Background: Digital interventions may assist patients with type 2 diabetes in improving glycemic control. We aimed to synthesize effect sizes of digital interventions on glycated hemoglobin (HbA1c) levels and to identify effective features of digital interventions targeting patients with poorly controlled type 2 diabetes. Materials and Methods: MEDLINE, ISI Web of Science, and PsycINFO were searched for randomized controlled trials (RCTs) comparing the effects of digital interventions with usual care. Two reviewers independently assessed studies for eligibility and determined study quality, using the Cochrane Risk of Bias Assessment Tool. The Behavioral Change Technique Taxonomy V1 (BCTTv1) was used to identify BCTs used in interventions. Mean HbA1c differences were pooled using analysis of covariance to adjust for baseline differences and pre–post correlations. To examine effective intervention features and to evaluate differences in effect sizes across groups, meta-regression and subgroup analyses were performed. Results: Twenty-three arms of 21 RCTs were included in the meta-analysis ( n = 3787 patients, 52.6% in intervention arms). The mean HbA1c baseline differences ranged from −0.2% to 0.64%. The pooled mean HbA1c change was statistically significant (−0.39 {95% CI: [−0.51 to −0.26]} with substantial heterogeneity [ I ² statistic, 80.8%]) and a significant HbA1c reduction was noted for web-based interventions. A baseline HbA1c level above 7.5%, β = −0.44 (95% CI: [−0.81 to −0.06]), the BCTs “problem solving,” β = −1.30 (95% CI: [−2.05 to −0.54]), and “self-monitoring outcomes of behavior,” β = −1.21 (95% CI: [−1.95 to −0.46]) were significantly associated with reduced HbA1c levels. Conclusions: Digital interventions appear effective for reducing HbA1c levels in patients with poorly controlled type 2 diabetes.

Background: A supportive environment is a key factor in addressing the issue of health among older adults. There is already sufficient evidence that objective and self-reported measures of the neighborhood environment should be taken into account as crucial components of active aging, as they have been shown to influence physical activity; particularly in people aged 60+. Thus, both could inform policies and practices that promote successful aging in place. An increasing number of studies meanwhile consider these exposures in analyzing their impact on physical activity in the elderly. However, there is a wide variety of definitions, measurements and methodological approaches, which complicates the process of obtaining comparable estimates of the effects and pooled results. The aim of this review was to identify and summarize these differences in order to emphasize methodological implications for future reviews and meta analyzes in this field and, thus, to create a sound basis for synthesized evidence.Methods: A systematic literature search across eight databases was conducted to identify peer-reviewed articles examining the association of objective and perceived measures of the neighborhood environment and objectively measured or self-reported physical activity in adults aged ≥ 60 years. Two authors independently screened the articles according to predefined eligibility criteria, extracted data, and assessed study quality. A qualitative synthesis of the findings is provided.Results: Of the 2967 records retrieved, 35 studies met the inclusion criteria. Five categories of methodological approaches, numerous measurement instruments to assess the neighborhood environment and physical activity, as well as several clusters of definitions of neighborhood, were identified.Conclusions: The strength of evidence of the associations of specific categories of environmental attributes with physical activity varies across measurement types of the outcome and exposures as well as the physical activity domain observed and the operationalization of neighborhood. The latter being of great importance for the targeted age group. In the light of this, future reviews should consider these variations and stratify their summaries according to the different approaches, measures and definitions. Further, underlying mechanisms should be explored.

Regular physical activity (PA) is of central importance for healthy ageing. However, in Germany, only 42% of older adults currently reach the PA recommendations of the World Health Organization. The aim of this study was to examine the effects of two web-based interventions on PA in adults aged 65-75 years living in Northwestern Germany compared to a delayed intervention control group (CG). 589 older adults were randomized to one of the three groups. Participants in intervention group 1 (IG1) received access to a web-based intervention for ten weeks assisting them in self-tracking PA behavior. Participants in IG2 received the intervention of IG1 and additionally an activity tracker to objectively track PA behavior. To analyze differences in objectively measured moderate-to-vigorous PA and sedentary time between baseline and follow-up (12 weeks after baseline), linear mixed models were used. The interaction effects revealed a decrease in minutes spent on moderate-to-vigorous PA in bouts of 10 min by 11 min per week in IG1 participants (β = -11.08, 95% CI: (-35.03; 12.87)). In comparison, IG2 participants were 7 min more physically active at follow-up (β = 7.48, 95% CI: (-17.64; 32.60)). Sedentary time in bouts of 30 min per week increased in IG1 participants (β = 106.77, 95% CI: (-47.69; 261.23)) and decreased in IG2 participants at follow-up (β = -16.45, 95% CI: (-178.83; 145.94)). Participation in the two web-based interventions did not lead to significant increases in moderate-to-vigorous PA or significant decreases in sedentary time compared to the CG. The study was registered at the German Clinical Trials Register (DRKS00010052, 07-11-2016).

