviewers (JSG and FPP) evaluated the methodological quality of the studies using the Quality in Prognosis Studies (QUIPS) tool [ 50 ] for observational cohort studies. The revised Cochrane Risk of Bias tool for randomized trials version 2 (RoB-2) [ 51 ] and the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool [ 52 ] were used for controlled clinical trials. The QUIPS includes six domains that can be categorized as a ‘low’, ‘moderate’, or ‘high’ risk of bias [ 50 ]. The RoB-2 tool includes five domains, with the overall score ranked as ‘low’, ‘some concerns’, or ‘high’ risk of bias [ 51 ]. The ROBINS-I tool consists of seven domains that can be judged as having a ‘low’, ‘moderate’, ‘serious’, or ‘critical’ risk of bias [52]. 2.7. Description of Interventions One researcher (JSG) used the Template for Intervention Description and Replication (TIDieR) 12-item checklist [ 53 ], and another researcher (PRSL) employed the mERA 16-item checklist [47] to determine the quality of reporting of mHealth interventions. 2.8. Spin of Information Two researchers (JSG and FPP) independently evaluated the quality and consistency of the information provided in the abstracts of the included studies. A seven-item checklist was used to quantify the amount of misleading information (‘yes’ or ‘no’) [ 54 ]. The spin overall score (after adding up the number of positive answers in each study) and the most prevalent items with some form of spin are reported [54]. 2.9. Certainty in the Evidence The certainty in the evidence was judged by two independent reviewers (PRSL and CGM) following the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach [ 55 ]. The GRADE includes five domains, risk of bias, imprecision, inconsistency, indirectness, and publication bias, and can be used in systematic reviews of both interventions and observational studies [ 55 ]. Randomized controlled trials start with high evidence, non-randomized clinical trials start with high evidence but should automatically be downgraded for limitations in design, and observational studies start with low evidence [55]. 2.10. Meta-Analysis Meta-analyses assessed those factors associated with changes in pain and physical function following mHealth exercise therapy for participants with chronic musculoskeletal pain. Possible direct associations between changes in pain and function after the intervention were also explored. Meta-analyses were performed when at least two studies evaluated the same bivariate association and could be only conducted for the association between pain and physical function (where a negative correlation indicates that lower pain intensity after the intervention is associated with higher function) and between anxiety and pain (where a positive correlation indicates that fewer anxiety symptoms following the intervention are associated with lower pain intensity). We used the Appl. Sci. 2024,14, 6632 5 of 21 R Studio software, v. 4.1.1 (Posit PBC, Boston, MA, USA) with the packages of meta (v.5.1-1) [ 56 ], metafor (v.3.0-2) [ 57 ], and dmetar (v.0.0.9000) [ 58 ]. When necessary, data were transformed into correlation coefficients (r) before the analysis. For example, the formula r = β + 0.05 λ (when β is nonnegative, λ is equal to 1; when β is negative, λ is equal to zero) was used to transform regression data [ 59 ]. Also, if necessary, the direction of the correlation data was adjusted based on the scoring interpretation of the assessment tool. For instance, an improvement in function can be represented by a lower score in the Australian/Canadian Hand Osteoarthritis Index (AUSCAN) or a higher score in the Hip Disability and Osteoarthritis Outcome Score (HOOS). Data were pooled using a random effect model. Correlation coefficients were transformed using Fisher’s Z and then back-transformed to correlation coefficients denoting ‘low’ (r = 0.10), ‘moderate’ (r = 0.30), and ‘high’ (r = 0.50) correlations [ 56 ]. Heterogeneity among the studies was explored using I2 statistics (high heterogeneity when I 2 > 50%) [ 60 ]. The Tau2 test and Q-test were also used to explore heterogeneity. Forest plots were generated to report the results of the meta-analyses. 