Background: Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients’ needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR. Objective: In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR. Methods: The scoping review followed the framework of Arksey and O’Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR. Results: A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies. Conclusions: Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients’ needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.

Background: Concomitant psychological and cognitive impairments modulate nociceptive processing and contribute to chronic low back pain (CLBP) maintenance, poorly correlated with radiological findings. Clinical practice guidelines recommend self-management and multidisciplinary educational and exercise-based interventions. However, these recommendations are based on self-reported measurements, which lack evidence of related electrophysiological changes. Furthermore, current mobile health (mHealth) tools for self-management are of low quality and scarce evidence. Thus, it is necessary to increase knowledge on mHealth and electrophysiological changes elicited by current evidence-based interventions. Objective: The aim of this study is to investigate changes elicited by a self-managed educational and exercise-based 4-week mHealth intervention (BackFit app) in electroencephalographic and electrocardiographic activity, pressure pain thresholds (PPTs), pain, disability, and psychological and cognitive functioning in CLBP versus the same intervention in a face-to-face modality. Methods: A 2-arm parallel nonrandomized clinical trial was conducted at the University of the Balearic Islands (Palma, Spain). A total of 50 patients with nonspecific CLBP were assigned to a self-managed group (23/50, 46%; mean age 45.00, SD 9.13 years; 10/23, 43% men) or a face-to-face group (27/50, 54%; mean age 48.63, SD 7.54 years; 7/27, 26% men). The primary outcomes were electroencephalographic activity (at rest and during a modified version of the Eriksen flanker task) and heart rate variability (at rest), PPTs, and pressure pain intensity ratings. The secondary outcomes were pain, disability, psychological functioning (mood, anxiety, kinesiophobia, pain catastrophizing, and fear-avoidance beliefs), and cognitive performance (percentage of hits and reaction times). Results: After the intervention, frequency analysis of electroencephalographic resting-state data showed increased beta-2 (16-23 Hz; 0.0020 vs 0.0024; P=.02) and beta-3 (23-30 Hz; 0.0013 vs 0.0018; P=.03) activity. In addition, source analyses revealed higher power density of beta (16-30 Hz) at the anterior cingulate cortex and alpha (8-12 Hz) at the postcentral gyrus and lower power density of delta (2-4 Hz) at the cuneus and precuneus. Both groups also improved depression (7.74 vs 5.15; P=.01), kinesiophobia (22.91 vs 20.87; P=.002), activity avoidance (14.49 vs 12.86; P<.001), helplessness (6.38 vs 4.74; P=.02), fear-avoidance beliefs (35 vs 29.11; P=.03), and avoidance of physical activity (12.07 vs 9.28; P=.01) scores, but there was an increase in the disability score (6.08 vs 7.5; P=.01). No significant differences between the groups or sessions were found in heart rate variability resting-state data, electroencephalographic data from the Eriksen flanker task, PPTs, subjective ratings, or cognitive performance. Conclusions: Both intervention modalities increased mainly beta activity at rest and improved psychological functioning. Given the limitations of our study, conclusions must be drawn carefully and further research will be needed. Nevertheless, to the best of our knowledge, this is the first study reporting electroencephalographic changes in patients with CLBP after an mHealth intervention. Trial Registration: ClinicalTrials.gov NCT04576611; https://clinicaltrials.gov/ct2/show/NCT04576611

Musculoskeletal disorders (MSD) are among the most frequent and costly occupational health problems with a rising prevalence globally. This systematic review with meta-analysis was conducted to know and evaluate the prevalence of MSD by anatomic location among hotel housekeepers (HHs) and cleaners. Electronic searches were conducted in PubMed, Web of Science, Scopus, Dialnet Plus, PEDro and Cochrane Database for Systematic Reviews using a search strategy to identify cross-sectional studies reporting on the prevalence of MSD in HHs or cleaners. The risk of bias was assessed with Joanna Briggs Institute tool for systematic reviews. A random-effects model was used in the meta-analysis. Nineteen studies were included in the systematic review, nine of them in the meta-analysis (n = 2299). The study sample sizes ranged from 24 to 1043 participants. The Standardized Nordic Musculoskeletal Questionnaire was the most common tool used to assess MSD among both HHs and cleaners (9/19 of the included studies). The three most affected anatomic locations were the low back 53.9% (95% CI: 43.3-64.6), shoulders 41.4% (95% CI: 27.1-55.8), and wrists/hands 40.1% (95% CI: 24.5-55.7). HHs and cleaners have a high prevalence of MSD. Low back pain is the most prevalent MSD among both HHs and cleaners affecting up to one of two people.

