https://orcid.org/0000-0003-0619-0661
Abstract
Introduction
Digital technology-based interventions have gained popularity over the last two decades, due to the ease with which they are scalable and low in implementation cost. Multicomponent health promotion programmes, with significant digital components, are increasingly being deployed in the workplace to assess and promote employees’ health behaviours and reduce risk of chronic diseases. However, little is known about workplace digital health interventions in low- and middle- income countries (LMICs).
Methods
Various combinations of keywords related to “digital health”, “intervention”, “workplace” and “developing country” were applied in Ovid MEDLINE, EMBASE, CINAHL Plus, PsycINFO, Scopus and Cochrane Library for peer-reviewed articles in English language. Manual searches were performed to supplement the database search. The screening process was conducted in two phases and a narrative synthesis to summarise the data. The review protocol was written prior to undertaking the review (OSF Registry:10.17605/OSF.IO/QPR9J).
Results
The search strategy identified 10,298 publications, of which 24 were included. Included studies employed the following study designs: randomized-controlled trials (RCTs) (n = 12), quasi-experimental (n = 4), pilot studies (n = 4), pre-post studies (n = 2) and cohort studies (n = 2). Most of the studies reported positive feedback of the use of digital wellness interventions in workplace settings.
Conclusions
This review is the first to map and describe the impact of digital wellness interventions in the workplace in LMICs. Only a small number of studies met the inclusion criteria. Modest evidence was found that digital workplace wellness interventions were feasible, cost-effective, and acceptable. However, long-term, and consistent effects were not found, and further studies are needed to provide more evidence. This scoping review identified multiple digital health interventions in LMIC workplace settings and highlighted a few important research gaps.
Figures
Citation: Thai YC, Sim D, McCaffrey TA, Ramadas A, Malini H, Watterson JL (2023) A scoping review of digital workplace wellness interventions in low- and middle-income countries. PLoS ONE 18(2): e0282118. https://doi.org/10.1371/journal.pone.0282118
Editor: Ali A. Weinstein, George Mason University, UNITED STATES
Received: June 21, 2022; Accepted: February 7, 2023; Published: February 28, 2023
Copyright: © 2023 Thai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data is available in existing library databases that we searched. Our methodology is outlined in the article to allow others to reproduce the search.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
According to the World Health Organisation (WHO), 3.5 billion people, nearly half of the world’s population, are employees. On average, a full-time employee spends more than one-third of his or her days, five days a week at their workplace. Due to the large population and long hours spent at work, the workplace has been a favourable setting to implement health promotion programs, motivating employee health behaviour change. WHO has estimated that 2.1% of all deaths and 2.7% of the global disease burden are attributed to quantified occupation risks [1]. Of these, employees in low-and middle-income countries (LMICs) have contributed to the largest portions of deaths and disability in the workplace settings. Evidence has suggested a rising need for WWPs in LMICs [2].
WWPs are typically designed to reduce medical spending, increase employee’s productivity and enhance their well-being [3]. Research explored the link between employee health and work productivity [4, 5]. Besides absenteeism as an indicator for work productivity, there is also an extent of limitation due to health problems even when employees are present at work. For example, obese workers may experience greater challenges at work compared to normal weight workers [6]. Both absenteeism and presenteeism are strongly associated with poor employee’s status and behaviours, including obesity, insomnia, depression or physical inactive which have been proven to cause detrimental burden to organizations’ economic [7–11]. This poor workplace performance which is caused by physical or mental health issues is often underestimated by the organisation. Thus, it is essential to build a healthy work environment and address employee’s health issues.
Studies have grown exponentially in a short time as digital health is progressing rapidly due to advances in technology and applications. Multicomponent design which involves various support from healthcare professionals, employees support groups, telephone-based coaching and more recently web and mobile-delivered programs, has been proven to be the most effective approach in addressing occupational health issues [12–17]. Digital technology-based intervention is increasingly being deployed in the workplace due in part to their scalability to a large population and cost-effective approach when compared with traditional health intervention used. Additionally, the COVID-19 pandemic has accelerated the digital transformation and brought more people on the digital health journey. The remote work model might affect the implementation of WWPs and thus, digital intervention may be more feasible and practical in this new norm. Also, digital workplace wellness allows all employees to access the health promotion content from anywhere at any time with the help of technology. Nonetheless, it is worth discovering whether digital workplace wellness is effective in modifying health behaviours. Employee populations potentially have much to gain from digital intervention for health behaviours promotion, yet little is known about the implementation of digital-based technology intervention in the LMICs workplace context as most of the studies reported were in developed countries. Hence, this scoping review aims to explore and provide a comprehensive synthesis of current evidence in relation to the effectiveness, feasibility, and acceptability of the digital workplace wellness intervention in the LMICs settings.
Methods
Study design
This scoping review utilises systematic searching methods and is guided by the Joanna Briggs Institute methodology [18] and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-Scr) [19]. Ethical approval was not sought as the data were publicly available. A scoping review approach was chosen to examine the volume of existing literature and provide an overview of its focus, such as the digital components used, and health outcomes targeted. Specifically, we aim to explore the following research questions:
- How have digital technology interventions been conducted in the workplace in LMICs?
- What research has been done and what are the effects of these interventions on health- and job performance-related outcomes?
- What are the research gaps that can be identified from these interventions as to improve health behaviours in the workplace in LMICs?
The protocol of this scoping review has been registered with Open Science Framework (10.17605/OSF.IO/QPR9J).
