## 4 Discussion
### Methodological and Clinical Considerations  
We were able to conduct meta-analyses on how changes in pain intensity and severity of anxiety symptoms after interventions could be associated with improvements in function and pain, respectively. Correlation analyses can be used as a measure of association, but they are not sufficient to infer causality [74]. In fact, observational studies should not be used to claim causation [75]. Ultimately, the longitudinal design of the included studies adds robustness to the observed association between pain and function. More RCTs with high methodological quality and a control of confounding variables would be needed to test causal hypotheses [74]. Using the GRADE framework, serious concerns were identified from the risk of bias, inconsistency among studies, and indirectness of the evidence. Bias due to uncontrolled confounding was common, and this represents an important source of error in the health literature [76]. Studies with various methodological designs were meta-analyzed together, which added more heterogeneity. The differences in populations, interventions, and assessment tools limit the generalizability of the findings. To our knowledge, this systematic review is the first to explore factors associated with changes in pain and function after mHealth exercise therapy for chronic musculoskeletal pain. We aimed to investigate the distinct effects of mHealth rather than those of digital health in general [35]. Studies used several types of mobile apps (with or without wearable devices), which included different monitoring systems, exercise presentation formats, and adjustment programs. Despite this possibly reflecting the existing variability within mHealth technology [36], it also detracts from the external validity of the findings. This review included patients with musculoskeletal disorders under the same diagnostic taxonomy [49]. However, participants reported a wide range of conditions and pain locations; thus, the results may not be specific to a particular disorder. Finally, one way to support the clinical applicability and future replication of interventions is to improve how these are reported. For this purpose, we used both the TIDieR and mERA checklists. In the TIDieR checklist, common concerns arose related to the lack of descriptions of who provided the intervention and the modifications implemented during the course of the study. Similarly, most studies did not meet the essential guidelines of reporting according to the mERA checklist, with especial attention to items of the technology platform, interoperability, data security, replicability, or limitations for scaling-up. Some of these shortcomings have already been mentioned by the 2020 WHO Digital Health Guideline for decision-makers and politicians [77] to facilitate the incorporation of mHealth systems in the clinical settings. Another important aspect is how accurate the information reported in the abstracts of the included studies was. The spin of information was mostly related to inaccuracies in reporting adverse events and the recommendation of the intervention when this failed to achieve a clinically important effect. This situation can have a detrimental influence on clinical decision-making for both healthcare professionals and the broader audience. Moreeffort 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].