External Validation of a Single-Item Scale to Measure Motivation to Stop Smoking: Findings from a Representative Population Survey (DEBRA Study)

Aims: The Motivation to Stop Scale (MTSS) is a single-item English language scale for predicting attempts to quit smoking. The aim of the study was an external validation of a German version of the MTSS (Motivation zum Rauchstopp Skala, MRS) based on a sample of current tobacco smokers in Germany. Methods: We used data from the first 18 waves (June 2016-May 2019) of the DEBRA study (German Study on Tobacco Use): a nationwide, face-to-face household survey of persons aged 14 years and older with one follow-up telephone interview after 6 months. We analysed data from 767 current smokers. The MRS was used at baseline (level 1-7 = no to highest motivation). At follow-up, the number of quit attempts since baseline were measured. We conducted logistic regression analyses and calculated the discriminant accuracy of MRS using the Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: At baseline, 61.1 % (n = 469; 95 % confidence interval (CI) = 57.7-64.6) of current 767 tobacco smokers were not motivated to quit smoking (MRS level 1-2). Overall, 185 of the 767 smokers (24.1 %; CI = 21.1-27.1) made at least one quit attempt between the baseline and follow-up survey. The odds of reporting a quit attempt increased with increasing motivation to stop smoking on the MRS: odds ratio = 1.37, 95 % CI = 1.25-1.51. The discriminative accuracy of the MRS was ROC-AUC = 0.64. Conclusion: The MRS is a brief and valid measurement for assessing the motivation to stop smoking in the German language

A Smartwatch Step-Counting App for Older Adults: Development and Evaluation Study

Background: Older adults who engage in physical activity can reduce their risk of mobility impairment and disability. Short amounts of walking can improve quality of life, physical function, and cardiovascular health. Various programs have been implemented to encourage older adults to engage in physical activity, but sustaining their motivation continues to be a challenge. Ubiquitous devices, such as mobile phones and smartwatches, coupled with machine-learning algorithms, can potentially encourage older adults to be more physically active. Current algorithms that are deployed in consumer devices (eg, Fitbit) are proprietary, often are not tailored to the movements of older adults, and have been shown to be inaccurate in clinical settings. Step-counting algorithms have been developed for smartwatches, but only using data from younger adults and, often, were only validated in controlled laboratory settings. Objective: We sought to develop and validate a smartwatch step-counting app for older adults and evaluate the algorithm in free-living settings over a long period of time. Methods: We developed and evaluated a step-counting app for older adults on an open-source wrist-worn device (Amulet). The app includes algorithms to infer the level of physical activity and to count steps. We validated the step-counting algorithm in the lab (counting steps from a video recording, n=20) and in free-living conditions—one 2-day field study (n=6) and two 12-week field studies (using the Fitbit as ground truth, n=16). During app system development, we evaluated 4 walking patterns: normal, fast, up and down a staircase, and intermittent speed. For the field studies, we evaluated 5 different cut-off values for the algorithm, using correlation and error rate as the evaluation metrics. Results: The step-counting algorithm performed well. In the lab study, for normal walking (R2=0.5), there was a stronger correlation between the Amulet steps and the video-validated steps; for all activities, the Amulet’s count was on average 3.2 (2.1%) steps lower (SD 25.9) than the video-validated count. For the 2-day field study, the best parameter settings led to an association between Amulet and Fitbit (R2=0.989) and 3.1% (SD 25.1) steps lower than Fitbit, respectively. For the 12-week field study, the best parameter setting led to an R2 value of 0.669. Conclusions: Our findings demonstrate the importance of an iterative process in algorithm development before field-based deployment. This work highlights various challenges and insights involved in developing and validating monitoring systems in real-world settings. Nonetheless, our step-counting app for older adults had good performance relative to the ground truth (a commercial Fitbit step counter). Our app could potentially be used to help improve physical activity among older adults

Coronavirus and financial stability 3.0: Try equity – risk sharing for companies, large and small

This policy letter adds to the current discussion on how to design a program of government assistance for firms hurt by the Coronavirus crisis. While not pretending to provide a cure-all proposal, the advocated scheme could help to bring funding to firms, even small firms, quickly, without increasing their leverage and default risk. The plan combines outright cash transfers to firms with a temporary, elevated corporate profit tax at the firm level as a form of conditional payback. The implied equity-like payment structure has positive risk-sharing features for firms, without impinging on ownership structures. The proposal has to be implemented at the pan-European level to strengthen Euro area resilience

Enxertia em figueira Roxo de Valinhos por borbulhia e garfagem.

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