Data sources and search strategy
We systematically searched 6 databases, including OVID Medline, Embase, PsycINFO, Scopus, CINAHL Plus and Cochrane Library. Four themes of keywords “digital health”, “intervention”, “workplace” and “developing country” were used to guide the search and a more detailed search strategy with relevant synonyms and medical subject heading (MeSH) terms, combined with Boolean Operator AND is provided below:
- "Digital health” OR ehealth OR mhealth OR "mobile health" OR digital* OR web* OR internet* OR online OR smartphone* OR "cell phone*" OR "mobile phone*" OR telephone* OR application* OR "activity monitor" OR tracker OR pedometer OR technolog* OR messaging OR whatsapp* OR "whatsapp-based" OR "wechat*" OR "wechat-based" OR "social media"
- Wellness OR wellbeing OR lifestyle* OR “workplace wellness” OR “occupational health” OR “health promotion” OR "health behavio?r*" OR intervention* OR program* OR education OR "physical activity" OR exercise* OR health* OR diet OR nutrition OR food OR "healthy eating" OR "mental health" OR "chronic disease*" OR sedentary OR "sedentary behavio?r*" OR stress* OR sleep*
- Workplace OR employee* OR occupation* OR work* OR industr* OR office OR job* OR "job performance" OR "work engagement"
- LMICs country list modified based on 2022 fiscal year classification [2].
The database search was limited to peer-reviewed original articles in the English language, human studies, and age group >18 years where possible restriction was imposed. Articles were only included from 2010–2021 to avoid the inclusion of obsolete digital components such as CD-ROMs and personal digital assistants (PDAs) which are not applicable in the current digital era. A sample of search strategy as performed in OVID MEDLINE on 15 December 2021 is shown in S2 Table.
This search strategy was refined after initial searches were run in October 2021 and resulted in few relevant studies. At the same time, the research team performed a manual search in Google Scholar and identified relevant studies that were not included in the database results. The search terms were reviewed and revised, drawing on terms used in the publications identified manually, and with input from Monash University librarians. These search term revisions resulted in a higher number of relevant results, including the papers that had been located through manual searching, indicating they were more effective than the initial search terms. All searches were rerun with these search terms in December 2021.
Study selection
Covidence software [20] was used to facilitate the screening process. All records retrieved were imported into Covidence where the duplicate records were automatically removed. Two reviewers first screened independently the titles and abstracts of the publications, and then proceeded to screen full text articles to determine the eligibility of the papers. Both reviewers screened all the articles. The screening was conducted according to predetermined eligibility criteria (Table 1). All conflicts were resolved through discussion with the research team. Emails were sent to corresponding authors as needed to get relevant information such as full texts of papers which could not be found online to further confirm the eligibility of the paper.
Data extraction
Two reviewers conducted the data extraction from half of the 24 finalised articles (12 each). Information that was extracted included: country, year of publication, study design, participant characteristics, inclusion and exclusion criteria, intervention duration and follow-up, intervention components, measured outcomes, and main findings. As the scoping review is qualitative in nature, we performed narrative data synthesis according to five groups of identified outcomes (as shown in Table 3), namely Lifestyle (A) including smoking and cardiovascular disease risk, Weight Management (B), Physical Activity (PA) (C), Job performance (D) including work engagement, as well as other health outcomes (E) such as sleep, stress, and ergonomic condition. The reviewers worked independently on extracting the data of 12 studies each using a shared Google document, then later finalised and refined the data extraction table together. All conflicts were resolved through discussion with the research team.
Results
Study selection and characteristics
Our database search identified a total of 10,298 studies. Fig 1 illustrates the PRISMA flow chart of the article selection process. After removing the 4266 duplicates from the imported studies and screening 6032 studies at the title or abstract level, we found 46 full text articles which were potentially relevant. Eventually, 24 studies were included in the current review after applying the inclusion and exclusion criteria.
A summary of the main characteristics of the included studies is provided in Table 2. All the studies were published between the years 2012 and 2021 (Fig 2). The study designs included randomized-controlled trials (RCTs) (n = 12), quasi-experimental (n = 4), pilot studies (n = 4), pre-post studies (n = 2) and cohort studies (n = 2). Six studies were conducted in China [22–27], four in Iran [28–31], four in India [32–35], two in Turkey [36, 37], two in Latin America [38, 39], one in South Africa [40], one in Brazil [41], one in Nigeria [42], and one in Vietnam [43]. Two studies were conducted in multiple countries [44, 45]: Ganesan A et al (2016) was conducted in LMICs in Asia and 90% of the total participants were from India and Montagni et al (2019) involved participants from China, France, Spain and the UK. For these studies including participants from both LMICs and high-income countries, results could not be separated by country, so the pooled results are presented in this review. Workplace settings included academic institutions [36, 40–42], hospitals or academic hospitals [22, 27, 31, 37, 39, 43], healthcare facilities [29, 30, 35], IT companies [25, 34], industrial units [33], and service companies [32]. Seven studies targeted public and private sector organisations from multiple worksites [23, 24, 26, 28, 38, 44, 45].
Theoretical frameworks
Some of the included studies drew on theoretical frameworks in their design or analysis. Three studies reported that their interventions drew on the Transtheoretical Model of Behaviour Change (n = 3) [33, 38, 40], with two [38, 40] used motivational messages and calls, and the other [33] providing health information. Two studies were based on Social Cognitive Theory (n = 2), with one [28] involving education/training, and the other [26] used a WeChat group for motivation and progress reporting. Two used the Health Belief Model (n = 2), with one [30] involving education material and the other [38] involving motivational calls and personal text messages. One study adopted the Theory of Planned Behaviour [29] with education training, messaging and knowledge sharing in a Telegram group. Another used Behaviour Change Techniques [25] with coaching and pedometer-generated personalised feedback. Another used Self Efficacy [27] while asking participants to post 3 good things every day. One study used Goal Setting [34] and provided health information through phone messages and emails. Another study drew on the Behaviour Change Wheel [41] and involved coaching and pedometer-generated personalised feedback. Another study used Influential Theory [32] and involved pictures, videos, and text messages on positive emotions. Eleven studies mentioned no clear theoretical basis [22, 24, 31, 35, 37, 39, 43–45].
