Simple and rationale-providing SMS reminders to promote accelerometer use: a within-trial randomised trial comparing persuasive messages

Reliable and valid assessment is necessary when evaluating whether public health policies or interventions change physical activity (PA) levels in the target group. Little consensus exists about what to measure, when, with what and for how long in PA research [1, 2]. While an inability of individuals to accurately remember their past PA and social desirability are clear problems with self-reported PA measures [3], objective measurements of PA (e.g. pedometers and accelerometers) have issues too. Zhuang et al. [4] found that missing accelerometry data was more common in 15- to 17-year-olds than among younger participants, especially during weekends (Sundays in particular), with missing data occurring increasingly from the first recording day to the last. This exemplifies a key issue in measurement: the proportion of an individual’s day or week captured by the measure. An extreme example would be an individual, who only wears the measurement device when undertaking PA. Thus, some guidelines suggest that a person should wear an accelerometer for a minimum of 10 h daily for at least 4 days in a 7-day measurement period in order to obtain an accurate reading of PA [1, 2]. Participants’ compliance with instructions on wearing the accelerometer is clearly very important in obtaining accurate PA measurements [5].

Research on enhancing accelerometer instruction compliance rates is rare [2, 6], particularly among older adolescents. One strategy has been monetary incentives contingent on proper wear-time [7]. Sallis et al. [8] used an alternative strategy, asking participants to re-wear the accelerometer if they had not worn it for at least 5 valid days (> 10 valid hours of data) or a minimum of 66 valid hours across 7 days.

Barak et al. [9] suggest that new opportunities to promote compliance—such as text messaging (SMS; Short Messaging Service)—may be more reliable and effective than traditional methods, such as written or verbal wear instructions by the investigator. Zhuang et al. [4], too, recommend SMS reminders. Toftager et al. [10] used SMS reminders to increase compliance but did not report effects or acceptability. In a self-selected Irish sample of adolescents [11], daily SMS reminders were associated with putting on the accelerometer in the morning, but not in increased overall compliance (defined as valid days of data or minutes of non-wear). The study did not report levels of wear or effects of the reminders. The discrepancy between remembering to put on the device and actually wearing it for a sufficient amount of time indicates that these may be separate behaviors.

Since the classic “Xerox machine study” by Langer, Blank and Chanowitz [12], providing reasons for compliance has been discussed in the social influence literature. The study indicated that placebic or pseudo-reasons [13] (“Excuse me, I have 5 pages. May I use the xerox machine, because I have to make copies?”; 93% compliance) could result in similar compliance rates as actual reasons (“[…] because I’m in a rush?”; 94% compliance) compared to the request only condition (“Excuse me, I have 5 pages. May I use the xerox machine”; 60% compliance). Pratkanis (2007), identified “placebic reasons” in his index of social influence tactics, but called for further research into the subject. Less careful are Cialdini, Goldstein and Martin [14], who tout the “unique motivational influence of the word because”, basing their claims on the importance of reasoning in social influence. To this day, the xerox machine study remains cited in the press as an example of the power of the word ‘because’ [15‐18].

A well-known principle of human behavior says that when we ask someone to do us a favor we will be more successful if we provide a reason. People simply like to have reasons for what they do. [19]

Following the terminology used by Key, Edlund, Sagaring and Bizer [20], the phenomenon of increased compliance by providing reasons is referred to as “the because-heuristic.” Let us accordingly define the naïve because-heuristic as “reasons increase compliance.”

In the Langer, Blank and Chanowitz study 1, this effect of reasons increasing compliance was only found when the confederate asked for ‘a small favor’ (five instead of ten pages, translating to effect sizes of d = 0.87 and d = 0.13, respectively) [12]. Still, the results in general, as well as their implications have been questioned [21, 22]. A study by Folkes suggests, that instead of the size of the request, the effect is moderated by controllability [21]. Pooling Folkes’ reason conditions results to an effect size of d = − 0.026, speaking against the quote above, and pointing out that the “power of reasons” effect is malleable, in the least.

To our knowledge, only one published direct replication of the Langer, Blank and Chanowitz study 1 exists [20]. The main effect of the study replicated (d = 0.67 for placebic over no reason and d = 0.69 for real over no reason conditions), although over 20% (34 out of 163) of the participants needed to be excluded for various reasons. Lack of published replication studies, of course, is not new in the field of psychology [23].