Background: Fewer than half of older German adults engage in the recommended levels of endurance training. Objective: The study aim is to compare the acceptance and effectiveness of two interventions for physical activity (PA) promotion among initially inactive community-dwelling older adults ≥60 years in a 9-month, crossover randomized trial. Methods: Participants were recruited in person and randomized to one of the following interventions for self-monitoring PA: a print-based intervention (PRINT: 113/242, 46.7%) or a web-based intervention (WEB: 129/242, 53.3%). Furthermore, 29.5% (38/129) of those in the web-based intervention group received a PA tracker in addition to WEB (WEB+). After randomization, the participants and researchers were not blinded. The participants’ baseline intervention preferences were retrospectively assessed. All the intervention groups were offered 10 weekly face-to-face group sessions. Afterward, participants could choose to stay in their group or cross over to one of the other groups, and group sessions were continued monthly for another 6 months. 3D accelerometers to assess PA and sedentary behavior (SB) at baseline (T0), 3-month follow-up (T1), and 9-month follow-up (T2) were used. Adherence to PA recommendations, attendance of group sessions, and intervention acceptance were assessed using self-administered paper-based questionnaires. Linear mixed models were used to calculate differences in moderate to vigorous PA (MVPA) and SB between time points and intervention groups. Results: Of the 242 initially recruited participants, 91 (37.6%) were randomized to the WEB group; 38 (15.7%) to the WEB+ group; and 113 (46.7%) to the PRINT group. Overall, 80.6% (195/242) of the participants completed T1. Only 0.4% (1/242) of the participants changed from the WEB group to the PRINT group and 6.2% (15/242) moved from the PRINT group to the WEB group (WEB-WEB: 103/249, (41.4%); PRINT-PRINT: 76/249, 30.5%) when offered to cross over at T1. Furthermore, 66.1% (160/242) of participants completed T2. MVPA in minutes per day increased between baseline and T1, but these within-group changes disappeared after adjusting for covariates. MVPA decreased by 9 minutes per day between baseline and T2 (βtime=−9.37, 95% CI −18.58 to −0.16), regardless of the intervention group (WEB vs PRINT: βgroup*time=−3.76, 95% CI −13.33 to 5.82, WEB+ vs PRINT: βgroup*time=1.40, 95% CI −11.04 to 13.83). Of the participants, 18.6% (38/204) met the PA recommendations at T0, 16.4% (26/159) at T1, and 20.3% (28/138) at T2. For SB, there were no significant group differences or group-by-time interactions at T1 or T2. Intervention acceptance was generally high. The use of intervention material was high to moderate at T1 and decreased by T2. Conclusions: There was little movement between intervention groups at T1 when given the choice, and participation was not associated with increases in PA or decreases in SB over time. Trial Registration: German Clinical Trials Register DRKS00016073; https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00016073

Background: It remains unclear how physical activity (PA) interventions need to be designed to reach older adults and to be widely accepted in this target group. The aim of this study was to assess the acceptance of a web-based PA program, including individual intervention components as well as relevant contextual factors, and to specify requirements for future interventions. Methods: Two hundred sixty-six participants of a PA intervention completed a questionnaire covering individual program components (content, structure, and context). Further, 25 episodic guided interviews focusing on reasons for (non-) participation were conducted with 8 participants and 17 non-participants. Following qualitative content analysis, different requirements were identified and organized based on the social-ecological model, resulting in a profile of requirements. Results: Based on the participants’ and non-participants’ statements, six different levels of requirements affecting acceptance of and successful participation in a web-based PA intervention were identified. The individual fit was influenced by an interaction of different factors at the intrapersonal, sociocultural, content, spatial, digital and organizational levels. Several age- and gender-specific requirements were noted in the interviewed older adults. Men and women, as well as younger (< 70 years) and older (≥70 years) adults differed in terms of perceived enjoyment and benefits of socializing while exercising together, the time expenditure perceived to be acceptable, previous digital skills, as well as in perceptions that ambience and accessibility of exercise facilities in the neighborhood were important. Conclusions: To motivate older adults to engage in PA and address different needs in terms of life circumstances and quality of life as well as differences in technical affinity, different requirement profiles should be included in the process of intervention development and implementation. Participatory development loops and modular offer formats are recommended for this.