Participant characteristics
The study sample sizes ranged from 41 in an RCT [31] to 26,562 in a prospective cohort study [44]. Of the 24 studies, two studies targeted single-gender participants: all male employees in the industrial sector [33] and all female staff working at a university [36], while all other studies involved participants of both genders. Overall, the proportion of females was higher than males, with 15 of 24 studies having ≥50% female participants. Employees’ participation in all studies was voluntary and neither incentives nor monetary compensation were specified in any study. Thus, the drop-out rate for one of the studies was as high as 47% from baseline participation [44]. Ganesan et al (2016) used a competition method among countries and that may have motivated participants to complete the intervention. Most studies employed inclusion criteria that required participants to be adult employees above the age of 18 years. The study by Beleigoli et al. (2020) included both staff and students at a university and, though studies of students did not meet the inclusion criteria for this review, the pooled results are presented here as it was not possible to separate the results for staff only. Table 3 shows the study characteristics and main findings of the included papers. Supplementary materials are available to provide more details on the studies included.
Target population
17 studies targeted general employees without any health condition and seven targeted the at-risk population. Four articles employed a weight management intervention with an overweight or obese population (n = 3) [28, 41, 46] or population intending to lose weight (n = 1) [23]. One article included employees having prehypertension, one included employees who showed high stress symptoms, and one included employees with a family history of risk factors of metabolic diseases. Of the 24 articles, two targeted at male-only [33] and female-only [36] population, the remaining included both genders.
Intervention and study characteristics
Duration of intervention and follow-up.
The duration of intervention delivery varied, ranging from 2 weeks to 2 years, with six studies running from 1 to 10 weeks, six lasting for 3 months, five studies for 6 months, three studies for 12 months and two studies for 24 months. The two shortest interventions lasted for 10 working days. One [30] involved education and messaging through Telegram, the other [32] provided two 20–40 second stimuli in the form of pictures, videos and text through emails. 11 studies [23, 26, 28, 30, 33, 35, 38, 40, 43, 45] had follow-up periods after the intervention ranging from 2 weeks to 6 months. However, most of the studies did not include a detailed description of their follow-up phase, if any.
Intervention provider.
The majority of studies involved interventions provided by the research team. Seven studies included interventions led by both the research team and participants. Of these, one study chose some participants to serve as role models for the remaining participants (using observational learning) while the research team were delivering educational content and consultations [28]. Two studies assigned some participants as leaders to facilitate the interventions and motivate participants to engage [25, 26]. Sasaki et al (2021) included participants in the program development, exploring the cultures and specific stressors in the workplace context. Additionally, Montagni and her team [45] built local teams in each participating country to lead adaptation and implementation of the pilot intervention with instructions from headquarters. For cohort studies which only involved data collection and analysis, one was a collaborative project with 2 universities (intervention provider) [44] and the other collaborated with the district government (intervention provider) [23].
Digital component.
The digital components of interventions included: educational content or video demonstrations shared on websites or by SMS, email consultations, telephone counselling, use of a pedometer (piezoelectric accelerometer technology), WeChat and WhatsApp groups (communication mobile applications), and smartphone applications. 10 studies [23, 32, 33, 35, 39, 43, 45] involved a single digital component, whereas 14 studies [22, 24, 31, 34, 40, 41, 43, 44] utilised interventions where more than one digital component was used. For example, Yu Y. et al (2018) used an individualised pedometer-assisted exercise prescription and asked participants to synchronise the data to the Health System Centre daily. Abdi J. et al (2015) used websites and SMS to deliver healthy nutrition and PA information, as well as providing telephone and email consultations every two weeks. Of the 24 studies, 7 studies [24, 25, 33, 36, 39, 42, 45] involved only a digital intervention, while the remaining 16 studies [22, 23, 26, 28, 32, 34, 35, 37, 38, 40, 41, 43, 44] involved both digital and in-person support. For instance, Beleigoli et al (2020) included an online weight loss program and dietitian-delivered personalized feedback.
Control and comparison.
A control or comparator group was present in 17 studies [22, 23, 25, 26, 28–34, 37, 38, 40, 41, 43]. There were a variety of control and comparison types adopted in the included studies. Three studies [25, 40, 43] adopted a waitlist control design where the control group did the same baseline assessment as the experimental group but were only involved in the same intervention upon the completion of the study. Five studies [26, 27, 32, 34, 37] reported no intervention adopted in the control groups. Among the remaining 9 studies, one [30] received a self-monitored intervention, eight [22, 23, 28, 29, 31, 33, 38, 40] received the usual intervention without a digital component involved or a partial component. For example, Liu et al. (2015) only provided usual medical examinations to the control group without follow-up calls and text messages. Pillay et al. (2014) provided a partial intervention to the control group, sending general motivational email messages without personalised pedometer feedback.
Physical, mental or other health-related measures.
All of the studies measured quantitative results, except for one study [42] that used qualitative and quantitative findings to develop an integrated technology-moderated institutional health promotion model. The outcome measures of the interventions were heterogeneous. Sixteen studies [22–26, 28, 30, 33, 34, 36, 38, 42, 44] assessed changes in physical health including PA, diet, and other lifestyle factors. Seven studies [27, 31, 32, 35, 37, 43, 45] measured changes in mental health factors, such as sleep, stress, work engagement and more. One study [29] measured improvements in occupational health and ergonomic conditions. Further details can be found in Table 3.
Effectiveness of interventions
Changes to weight.