In a conceptual replication of the phenomenon, in small request conditions, reasons (either placebic or real) increased compliance by an equivalent of d = 0.43 (calculated from Table 1 of [24]) when including their additional persuasion group and d = 0.22 when excluding it. Another conceptual replication [25] found d = 0.15 for requests perceived as small, and d = 0.21 for requests perceived as large (as calculated from Figure 3 of [25]).

These studies seem to temper earlier claims for the power of reasons in increasing compliance. In contrast to the naïve because-heuristic, let us define the weak because-heuristic as “reasons increase compliance, but only if the perceived favour is small”.

This study will investigate the effects of the because heuristic on compliance with the physical activity measurement procedures in the context of baseline measurements of a large school-based intervention.

Inadequate PA predicts increased morbidity and mortality in people of low socioeconomic status (SES) [26], with SES differences in PA emerging already in adolescence [27]. Finnish vocational school students are less physically active than those in high school [28]. The Let’s Move It intervention aimed to increase PA and decrease sedentary behaviors in older adolescents in vocational schools.

The current study was conducted as a sub-study of the cluster randomised effectiveness evaluation trial of the Let’s Move It intervention [29]. In a preceding feasibility study [30], participants’ accelerometer wear times were suboptimal; 47% (18/38) of baseline participants reached the cutoff of 10 h per day for at least 4 days, 63% (17/27) for the first and 75% (9/12) for the second follow-up. A frequently cited explanation for not wearing the accelerometer was forgetting to put on the device.

In this within-trial study, we investigate SMS-reminder strategies to improve the duration of accelerometer wear time. The literature cited previously lead us to hypothesise that reminders would increase accelerometer wear time and that citing reasons would amplify the effect. In addition to daily wear hours, we are interested in the number of days our participants provide valid activity data (i.e. days of ≥10 h of activity data). The target behavior is thus twofold: 1) putting on the accelerometer in the morning for as many days as possible, 2) wearing the accelerometer for as long as possible in the waking hours each day. In this study, two main research questions are posited:

The current study investigated this by comparing the compliance rates across a) participants who opted to receive SMS reminders to wear their accelerometer, and b) participants who opted not to receive the reminders (non-randomised control group). If forgetting is an important reason for non-compliance, in the absence of intervening factors, reminders should increase compliance.

Statistical hypothesis H₁: Those who receive SMS reminders will have higher accelerometer wear times than those who do not.

If reasons increase compliance, SMS reminders containing reasons to wear an accelerometer should lead to greater compliance.

Statistical hypothesis H₂: Those who receive reasons in the SMS reminders have more minutes of accelerometer wear and more days of valid data (≥10 h of activity) than those who do not receive reminders containing a reason.

An additional research question, on whether providing reasons to comply with accelerometer wear increases trial retention, is omitted here. These null results are reported in [31].

To be included in the study, the participants had to fulfill inclusion criteria of the Let’s Move It study [29] and had to have consented to the accelerometry measurements: all were at least 16 years old and were vocational school students. The reminder arms consisted of the participants who opted in to receive reminders for accelerometer wear.

During baseline recruitment of the first two recruitment waves of the Let’s Move It trial, students in two vocational schools were approached during class and informed about their school’s study participation in the study. After the invitation to participate in the main trial and collection of signed informed consent forms, those who consented were given an online questionnaire to complete. Details of trial procedures are reported in the protocol [29].

After 1–3 days, research assistants gave the participants a waist-worn accelerometer (Hookie AM 20, Traxmeet Ltd., Espoo, Finland) and instructed them on how to wear it for a duration of seven consecutive days (including the day of receiving the device). The used Hookie accelerometer is a tri-axial accelerometer that collects data at 100 Hz sampling rate without preprocessing. The measurement range of the accelerometer is ±16 g and the resolution is 4 mg (milligravity). The Hookie accelerometer employs the same tri-axial acceleration sensor component (ADXL345; Analog Devices, Norwood MA) that is used in widely used research-grade accelerometers [33]. The validation of the Hookie accelerometer has been reported in both children [34] and adults [35] in studies comparing analysis of raw acceleration data from different accelerometers.