2.11. Meta-Regression and Sensitivity Analyses A sensitivity exploratory analysis was conducted to identify outliers and potential sources of heterogeneity using influence analysis, the leave-one-out method, or Baujat plots. Outliers were excluded from the meta-analyses. We included prediction intervals in each forest plot as a measure of heterogeneity. These intervals provide 95% confidence for how likely future results may fall within their range. In addition, subgroup analyses were performed when at least two studies included the same covariate. These analyses were illustrated using forest plots with their prediction intervals. Q-tests within and between subgroups were carried out. In addition, mixed-model effect meta-regressions were performed to explore sources of heterogeneity and potential predictors of the treatment effects. Meta-regression analyses were developed when one of these predictors was included in at least three trials: age, body mass index, number of female or male participants, sample size, control comparator, duration of symptoms, number and frequency of sessions in the mHealth and control groups, type of mHealth, completion rate, and outcome measure for pain and physical function. Bubble plots were generated to visually represent the results of meta-regressions involving continuous variables. 2.12. Publication Bias Publication bias was explored using funnel plots and the Egger’s test (a pvalue < 0.05 indicates the presence of publication bias) when there were at least three studies in the same meta-analysis [61]. 3. Results 3.1. Study Selection A total of 1462 citations were retrieved from databases and manual search strategies. From these potentially eligible records, eight studies were finally included in the systematic review (Figure 1). A list of the records excluded after full-text reading (n= 124), with the reasons for exclusion, can be found in Supplementary File S2. Appl. Sci. 2024,14, 6632 6 of 21 Appl. Sci. 2024, 14, x FOR PEER REVIEW 6 of 21 Figure 1. Flow diagram of studies through the review. 3.2. Description of Clinical Trials One randomized controlled trial (RCT) [37], two non-RCTs [45,62], and five observational cohort studies [40,43,44,46,63] were included. The total number of participants was 51,755 (27,127 females, 51.41%); they were diagnosed with chronic musculoskeletal pain in the lower back (four studies) [40,45,46,63], shoulder (three studies) [40,46,62], and wrist–hand (three studies) [37,40,44] as the most frequent pain locations. mHealth interventions lasted from 4 to 12 weeks, were delivered using a mobile [37,45,62,63] or a Tablet app [40,43,44,46], and involved wearable devices in three studies: BoostFix (COMPAL Electronics Inc., Taipei, Taiwan) [62], Hinge Health (Hinge Health Inc., San Francisco, CA, USA) [46], and SWORD health (SWORD Health Inc., New York, NY, USA) [40,43,44]. The comparator groups included home-based [37,62] or face-to-face exercise programs [45], combined with educational material [45,62] or regular medical visits [37]. A follow-up evaluation was conducted by a single study [37] at 12 weeks after the intervention. Table 1 shows the characteristics of the included studies. A detailed description of the mHealth interventions in primary studies is reported in Supplementary File S3. Figure 1. Flow diagram of studies through the review. 3.2. Description of Clinical Trials One randomized controlled trial (RCT) [ 37 ], two non-RCTs [ 45 , 62 ], and five observational cohort studies [ 40 , 43 , 44 , 46 , 63 ] were included. The total number of participants was 51,755 (27,127 females, 51.41%); they were diagnosed with chronic musculoskeletal pain in the lower back (four studies) [ 40 , 45 , 46 , 63 ], shoulder (three studies) [ 40 , 46 , 62 ], and wrist–hand (three studies) [ 37 , 40 , 44 ] as the most frequent pain locations. mHealth interventions lasted from 4 to 12 weeks, were delivered using a mobile [ 37 , 45 , 62 , 63 ] or a Tablet app [ 40 , 43 , 44 , 46 ], and involved wearable devices in three studies: BoostFix (COMPAL Electronics Inc., Taipei, Taiwan) [62], Hinge Health (Hinge Health Inc., San Francisco, CA, USA) [ 46 ], and SWORD health (SWORD Health Inc., New York, NY, USA) [ 40 , 43 , 44 ]. The comparator groups included home-based [ 37 , 62 ] or face-to-face exercise programs [ 45 ], combined with educational material [ 45 , 62 ] or regular medical visits [ 37 ]. A follow-up evaluation was conducted by a single study [ 37 ] at 12 weeks after the intervention. Table 1 shows the characteristics of the included studies. A detailed description of the mHealth interventions in primary studies is reported in Supplementary File S3. Appl. Sci. 2024,14, 