Five studies reported weight as an outcome and four found that the WWPs had a significant effect on reducing weight. Among the four RCTs finding evidence of effectiveness, weight loss ranged from about 1–2 kilograms, including: web-assisted and telephone-assisted education (-1.08kg, p = 0.001; -1.92kg, p = 0.001) [28], web-based coach-delivered and computer-delivered feedback (-1.57 kg, [95%CI: –1.92 to –1.22], p = 0.001; –1.08kg, [95%CI: –1.41 to –0.75], p = 0.001) [41], phone messages and emails (-1.1kg [95%CI: -1.5 to -0.7], p<0.001) [34], and a pedometer and web-assisted exercise prescription (Males (BMI ≥24kg/m2) = -1.0kg [95%CI: –2.5 to –1.4], p<0.001; Females (BMI ≥24kg/m2) = -1.2kg [95%CI: –1.7 to –0.6], p<0.001]) [24]. One cohort study [23] of social media-based (WeChat) support and consultations found mixed evidence of effectiveness, with results suggesting the intervention was more effective among males and those who were actively engaged in the groups.
Changes to physical activity level.
Four articles reported PA as a primary outcome, with three indicating a significant effect. Multiple intervention components were used in two studies. One prospective study [44] involved use of pedometers and a website for education and communication and found significant improvements in the intervention group (+3,519 steps/day; [95%CI: 3,484 to 3,553]; p <0.0001). Another quasi experimental study [26] using pedometers and a social media application (WeChat) for communication found a significant increase in walking from baseline to the end of intervention period (22%, p<0.05). There were two studies involving a single component. One RCT [25] that included online exercise videos led to a significant increase in PA (+5.8hrs/week, p = 0.04). One pilot study [40] that included a pedometer and motivational emails didn’t not show a statistically significant improvement in steps between groups (IG = +996±1748 steps/day, CG = +97±750 steps/day).
Changes to lifestyle beliefs, knowledge, behaviors, or chronic disease risk.
Seven studies reported lifestyle-related belief, knowledge, or behavioral outcomes or chronic disease risk as an outcome, with six indicating that WWP had a significant effect [22, 30, 32, 36, 39, 42]. For example, Liu, et al found a significant decrease in 10-year risk of CVD between the control and intervention groups in a cluster RCT ([95%CI: -4.47 to -1.18], p = 0.001) [22] and Ramachandran et al found a significantly lower incidence of type 2 diabetes ([95%CI: 0·45 to 0·92], p = 0·015) [41] in the intervention group. Only one study [38] did not find a statistically significant improvement in the primary outcome of interest (blood pressure) but did find a small reduction in body weight (−4·85kg, [95%CI: –8·21 to −1·48], p<0.05) and waist circumferences (−3·31cm, [95%CI: –5·95 to −0·67], p<0.05) in the intervention group of a parallel RCT.
Changes to job performance.
Two studies reported job performance as outcome, with one RCT indicating that the WWP created a significant effect. The RCT [27] using a social media application (WeChat) for positive psychotherapy messages, showed significant improvement in job performance (job contribution: F = 6.425, p = .013; Task performance: F = 29.252, p = .000) and self-efficacy (F = 13.326, p = .000). One RCT [43] with 951 participants which involved an app-based cognitive behavioral therapy and messaging for technical support did not find a statistically significant effect at long term follow-up at 7 months (Program A: 95%CI: –0.11 to 0.12, p = 0.94; Program B: 95%CI:–0.08 to 0.16, p = 0.5).
Changes to other health outcomes, such as stress, sleep and ergonomic condition.
Three studies reported stress as outcome and indicated that WWP was effective in reducing stress and burnout level among the employees. A RCT [35] conducted with 92 participants receiving online group yoga teaching showed significant reduction in stress, anxiety and depression level (p<0.001) immediately after the intervention, but not at 40 days (p = 0.49, p = 0.613, p = 0.563). A pilot study [37] conducted with 72 participants receiving online group educational treatment found evidence of reductions in stress, anxiety and depression level (95%CI:−5201 to −389, p<0.001; 95%CI: −35.18 to −29.16, p<0.001; 95%CI:−1.38 to—0.511, p<0.001). A pilot study [32] which assessed 81 participants adopting digital components of videos and email messages had shown significant improvement in quality of work life and resilience (r = 0.378, p<0.01; r = 0.365, p<0.05).
Two studies reported sleep quality as outcome with one of them finding a significant improvement to sleep quality post intervention. A pilot study [45] involving a sleep survey and recommendations delivered by tablet application showed significant effect on sleep awareness, total sleep duration during the weekend (p = 0.046), sleep debt (p = 0.019), sleep difficulties (p<0.001), and sleepiness (p = 0.026). Another RCT [31] using a multicomponent intervention involving both a messaging application (WhatsApp) for education and human support showed statistically significant improvement in other indicators, but not in overall sleep quality.
One quasi experimental study [29] involving a messaging application (Telegram), online education material, and text messages did not find significant improvement in workplace ergonomic condition.
Feasibility and acceptability of interventions
Four studies assessed the feasibility and acceptability of the studies through questionnaires [25, 33, 34, 40] testing acceptability. All of them found that the interventions were feasible and/or acceptable. Blake et al. also identified facilitators to successful implementation, such as organizational support and participating in groups, as well as barriers, such as team leaders not adequately leading the exercise activities and limited space in the offices.
Study quality
This scoping review did not include any formal quality assessment. Many of the studies were RCTs or quasi-experimental studies that aimed to reduce potential sources of bias. However, some of the limitations of the included studies were small sample sizes affecting the generalisability and results, and short study durations, leading to an unknown long-term effect of the intervention.
Excluded or near-miss studies
Throughout the process of final study selection, a total of 22 studies were found to be highly relevant but did not meet certain inclusion criteria. Four of them were conducted in the wrong settings, as they did not take place in LMICs [47–50]. Articles were also excluded when the participants were born in LMICs but currently working in high-income countries, for example, one article which was conducted among South Asian (India, Pakistan, or Bangladesh) workers in New York City was excluded. Three studies took place in atypical workplaces excluded from the current review (e.g., sex workers [46, 51], soldiers [52]). Four studies did not report health-related, work-related, or design-related outcomes. Four studies were excluded due to a lack of digital components in their workplace wellness interventions, including one article using a pedometer only for step count recording, but not to promote healthier behaviours [53].