When participants received the accelerometers, they were asked whether they would like to receive SMS messages to help them remember to put it on every morning. Those who consented to the messages were subsequently randomised to one of two message conditions, and those who opted not to receive the reminders were treated as a self-selected control arm.

After 7 days, participants returned their devices to research assistants and were asked to fill out a short questionnaire assessing process measures (see Additional file 1: Appendix S1 and Additional file 2: Appendix S2.

Participants were assigned to the reason and succinct arms after they were recruited. The first author extracted the phone numbers from the list and used R code to create an amount of random numbers equal to the number of new participants. The vector of random numbers was then assigned to the participants. Participants with a number equal to or smaller than the median of the vector were allocated to the reason-condition. Others were allocated to the succinct condition. Research assistants working in the field to assess were blind to group allocation. Recruitment and randomisation took place on the same day, and restrictions such as blocking or stratification were not used.

Recruitment took place in two waves, alongside the recruitment of the main trial. In order to increase the rates of participants opting in for the reminders, the recruitment prompt was slightly modified for the second wave. The research assistants presented the SMS reminders as the default option, and asked whether this is acceptable to the participants.

Random assignment was not visible to the participants and the research assistants did not mention that different kinds of messages were going to be sent. The statistician who analysed the raw accelerometer data was blind to group assignment.

An important issue regarding the current study was to avoid tampering with the effects of the main trial. In other words, it should not affect main trial outcome measures in any other ways except for increased data quality. Care was taken to formulate the SMS messages to not pressure participants or provoke changes in main trial outcome measures such as PA.

We altered a previous procedure [11] by varying the message content slightly each day to reduce habituation and thus expected to increase the chances of the message being read, for both arms.

The two arms received different message content.

Messages are presented in detail in Table 1 below.

We sent the messages using an SMS Gateway device MT-SF100-G-EU (MultiModem iSMS Server 1-port) by Multi-Tech Systems (http://​www.​multitech.​com/​brands/​multimodem-isms). We used a manufacturer-designed guided user interface for the first recruitment wave and a custom interface designed by a local service provider for the second wave.

The study plan was reviewed by the Ethics Committee for Gynaecology and Obstetrics, Pediatrics and Psychiatry of the Hospital District of Helsinki and Uusimaa (decision number 367/13/03/03/2014).

Official public registration in the German Clinical Trials Register (DRKS-ID: DRKS00007721) was completed 3 months after recruitment of the first wave had been initiated, but before data was available. Pre-registration (before starting data collection) failed due to lack of available resources at the time.

The original plan was to establish the additive effect of messages containing a reason and those not containing one over a no-message condition during the baseline measurement of the first batch. With the sample size we expected (n = 140), we would have had over 95% power to detect an effect of d = 0.6 (slightly smaller than the one discovered in the Langer, Blank and Chanowitz replication [20]). We had then planned to pit the more successful message type against a third message in the second wave. Instead of going forward with the plan of using a third message, we made the decision to gather another wave of participants with the same message types after the data from the first wave was analysed. This was due to the fact that, contrary to our expectations, no difference between the two messages was detected. This is important to note, as it means we can no longer rely on a long-term error rate of 5% [36] and—as p-values depend on the sampling distribution—default p-values from common statistical programs no longer apply [37].

To address the issue of inadequate reporting in the sciences [38], the current report complies with the Consolidated Standards of Reporting Trials (CONSORT) statement [39]. Contributor roles are clarified in Additional file 3: Appendix S3, according to a taxonomy for this purpose [40].

All non-Bayesian analyses were conducted using RStudio running R [41, 42]. Plots were drawn using R packages ‘ggplot2’ [43] and ‘yarrr’ [44]. Distributions between the reason and succinct groups in the implementation assessment questions were compared using the chi-square test.

Accelometer wear times were analysed using bootstrapping methods. A 95% bootstrap confidence interval for a mean can be acquired by resampling observed data to simulate a sampling distribution, obtaining the values for the 0.025th and 0.975th percentiles of resampled means [45]. A kernel density plot, bootstrap confidence interval and a bootstrap test of equivalence were conducted using R package ‘sm’ [46] for differences of distributions of the two reminder arms. Wilcoxon rank sum test with continuity correction was used to compare medians between groups.