6632 7 of 21 Table 1. Characteristics of the included studies. Author(s), Year, and Country Study Design Participants (Sex, Mean Age), BMI, and Race/Ethnicity Educational Stage, Employment, Family, and Financial Status Diagnosis mHealth Type Symptom Duration; Previous History mHealth Intervention Group Comparison Group Outcomes of Interest; Assessment Points Treatment Completion Rate a Main Findings of Interest Chen et al., 2020 Taiwan [62] Two-arm parallel non-RCT N= 15 (5 females) b: EG = 8, CG = 7. Mean age, EG: 53(6.2), CG: 56.1(13.3) BMI: UR Ethnicity: UR Educational stage: senior high (EG, n= 2; CG, n= 4); bachelor’s degree or higher (EG, n= 5; CG, n= 3). Employment status: UR Family status: UR Financial status: UR Adhesive capsulitis of the shoulder > 12 wks. Mobile app and wearable inertial sensors Symptom duration: 3 to 6 mo. (n= 7); 26 to 52 mo. (n= 7) Previous history: UR Motion sensor-assisted home-based shoulder exercise program via the Patient App. Daily, 12 wks. Home-based shoulder exercise program, with advices on sleep, posture, and pain relief. Daily (10 times per exercise per day), 12 wks. Pain intensity (worst pain last week): VAS Function: QDASH Shoulder ROM (active and passive): motion sensor T0, T1 (week 4), T2 (week 8), T3 (week 12) Overall: 93.3% (14/15) EG: 87.5% CG: 100% Correlations: Significant correlation between passive internal rotation and changes in QDASH at T1 (r = −0.539, p< 0.05) Costa et al., 2022 USA [44] Single-arm cohort study N= 189 (115 females): Mean age, 47.3(11.1) BMI: 28.7(6.8) Ethnicity: UR Educational stage: UR Employment status: Full- or part-time (n= 174) Family status: UR Financial status: UR Wrist or hand pain: CTS (n= 50), tendinopathy (n= 45), non-specific wrist pain (n= 28), DeQuervain tenosynovitis (n= 16), wrist/hand OA (n= 16), sprain or fracture (n= 14), systemic disease (n= 11), other (n= 9) Tablet App, Inertial motion trackers and Cloud-based portal. Symptom duration: <3 mo. (n= 69); >3 mo. (n= 120) Previous history: UR SWORD Health’s digital treatment (CBT, exercise program, and education). Three times per week, 8 wks. N/A Anxiety: GAD-7 Depression: PHQ-9 Pain intensity (average pain last week): NRS Function: QDASH T0, T1 (week 4), T2 (week 8) Overall: 78.8% (149/189) OR (95% CI), pvalue Considering an MCID (30% change) in pain intensity at T2: Age, 1.03 (0.97 to 1.10), 0.34 BMI, 1.08 (0.96 to 1.26), 0.24 Female, 0.94 (0.25 to 3.58), 0.92 GAD-7, 0.99 (0.67 to 1.53), 0.96 PHQ-9, 1.04 (0.78 to 1.38), 0.80 Correlations: Significant (p< 0.001) between ↓pain and ↑function: r = 0.659, Latent growth curve model: Female sex was associated with faster-paced recovery for QDASH = −0.85 per week, p= 0.029 Appl. Sci. 2024,14, 6632 8 of 21 Table 1. Cont. Author(s), Year, and Country Study Design Participants (Sex, Mean Age), BMI, and Race/Ethnicity Educational Stage, Employment, Family, and Financial Status Diagnosis mHealth Type Symptom Duration; Previous History mHealth Intervention Group Comparison Group Outcomes of Interest; Assessment Points Treatment Completion Rate a Main Findings of Interest Janela et al., 2022 USA [43] Single-arm cohort study N= 534 (363 females): Mean age, 50.2(11.3) BMI: 29.1(6.4) Ethnicity: UR Educational stage: UR Employment status: Full or part time (n= 480) Family status: UR Financial status: UR Hip pain > 12 wks.: hip OA (n= 106), other conditions, e.g., non-specific pain, bursitis, sprain/strain, tendinopathy (n= 428) Tablet App, Inertial motion trackers, and Cloud-based portal. Symptom duration: UR Previous history: UR SWORD Health’s digital treatment: (CBT, exercise program, and education). Three times per week, 12 wks. N/A Anxiety: GAD-7 Pain intensity (average pain last week): NRS Pain: HOOS-pain Function: HOOS-function T0, T1 (week 4), T2 (week 8), T3 (week 12) Overall: 74.2% (396/534) Correlations: Significant (p< 0.001) between ↓pain and positive change in HOOS-function, r = −0.404 HOOS-pain, r = −0.556 HOOS-QoL, r = −0.357 GAD-7, r = 0.265 Significant between change in surgery intent and ↓Pain, r = 0.155, p= 0.033 ↑ HOOS-function, r = −0.28, p= 0.004 Latent growth curve model: Older age was associated with ↓ pain (NRS) = −0.01, p= 0.012. Female sex was associated with change in HOOS- pain = −1.00, p= 0.016. ↑BMI was associated with ↓ in HOOS- function = 0.14, p= 0.003 Appl. Sci. 2024,14, 6632 9 of 21 Table 1. Cont. Author(s), Year, and Country Study Design Participants (Sex, Mean