During the screening stage, we found two articles from the same research study, but one was excluded because it was a protocol and therefore did not meet the inclusion criteria [43].
Discussion
Main findings
This scoping review aimed to assess the implementation of digital workplace wellness interventions in LMICs. Many studies were excluded from the review due to the fact that most digital workplace wellness research has been done in developed countries, such as the US and the UK. From this review, we see that digital workplace wellness interventions have been used to address a broad range of health behaviours (physical activity level, smoking cessation, sleep quality, burnout, etc.) in LMICs, but targeting outcomes with the goal of reducing the risk of chronic diseases seem to be most common. No other systematic or scoping reviews focus on the same criteria as our study, and this review therefore can help to further our understanding of digital workplace wellness interventions specifically in LMICs.
The final included 24 articles cover a wide range of interventions and measured outcomes. The content of the interventions varied, yet most of them involved mixed digital components, including websites, educational videos, social media or messaging applications, and phone calls or messages. As the studies showed a high level of heterogeneity in terms of intervention aims, digital components involved, outcomes and measures, it is difficult to compare or discern a clear pattern of effectiveness among the 24 studies.
Of the 24 articles, statistically significant improvements were found in all the studies except for two which found no changes [31, 38]. Therefore, it seems that digital WWPs hold promise for improving outcomes in LMICs, however, it is not possible to discern specific patterns between intervention components and outcomes as all the studies varied in intervention components, study design, duration, target outcomes and so on. For example, the duration of interventions varied greatly. Two studies that did not find statistically significant results had very different durations, one [31] was a 7-week intervention without follow up, and the other [38] was a 12 month intervention with 6 month follow up. The remaining 22 studies included interventions ranging from 2 weeks to 2 years (with or without follow-up) and found statistically significant improvements on the targeted outcomes, suggesting that there was no clear pattern of duration and follow-up for study effectiveness in the current review.
Generally, the studies concluded that digital interventions were well-accepted and feasible for the employees. Based on the findings of the reported studies, digital health interventions are potentially effective and feasible for improving employees’ physical and mental wellbeing. The fact that digital interventions can be low-cost and more easily scalable have made them an attractive approach in low-resource settings. However, these findings are mixed and small in effect size, and long-term effects were not studied.
Comparison with related literature
There are many studies on similar topics that did not fulfil all our inclusion criteria, particularly the criterion that the study should be conducted in an LMIC setting. Nonetheless, it is worth discussing the currently available research on digital workplace wellness in high-income countries to compare our findings. For instance, a systematic review analysed the impact of pure digital health interventions in the workplace in high-income countries [54]. The review found that digital-only interventions can improve health-related outcomes in the workplace. They also found that they were more effective when tightly embedded in the work environment (such as downloading a software onto a work computer) and limited to distinct health behaviours that are regularly performed at work, such as physical activity and eating. On the other hand, more complex health behaviours that extend outside the workplace may require human support as a more effective approach [54]. Another systematic review also assessed mobile health interventions to encourage physical activity in the workplace in high-income countries [55]. It was found that commonly used behaviour change techniques were self-monitoring, feedback, goal-setting, and social comparison. Simultaneously, the main mHealth tools used were wearable activity trackers, smartphone apps, or both. Some studies also utilised text messaging, e-mails, social media groups or websites to deliver motivational messages. Approximately half of the studies found a significant increase in physical activity while 4 out of 10 studies reported significant reduction in sedentary time. The findings from these reviews in high-income settings are consistent with the findings of our review, which show that digital health workplace interventions can be feasible and effective [54–56]. Another scoping review [57] that examined the return on investment of WWPs in high-income countries found greater returns in larger companies (>500 employees), however, our scoping review was unable to identify any patterns by company size and many studies did not report this information.
Aside from digital health interventions in workplaces in LMICs, there are many studies examining digital interventions in LMICs, but without the focus on the workplace. One systematic review examined the use of short message service (SMS) interventions for disease prevention in developing countries [58]. The review concluded that, while there are many existing SMS applications for disease prevention, very limited evaluation is done to assess their effectiveness. It was also stated that the majority of the selected studies were from grey literature sources. Implementation barriers that were identified included language, timing of messages, network connection issues, high mobile phone turnover, data privacy and lack of financial incentives [58]. These same barriers might also apply to the implementation of digital workplace wellness interventions in LMICs, but the studies in this review did not discuss these barriers. Another literature review found 53 mHealth studies in LMICs [59]. However, the majority of these studies lacked a theoretical framework and outcome measures. Similar to our findings in workplaces specifically, these reviews suggest that there are small numbers of peer-reviewed studies that examine digital health in LMICs. In both cases, it is important to improve future work on digital health interventions in LMICs through the use of theoretical frameworks in the design of interventions and ensuring that programs are evaluated and measure health outcomes.
Evidence gap
Based on our findings, there is a lack of information and evidence supporting the feasibility and effectiveness of digital health interventions among employees in LMICs. The locations of the studies only covered a few specific countries, mainly in China (n = 4), India (n = 4) and Iran (n = 4). Hence, future research is required as factors such as culture, ethnic groups, and lifestyle habits vary across countries and one successful intervention does not fit all.
Future research directions
There are two implications for future research, based on the findings of this review. First, this review highlighted the relatively small amount of research that has been done on digital workplace wellness interventions in LMICs, demonstrating the need for ongoing research in this area. Second, the review identified that there is no clear consensus on the theoretical frameworks that apply to the development of digital workplace wellness interventions in LMICs, which is likely due to the included studies involving varied approaches to health promotion, which may require different theoretical frameworks. These areas are also in need of future research to further our understanding of the mechanisms of workplace behaviour change and for which health outcomes digital workplace wellness is most effective.