Objectives: To estimate the prevalence of musculoskeletal pain of hotel housekeepers (HHs) and to describe the work conditions and perception of health in this occupational group in the Balearic Islands, Spain. Methods: Cross-sectional descriptive study with HHs of the Balearic Islands, performed in primary care. Random sample of HHs who worked during the 2018 season. We collected information on sociodemographic variables, job characteristics, workload, pain, perceived health, and physical activity. After participants signed the informed consent form, we conducted a face-to-face interview in the primary care centre and accessed the participants’ electronic health records. Results: 1043 HHs aged 43.3 ± 10 years and with 10.7 ± 9.1 years worked as HHs were included. 51% (95% CI: 48–54%) reported chronic pain, mainly in the lower back 28.7% (95% CI: 25.9–31.5%), hands/wrists 23.7% (95% CI: 21.1–26.4%), neck 21.6% (95% CI: 19.1–24.3%), shoulders 19.9% (95% CI: 17.4–22.4%), and back 17.8% (95% CI: 15.4–20.2%). Pain was associated with older age, more years worked, more beds made/day and difficulty in pushing the housekeeping cart. More than half HHs reported that they did not incorporate occupational risk prevention measures (ORPMs) into their routine; 17.3% (95% CI: 15.1–19.7%) HHs considered their health as poor or very poor. Perception of health was worse in HHs with chronic pain. Conclusions: A high percentage of HHs of the Balearic Islands reported chronic pain, a low compliance with ORPMs and compared to women of the same sociodemographic profile they perceive a worse health status.

Background: Evidence-based clinical guidelines consider physical exercise one of the best nonpharmacological interventions for low-back pain (LBP), but it is necessary to clarify the exercise-induced hypoalgesia effect of different modalities of exercise in chronic pain populations. Purpose: This study focused on exploring acute changes in tactile and pressure-pain perception and lumbar strength and flexibility in patients with nonspecific chronic LBP (NSCLBP) after performing one of three 20-minute physical exercise modalities. Methods: A total of 81 patients with NSCLBP were pseudorandomly distributed into three groups of 20-minute physical exercise — 1) aerobic (n=21, mean age 42± 9.72 years, nine men), 2) stretching (n=21, mean age 40± 11.37 years, ten men), and 3) strengthening (n=20, mean age 35.80± 11.56 years, ten men) — and 4) a control group (n=19, mean age 38.64± 10.24 years, eight men), and completed self-reported questionnaires during the same period. Tactile and pressure-pain thresholds and isometric lumbar muscle endurance and flexibility were assessed before and after this brief exercise-based intervention. Results: All groups were comparable in terms of sociodemographic and clinical data, cardiovascular capacity, and self-reported data onphysical disability, mood, motivation, psychological response to stimulus properties of physical exercise, and physical activity enjoyment. Our analyses revealed higher tactile sensitivity (p< 0.001) and pressure-pain thresholds (p< 0.001) at the forefinger than other body locations. We also found lower pain sensitivity (p=0.010) and pressure pain–intensity ratings (p=0.001) and higher lumbar flexibility (p< 0.001) after intervention. After calculation of absolute pre–post differences, higher tactile sensitivity was observed at the gluteus medius muscle than the erector spinal muscle only after aerobic intervention (p=0.046). Conclusion: These results add some evidence about different modalities of exercise-induced hypoalgesia in NSCLBP. However, the fact that we also found improvements in the control group limits our conclusions.

BACKGROUND Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients’ needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR. OBJECTIVE In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR. METHODS The scoping review followed the framework of Arksey and O’Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR. RESULTS A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies. CONCLUSIONS Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients’ needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.