Physical activity and depression: Towards understanding the antidepressant mechanisms of physical activity.

Introduction Personalization is a much-discussed approach to improve adherence and outcomes for Digital Mental Health interventions (DMHIs). Yet, major questions remain open, such as (1) what personalization is, (2) how prevalent it is in practice, and (3) what benefits it truly has. Methods We address this gap by performing a systematic literature review identifying all empirical studies on DMHIs targeting depressive symptoms in adults from 2015 to September 2022. The search in Pubmed, SCOPUS and Psycinfo led to the inclusion of 138 articles, describing 94 distinct DMHIs provided to an overall sample of approximately 24,300 individuals. Results Our investigation results in the conceptualization of personalization as purposefully designed variation between individuals in an intervention's therapeutic elements or its structure. We propose to further differentiate personalization by what is personalized (i.e., intervention content, content order, level of guidance or communication) and the underlying mechanism [i.e., user choice, provider choice, decision rules, and machine-learning (ML) based approaches]. Applying this concept, we identified personalization in 66% of the interventions for depressive symptoms, with personalized intervention content (32% of interventions) and communication with the user (30%) being particularly popular. Personalization via decision rules (48%) and user choice (36%) were the most used mechanisms, while the utilization of ML was rare (3%). Two-thirds of personalized interventions only tailored one dimension of the intervention. Discussion We conclude that future interventions could provide even more personalized experiences and especially benefit from using ML models. Finally, empirical evidence for personalization was scarce and inconclusive, making further evidence for the benefits of personalization highly needed. Systematic Review Registration Identifier: CRD42022357408.

/pdf/personalization-strategies-in-digital-mental-health-1jf20qxe.pdf

Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms

Antidepressants or running therapy: Comparing effects on mental and physical health in patients with depression and anxiety disorders.

Background: Sedentary behavior (SB) negatively impact health and is highly prevalent in the population. Digital behavior change interventions (DBCIs) have been developed to modify behaviors such as SB by technologies. However, it is unknown which behavior change techniques (BCTs) are most frequently employed in SB as well as the effect associated with DBCIs in this field. The aim of this systematic review was: (a) to evaluate the BCT most frequently employed in digital health including all technologies available and interventions aimed at increasing physical activity (PA), reducing sedentary time, and improving adherence to exercise in the clinical population, and (b) to review the effect associated with DBCIs in this field. Methods: The database used was Medline, as well as Scopus, Scielo, and Google Scholar. For the search strategy, we considered versions of behavior/behavioral, mHealth/eHealth/telemedicine/serious game/gamification. The terms related to PA and SB were included, the criteria for inclusion were randomized clinical trials (RCTs), adults, intervention based on digital media, and outcome variable lifestyle modification; a last 5 years filter was included. Michie's Taxonomy was used to identify BCTs. The study was registered under the number PROSPERO CRD42019138681. Results: Eighteen RCTs were included in the present systematic review, 5 of them healthy adults, and 13 of them with some illness. Studies included 2298 sedentary individuals who were followed up for 5 weeks-3 years. The most used BCTs were goal setting, problem solving, review outcomes/goals, feedback on behavior and outcomes of behavior, self-monitoring of behavior, social support, information about health consequences, and behavior practice/rehearsal. The effect associated with DBCIs showed improvements, among several related to PA and physiologic self-reported and anthropometric outcomes. Conclusion: The BCTs most used in digital health to change outcomes related to SB were goals and planning, feedback and monitoring, social support, natural consequences, repetition, and substitution. Besides these findings, DBCIs are influenced by several factors like the type of intervention, patients' preferences and values, or the number of BCTs employed. More research is needed to determine with precision which DBCIs or BCTs are the most effective to reduce SB in the clinical population.

/pdf/behavior-change-techniques-and-the-effects-associated-with-2dtgr1ls2o.pdf

Behavior Change Techniques and the Effects Associated With Digital Behavior Change Interventions in Sedentary Behavior in the Clinical Population: A Systematic Review.