Strengths and limitations
The current review was conducted in accordance with PRISMA guidelines, including developing a robust search strategy, study selection, data extraction and synthesis. This review covered a broad range of study types involving quantitative and mixed method designs, the use of the most recent digital technologies in the studies, as well as targeting both physical and mental outcomes. One limitation is that the heterogeneous outcomes and incomplete reporting of the studies, affected the level of data synthesis that was possible. Another limitation was that non-peer-reviewed studies were not included, and relevant grey literature could have been missed.
Conclusion
To our knowledge, this is the first scoping review to explore the nature of existing digital health interventions in workplace settings in LMICs. This scoping review gathered recent evidence of digital workplace wellness programs in LMICs. Based on our findings, there is relatively less evidence found when compared to developed or high-income countries. Positive improvements were found in the employees’ mental and physical well-being with the implementation of digital health interventions, yet the effect of the interventions remain unclear in the LMIC context due to the small number of studies identified. Thus, there is a clear need for new high-quality studies with better reporting of interventions and outcomes to be conducted. Future studies should also adopt the use of theoretical frameworks into the research design, exploring more reliable and sustainable wellness programs to enhance the practice of digital health in the workplace in LMICs.
References
- 1.
World Health Organization, Wolf J., Prüss-Ustün A, Ivanov I., Mugdal S., Corvalán C., et al. Preventing disease through a healthier and safer workplace. Geneva: World Health Organization; 2018.
- 2. Wipfli H, Zacharias KD, (Nivvy) Hundal N, Shigematsu LMR, Bahl D, Arora M, et al. Workplace wellness programming in low-and middle-income countries: a qualitative study of corporate key informants in Mexico and India. Global Health 2018;14:46. pmid:29739444
- 3. Baicker K. Do Workplace Wellness Programs Work? JAMA Health Forum 2021;2:e213375. https://doi.org/10.1001/jamahealthforum.2021.3375. pmid:36218657
- 4. Kigozi J, Jowett S, Lewis M, Barton P, Coast J. The Estimation and Inclusion of Presenteeism Costs in Applied Economic Evaluation: A Systematic Review. Value Health 2017;20:496–506. https://doi.org/10.1016/j.jval.2016.12.006. pmid:28292496
- 5. Kirsten W. Making the Link between Health and Productivity at the Workplace ―A Global Perspective. INDUSTRIAL HEALTH 2010;48:251–5. https://doi.org/10.2486/indhealth.48.251. pmid:20562499
- 6. Hertz RP, Unger AN, McDonald M, Lustik MB, Biddulph-Krentar J. The Impact of Obesity on Work Limitations and Cardiovascular Risk Factors in the U.S. Workforce. Journal of Occupational & Environmental Medicine 2004;46:1196–203. https://doi.org/10.1097/01.JOM.0000147222.53729.1E. pmid:15591970
- 7. Finkelstein EA, DiBonaventura M daCosta, Burgess SM, Hale BC. The Costs of Obesity in the Workplace. Journal of Occupational & Environmental Medicine 2010;52:971–6. https://doi.org/10.1097/JOM.0b013e3181f274d2. pmid:20881629
- 8. Kannan H, Thompson S, Bolge SC. Economic and Humanistic Outcomes Associated With Comorbid Type-2 Diabetes, High Cholesterol, and Hypertension Among Individuals Who Are Overweight or Obese. Journal of Occupational & Environmental Medicine 2008;50:542–9. https://doi.org/10.1097/JOM.0b013e31816ed569. pmid:18469623
- 9. Daley M, Morin CM, LeBlanc M, Grégoire J-P, Savard J. The Economic Burden of Insomnia: Direct and Indirect Costs for Individuals with Insomnia Syndrome, Insomnia Symptoms, and Good Sleepers 2009;32:10. pmid:19189779
- 10. Stewart WF, Ricci JA, Chee E, Hahn SR, Morganstein D. Cost of Lost Productive Work Time Among US Workers With Depression. JAMA 2003;289:3135–44. pmid:12813119
- 11. Wang F, McDonald T, Champagne LJ, Edington DW. Relationship of Body Mass Index and Physical Activity to Health Care Costs Among Employees: Journal of Occupational and Environmental Medicine 2004;46:428–36. pmid:15167389
- 12. Merrill RM, Aldana SG, Garrett J, Ross C. Effectiveness of a Workplace Wellness Program for Maintaining Health and Promoting Healthy Behaviors. Journal of Occupational & Environmental Medicine 2011;53:782–7. pmid:21670705
- 13. Patel DN, Lambert EV, da Silva R, Greyling M, Nossel C, Noach A, et al. The Association between Medical Costs and Participation in the Vitality Health Promotion Program among 948,974 Members of a South African Health Insurance Company. Am J Health Promot 2010;24:199–204. https://doi.org/10.4278/090217-QUAN-68R2.1. pmid:20073387
- 14. Baicker K, Cutler D, Song Z. Workplace Wellness Programs Can Generate Savings. Health Affairs 2010;29:304–11. pmid:20075081
- 15. Beiwinkel T, Eißing T, Telle N-T, Siegmund-Schultze E, Rössler W. Effectiveness of a Web-Based Intervention in Reducing Depression and Sickness Absence: Randomized Controlled Trial. J Med Internet Res 2017;19:e213. pmid:28619701
- 16. Mills PR, Kessler RC, Cooper J, Sullivan S. Impact of a Health Promotion Program on Employee Health Risks and Work Productivity. Am J Health Promot 2007;22:45–53. pmid:17894263
- 17. van Wier MF, Dekkers JC, Bosmans JE, Heymans MW, Hendriksen IJ, Pronk NP, et al. Economic evaluation of a weight control program with e-mail and telephone counseling among overweight employees: a randomized controlled trial. Int J Behav Nutr Phys Act 2012;9:1–12. https://doi.org/10.1186/1479-5868-9-112.
- 18.
Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC. Chapter 11: Scoping reviews—JBI Manual for Evidence Synthesis—JBI Global Wiki n.d. https://jbi-global-wiki.refined.site/space/MANUAL/3283910770/Chapter+11%3A+Scoping+reviews (accessed February 10, 2022).
- 19. Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018;169:467–73. pmid:30178033
- 20.
Covidence—Better systematic review management. Covidence n.d. https://www.covidence.org/ (accessed February 10, 2022).
- 21.
World Bank Country and Lending Groups–World Bank Data Help Desk n.d. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519 (accessed February 10, 2022).
- 22. Liu Z, Chen S, Zhang G, Lin A. Mobile Phone-Based Lifestyle Intervention for Reducing Overall Cardiovascular Disease Risk in Guangzhou, China: A Pilot Study. IJERPH 2015;12:15993–6004. pmid:26694436
- 23. He C, Wu S, Zhao Y, Li Z, Zhang Y, Le J, et al. Social Media–Promoted Weight Loss Among an Occupational Population: Cohort Study Using a WeChat Mobile Phone App-Based Campaign. Journal of Medical Internet Research 2017;19. pmid:29061555
- 24. Yu Y, Lv Y, Yao B, Duan L, Zhang X, Xie L, et al. A novel prescription pedometer-assisted walking intervention and weight management for Chinese occupational population. PLoS ONE 2018;13. https://doi.org/10.1371/journal.pone.0190848. pmid:29324808
- 25. Blake H, Lai B, Coman E, Houdmont J, Griffiths A. Move-It: A Cluster-Randomised Digital Worksite Exercise Intervention in China: Outcome and Process Evaluation. IJERPH 2019;16:3451. pmid:31533292
- 26. Gu M, Wang Y, Shi Y, Yu J, Xu J, Jia Y, et al. Impact of a group-based intervention program on physical activity and health-related outcomes in worksite settings. BMC Public Health 2020;20:1–11. https://doi.org/10.1186/s12889-020-09036-2.
- 27. Guo Y-F, Lam L, Plummer V, Cross W, Zhang J-P. A WeChat-based “Three Good Things” positive psychotherapy for the improvement of job performance and self-efficacy in nurses with burnout symptoms: A randomized controlled trial. Journal of Nursing Management 2020;28:480–7. pmid:31811737
- 28. Abdi J, Eftekhar H, Mahmoodi M, Shojayzadeh D, Sadeghi R, Saber M. Effect of the Intervention Based on New Communication Technologies and the Social-Cognitive Theory on the Weight Control of the Employees with Overweight and Obesity. J Res Health Sci 2015;15:256–61. pmid:26728913
- 29. Aliakbari R, Vahedian-Shahroodi M, Abusalehi A, Jafari A, Tehrani H. A digital-based education to improve occupational health and ergonomic conditions of dentists: an application of theory of planned behavior. International Journal of Health Promotion and Education 2020;58:268–81. https://doi.org/10.1080/14635240.2019.1687316.
- 30. Jorvand R, Ghofranipour F, HaeriMehrizi A, Tavousi M. Evaluating the impact of HBM-based education on exercise among health care workers: the usage of mobile applications in Iran. BMC Public Health 2020;20:1–11. pmid:32321484
- 31. Nourian M, Nikfarid L, Khavari AM, Barati M, Allahgholipour AR. The Impact of an Online Mindfulness-Based Stress Reduction Program on Sleep Quality of Nurses Working in COVID-19 Care Units: A Clinical Trial. Holistic Nursing Practice 2021;35:257–63. https://doi.org/10.1097/HNP.0000000000000466. pmid:34407023
- 32. Pendse Maithily, Ruikar Sheetal. The Relation between Happiness, Resilience and Quality of Work Life and Effectiveness of a Web-Based Intervention at Workplace: A Pilot Study. Journal of Psychosocial Research 2013;8:189–97.
- 33. Ramachandran A, Snehalatha C, Ram J, Selvam S, Simon M, Nanditha A, et al. Effectiveness of mobile phone messaging in prevention of type 2 diabetes by lifestyle modification in men in India: a prospective, parallel-group, randomised controlled trial. The Lancet Diabetes & Endocrinology 2013;1:191–8. pmid:24622367
- 34. Limaye T, Kumaran K, Joglekar C, Bhat D, Kulkarni R, Nanivadekar A, et al. Efficacy of a virtual assistance-based lifestyle intervention in reducing risk factors for Type 2 diabetes in young employees in the information technology industry in India: LIMIT, a randomized controlled trial. Diabet Med 2017;34:563–8. https://doi.org/10.1111/dme.13258. pmid:27589695
- 35. Divya K, Bharathi S, Somya R, Darshan MH. Impact of a Yogic Breathing Technique on the Well-Being of Healthcare Professionals During the COVID-19 Pandemic. Glob Adv Health Med 2021;10:216495612098295. pmid:33623726
- 36. Nurgul K, Nursan C, Dilek K, Over OT, Sevin A. Effect of Web-supported Health Education on Knowledge of Health and Healthy-living Behaviour of Female Staff in a Turkish University. Asian Pacific Journal of Cancer Prevention 2015;16:489–94. pmid:25684476
- 37. Dincer B, Inangil D. The effect of Emotional Freedom Techniques on nurses’ stress, anxiety, and burnout levels during the COVID-19 pandemic: A randomized controlled trial. EXPLORE 2021;17:109–14. pmid:33293201
- 38. Rubinstein A, Miranda JJ, Beratarrechea A, Diez-Canseco F, Kanter R, Gutierrez L, et al. Effectiveness of an mHealth intervention to improve the cardiometabolic profile of people with prehypertension in low-resource urban settings in Latin America: a randomised controlled trial. The Lancet Diabetes & Endocrinology 2016;4:52–63. pmid:26653067
- 39. Martínez C, Castellano Y, Company A, Guillen O, Margalef M, Alicia Arrien M, et al. Impact of an online training program in hospital workers’ smoking cessation interventions in Bolivia, Guatemala and Paraguay. Gaceta Sanitaria 2018;32:236–43. pmid:29398107
- 40. Pillay JD, Kolbe-Alexander TL, Proper KI, van Mechelen W, Lambert EV. Steps that count! A feasibility study of a pedometer-based, health-promotion intervention in an employed, South African population. South African Journal of Sports Medicine 2014;26:15–9. https://doi.org/10.7196/SAJSM.500.