ANOVA for equivalence of means between the two reminder groups and the no-reminder group, as well as its illustration, was performed using R package ‘userfriendlyscience’ [47]. Additionally, a MANOVA with wear time minutes and wear days with valid data as dependent variables, and SMS group as an independent variable, was used to test robustness of results.

A 95% Bayesian Highest Density Interval (HDI) [44] of the means of valid wear days was plotted using R package ‘yarrr’. HDI refers to the most likely population parameter values (here: means) given the data; information which is not delivered by frequentist confidence intervals [48, 49].

A participant flow diagram presented in Fig. 1 indicates how the messages were sent to almost all participants as intended.

Of the 375 participants consenting to accelerometer measurements as part of the main trial, 95 opted out of receiving reminders and an additional 7 did not receive messages due to technical difficulties. In the end, the SMS messages with reasons were sent to 138 and the succinct messages to 135 participants. Consent rate for reminders was 54% (101 out of 186) in the first wave and 95% for the second wave (179 out of 189).

Table 2 shows the sample characteristics for the baseline data.

Manipulation and contamination check, as well as satisfaction with the messages and discussing their content are presented in the supplement. In brief, we did not detect differences across any groups, with Bayes Factors indicating strong support for the null hypotheses. As shown in Fig. 3. Seventy four point nine percent of respondents reported having opened and read the SMS at least four mornings. Discussing the content of the messages with peers was not common; 91.1% answered having done so never or just once Fig. 4.

Open comments did not reveal unforeseen negative effects. In addition, 13% (9 out of 70) of participants who answered the question explicitly added, that remembering to wear the device was due to receiving the messages.

There are a number of ways this study could have been improved on.

In this paper, we attempted to answer to the call of more stringent methodology by pre-registration. Optimally, this would have been done prior to beginning data collection. In these cases, it has been proposed that analyses should be considered exploratory [67]—especially in the presence of researcher degrees of freedom or data-dependent analysis decisions [68]—and can render p-values meaningless. In our case, this mistake turned out to be nonconsequential. We used Bayes factors to avoid claiming findings based on p-values alone, as recently warned against by the American Statistical Association [69]. Other approaches we used to address the replicability problem were transparent reporting and open data.

Our results, in line with some other studies e.g. [11] indicate that researchers should not expect simple reminders to have strong effects on accelerometer wear times among youth. Also, despite previous strong claims, the because-heuristic in this context lacks the strength attributed to it in the popular literature. When considering using SMS reminders for youth, we suggest ensuring that remembering plausibly plays the key role in compliance with the behavior and target group in question, instead of other determinants/factors (such as social norms or motivation). Participants’ coping skills and attention span may act as a ceiling to the potential effect of the reminder in situations where the target behavior can not immediately be carried out, so suitability of SMS reminders could be assessed in these respects as well.

To an extent, the findings here apply to situations where cost-effective reminders can potentially improve compliance. These areas may range from medication adherence [70] to sunscreen use [71]. An interesting hypothesis to test, would be whether reminders actually reduce active coping strategies that people use spontaneously – this could partly explain some null findings in the literature on technical reminder systems [72]. Second, the delivery of the reminders should optimally be objectively trackable, in order to make firm conclusions about the independent effects of delivery and receipt. Third, the context (including timing and location) where the participant receives the reminder is likely to be important, as well as the coping behaviour of the control group. It may also be worthwhile to gauge whether altering frequency of reminders affects the target behavior [70], or if the system can be made such that it adapts to the users and their environments [73]. Lastly, it might be worthwhile to investigate, if personally meaningful persuasive arguments work better than vague and general ones (e.g. contributing to science), which were used in order to minimise risk of participants changing their activity behaviour instead of merely the wear time behaviour. As the literature presented earlier suggests, any reasons should be enough for heuristic decision making, whereas good reasons may be needed for more reflective decisions. Further theorising and additional measures to test hypotheses based on dual process models may be fruitful – but we encourage researchers to see [74‐79], and also consider a wider perspective from complexity and systems theory [80‐83], which have recently been applied in public health [84‐86].