Age), BMI, and Race/Ethnicity Educational Stage, Employment, Family, and Financial Status Diagnosis mHealth Type Symptom Duration; Previous History mHealth Intervention Group Comparison Group Outcomes of Interest; Assessment Points Treatment Completion Rate a Main Findings of Interest Rodríguez- Sánchez-Laulhé et al., 2023 Spain [37] Two-arm parallel RCT N= 74 (50 females): EG = 34, CG = 40. Mean age, EG: 62.2(8.8) CG: 64.3(7.7) BMI: UR Ethnicity: UR Educational stage: UR Employment status: UR Family status: UR Financial status: UR Unilateral or bilateral hand OA > 6 mo. Mobile App Symptom duration: UR Previous history: UR CareHand app: exercise (15 to 20 min.), info about the disease, joint protection, and selfmanagement advice. Four times per week, 12 wks. Written home exercise program and regular medical visits (info about the disease). Four times per week, 12 wks. Pain intensity: NRS, AUSCAN Function: AUSCAN, QDASH Overall status: AUSCAN total Pinch and grip strength: pinch gauge, dynamometer. T0, T1 (week 4), T2 (week 12), T3 (week 24) Overall: 85.1% (63/74) EG: 85.3% CG: 85% Regression β(95% CI), pvalue: Change AUSCAN function at T2: AUSCAN pain at T0: 0.995 (0.788 to 2.639), p= 0.001 AUSCAN total at T0: −1.054 (−1.060 to −0.206), p= 0.005 Change in AUSCAN pain at T2: 0.592 (0.406 to 1.431), p= 0.001. Change NRS at T2: AUSCAN pain at T0: −0.603 (−0.997 to −0.334), p= 0.001 QDASH at T0: 0.433 (0.008 to 0.106), p= 0.023 Change in QDASH at T2: 0.469 (0.016 to 0.112), p= 0.010 Scheer et al., 2022 USA [40] Single-arm cohort study N= 9550 (5589 females): Mean age, 49.4 (12.9) BMI: 29.2 (6.7) Ethnicity: Asian (n= 910); Black (n= 1025); Hispanic (n= 913); non-Hispanic white (n= 6240); other (n= 462) Educational stage: middle, elementary, high school, or college (n= 3649); bachelor or higher (n= 5763). Employment status: Full- or part-time (n= 8080); not employed (n= 414) Family status: UR Financial status: UR Chronic pain > 12 wks.: ankle (n= 352), hip (n= 817), knee (n= 1275), low back (n= 4097), neck (n= 882), wrist and hand (n= 335), elbow (n= 191), shoulder (n= 1431). Tablet App, Inertial motion trackers, and Cloud-based portal. Symptom duration: UR Previous history: UR SWORD tablet app: exercise program, education, and CBT (60 min total). Three times per week, 12 wks. N/A Anxiety: GAD-7 Depression: PHQ-9 Pain intensity (average pain last week): NRS T0, T1 (week 4), T2 (week 8), T3 (week 12) Overall: 72.8% (6949/9550) Logistic regression OR (95% CI), pvalue Clinically meaningful change in pain intensity at T3: Hispanic vs. non-Hispanic: 1.74 (1.24 to 2.45), p= 0.001 Men vs. women: N/S, p= 0.007 Prior upper limb pain vs. no prior: N/S, p< 0.001 Appl. Sci. 2024,14, 6632 16 of 21 effort is needed for editors, reviewers, and authors to improve the quality of abstracts to provide transparent and accurate information. mHealth technology offers great opportunities for musculoskeletal research [ 35 ] and practice [ 77 ]. Mobile devices facilitate data collection and increase the statistical power of studies to find associations. They also represent an easy-to-use approach to influence patients’ behavior, i.e., the delivery of follow-up educational information [ 78 ]. However, there are still significant barriers to overcome [ 78 ]. For instance, high participant attrition is a prevalent issue in the mHealth literature [ 79 , 80 ]. We identified a moderate or high risk of bias due to attrition in 50% of the studies [ 40 , 43 , 46 , 63 ]. Also, the accessibility of mHealth for all sociodemographic groups remains questionable. In this systematic review, aspects such as the educational level and the financial status of participants were not reported in most studies. Indeed, it is worth noting that all the studies included were conducted in high-income countries; thus, we must be cautious to extrapolate the results to broader population groups. Overall, the limited number of studies and the substantial heterogeneity among them, along with serious methodological concerns and a very low certainty in the evidence, prevent us from making strong clinical recommendations. 