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Smartphone-embedded inertia sensors are widely available nowadays. We have developed a smartphone application that could assess temporal gait characteristics using the built-in inertia measurement unit with the aim of enabling mass screening for gait abnormality. This study aimed to examine the test–retest reliability and concurrent validity of the smartphone-based gait assessment in assessing temporal gait parameters in level-ground walking. Twenty-six healthy young adults (mean age: 20.8 ± 0.7) were recruited. Participants walked at their comfortable pace on a 10 m pathway repetitively in two walking sessions. Gait data were simultaneously collected by the smartphone application and a VICON system during the walk. Gait events of heel strike and toes off were detected from the sensors signal by a peak detection algorithm. Further gait parameters were calculated and compared between the two systems. Pearson Product–Moment Correlation was used to evaluate the concurrent validity of both systems. Test–retest reliability was examined by the intraclass correlation coefficients (ICCs) between measurements from two sessions scheduled one to four weeks apart. The validity of smartphone-based gait assessment was moderate to excellent for parameters involving only heel strike detection (r = 0.628–0.977), poor to moderate for parameters involving detection of both heel strike and toes off (r = 0.098–0.704), and poor for the proportion of gait phases within a gait cycle. Reliability was good to fair for heel strike-related parameters (ICC = 0.845–0.388), good to moderate for heel strike and toes-off-related parameters (ICC = 0.827–0.582), and moderate to fair for proportional parameters. Validity was adversely affected when toe off was involved in the calculation, when there was an insufficient number of effective steps taken, or when calculating sub-phases with short duration. The use of smartphone-based gait assessment is recommended in calculating step time and stride time, and we suggest collecting no less than 100 steps per leg during clinical application for better validity and reliability.

Objective This article describes a protocol for a randomized controlled trial to evaluate the effects of a three-level Health App for Post-Pandemic Years (HAPPY) on alleviating post-pandemic physiological and psychosocial distress. Methods Convenience and snowball sampling methods will be used to recruit 814 people aged 18+ with physiological and/or psychosocial distress. The experimental group will receive a 24-week intervention consisting of an 8-week regular supervision phase and a 16-week self-help phase. Based on their assessment results, they will be assigned to receive interventions on mindfulness, energy conservation techniques, or physical activity training. The waitlist control group will receive the same intervention in Week 25. The primary outcome will be changes in psychosocial distress, measured using the Kessler Psychological Distress Scale (K10). Secondary outcomes will include changes in levels of fatigue (Chinese version of the Brief Fatigue Inventory), sleep quality (Chinese version of the Pittsburgh Sleep Quality Index), pain intensity (Numeric Rating Scale), positive appraisal (Short version of the 18-item Cognitive Emotion Regulation Questionnaire), self-efficacy (Chinese version of the General Self-efficacy Scale), depression and anxiety (Chinese version of the 21-item Depression Anxiety Stress Scale), and event impact (Chinese version of the 22-item Impact of Event Scale–Revised). All measures will be administered at baseline (T0), Week 8 after the supervision phase (T1), and 24 weeks post-intervention (T2). A generalized estimating equations model will be used to examine the group, time, and interaction (Time × Group) effect of the interventions on the outcome assessments (intention-to-treat analysis) across the three time points, and to compute a within-group comparison of objective physiological parameters and adherence to the assigned interventions in the experimental group. Conclusions The innovative, three-level mobile HAPPY app will promote beneficial behavioral strategies to alleviate post-pandemic physiological and psychosocial distress. Trial registration ClinicalTrials.gov, NCT05459896. Registered on 15 July 2022.

Background Motor synergy patterns are recognized as physiological markers of motor cortical damage, providing insights into how motor cortex coordinates spinal motor modules to generate movements. However, how these patterns adapt to tasks of varying complexity following post-stroke cortical damage is not yet fully understood. Objective We aimed to understand how motor synergy patterns are distorted across tasks of increasing complexity after stroke induced cortical damage, also to provide a reference for task selection when using muscle synergy patterns as biomarkers for stroke evaluation or intervention. Methods This was a pilot, cross sectional study. We investigated muscle synergies during five tasks with varying complexity in 20 healthy individuals (13 females and 7 males, aged 64.33 ± 6.94 years) and in 12 participants with chronic stroke (4 females and 8 males, aged 64.4 ± 6.54 years). Surface electromyographic activities were recorded from 16 upper limb muscles (8 muscles per limb: upper/lower trapezius, anterior/posterior deltoid, triceps brachii lateral head, biceps brachii short head, flexor digitorum superficialis, and extensor digitorum communis). Non-negative matrix factorization was performed to extract the muscle synergies. We categorized the stroke-induced synergy plasticity based on healthy synergy centroids, compared the synergy plasticity between affected and unaffected limbs, and investigated the correlation between synergy plasticity and patient’s motor function, Results In healthy individuals, the number of muscle synergies exhibited a U-shaped pattern as task complexity increased, whereas in stroke patients, both the affected and unaffected limbs showed a decreasing trend in muscle synergy number with increasing task complexity. Besides, the unaffected arm exhibited significantly more preservation synergies (synergies resembling healthy patterns) than the affected arm in moderate (Placing 30 cm: Z(11) = -2.144, corrected p  = 0.031, Rosenthal’s r = -0.646) and high complexity tasks (Z(11) = -2.558, corrected p  = 0.028, Rosenthal’s r = -0.771), and fewer mutation synergies (synergies deviating from healthy patterns) with marginal significance (Placing 30 cm: Z(11) = -1.992, corrected p  = 0.058, Rosenthal’s r = -0.600; Drinking: Z(11) = -2.070, corrected p  = 0.058, Rosenthal’s r = -0.624). Notably, this asymmetry in preservation synergies was significantly correlated with patients’ motor function (Fugl-Meyer Assessment-Upper Limb: R = -0.711, permutation p  = 0.010; Modified Ashworth Scale for elbow flexion: R  = 0.603, permutation p  = 0.044). Conclusion This study is among the first to investigate how task complexity influences muscle synergy plasticity and its asymmetry in participants with chronic stroke. Patients demonstrate spatial asymmetry in muscle synergies between the unaffected and affected sides. This asymmetry is magnified by task complexity and shows a strong correlation with motor performance. Therefore, we recommend that when using muscle synergy patterns as biomarkers for stroke assessment, the influence of task complexity should be explicitly considered, as it plays a critical role in shaping these patterns.