- 41. Beleigoli A, Andrade AQ, Diniz MDF, Ribeiro AL. Personalized Web-Based Weight Loss Behavior Change Program With and Without Dietitian Online Coaching for Adults With Overweight and Obesity: Randomized Controlled Trial. J Med Internet Res 2020;22:e17494. https://doi.org/10.2196/17494.
- 42. Joseph-Shehu EM, Ncama BP, Irinoye O. Developing and Pilot Testing an Integrated Technology–Moderated Institutional Health Promotion Model Using Operational Research Approach. CIN: Computers, Informatics, Nursing 2019;37:532–40. pmid:31449142
- 43. Sasaki N, Imamura K, Tran TTT, Nguyen HT, Kuribayashi K, Sakuraya A, et al. Effects of Smartphone-Based Stress Management on Improving Work Engagement Among Nurses in Vietnam: Secondary Analysis of a Three-Arm Randomized Controlled Trial. Journal of Medical Internet Research 2021;23. pmid:33620328
- 44. Ganesan AN, Louise J, Horsfall M, Bilsborough SA, Hendriks J, McGavigan AD, et al. International Mobile-Health Intervention on Physical Activity, Sitting, and Weight. Journal of the American College of Cardiology 2016;67:2453–63. https://doi.org/10.1016/j.jacc.2016.03.472.
- 45. Montagni I, Dehman A, Yu Z, Martinez MJ, Banner S, Rimbert S, et al. Effectiveness of a Blended Web-Based Intervention to Raise Sleep Awareness at Workplace: The WarmUappTM Pilot Study. Journal of Occupational & Environmental Medicine 2019;61:e253–9. https://doi.org/10.1097/JOM.0000000000001589.
- 46. Ampt FH, Lim MSC, Agius PA, L’Engle K, Manguro G, Gichuki C, et al. Effect of a mobile phone intervention for female sex workers on unintended pregnancy in Kenya (WHISPER or SHOUT): a cluster-randomised controlled trial. The Lancet Global Health 2020;8:e1534–45. pmid:33220217
- 47. Garner SL, Killingsworth E, Bradshaw M, Raj L, Johnson SR, Abijah SP, et al. The impact of simulation education on self-efficacy towards teaching for nurse educators. Int Nurs Rev 2018;65:586–95. pmid:29571221
- 48. Gany F, Gill P, Baser R, Leng J. Supporting South Asian Taxi Drivers to Exercise through Pedometers (SSTEP) to Decrease Cardiovascular Disease Risk. J Urban Health 2014;91:463–76. pmid:24500026
- 49. Clement S, Lassman F, Barley E, Evans-Lacko S, Williams P, Yamaguchi S, et al. Mass media interventions for reducing mental health-related stigma. Cochrane Database of Systematic Reviews 2013. pmid:23881731
- 50. Wang MP, Suen YN, Li WHC, Lau OS, Lam TH, Chan SSC. Proactive outreach smoking cessation program for Chinese employees in China. Archives of Environmental & Occupational Health 2018;73:67–78. pmid:28350250
- 51. Mimiaga MJ, Thomas B, Biello K, Johnson BE, Swaminathan S, Navakodi P, et al. A Pilot Randomized Controlled Trial of an Integrated In-person and Mobile Phone Delivered Counseling and Text Messaging Intervention to Reduce HIV Transmission Risk among Male Sex Workers in Chennai, India. AIDS Behav 2017;21:3172–81. pmid:28831618
- 52. Wesemann U, Kowalski JT, Jacobsen T, Beudt S, Jacobs H, Fehr J, et al. Evaluation of a Technology-Based Adaptive Learning and Prevention Program for Stress Response—A Randomized Controlled Trial. Military Medicine 2016;181:863–71. pmid:27483525
- 53. Abdullah M, Saat NZM, Fauzi NFM, Hui CY, Kamaralzam S. Association between Walking and Cardiovascular Risk Factors in University Employees. J of Medical Sciences 2015;15:105–9. https://doi.org/10.3923/jms.2015.105.109.
- 54. Howarth A, Quesada J, Silva J, Judycki S, Mills PR. The impact of digital health interventions on health-related outcomes in the workplace: A systematic review. DIGITAL HEALTH 2018;4:205520761877086. pmid:29942631
- 55. Buckingham SA, Williams AJ, Morrissey K, Price L, Harrison J. Mobile health interventions to promote physical activity and reduce sedentary behaviour in the workplace: A systematic review. DIGITAL HEALTH 2019;5:205520761983988. pmid:30944728
- 56. Schoeppe S, Alley S, Van Lippevelde W, Bray NA, Williams SL, Duncan MJ, et al. Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review. Int J Behav Nutr Phys Act 2016;13:127. pmid:27927218
- 57. Unsal N, Weaver G, Bray J, Bibeau D. A Scoping Review of Economic Evaluations of Workplace Wellness Programs. Public Health Rep 2021;136:671–84. pmid:33541206
- 58. Déglise C, Suggs LS, Odermatt P. Short Message Service (SMS) Applications for Disease Prevention in Developing Countries. J Med Internet Res 2012;14:e3. pmid:22262730
- 59. Chib A, van Velthoven MH, Car J. mHealth Adoption in Low-Resource Environments: A Review of the Use of Mobile Healthcare in Developing Countries. Journal of Health Communication 2015;20:4–34. pmid:24673171