4.2. Study Limitations Although many citations were initially reviewed, few studies fulfilled the eligibility criteria. The Cochrane Handbook recommends a minimum of 10 studies for each examined covariate in a meta-regression [ 60 ], although this remains controversial, with other studies suggesting that a lower number of observations may be sufficient [ 81 ]. All in all, conducting meta-analyses and meta-regressions with a limited number of studies limits the generalizability of the results and does not allow for establishing definite conclusions. In addition, most studies showed an overall high risk of bias and high heterogeneity in patients’ characteristics, mHealth interventions, and outcome measures. This heterogeneity remained high even after the sensitivity analyses. Finally, no study assessed outcomes in the long term, which detracts from their clinical applicability. More longitudinal cohort studies and RCTs with high methodological quality and long-term follow-ups are warranted to understand the possible moderators of the effect of digitally based exercise programs for chronic pain. 5. Conclusions The present systematic review suggests that a decrease in pain intensity after mHealthbased exercise therapy is associated with higher physical function in patients with chronic musculoskeletal pain. However, no association was found between changes in the severity of anxiety symptoms and pain intensity. Potential factors modulate the association between pain and function, such as the BMI and the mHealth intervention dose and completion rate. Given the low certainty in the evidence and the presence of serious methodological concerns in primary studies, no clinical recommendations are warranted at this time. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/app14156632/s1, Supplementary File S1: Search strategies; Supplementary File S2: List of studies excluded after full text reading, including the specific reasons for exclusion (n= 124); Supplementary File S3: Characteristics of the mHealth interventions; Supplementary File S4: Risk of Bias Assessment QUIPS; Supplementary File S5: Risk of Bias Assessment RoB-2; Supplementary File S6: Risk of Bias Assessment ROBIN-I; Supplementary File S7: The Template for Intervention Description and Replication (TIDieR) Checklist Results; Supplementary File S8: mHealth evidence reporting and assessment (mERA) checklist; Supplementary File S9: Spin of Information in the abstracts; Supplementary File S10: Forest Plot Sensitivity Analysis Pain and Function; Supplementary File S11: Baujat Plots Analysis Pain and Function; Supplementary File S12: Influence Analysis Pain and Function; Supplementary File S13: Leave one out method analysis Pain and Function; Supplementary File S14: Funnel Plot for Publication Bias for Pain and Function; Supplementary File S15: Univariate meta-regression analysis of covariates that could moderate the correlation between pain and function in patients with chronic musculoskeletal pain; Supplemen- Appl. Sci. 2024,14, 6632 17 of 21 tary File S16: Subgroup meta-analyses for the correlation between pain and function in patients with chronic musculoskeletal pain; Supplementary File S17: Forest Plot subgroup analysis on Pain Assessment tools in Pain and Function association; Supplementary File S18: Forest Plot subgroup analysis on Function Assessment tools in Pain and Function association; Supplementary File S19: Forest Plot subgroup analysis on Comparators in Pain and Function association; Supplementary File S20: Forest Plot subgroup analysis on study design in Pain and Function association; Supplementary File S21: Forest Plot subgroup analysis on duration of symptoms in Pain and Function association; Supplementary File S22: Forest Plot subgroup analysis on type of mHealth in Pain and Function association; Supplementary File S23: Forest Plot subgroup analysis on location of the lesion in Pain and Function association; Supplementary File S24. Funnel Plot for Publication Bias for Pain and Anxiety. Author Contributions: Conceptualization, P.R.-S.-L. and A.M.H.-R.; methodology, P.R.-S.-L., A.M.H.-R., and C.G.-M.; software, C.G.-M.; validation, P.R.-S.-L. and A.M.H.-R.; investigation, P.R.-S.-L., A.M.H.-R. , J.S.-G., F.P.-P., and L.M.F.-S.; formal analysis, C.G.-M.; data curation, P.R.-S.-L.; writing—original draft preparation, P.R.-S.-L. and A.M.H.-R.; writing—review and editing, P.R.-S.-L., A.M.H.-R., J.S.-G., F.P.-P., L.M.F.-S., and C.G.-M.; visualization, P.R.-S.-L. and C.G.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: We are grateful for the support of the CTS:1110 Understanding Movement and Self in health from Science (UMSS) Research Group, Andalusia, Spain. 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