Background Digital health interventions for managing chronic conditions have great potential. However, the benefits and harms are still unclear. Objective This systematic review and meta-analysis aimed to investigate the benefits and harms of digital health interventions in promoting physical activity in people with chronic conditions. Methods We searched the MEDLINE, Embase, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to October 2022. Eligible randomized controlled trials were included if they used a digital component in physical activity promotion in adults with ≥1 of the following conditions: depression or anxiety, ischemic heart disease or heart failure, chronic obstructive pulmonary disease, knee or hip osteoarthritis, hypertension, or type 2 diabetes. The primary outcomes were objectively measured physical activity and physical function (eg, walk or step tests). We used a random effects model (restricted maximum likelihood) for meta-analyses and meta-regression analyses to assess the impact of study-level covariates. The risk of bias was assessed using the Cochrane Risk of Bias 2 tool, and the certainty of the evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation. Results Of 14,078 hits, 130 randomized controlled trials were included. Compared with usual care or minimal intervention, digital health interventions increased objectively measured physical activity (end of intervention: standardized mean difference [SMD] 0.29, 95% CI 0.21-0.37; follow-up: SMD 0.17, 95% CI 0.04-0.31) and physical function (end of intervention: SMD 0.36, 95% CI 0.12-0.59; follow-up: SMD 0.29, 95% CI 0.01-0.57). The secondary outcomes also favored the digital health interventions for subjectively measured physical activity and physical function, depression, anxiety, and health-related quality of life at the end of the intervention but only subjectively measured physical activity at follow-up. The risk of nonserious adverse events, but not serious adverse events, was higher in the digital health interventions at the end of the intervention, but no difference was seen at follow-up. Conclusions Digital health interventions improved physical activity and physical function across various chronic conditions. Effects on depression, anxiety, and health-related quality of life were only observed at the end of the intervention. The risk of nonserious adverse events is present during the intervention, which should be addressed. Future studies should focus on better reporting, comparing the effects of different digital health solutions, and investigating how intervention effects are sustained beyond the end of the intervention. Trial Registration PROSPERO CRD42020189028; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=189028

Benefits and Harms of Digital Health Interventions Promoting Physical Activity in People With Chronic Conditions: Systematic Review and Meta-Analysis

Background: Due to the high prevalence of depressive disorders, it is mandatory to develop therapeutic strategies that provide universal access and require limited financial and human resources. Web-based therapeutic approaches fulfill these conditions. Objective: The objective of our study was to assess the feasibility, acceptability, and efficacy of a supervised, individualized 8-week Web-based exercise intervention conducted for patients with moderate to severe depression. Methods: We recruited 20 patients with unipolar depression and randomly assigned them into 2 groups (intervention, exercise program group, n=14, and control, treatment-as-usual group, n=6). At baseline, depressive symptoms were rated via the Quick Inventory of Depressive Symptomatology (QIDS) by patients themselves (QIDS–self-report, QIDS-SR) and by a blinded psychiatrist (QIDS–clinician rating, QIDS-C). In addition, performance diagnostics (lactate analysis, spiroergometry during a treadmill walking test) were conducted. Quality of life was assessed via the Short Form-36 questionnaire (SF-36) and self-efficacy via the General Self-Efficacy scale (GSE). In addition, habitual physical activity (HPA) was determined via the Baecke questionnaire. Participants of the intervention group received exercise schedules once weekly with endurance and strength training instructions. Rating of depressive symptoms was repeated after 6-12 days and 8 weeks; performance diagnostics and the completion of all the questionnaires were repeated after 8 weeks only. Results: The severity of depression subsided significantly in the intervention group after 8 weeks (median change in QIDS-SR: −5; interquartile range, IQR: −2 to −10), although it was already evident within the first 6-12 days (median change in QIDS-SR: −6; IQR: −2 to −8). During the intervention, participants undertook a median of 75 (IQR: 63 to 98) minutes of endurance training per week or 84% (16 [IQR: 9 to 19] of 19 [IQR: 15 to 21]) recommended endurance units in total. In addition, 9 (IQR: 4 to 12) of 10 (IQR: 8 to 13) recommended strength training exercise units were conducted during the 8 weeks. Performance diagnostics revealed a substantial increase in the maximum output in Watt for the intervention group after 8 weeks. Moreover, the intervention showed a favorable effect on SF-36 items “emotional well-being” and “social functioning” as well as on GSE and HPA scores. Conclusions: Our individualized Web-based exercise intervention for moderate to severe depression was highly accepted by the patients and led to a significant and clinically relevant improvement of depressive symptoms. Trial Registration: ClinicalTrials.gov NCT02874833; https://clinicaltrials.gov/ct2/show/NCT02874833 (Archived by WebCite at http://www.webcitation.org/72ZUUR4tE)

/pdf/individualized-web-based-exercise-for-the-treatment-of-3boxl4upjc.pdf

Individualized Web-Based Exercise for the Treatment of Depression: Randomized Controlled Trial

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Individualized Web-Based Exercise for the Treatment of Depression: Randomized Controlled Trial